US20220246242A1

US20220246242A1 - Methods of assessing risk of developing a severe response to coronavirus infection

Info

Publication number: US20220246242A1
Application number: US17/667,282
Authority: US
Inventors: Gillian Sue Dite; Nicholas Mark Murphy; Richard Allman
Original assignee: Genetic Technologies Ltd
Current assignee: Genetic Technologies Ltd
Priority date: 2020-05-27
Filing date: 2022-02-08
Publication date: 2022-08-04
Also published as: CN116134152A; IL298097A; EP4158067A1; MX2022014931A; WO2021237292A1; CA3179313A1; KR20230019872A; US11257569B1; AU2021281702A1; JP2023527834A

Abstract

The present disclosure relates to methods and systems for assessing the risk of a human subject developing a severe response to a Coronavirus infection, such as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infection.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT International Application No. PCT/AU2021/050507, filed May 26, 2021, which claims the priority of each of Australian Application No. 2020901739, filed May 27, 2020, Australian Application No. 2020902052, filed Jun. 19, 2020, Australian Application No. 2020903536, filed Sep. 30, 2020, and Australian Application No. 2021900392, filed Feb. 17, 2021 the contents of each of which are hereby incorporated by reference in their entirety into this application.

REFERENCE TO SEQUENCE LISTING

This application incorporates-by-reference nucleotide and/or amino acid sequences which are present in the file named “210706_91753_SequenceListing_DH.txt”, which is 4 kilobytes in size, and which was created Jul. 5, 2021 in the IBM-PC machine format, having an operating system compatibility with MS-Windows, which is contained in the text file filed Jul. 6, 2021 as part of this application.

FIELD OF THE INVENTION

The present disclosure relates to methods and systems for assessing the risk of a human subject developing a severe response to a coronavirus infection such as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral infection.

BACKGROUND OF THE INVENTION

In December 2019, there were a series of unexplained cases of pneumonia reported in Wuhan, China. On 12 Jan. 2020, the World Health Organization (WHO) tentatively named this new virus as the 2019 novel coronavirus (2019-nCoV). On 11 Feb. 2020, the WHO formally named the disease triggered by 2019-nCoV as coronavirus disease 2019 (COVID-19). The coronavirus study group of the International Committee on Taxonomy of Viruses named 2019-nCoV as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The WHO declared the virus a Public Health Emergency of International Concern on 30 Jan. 2020. The WHO eventually declared a pandemic on 11 Mar. 2020.
Like many complex diseases, there are a multitude of host factors that influence the severity of disease once infected with a virus. This means viral infections are complex multifactorial diseases like many cancers, cardiovascular disease and diabetes.
As global health systems try to manage resources and governments attempt to manage their respective economies there is a need to identify which people are at most risk of developing severe symptoms in response to the viral infection. Such a tool would enable earlier hospitalization and targeted treatments which may lead to the saving of lives. Of great importance to the economy, there is potential that lower risk individuals could be recommended to continue their normal employment given the lower risk of developing a life threatening disease should they contract a Coronavirus infection such as a SARS-Cov-2 viral infection.

SUMMARY OF THE INVENTION

The present inventors have found that a severe response to a Coronavirus infection risk model provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection.
In an aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, the presence at least two polymorphisms associated with a severe response to a Coronavirus infection.
In an embodiment, the Coronavirus is an Alphacoronavirus, Betacoronavirus, Gammacoronavirus or an Deltacoronavirus.
In an embodiment, the Coronavirus is Alphacoronavirus 1, Human coronavirus 229E, Human coronavirus NL63, Miniopterus bat coronavirus 1, Miniopterus bat coronavirus HKU8, Porcine epidemic diarrhea virus, Rhinolophus bat coronavirus HKU2, Scotophilus bat coronavirus 512, Betacoronavirus 1 (Bovine Coronavirus, Human coronavirus OC43), Hedgehog coronavirus 1, Human coronavirus HKU1, Middle East respiratory syndrome-related coronavirus (MERS), Murine coronavirus, Pipistrellus bat coronavirus HKU5, Rousettus bat coronavirus HKU9, Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Tylonycteris bat coronavirus HKU4, Avian coronavirus, Beluga whale coronavirus SW1, Bulbul coronavirus HKU11 or Porcine coronavirus HKU15.
In an embodiment, the Coronavirus is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E or Human coronavirus NL63.
In an embodiment, the Coronavirus is a Betacoronavirus.
In an embodiment, the Betacoronavirus is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43 or Human coronavirus HKU1.
In an embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43 or Human coronavirus HKU1.
In an embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2) or Middle East respiratory syndrome-related coronavirus (MERS).
In a preferred embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
In an embodiment, the method comprises detecting the presence of at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection.
In an embodiment, the polymorphisms are selected from Tables 1 to 6, 8, 19 or 22 or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the method at least comprises detecting polymorphisms at one or more or all of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792, or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the method at least comprises detecting polymorphisms at one or more or all of rs10755709, rs112317747, rs112641600, rs115492982, rs118072448, rs1984162, rs2034831, rs7027911 and rs71481792, or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the polymorphisms are selected from Table 1, Table 6a, Table 6b or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the polymorphisms are selected from any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the polymorphisms are selected from Table 3 or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, at least three polymorphisms are analysed.
In an embodiment, the method comprises, or consists of, detecting the presence of at least 60, or each, of the polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.
In another embodiment, the polymorphisms are selected from Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.
In a further embodiment, the polymorphisms are selected from Table 3 and/or Table 8 or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the polymorphisms are selected from Table 3 or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, the method comprises, or consists of, detecting the presence of each of the polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.
The genetic risk assessment may be combined with clinical risk factors to further improve the risk analysis. Thus, in an embodiment, the method further comprises
performing a clinical risk assessment of the human subject; and
combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on, but not necessarily limited to, one or more of the following: age, family history of a severe response to a Coronavirus infection, race/ethnicity, gender, body mass index, total cholesterol level, systolic and/or diastolic blood pressure, smoking status, does the human have diabetes, does the human have a cardiovascular disease, is the subject on hypertension medication, loss of taste, loss of smell and white blood cell count.
In another embodiment, the clinical risk assessment is based only on one or more or all of age, body mass index, loss of taste, loss of smell and smoking status.
In a further embodiment, the clinical risk assessment is based only on one or more or all of age, loss of taste, loss of smell and smoking status.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of: age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma). In an embodiment, the autoimmune disease is rheumatoid arthritis, lupus or psoriasis.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of: age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
The skilled person would appreciate that numerous different procedures can be followed to combine the clinical and genetic risk assessments. In an embodiment, combining the clinical risk assessment and the genetic risk assessment comprises multiplying the risk assessments. In an embodiment, combining the clinical risk assessment and the genetic risk assessment comprises adding the risk assessments.
The inventors, for the first time, have identified numerous polymorphisms associated with a subject's risk of developing a severe response to a Coronavirus infection. Thus, in another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus, the method comprising detecting, in a biological sample derived from the human subject, the presence of a polymorphism provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith.
In an embodiment, the polymorphism is provided in Table 19 and/or 22 or is a polymorphism in linkage disequilibrium therewith.
In an embodiment, the polymorphism is provided in Table 1 or Table 6a or is a polymorphism in linkage disequilibrium therewith.
In an embodiment, the polymorphism is provided in Table 3 or Table 6a or is a polymorphism in linkage disequilibrium therewith.
In an embodiment, the polymorphism is provided in Table 3, Table 6, is rs2274122, is rs1868132, is rs11729561, is rs1984162, is rs8105499 or is a polymorphism in linkage disequilibrium therewith.
In an embodiment, the polymorphism is provided in Table 3, is rs2274122, is rs1868132, is rs11729561, is rs1984162, is rs8105499 or is a polymorphism in linkage disequilibrium therewith.
In another aspect, the present invention provides a method of determining the identity of the alleles of fewer than 100,000 polymorphisms in a human subject selected from the group of subjects consisting of humans in need of assessment for the risk of developing a severe response to a Coronavirus infection to produce a polymorphic profile of the subject, comprising
(i) selecting for allelic identity analysis at least two polymorphisms provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof,
(ii) detecting, in a biological sample derived from the human subject, the polymorphisms, and
(iii) producing the polymorphic profile of the subject screening based on the identity of the alleles analysed in step (ii), wherein fewer than 100,000 polymorphisms are selected for allelic identity analysis in step (i) and the same fewer than 100,000 polymorphisms are analysed in step (ii).
In an embodiment of the above aspect, fewer than 100,000 polymorphisms, fewer than 50,000 polymorphisms, fewer than 40,000 polymorphisms, fewer than 30,000 polymorphisms, fewer than 20,000 polymorphisms, fewer than 10,000 polymorphisms, fewer than 7,500 polymorphisms, fewer than 5,000 polymorphisms, fewer than 4,000 polymorphisms, fewer than 3,000 polymorphisms, fewer than 2,000 polymorphisms, fewer than 1,000 polymorphisms, fewer than 900 polymorphisms, fewer than 800 polymorphisms, fewer than 700 polymorphisms, fewer than 600 polymorphisms, fewer than 500 polymorphisms, fewer than 400 polymorphisms, fewer than 300 polymorphisms, fewer than 200 polymorphisms, or fewer than 100 polymorphisms, are selected for allelic identity.
In an embodiment of each of the above aspects, the human subject can be Caucasian, African American, Hispanic, Asian, Indian, or Latino. In a preferred embodiment, the human subject is Caucasian.
In an embodiment of each of the above aspects, the method further comprises obtaining the biological sample.
In an embodiment, the polymorphism(s) in linkage disequilibrium has linkage disequilibrium above 0.9. In another embodiment, the polymorphism(s) in linkage disequilibrium has linkage disequilibrium of 1.
The present inventors have also found that a severe response to a Coronavirus infection risk model that relies solely on clinical factors provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection. Such a test may be particularly useful in circumstances where a rapid decision needs to be made and/or when genetic testing is not readily available. Thus, in another aspect the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on two, three, four, five or more or all of age, gender, race/ethnicity, height, weight, blood type, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had liver disease, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the method comprises obtaining information from the subject on age and gender.
In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the method comprises obtaining information from the subject on one or more of all of age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).
In another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising
i) performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, polymorphisms at rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792,
ii) performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma), and
iii) combining the genetic risk assessment with the clinical risk assessment to determine the risk of a human subject developing a severe response to a Coronavirus infection.
In an embodiment,
a) a β coefficient of 0.124239 is assigned for each G allele at rs10755709;
b) a β coefficient of 0.2737487 is assigned for each C allele at rs112317747;
c) a β coefficient of −0.2362513 is assigned for each T allele at rs112641600;
d) a β coefficient of −0.1995879 is assigned for each C allele at rs118072448;
e) a β coefficient of 0.2371955 is assigned for each C allele at rs2034831;
f) a β coefficient of 0.1019074 is assigned for each A allele at rs7027911; and
g) a β coefficient of −0.1058025 is assigned for each T allele at rs71481792.
In an embodiment, the subject is between 50 and 84 years of age and
a) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age;
b) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age;
c) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age;
d) a β coefficient of 0.2444891 is assigned if the subject is male;
e) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;
f) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;
g) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;
h) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;
i) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;
j) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;
k) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;
l) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and
m) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).
In an embodiment, the subject is between 18 and 49 years of age and
a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age;
b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age;
c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age;
d) a β coefficient of 0.2444891 is assigned if the subject is male;
e) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;
f) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;
g) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;
h) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;
i) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;
j) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;
k) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;
l) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and
m) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).
In an embodiment, the subject is between 18 and 84 years of age and
a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age;
b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age;
c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age;
d) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age;
e) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age;
f) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age;
g) a β coefficient of 0.2444891 is assigned if the subject is male;
h) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;
i) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;
j) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;
k) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;
l) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;
m) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;
n) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;
o) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and
p) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).
In an embodiment, in step iii) the genetic risk assessment is combined with the clinical risk assessment using the following formula:
Long Odds (LO)=−1.36523+SRF+Σ Clinical β coefficients, and wherein SRF is the SNP Risk Factor which is determined using the following formula:
Σ(No of Risk Alleles×SNP β coefficient).
In another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising
i) performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, polymorphisms at rs10755709, rs112317747, rs112641600, rs115492982, rs118072448, rs1984162, rs2034831, rs7027911 and rs71481792,
ii) performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma), and
iii) combining the genetic risk assessment with the clinical risk assessment to determine the risk of a human subject developing a severe response to a Coronavirus infection.
In an embodiment,
a) a β coefficient of 0.1231766 is assigned for each G allele at rs10755709;
b) a β coefficient of 0.2576692 is assigned for each C allele at rs112317747;
c) a β coefficient of −0.2384001 is assigned for each T allele at rs112641600;
d) a β coefficient of −0.1965609 is assigned for each C allele at rs118072448;
e) a β coefficient of 0.2414792 is assigned for each C allele at rs2034831;
f) a β coefficient of 0.0998459 is assigned for each A allele at rs7027911;
g) a β coefficient of −0.1032044 is assigned for each T allele at rs71481792;
h) a β coefficient of 0.4163575 is assigned for each A allele at rs115492982; and
i) a β coefficient of 0.1034362 is assigned for each A allele at rs1984162.
In a further embodiment, the subject is between 50 and 84 years of age and
a) a β coefficient of 0.1677566 is assigned if the subject is between 65 and 69 years of age;
b) a β coefficient of 0.6352682 is assigned if the subject is between 70 and 74 years of age;
c) a β coefficient of 0.8940548 is assigned if the subject is between 75 and 79 years of age;
d) a β coefficient of 1.082477 is assigned if the subject is between 80 and 84 years of age;
e) a β coefficient of 0.2418454 is assigned if the subject is male;
f) a β coefficient of 0.2967777 is assigned if the subject is an ethnicity other than Caucasian;
g) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.560943 to provide the β coefficient to be assigned;
h) a β coefficient of 0.3950113 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;
i) a β coefficient of 0.6650257 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;
j) a β coefficient of 0.4126633 is assigned if the subject has ever been diagnosed as having diabetes;
k) a β coefficient of 1.001079 is assigned if the subject has ever been diagnosed as having haematological cancer;
l) a β coefficient of 0.2640989 is assigned if the subject has ever been diagnosed as having hypertension;
m) a β coefficient of 0.2381579 is assigned if the subject has ever been diagnosed as having a non-haematological cancer;
n) a β coefficient of 1.148496 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma);
o) a β coefficient of −0.229737 is assigned if the subject has an ABO blood type;
p) a β coefficient of 0.6033541 is assigned if the subject has ever been diagnosed as having a immunocompromised disease;
q) a β coefficient of 0.2301902 is assigned if the subject has ever been diagnosed as having liver disease.
In an embodiment, in step iii) the genetic risk assessment is combined with the clinical risk assessment using the following formula:
Long Odds (LO)=1.469939+SRF+Σ Clinical β coefficients,
and wherein SRF is the SNP Risk Factor which is determined using the following formula:
Σ(No of Risk Alleles×SNP β coefficient).
In an embodiment, a method of the invention further comprises determining the probability the subject would require hospitalisation if infected with a Coronavirus using the following formula:
e ^LO/(1+e ^LO),
which is then multiplied by 100 to obtain a percent chance of hospitalisation being required.
In an embodiment of each of the above aspects, the risk assessment produces a score and the method further comprises comparing the score to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.
In an embodiment, if it is determined the subject has a risk of developing a severe response to a Coronavirus infection, the subject is more likely than someone assessed as low risk, or when compared to the average risk in the population, to be admitted to hospital for intensive care.
In a further aspect, the present invention provides a method for determining the need for routine diagnostic testing of a human subject for a Coronavirus infection comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention.
In another aspect, the present invention provides a method of screening for a severe response to a Coronavirus infection in a human subject, the method comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention, and routinely screening for a Coronavirus infection in the subject if they are assessed as having a risk for developing a severe response to a Coronavirus infection.
In an embodiment of the above two aspects, the screening involves analysing the subject for the virus or a symptom thereof.
In a further aspect, the present invention provides a method for determining the need of a human subject for prophylactic anti-Coronavirus therapy comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention.
In yet another aspect, the present invention provides a method for preventing or reducing the risk of a severe response to a Coronavirus infection in a human subject, the method comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention, and if they are assessed as having a risk for developing a severe response to a Coronavirus infection
1) administering an anti-Coronavirus therapy and/or
2) isolating the subject.
In an aspect, the present invention provides an anti-Coronavirus infection therapy for use in preventing a severe response to a Coronavirus infection in a human subject at risk thereof, wherein the subject is assessed as having a risk for developing a severe response to a Coronavirus infection using a method of the invention.
Many anti-Coronavirus therapies, such as anti-SARS-CoV-2 virus therapies, are in development. The skilled person would appreciate that any therapy shown to be successful can be used in the above methods. Possible examples include, but are not limited to, intubation to assist breathing, an anti-Coronavirus—such as anti-SARS-CoV-2 virus—vaccine, convalescent plasma (plasma from people who have been infected, developed antibodies to the virus, and have then recovered), chloroquine, hydroxychloroquine (with or without zinc), Favipiravir, Remdesivir, Ivermectin, Quercetin, Kaletra (lopinavir/ritonavir), Arbidol, Baricitinib, CM4620-IE, an IL-6 inhibitor, Tocilizumab and stem cells such as mesenchymal stem cells. In another embodiment, the therapy is Vitamin D. Other examples of therapy include, Dexamethasone (or other corticosteroids such as prednisone, methylprednisolone, or hydrocortisone), Baricitinib in combination with remdesivir, anticoagulation drugs (“blood thinners”), bamlanivimab and etesevimab, convalescent plasma, tocilizumab with corticosteroids, Casirivimab and Imdevimab, Atorvastatin, GRP78 and siRNA-nanoparticle formulations.
Once a vaccine (or indeed possibly many different anti-Coronavirus therapies) is developed it is highly likely there will be supply issues and decisions will need to be made about why one person will receive the vaccine first when compared to another person. The present invention can thus be used to determine who is at most risk, and the anti-Coronavirus therapy (such as a vaccine) first administered to people assessed as likely to develop a severe response to a Coronavirus infection.
In an embodiment, the vaccine is an mRNA vaccine. In an embodiment, the vaccine is a protein vaccine. Examples of vaccines that can be administered include, but are not limited to, the Pfizer-BioNTech vaccine, the Moderna vaccine, the Johnson & Johnson vaccine, the Oxford-AstraZeneca vaccine and the Novavax vaccine (see, for example, Katella, 2021).
In another embodiment, the present invention provides a method for stratifying a group of human subjects for a clinical trial of a candidate therapy, the method comprising assessing the individual risk of the subjects for developing a severe response to a Coronavirus infection using a method of the invention, and using the results of the assessment to select subjects more likely to be responsive to the therapy.
Also provided is a kit comprising at least two sets of primers for amplifying two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising each of the polymorphisms provided in Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In another aspect, the present invention provides a genetic array comprising at least two sets of probes for hybridising to two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the array comprises probes hybridising to nucleic acids comprising each of the polymorphisms provided in Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an aspect, the present invention provides a computer implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:
receiving genetic risk data for the human subject, wherein the genetic risk data was obtained by a method of the invention;
processing the data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and
outputting the risk of a human subject developing a severe response to a Coronavirus infection.
In an aspect, the present invention provides a computer implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:
receiving clinical risk data and genetic risk data for the human subject, wherein the clinical risk data and genetic risk data were obtained by a method of the invention;
processing the data to combine the clinical risk data with the genetic risk data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and
outputting the risk of a human subject developing a severe response to a Coronavirus infection.
In a further aspect, the present invention provides a computer-implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:
receiving at least one clinical variable associated with the human subject, wherein at least one clinical variable was obtained by a method of the invention;
processing the data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and
outputting the risk of a human subject developing a severe response to a Coronavirus infection.
In an embodiment of the three above aspects, processing the data is performed using a risk assessment model, where the risk assessment model has been trained using a training dataset comprising data relating to Coronavirus infection response severity and the genetic data and/or clinical data. In another embodiment, the method further comprises displaying or communicating the risk to a user.
In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:
system instructions for performing a genetic risk assessment of the human subject according to a method of the invention; and
system instructions to obtain the risk of a human subject developing a severe response to a Coronavirus infection.
In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:
system instructions for performing a clinical risk assessment and a genetic risk assessment of the human subject according to a method of the invention; and
system instructions for combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection.
In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:
system instructions for performing a clinical risk assessment of the human subject using the method according to any one of claims 20 to 26 or 36 to 39; and
system instructions to obtain the risk of a human subject developing a severe response to a Coronavirus infection.
In an embodiment, the risk data for the subject is received from a user interface coupled to the computing system. In another embodiment, the risk data for the subject is received from a remote device across a wireless communications network. In another embodiment, the user interface or remote device is a SNP array platform. In another embodiment, outputting comprises outputting information to a user interface coupled to the computing system. In another embodiment, outputting comprises transmitting information to a remote device across a wireless communications network.
Any embodiment herein shall be taken to apply mutatis mutandis to any other embodiment unless specifically stated otherwise.
The present invention is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only.
Functionally-equivalent products, compositions and methods are clearly within the scope of the invention, as described herein.
Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.
The invention is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

FIG. 1. Receiver operating characteristic curves for models with different amounts of information. The area under the receiver operating characteristic curve was 0.786 for the combined model, 0.723 for the clinical model, 0.680 for the SNP score, and 0.635 for the age and sex model.

FIG. 2. Distribution of COVID risk score for (a) cases and (b) controls. Note that 130 (13%) cases and 6 (1%) controls with scores over 15 have been omitted to facilitate the display of the distribution.

FIG. 3. Distribution of COVID-19 risk score in UK Biobank. Note that 7,769 (1.8%) scores over 15 have been omitted to facilitate the display of the distribution.

FIG. 4. Receiver operating characteristic curves for the age and sex model and the “full model” in the 30% validation dataset. The new model has an area under the curve (AUC) of 0.732 (95% CI=0.708, 0.756) and the age and sex model has an AUC of 0.671 (95% CI=0.646, 0.696).

FIG. 5. Calibration plots for the (A) age and sex model and (B) “full model” in the validation dataset.

FIG. 6. Distribution of probability of severe COVID-19 in all of UK Biobank for (A) age and sex model and (B) the full model.

DETAILED DESCRIPTION OF THE INVENTION

General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., epidemiological analysis, molecular genetics, risk assessment and clinical studies).
Unless otherwise indicated, the recombinant protein, cell culture, and immunological techniques utilized in the present invention are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T. A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J. E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).
It is to be understood that this disclosure is not limited to particular embodiments, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, terms in the singular and the singular forms “a,” “an” and “the,” for example, optionally include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a probe” optionally includes a plurality of probe molecules; similarly, depending on the context, use of the term “a nucleic acid” optionally includes, as a practical matter, many copies of that nucleic acid molecule.
The term “and/or”, e.g., “X and/or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.
As used herein, the term “about”, unless stated to the contrary, refers to +/−10%, more preferably +/−5%, more preferably +/−1%, of the designated value.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
“Coronavirus” is a group of related RNA viruses that typically cause diseases in mammals and birds, such as respiratory tract infections in humans. Coronaviruses constitute the subfamily Orthocoronavirinae in the family Coronaviridae. Coronaviruses are enveloped viruses with a positive-sense single-stranded RNA genome and a nucleocapsid of helical symmetry. Coronaviruses have characteristic club-shaped spikes that project from their surface. Examples of Coronaviruses which cause disease in humans include, but are not necessarily limited to, Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E and Human coronavirus NL63. In some embodiments, the SARS-CoV-2 the strain is selected from, but not limited to, the L strain, the S strain, the V strain, the G strain, the GR strain, the GH strain, hCoV-19/Australia/VIC01/2020, BetaCoV/Wuhan/WIV04/2019, B.1.1.7 variant, B.1.351 variant, B.1.427 variant, B.1.429 variant and P.1 variant.
As used herein, “risk assessment” refers to a process by which a subject's risk of developing a severe response to a Coronavirus infection can be assessed. A risk assessment will typically involve obtaining information relevant to the subject's risk of developing a severe response to a Coronavirus infection, assessing that information, and quantifying the subject's risk of developing a severe response to a Coronavirus infection, for example, by producing a risk score.
As used herein, the term “a severe response to a Coronavirus infection” encompasses any factor, or a symptom thereof, considered by a medical practitioner that would warrant the subject being hospitalised, the subject's life being at risk, or the subject requiring assistance to breath. Examples of symptoms of a severe response to a Coronavirus infection include, but are not limited to, difficulty breathing or shortness of breath, chest pain or pressure, loss of speech or loss of movement. A phenotype that displays a predisposition for a severe response to a Coronavirus infection, can, for example, show a higher likelihood that a severe response to a Coronavirus infection will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.).
As used herein, “biological sample” refers to any sample comprising nucleic acids, especially DNA, from or derived from a human patient, e.g., bodily fluids (blood, saliva, urine etc.), biopsy, tissue, and/or waste from the patient. Thus, tissue biopsies, stool, sputum, saliva, blood, lymph, or the like can easily be screened for polymorphisms, as can essentially any tissue of interest that contains the appropriate nucleic acids. In one embodiment, the biological sample is a cheek cell sample. These samples are typically taken, following informed consent, from a patient by standard medical laboratory methods. The sample may be in a form taken directly from the patient, or may be at least partially processed (purified) to remove at least some non-nucleic acid material.
As used herein, “gender” and “sex” are used interchangeably and refer to an individual's biological reproductive anatomy. In an embodiment, an individual's gender/sex is self-identified.
As used herein, “human subject”, “human” and subject” are used interchangeably and refer to the individual being assessed for risk of developing a severe response to a coronavirus infection.
A “polymorphism” is a locus that is variable; that is, within a population, the nucleotide sequence at a polymorphism has more than one version or allele. One example of a polymorphism is a “single nucleotide polymorphism” (SNP), which is a polymorphism at a single nucleotide position in a genome (the nucleotide at the specified position varies between individuals or populations). Other examples include a deletion or insertion of one or more base pairs at the polymorphism locus.
As used herein, the term “SNP” or “single nucleotide polymorphism” refers to a genetic variation between individuals; e.g., a single nitrogenous base position in the DNA of organisms that is variable. As used herein, “SNPs” is the plural of SNP. Of course, when one refers to DNA herein, such reference may include derivatives of the DNA such as amplicons, RNA transcripts thereof, etc.
The term “allele” refers to one of two or more different nucleotide sequences that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population. An allele “positively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele. An allele “negatively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.
A marker polymorphism or allele is “correlated” or “associated” with a specified phenotype (a severe response to a Coronavirus infection susceptibility, etc.) when it can be statistically linked (positively or negatively) to the phenotype. Methods for determining whether a polymorphism or allele is statistically linked are known to those in the art. That is, the specified polymorphism occurs more commonly in a case population (e.g., a severe response to a Coronavirus infection patients) than in a control population (e.g., individuals that do not have a severe response to a Coronavirus infection). This correlation is often inferred as being causal in nature, but it need not be, simple genetic linkage to (association with) a locus for a trait that underlies the phenotype is sufficient for correlation/association to occur.
The phrase “linkage disequilibrium” (LD) is used to describe the statistical correlation between two neighbouring polymorphic genotypes. Typically, LD refers to the correlation between the alleles of a random gamete at the two loci, assuming Hardy-Weinberg equilibrium (statistical independence) between gametes. LD is quantified with either Lewontin's parameter of association (D′) or with Pearson correlation coefficient (r) (Devlin and Risch, 1995). Two loci with a LD value of 1 are said to be in complete LD. At the other extreme, two loci with a LD value of 0 are termed to be in linkage equilibrium. Linkage disequilibrium is calculated following the application of the expectation maximization algorithm (EM) for the estimation of haplotype frequencies (Slatkin and Excoffier, 1996). LD (r²) values according to the present disclosure for neighbouring genotypes/loci are selected above 0.1, preferably, above 0.2, more preferable above 0.5, more preferably, above 0.6, still more preferably, above 0.7, preferably, above 0.8, more preferably above 0.9, ideally about 1.0.
Another way one of skill in the art can readily identify polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure is determining the LOD score for two loci. LOD stands for “logarithm of the odds”, a statistical estimate of whether two genes, or a gene and a disease gene, are likely to be located near each other on a chromosome and are therefore likely to be inherited. A LOD score of between about 2-3 or higher is generally understood to mean that two genes are located close to each other on the chromosome. Various examples of polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure are shown in Tables 1 to 6, 8, 19 or 22. The present inventors have found that many of the polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure have a LOD score of between about 2-50. Accordingly, in an embodiment, LOD values according to the present disclosure for neighbouring genotypes/loci are selected at least above 2, at least above 3, at least above 4, at least above 5, at least above 6, at least above 7, at least above 8, at least above 9, at least above 10, at least above 20 at least above 30, at least above 40, at least above 50.
In another embodiment, polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure can have a specified genetic recombination distance of less than or equal to about 20 centimorgan (cM) or less. For example, 15 cM or less, 10 cM or less, 9 cM or less, 8 cM or less, 7 cM or less, 6 cM or less, 5 cM or less, 4 cM or less, 3 cM or less, 2 cM or less, 1 cM or less, 0.75 cM or less, 0.5 cM or less, 0.25 cM or less, or 0.1 cM or less. For example, two linked loci within a single chromosome segment can undergo recombination during meiosis with each other at a frequency of less than or equal to about 20%, about 19%, about 18%, about 17%, about 16%, about 15%, about 14%, about 13%, about 12%, about 11%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.75%, about 0.5%, about 0.25%, or about 0.1% or less.
In another embodiment, polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure are within at least 100 kb (which correlates in humans to about 0.1 cM, depending on local recombination rate), at least 50 kb, at least kb or less of each other.
For example, one approach for the identification of surrogate markers for a particular polymorphism involves a simple strategy that presumes that polymorphisms surrounding the target polymorphism are in linkage disequilibrium and can therefore provide information about disease susceptibility. Thus, as described herein, surrogate markers can therefore be identified from publicly available databases, such as HAPMAP, by searching for polymorphisms fulfilling certain criteria which have been found in the scientific community to be suitable for the selection of surrogate marker candidates (see, for example, Table 6a which provides surrogates of the polymorphisms in Table 3, and Table 6b which provides surrogates of the polymorphisms in Table 4).
“Allele frequency” refers to the frequency (proportion or percentage) at which an allele is present at a locus within an individual, within a line or within a population of lines. For example, for an allele “A,” diploid individuals of genotype “AA,” “Aa,” or “aa” have allele frequencies of 1.0, 0.5, or 0.0, respectively. One can estimate the allele frequency within a line or population (e.g., cases or controls) by averaging the allele frequencies of a sample of individuals from that line or population. Similarly, one can calculate the allele frequency within a population of lines by averaging the allele frequencies of lines that make up the population. In an embodiment, the term “allele frequency” is used to define the minor allele frequency (MAF). MAF refers to the frequency at which the least common allele occurs in a given population.
An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes). An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles). The term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.
A “locus” is a chromosomal position or region. For example, a polymorphic locus is a position or region where a polymorphic nucleic acid, trait determinant, gene or marker is located. In a further example, a “gene locus” is a specific chromosome location (region) in the genome of a species where a specific gene can be found.
A “marker,” “molecular marker” or “marker nucleic acid” refers to a nucleotide sequence or encoded product thereof (e.g., a protein) used as a point of reference when identifying a locus or a linked locus. A marker can be derived from genomic nucleotide sequence or from expressed nucleotide sequences (e.g., from an RNA, nRNA, mRNA, a cDNA, etc.), or from an encoded polypeptide. The term also refers to nucleic acid sequences complementary to or flanking the marker sequences, such as nucleic acids used as probes or primer pairs capable of amplifying the marker sequence. A “marker probe” is a nucleic acid sequence or molecule that can be used to identify the presence of a marker locus, e.g., a nucleic acid probe that is complementary to a marker locus sequence. Nucleic acids are “complementary” when they specifically hybridize in solution, e.g., according to Watson-Crick base pairing rules. A “marker locus” is a locus that can be used to track the presence of a second linked locus, e.g., a linked or correlated locus that encodes or contributes to the population variation of a phenotypic trait. For example, a marker locus can be used to monitor segregation of alleles at a locus, such as a quantitative trait locus (QTL), that are genetically or physically linked to the marker locus. Thus, a “marker allele,” alternatively an “allele of a marker locus” is one of a plurality of polymorphic nucleotide sequences found at a marker locus in a population that is polymorphic for the marker locus. Each of the identified markers is expected to be in close physical and genetic proximity (resulting in physical and/or genetic linkage) to a genetic element, e.g., a QTL, that contributes to the relevant phenotype. Markers corresponding to genetic polymorphisms between members of a population can be detected by methods well-established in the art. These include, e.g., DNA sequencing, PCR-based sequence specific amplification methods, detection of restriction fragment length polymorphisms (RFLP), detection of isozyme markers, detection of allele specific hybridization (ASH), detection of single nucleotide extension, detection of amplified variable sequences of the genome, detection of self-sustained sequence replication, detection of simple sequence repeats (SSRs), detection of single nucleotide polymorphisms (SNPs), or detection of amplified fragment length polymorphisms (AFLPs).
The term “amplifying” in the context of nucleic acid amplification is any process whereby additional copies of a selected nucleic acid (or a transcribed form thereof) are produced. Typical amplification methods include various polymerase based replication methods, including the polymerase chain reaction (PCR), ligase mediated methods such as the ligase chain reaction (LCR) and RNA polymerase based amplification (e.g., by transcription) methods.
An “amplicon” is an amplified nucleic acid, e.g., a nucleic acid that is produced by amplifying a template nucleic acid by any available amplification method (e.g., PCR, LCR, transcription, or the like).
A “gene” is one or more sequence(s) of nucleotides in a genome that together encode one or more expressed molecules, e.g., an RNA, or polypeptide. The gene can include coding sequences that are transcribed into RNA which may then be translated into a polypeptide sequence, and can include associated structural or regulatory sequences that aid in replication or expression of the gene.
A “genotype” is the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci of the individual, typically, the compilation of alleles inherited from its parents.
A “haplotype” is the genotype of an individual at a plurality of genetic loci on a single DNA strand. Typically, the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome strand.
A “set” of markers (polymorphisms), probes or primers refers to a collection or group of markers probes, primers, or the data derived therefrom, used for a common purpose, e.g., identifying an individual with a specified genotype (e.g., risk of developing a severe response to a Coronavirus infection). Frequently, data corresponding to the markers, probes or primers, or derived from their use, is stored in an electronic medium. While each of the members of a set possess utility with respect to the specified purpose, individual markers selected from the set as well as subsets including some, but not all of the markers, are also effective in achieving the specified purpose.
The polymorphisms and genes, and corresponding marker probes, amplicons or primers described above can be embodied in any system herein, either in the form of physical nucleic acids, or in the form of system instructions that include sequence information for the nucleic acids. For example, the system can include primers or amplicons corresponding to (or that amplify a portion of) a gene or polymorphism described herein. As in the methods above, the set of marker probes or primers optionally detects a plurality of polymorphisms in a plurality of said genes or genetic loci. Thus, for example, the set of marker probes or primers detects at least one polymorphism in each of these polymorphisms or genes, or any other polymorphism, gene or locus defined herein. Any such probe or primer can include a nucleotide sequence of any such polymorphism or gene, or a complementary nucleic acid thereof, or a transcribed product thereof (e.g., a nRNA or mRNA form produced from a genomic sequence, e.g., by transcription or splicing).
As used herein, “Receiver operating characteristic curves” (ROC) refer to a graphical plot of the sensitivity vs. (1—specificity) for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives (TPR=true positive rate) vs. the fraction of false positives (FPR=false positive rate). Also known as a Relative Operating Characteristic curve, because it is a comparison of two operating characteristics (TPR & FPR) as the criterion changes. ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. Methods of using in the context of the disclosure will be clear to those skilled in the art.
As used herein, the phrase “combining the first clinical risk assessment and the genetic risk assessment” refers to any suitable mathematical analysis relying on the results of the assessments. For example, the results of the first clinical risk assessment and the genetic risk assessment may be added, more preferably multiplied.
As used herein, the terms “routinely screening for a severe response to a Coronavirus infection” and “more frequent screening” are relative terms, and are based on a comparison to the level of screening recommended to a subject who has no identified risk of developing a severe response to a Coronavirus infection.

Genetic Risk Assessment

In an aspect, a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection of the invention involves detecting the presence of a polymorphism provided in any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith. In another aspect, a method of the invention involves a genetic risk assessment performed by analysing the genotype of the subject at two or more loci for polymorphisms associated with a severe response to a Coronavirus infection. Various exemplary polymorphisms associated with a severe response to a Coronavirus infection are discussed in the present disclosure. These polymorphisms vary in terms of penetrance and many would be understood by those of skill in the art to be low penetrance polymorphisms.
The term “penetrance” is used in the context of the present disclosure to refer to the frequency at which a particular polymorphism manifests itself within human subjects with a severe response to a Coronavirus infection. “High penetrance” polymorphisms will almost always be apparent in a human subject with a severe response to a Coronavirus infection while “low penetrance” polymorphisms will only sometimes be apparent. In an embodiment polymorphisms assessed as part of a genetic risk assessment according to the present disclosure are low penetrance polymorphisms. As the skilled addressee will appreciate, each polymorphism which increases the risk of developing a severe response to a Coronavirus infection has an odds ratio of association with a severe response to a Coronavirus infection of greater than 1.0. In an embodiment, the odds ratio is greater than 1.02. Each polymorphism which decreases the risk of developing a severe response to a Coronavirus infection has an odds ratio of association with a severe response to a Coronavirus infection of less than 1.0. In an embodiment, the odds ratio is less than 0.98. Examples of such polymorphisms include, but are not limited to, those provided in Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, the genetic risk assessment involves assessing polymorphisms associated with increased risk of developing a severe response to a Coronavirus infection. In another embodiment, the genetic risk assessment involves assessing polymorphisms associated with decreased risk of developing a severe response to a Coronavirus infection. In another embodiment, the genetic risk assessment involves assessing polymorphisms associated with an increased risk of developing a severe response to a Coronavirus infection and polymorphisms associated with a decreased risk of developing a severe response to a Coronavirus infection.
In an embodiment, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are analysed.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided in Tables 1 to 3, 5a or 6, Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof

TABLE 1

Informative polymorphisms of the invention.

				p-value
Chro-				for
mo-				associ-
some	Position	SNP ID	Alleles	ation

1	31624029	rs12083278	G, C	0.00000243

1	87628173	rs10873821	C, T	0.0000228

1	63766718	rs112728381	C, T	0.0000252

1	2998313	rs12745140	G, A	0.0000317

1	36374101	rs2765013	C, T	0.000039

1	36549664	rs2274122	G, A	0.000107

1	186287454	rs1830344	T, C	0.000127

1	187364290	rs7517532	G, C	0.000136

1	114893146	rs574339	T, C	0.000169

1	222722631	rs61825527	A, C	0.000194

1	31380174	rs4303117	A, C	0.000199

1	88237749	rs72714531	T, C	0.000223

1	101661978	rs11166552	C, T	0.000257

1	37147203	rs219007	C, T	0.000271

1	184803508	rs630030	A, G	0.000271

1	60210649	rs1004772	T, C	0.000289

1	83665753	rs9432945	C, T	0.000293

1	36172029	rs6664663	C, G	0.000304

1	161229986	rs5778200	A, AC	0.000326

1	107941708	rs17018870	G, A	0.000328

1	14216150	rs17350970	C, T	0.000346

1	55046392	rs300269	G, A	0.000355

1	230906775	rs3790971	A, G	0.000357

1	60214250	rs3990361	A, G	0.000386

1	208388679	rs78771609	A, G	0.000395

1	3680362	rs146866117	T, C	3.21516E−05

1	10993680	rs75721992	C, T	8.03153E−09

1	15698556	rs12562412	G, C	7.34872E−05

1	15758944	rs117338853	A, G	9.31685E−05

1	16109212	rs72647169	G, C	1.72286E−05

1	17295659	rs199765517	T, C	6.47434E−05

1	20072025	rs199727655	A, G	3.41489E−05

1	22538788	rs78360109	A, G	1.32276E−06

1	34829829	rs79955780	G, A	8.74145E−05

1	38661814	rs61778695	C, A	4.43093E−05

1	67804320	rs578200723	GTTA, G	0.000020482

1	72488455	rs116544454	T, G	1.85829E−05

1	109823418	rs144022094	T, C	3.57493E−05

1	109933450	rs56072034	A, G	4.12342E−05

1	168696733	rs76129265	T, G	7.51539E−05

1	204092087	rs201772428	A, G	4.3011E−06

1	206776460	rs35252702	A, G	1.56671E−07

1	207610967	rs61821114	T, C	3.00757E−05

1	210901242	rs1934624	A, T	5.48665E−05

1	218938774	rs76354174	A, G	7.54499E−05

1	230907829	rs143186556	A, G	5.46536E−05

1	231133014	rs200114138	A, G	3.96515E−06

1	46618634	rs17102023	G, A	0.5

1	150271556	rs115492982	A, G	0.01

1	152684866	rs2224986	T, C	0.8

1	192526317	rs74508649	T, C	0.9

1	239197542	rs112317747	C, T	0.05

2	36905013	rs6714112	C, A	0.00000781

2	217524986	rs2270360	A, C	0.0000245

2	11239618	rs62120103	C, T	0.000116

2	42181679	rs6740960	A, T	0.00012

2	137073048	rs6430625	A, G	0.000144

2	75788396	rs759255	C, A	0.000181

2	11694251	rs4313952	A, G	0.000194

2	140976470	rs9941558	A, G	0.000196

2	13900135	rs61101702	TAATA, T	0.0002

2	182396974	rs6760007	T, C	0.0002

2	46584059	rs34136947	T, G	0.000222

2	6948980	rs55900661	G, A	0.000235

2	175367762	rs4972443	T, C	0.000237

2	217553774	rs3755137	T, A	0.000263

2	2965401	rs1729903	C, T	0.000271

2	182359592	rs16867434	T, C	0.000275

2	50825372	rs116302817	C, T	0.000322

2	217701606	rs111437052	A, G	0.000343

2	52551249	rs115352379	T, C	0.000344

2	45503541	rs6749256	G, C	0.000397

2	11662023	rs62120186	C, T	0.000403

2	52998723	rs62127009	A, G	0.000408

2	22958939	rs59447738	G, T	8.21388E−05

2	23005876	rs73918088	A, C	7.50922E−05

2	25861939	rs189303418	T, C	0.000040202

2	34108876	rs1718746	C, G	8.52475E−05

2	34119632	rs1705143	A, G	8.08594E−05

2	64630299	rs872241	G, A	7.17367E−05

2	64641736	rs11676644	C, T	0.000032141

2	115258481	rs56735442	A, G	3.59163E−05

2	122952399	rs75945051	G, A	4.38486E−05

2	126726522	rs76187206	C, G	5.74668E−05

2	160721407	chr2	A, AC	4.45501E−05
		160721407

2	179428061	rs11896637	T, C	1.81846E−06

2	179441917	rs72646881	C, T	1.59099E−05

2	179612315	rs145581345	C, T	8.45307E−05

2	181910717	rs78593095	A, G	2.26398E−05

2	191278341	rs6725814	G, A	1.88302E−05

2	79895332	rs183569214	G, T	0.7

2	80029580	rs77764981	T, G	0.6

2	182353446	rs2034831	A, G	0.02

3	141408691	rs6440031	A, G	0.0000714

3	1093795	rs1504061	C, G	0.0000716

3	125837737	rs1868132	C, T	0.000102

3	125649876	rs6438947	T, C	0.000126

3	38759465	rs9990137	A, G	0.000148

3	155736888	rs4680228	C, T	0.000157

3	24759067	rs71328493	C, G	0.000213

3	46628726	rs1829538	G, T	0.000257

3	71238088	rs111323182	T, A	0.00027

3	143909347	rs13089585	T, C	0.000302

3	2357581	rs56035150	A, G	0.000318

3	5893097	rs74827709	G, T	0.000342

3	21751839	rs1080021	A, G	0.000366

3	25519583	rs1864903	A, G	0.000366

3	173319456	rs6800283	T, C	0.000366

3	195949310	rs35516030	AG, A	0.000375

3	170214459	rs2008829	G, T	0.000376

3	38736230	rs12639182	C, T	0.000395

3	18486173	rs62240975	G, A	0.000405

3	170266664	rs139670481	AAATT, A	0.000406

3	170961913	rs115037737	G, A	0.000407

3	2748191	rs80225140	G, A	8.74289E−06

3	34944013	rs17032477	G, A	3.63638E−05

3	65100060	rs2128405	C, A	1.71219E−05

3	70783491	rs6766000	T, C	2.00031E−05

3	81810551	rs35196441	T, G	3.53506E−05

3	154812638	rs2196521	G, A	3.73634E−05

3	160379672	rs4679910	G, A	1.90222E−05

3	171853417	rs73167212	G, A	0.00002702

3	27188298	rs17317135	A, G	0.0000641

3	177796194	rs74911757	T, C	1.43291E−05

3	3184653	rs1705826	G, C	0.5

3	45841938	rs35896106	T, C	0.04

3	45900634	rs76374459	C, G	0.03

3	45908514	rs35652899	G, C	0.04

3	45916222	rs12639224	T, C	0.7

3	45916786	rs34901975	A, G	0.09

3	46018781	rs71615437	G, A	0.1

3	46049765	rs13433997	C, T	0.1

3	46180416	rs10510749	T, C	0.9

3	46222037	rs115102354	G, A	0.7

3	62936766	rs13062942	G, A	0.09

3	148718087	rs76488148	T, G	0.03

4	69705994	rs115162070	G, A	0.00000356

4	44418592	rs35540967	T, C	0.0000289

4	5821922	rs3774882	C, G	0.0000349

4	5821877	rs3774881	T, C	0.0000459

4	112613026	rs112641600	C, T	0.000058

4	106943200	rs11729561	T, C	0.000104

4	163076494	rs62331317	G, A	0.000113

4	5820342	rs28426993	C, T	0.000114

4	35945837	rs111866232	G, C	0.000157

4	106909473	rs78699658	A, G	0.000213

4	99439788	rs7668981	C, T	0.000273

4	187239747	rs7687352	A, G	0.000288

4	60236874	rs12498396	G, A	0.000292

4	60147523	rs13126577	T, C	0.000293

4	24316054	rs28735003	T, C	0.000295

4	96348686	rs4277782	T, C	0.000314

4	162009814	rs35850287	C, T	0.00033

4	39943691	rs115509062	G, C	6.54964E−05

4	45117625	rs75072424	A, G	2.80438E−05

4	69795670	rs144454074	A, G	4.19779E−05

4	73555556	rs28616128	G, A	3.02015E−06

4	75191300	rs115044024	C, T	4.15557E−05

4	84216649	rs144185023	A, G	3.72469E−05

4	87939942	rs76456240	C, T	9.48747E−05

4	121958473	rs202221151	C, T	1.61977E−06

4	142326546	rs76589765	G, A	4.20983E−06

4	151724769	rs116015734	T, C	9.26679E−05

4	153821201	rs62319956	C, A	2.01266E−06

4	170238293	rs76519323	A, C	2.30315E−05

4	170498270	rs74557505	C, T	6.57926E−05

4	181162752	rs17069033	T, G	1.35337E−05

4	185567903	rs4647611	C, G	2.72675E−05

4	27383278	rs6810404	A, C	0.0000988

5	173989338	rs2220543	T, A	0.0000187

5	180216905	rs10577599	CAT, C	0.000025

5	142252549	rs10039856	C, T	0.0000585

5	123950404	rs4240376	G, T	0.000065

5	122832716	rs62377777	T, C	0.0000653

5	59077872	rs62370540	T, C	0.000116

5	122559297	rs72787582	C, T	0.000117

5	124143238	rs71594388	C, T	0.000136

5	159258092	rs78045322	G, C	0.000172

5	135351183	rs72794907	G, A	0.000174

5	135435801	rs7720483	C, T	0.000174

5	122958050	rs70988587	ATTC, A	0.000215

5	176737309	rs7732626	A, G	0.000256

5	59191612	rs159616	C, T	0.000259

5	135360738	rs35901765	C, T	0.000275

5	106396918	rs6864971	C, T	0.000373

5	6471344	rs507971	C, T	0.00038

5	161475413	rs17548653	C, T	0.00041

5	6826973	rs275444	C, G	3.53004E−05

5	19993490	rs4466171	A, T	3.08251E−06

5	43280480	rs55770078	A, G	1.16965E−05

5	99815379	rs115319054	A, C	8.88589E−05

5	133519273	rs79601653	G, A	9.89897E−05

5	155405405	rs958444	T, C	2.01272E−05

5	160448591	rs11749317	C, T	0.000080983

5	180237828	rs113791144	T, C	0.000144

5	169590905	rs4478338	G, T	0.3

5	171480160	rs111265173	T, C	1

6	106326754	rs9386484	T, A	0.00000617

6	45704813	rs16873740	T, A	0.0000296

6	12216966	rs10755709	A, G	0.00003

6	18015447	rs140247774	C, T	0.000047

6	6925195	rs6933436	A, C	0.0000983

6	151433505	rs6928557	A, C	0.000121

6	170445103	rs9366129	C, T	0.000133

6	148557644	rs9390548	G, A	0.000149

6	137545805	rs11759276	A, T	0.000183

6	54128003	rs12193921	T, C	0.000184

6	54353221	rs76378690	G, A	0.000206

6	20013937	rs13213659	A, G	0.000233

6	148522455	rs117687499	G, A	0.000262

6	154813083	rs6935885	G, C	0.000306

6	18739469	rs2328283	A, C	0.000333

6	18271083	rs10949488	C, T	0.000355

6	20044587	rs594259	A, G	0.000375

6	44184719	rs2297393	T, C	0.000409

6	2885791	rs318470	C, A	9.76222E−05

6	16217762	rs149442766	A, G	3.42524E−05

6	26408145	rs144114619	A, T	4.57681E−05

6	33156845	rs41268014	G, C	1.61406E−05

6	36976747	rs140572234	C, G	6.20165E−08

6	36999980	rs114925152	G, C	0.000019845

6	37139030	rs35760989	C, G	2.15792E−05

6	101279459	rs9485415	T, C	2.95345E−05

6	147885588	rs140643252	A, G	0.000080315

6	27604726	rs61611950	T, C	0.8

7	114940068	rs7800941	G, A	0.000199

7	138401542	rs3778698	T, C	0.0002

7	99987236	rs7798226	T, G	0.000222

7	154700565	rs882469	G, A	0.000222

7	105125204	rs111283303	G, A	0.000232

7	11301277	rs7807069	G, A	0.000271

7	11362820	rs76731008	G, A	0.000277

7	9397414	rs13238693	T, G	0.000286

7	52994281	rs7804465	T, C	0.000342

7	36662714	rs58016731	AGTCTT, A	0.000391

7	6687563	rs55944391	C, A	0.000395

7	45244259	rs1294888	T, C	0.000395

7	14862658	rs2109498	A, T	0.000419

7	21730647	rs7790948	G, T	3.13489E−05

7	35720134	rs79496619	T, G	3.21712E−05

7	38677134	rs78966608	A, C	7.06676E−05

7	100483400	rs200675508	A, G	7.25535E−05

7	104782689	rs55743527	G, T	1.36022E−06

7	122122078	rs73431600	A, G	5.15467E−05

7	122196872	rs73433754	A, C	3.48011E−05

7	154926067	rs117164958	C, G	7.61301E−06

7	158928541	rs13225056	A, G	1.53596E−05

7	152960930	rs6967210	C, T	0.3

8	16790149	rs118072448	T, C	0.0000235

8	74268198	rs2010843	T, C	0.0000556

8	38897470	rs13282163	A, C	0.0000739

8	40181978	rs11779911	C, A	0.0000841

8	38821327	rs10808999	A, G	0.0000884

8	12850333	rs2947375	A, G	0.000162

8	143261211	rs72685583	T, C	0.000218

8	55488334	rs74458553	C, T	0.000254

8	32590598	rs4351382	T, A	0.000273

8	61303777	rs147353244	G, GA	0.000327

8	2044114	rs147776183	T, C	8.64871E−06

8	9478274	rs78876374	T, C	8.85355E−05

8	19218826	rs145746072	A, G	1.91735E−05

8	20354600	rs13249119	G, A	1.33633E−05

8	20447019	rs73626732	A, G	1.40229E−05

8	20460661	rs35258926	G, T	9.04111E−06

8	39799765	rs7845003	T, C	9.39614E−05

8	125839433	rs118040942	T, C	0.000059504

8	130677813	rs57557483	A, G	8.17573E−06

8	130694201	rs75572486	A, G	2.53314E−05

8	8730488	rs332040	A, G	0.9

9	4329170	rs3895472	T, C	0.0000235

9	21131627	rs12236000	G, C	0.0000452

9	75127404	rs35460846	A, AT	0.0000819

9	81158113	rs7027911	A, G	0.0000905

9	81158443	rs3009696	C, T	0.00013

9	140227646	rs11523787	C, T	0.000137

9	75061917	rs11143296	C, T	0.00014

9	78174461	rs13298924	T, C	0.00021

9	23650820	rs4584238	G, C	0.00022

9	35490636	rs10814241	C, G	0.00022

9	138202418	rs7032559	C, T	0.00025

9	122045518	rs487545	T, C	0.000269

9	139024232	rs7021573	G, A	0.000309

9	75136789	rs7022441	G, A	0.000343

9	139047766	rs10858230	G, A	0.000367

9	138006474	rs61018036	G, A	0.000373

9	116468053	rs75260470	C, T	0.000402

9	10276812	rs10959000	A, G	7.85152E−05

9	22080363	rs16905613	G, A	5.78633E−06

9	38669062	rs2993177	A, G	7.37838E−05

9	79453107	rs7853555	T, C	5.25711E−05

9	83032179	rs72744937	G, A	6.92799E−05

9	100137855	rs200908751	A, G	6.57652E−05

9	101874496	rs76094400	A, G	3.95481E−08

9	125704675	rs76670825	A, G	5.01185E−05

9	125903047	rs77089732	A, G	0.00005145

9	128794405	rs62570501	T, C	7.35517E−05

9	131771551	rs17455482	A, G	5.2118E−06

9	27121456	rs71480372	T, A	0.7

9	29688719	rs74790577	T, A	0.9

10	44015051	rs10793436	G, T	0.0000302

10	54100345	rs1441121	A, T	0.0000322

10	37454397	rs1892429	A, G	0.0000335

10	37277870	rs2091431	A, G	0.0000935

10	68576077	rs12218365	T, C	0.000113

10	54088932	rs75242872	C, G	0.000117

10	37370440	rs1794410	G, A	0.000118

10	90464714	rs303509	T, G	0.000118

10	43942080	rs12355127	G, A	0.000182

10	6079344	rs11256442	T, C	0.000183

10	6042472	rs17322780	A, G	0.000185

10	63303879	rs79189092	GA, G	0.000234

10	43563260	rs3026716	A, G	0.000246

10	109569850	rs12570947	T, C	0.000269

10	27204531	rs1815323	T, C	0.000276

10	6128547	rs7078273	G, T	0.000303

10	115225859	rs10430681	T, A	0.000303

10	63420128	rs11595927	C, T	0.000333

10	8109274	rs520236	C, G	0.000349

10	27929163	rs12774308	G, A	0.000354

10	31913890	rs11008551	G, T	0.000366

10	57401482	rs2463950	C, T	0.000366

10	72532238	rs2253801	T, C	0.000387

10	72223789	rs10740349	C, G	0.000394

10	3096047	rs12241312	C, A	0.000403

10	50140588	rs2940708	G, A	0.000404

10	67680203	rs41313840	G, A	7.19977E−08

10	127258691	rs7084502	A, G	7.81329E−05

10	9030308	rs71481792	A, T	0.0000223

10	131456440	rs79858702	T, C	0.000070971

10	123000638	rs5016035	G, T	0.9

11	2893867	rs10766439	A, G	0.0000937

11	2897875	rs4929952	C, T	0.00011

11	10531548	rs142012992	CTTAG, C	0.000111

11	69896252	rs4980753	A, G	0.000199

11	2902105	rs3864883	C, T	0.000222

11	126061372	rs35747384	G, T	0.000237

11	270987	rs7396066	C, T	0.000245

11	117755082	rs491292	C, T	0.000246

11	130323805	rs7109513	C, T	0.000256

11	120784855	rs2852238	G, C	0.000274

11	119248855	rs76560104	T, C	0.000279

11	1795214	rs79194907	G, A	6.83674E−05

11	5146083	rs12365149	T, C	3.11673E−05

11	44547746	rs12275504	G, T	1.40655E−05

11	46727333	rs149066130	A, G	7.98651E−05

11	57982229	rs1966836	A, G	6.87681E−05

11	65414949	rs199717374	T, C	1.05822E−05

11	78904266	rs75970706	T, C	0.000040305

11	94665002	rs76360689	A, G	1.05432E−06

11	133713033	rs75786498	A, G	3.13102E−05

12	8760610	rs11613792	A, G	0.00000256

12	106624953	rs12823094	T, A	0.0000633

12	25427178	rs140584644	C, T	0.000107

12	50375477	rs7979554	A, G	0.000158

12	76404693	rs1433362	T, G	0.000196

12	95127606	rs6538530	C, T	0.000207

12	77114964	rs12827237	A, G	0.000227

12	7606158	rs11611785	C, T	0.000234

12	126551205	rs11058488	C, T	0.000239

12	58267346	rs112976255	C, A	0.000259

12	129567952	rs58003804	A, G	0.000265

12	130254478	rs73160210	G, T	0.000267

12	68010614	rs1904551	T, C	0.000272

12	116784113	rs1732329	A, G	0.000287

12	25159263	rs859134	A, G	0.000368

12	129563020	rs2002553	G, A	0.000387

12	130242016	rs73436164	A, G	0.000389

12	125537439	rs145578156	AATTTTTT, A	0.000402

12	67821215	rs7955453	C, A	0.000403

12	41970164	rs970970	G, A	0.000405

12	2144357	rs75442877	T, C	0.000407

12	2174748	rs117821007	C, T	4.86991E−05

12	25681286	rs58907459	T, C	6.07118E−05

12	57589676	rs143285614	A, G	2.69071E−05

12	125302200	rs143093152	G, A	6.62377E−06

12	56084466	rs7397549	C, T	0.9

13	74558505	rs12871414	C, T	0.000094

13	23658838	rs1984162	A, G	0.000103

13	99778655	rs35784338	CT, C	0.000199

13	102622688	rs7339161	T, G	0.000203

13	101425653	rs9585503	T, G	0.00025

13	74804797	rs2039342	C, T	0.000345

13	98753461	rs545096	C, G	0.000349

13	71296869	rs2249209	A, G	0.000385

13	28230642	rs10712355	AT, A	0.000415

13	39433574	rs138539682	A, G	3.32149E−05

13	68302925	rs75022796	T, C	7.24669E−05

13	63178476	rs2649134	T, C	0.5

14	72908102	rs2238187	A, G	0.00000821

14	72934229	rs12587980	C, T	0.0000933

14	76011690	rs2734265	G, A	0.000178

14	72891494	rs2238191	C, A	0.000196

14	65225452	rs58725048	G, C	0.000269

14	41523312	rs11157189	A, G	0.000314

14	35857405	rs61988300	A, G	0.000315

14	97526363	rs75607541	T, A	0.000337

14	52276643	rs117852779	C, T	2.89233E−06

14	80405359	rs11159425	T, G	4.36025E−07

14	80570671	rs114463019	G, T	3.85403E−05

14	87813714	rs28450466	A, G	9.45754E−05

14	93016441	rs57851052	C, T	5.53318E−05

14	104863663	rs80083325	A, G	0.000073746

14	77692036	rs144114696	A, G	0.3

15	33908103	rs12593288	C, T	0.000023

15	33916053	rs2229117	G, C	0.0000561

15	27274425	rs149380649	CT, C	0.000112

15	81412674	rs2683240	C, T	0.000184

15	89165665	rs73451724	G, A	0.000235

15	33407307	rs17816808	A, G	0.000295

15	46666881	rs1994195	A, C	0.000295

15	33914240	rs16973353	A, T	0.000307

15	53279291	rs719715	G, A	0.000321

15	89162709	rs112248718	G, A	0.000406

15	22845849	rs150408740	G, A	1.76793E−05

15	34498314	rs75915717	T, C	1.0293E−06

15	41047777	rs35673728	C, T	6.67895E−05

15	41254865	rs12915860	C, A	1.01336E−05

15	41712936	rs62001419	A, C	9.84979E−05

15	52689631	rs1724577	T, G	2.42041E−05

15	58047086	rs77910305	G, C	1.30369E−05

15	65851028	rs200531541	A, G	3.83063E−06

15	78471034	rs34921279	T, C	2.40367E−05

15	84063245	rs12591031	G, A	1.16687E−05

15	91452594	rs142925505	T, C	6.04339E−07

15	45858905	rs77055952	G, A	0.5

15	48984345	rs74750712	G, T	0.4

16	78624025	rs72803978	A, G	0.0000612

16	4065412	rs12448453	A, G	0.000148

16	84006469	rs2250573	C, A	0.000148

16	6653119	rs12934582	T, C	0.000264

16	27040235	rs2063839	G, A	0.000272

16	6081670	rs8053942	C, T	0.000304

16	31404502	rs2454907	G, A	0.000304

16	85992829	rs11117428	T, C	0.000318

16	31392047	rs11574646	C, T	0.000358

16	78865144	rs68020681	T, G	0.000404

16	5898969	rs11647387	A, C	0.000406

16	49391921	rs62029091	A, G	7.11949E−05

16	49394276	rs8057939	C, T	1.12522E−05

16	60671279	rs118097562	T, A	1.90641E−05

16	61851413	rs151208133	T, C	6.3915E−06

16	81194912	rs11642802	C, A	4.32104E−06

16	90075827	rs201800670	T, C	1.60067E−05

16	10579876	rs72779789	C, G	0.9

16	49311043	rs145643452	A, G	0.9

17	9170408	rs34761447	C, T	0.0000262

17	29737612	rs178840	G, A	0.0000753

17	29740894	rs35054028	G, T	0.00025

17	18671675	rs55828488	G, A	0.000284

17	31676083	rs59341815	T, C	0.000294

17	3110572	rs34259120	C, A	0.00032

17	15548222	rs55821658	C, T	0.000365

17	1462712	rs73298816	G, A	5.44207E−05

17	3844344	rs144535413	T, C	1.11488E−05

17	36485146	rs147966258	A, G	1.64933E−05

17	39240563	rs193005959	A, C	3.72836E−05

17	55803083	rs72841559	C, G	2.26086E−05

17	56329775	rs368901060	ACCAT, A	4.70507E−06

17	63919929	rs7220318	G, A	2.39945E−05

17	72890474	rs689992	T, A	8.97907E−05

17	78215658	rs117140258	A, G	4.15567E−05

17	80443309	rs9890316	A, G	0.9

18	67208392	rs12958013	T, C	0.0000319

18	10016417	rs618909	C, A	0.000193

18	649311	rs9966612	A, G	0.00021

18	3899729	rs2667396	C, T	0.000259

18	59747387	rs652473	C, T	0.000338

18	9095227	rs16954792	T, C	0.000368

18	76506592	rs35409638	C, T	0.00039

18	67209524	rs34527658	A, AT	0.000391

18	4610215	rs76902871	G, T	2.95635E−05

18	13501162	rs2298530	C, T	6.48889E−05

18	14310187	rs117505121	G, A	3.59448E−05

18	49288587	rs117781678	A, C	4.32852E−05

18	76650871	rs7240086	G, A	6.95487E−06

18	30006171	rs142257532	C, T	1

19	44492164	rs60744406	A, G	0.000019

19	32023957	rs8105499	C, A	0.000103

19	3058098	rs3217064	C, CT	0.000188

19	50693096	rs648691	T, C	0.000229

19	6533402	rs3097296	T, C	0.000306

19	55500034	rs76616660	T, C	0.000312

19	15565046	rs9646651	G, A	0.000323

19	44436733	rs8100011	G, A	0.000334

19	57133633	rs35011777	C, T	0.000365

19	36018109	rs74726174	C, T	1.82372E−05

19	53333975	rs10411226	G, A	0.0000923

19	38867031	rs200403794	A, G	8.36021E−05

20	20344377	rs7270923	A, C	0.000159

20	53043792	rs6023232	G, T	0.000186

20	38792298	rs6016275	C, T	0.000192

20	45355986	rs2076293	A, G	0.000217

20	52985158	rs6097944	T, G	0.000284

20	8782776	rs138434221	A, C	4.80296E−06

20	15632993	rs6110707	C, T	1.31264E−05

20	55021575	rs6069749	T, C	2.92257E−05

20	55111747	rs6014757	A, G	6.44584E−05

20	39389409	rs56259900	G, A	0.6

20	60473717	rs76253189	G, C	1

21	43080428	rs2252109	A, T	0.0000428

21	43086264	rs2849697	T, C	0.000144

21	46991937	rs76902403	G, A	0.000155

21	41321695	rs11701006	G, A	0.000196

21	39963301	rs975846	A, G	0.000268

21	35423390	rs1986076	C, T	0.000297

21	39962001	rs9789875	C, T	0.000299

21	20402128	rs62216866	A, G	0.000367

21	19045795	rs73200561	A, T	9.40896E−05

21	37444937	rs2230191	A, G	3.4537E−07

21	44424444	rs75994231	T, C	0.7

22	22564734	rs5757427	A, T	0.00000237

22	49677464	rs62220604	G, A	0.0000355

22	24407483	rs11090305	T, C	0.0000377

22	44341300	rs17494724	G, A	0.000157

22	47986266	rs56813510	C, A	0.000189

22	44323597	rs139049	C, T	0.000306

22	28016883	rs1885362	A, C	4.65803E−05

22	40056937	rs113038998	T, C	1.37758E−05

22	44285118	rs117421847	A, G	6.60782E−05

22	22724951	rs7290963	T, G	0.0000716

TABLE 2

Informative polymorphisms of the invention - 306 polymorphism panel.

					Frequency	Frequency
			Allele	Allele	Allele	Allele	p-value for
Chromsome	Position	SNP ID	1	2	1	2	association	OR

1	2998313	rs12745140	A	G	0.088689	0.911311	0.000032	1.832076

1	14216150	rs17350970	T	C	0.077425	0.922575	0.000346	0.7217155

1	31380174	rs4303117	A	C	0.308602	0.691398	0.000199	0.725645

1	31624029	rs12083278	G	C	0.295029	0.704971	0.000002	0.751250

1	36172029	rs6664663	G	C	0.134612	0.865388	0.000304	0.816236

1	36374101	rs2765013	T	C	0.086283	0.913717	0.000039	0.749941

1	36549664	rs2274122	G	A	0.185863	0.814137	0.000107	0.810774

1	37147203	rs219007	T	C	0.373057	0.626943	0.000271	1.161837

1	55046392	rs300269	G	A	0.475448	0.524552	0.000355	1.118490

1	60210649	rs1004772	T	C	0.156424	0.843576	0.000289	1.537451

1	60214250	rs3990361	G	A	0.314799	0.685201	0.000386	0.880359

1	63766718	rs112728381	T	C	0.310873	0.689127	0.000025	0.829006

1	83665753	rs9432945	T	C	0.195772	0.804228	0.000293	1.244035

1	87628173	rs10873821	T	C	0.247509	0.752491	0.000023	0.654805

1	88237749	rs72714531	C	T	0.061964	0.938036	0.000223	0.615057

1	101661978	rs11166552	T	C	0.338277	0.661723	0.000257	0.740338

1	107941708	rs17018870	A	G	0.123982	0.876018	0.000328	1.316821

1	114893146	rs574339	C	T	0.292471	0.707529	0.000169	0.838798

1	161229986	rs5778200	AC	A	0.166927	0.833073	0.000326	1.100723

1	184803508	rs630030	A	G	0.459053	0.540947	0.000271	0.776025

1	186287454	rs1830344	C	T	0.099058	0.900942	0.000127	0.715555

1	187364290	rs7517532	C	G	0.277242	0.722758	0.000136	1.274559

1	208388679	rs78771609	G	A	0.057479	0.942521	0.000395	0.682444

1	222722631	rs61825527	C	A	0.055134	0.944866	0.000194	0.684243

1	230906775	rs3790971	A	G	0.296911	0.703089	0.000357	0.809538

2	2965401	rs1729903	T	C	0.431488	0.568512	0.000271	1.363332

2	6948980	rs55900661	A	G	0.069029	0.930971	0.000235	1.467029

2	11239618	rs62120103	T	C	0.447468	0.552532	0.000116	0.728192

2	11662023	rs62120186	T	C	0.141992	0.858008	0.000403	0.679629

2	11694251	rs4313952	G	A	0.128053	0.871947	0.000194	0.692882

2	13900135	rs61101702	T	TAATA	0.200010	0.799990	0.000200	0.686034

2	36905013	rs6714112	A	C	0.138077	0.861923	0.000008	0.702041

2	42181679	rs6740960	A	T	0.484269	0.515731	0.000120	0.838653

2	45503541	rs6749256	C	G	0.125856	0.874144	0.000397	0.698683

2	46584059	rs34136947	G	T	0.150999	0.849001	0.000222	0.764697

2	50825372	rs116302817	T	C	0.055823	0.944177	0.000322	0.502987

2	52551249	rs115352379	C	T	0.052932	0.947068	0.000344	0.802121

2	52998723	rs62127009	G	A	0.146798	0.853202	0.000408	1.315507

2	75788396	rs759255	A	C	0.179965	0.820035	0.000181	0.785823

2	137073048	rs6430625	G	A	0.377976	0.622024	0.000144	0.874221

2	140976470	rs9941558	G	A	0.215540	0.784460	0.000196	0.758804

2	175367762	rs4972443	C	T	0.185607	0.814393	0.000237	0.755393

2	182359592	rs16867434	C	T	0.086231	0.913769	0.000275	1.665760

2	182396974	rs6760007	C	T	0.202409	0.797591	0.000200	1.303906

2	217524986	rs2270360	C	A	0.265435	0.734565	0.000025	0.806294

2	217553774	rs3755137	A	T	0.167060	0.832940	0.000263	0.869056

2	217701606	rs111437052	G	A	0.045066	0.954934	0.000343	0.658309

3	1093795	rs1504061	G	C	0.050105	0.949895	0.000072	1.878863

3	2357581	rs56035150	G	A	0.051997	0.948003	0.000318	1.203568

3	5893097	rs74827709	T	G	0.109834	0.890166	0.000342	0.744145

3	18486173	rs62240975	A	G	0.238497	0.761503	0.000405	1.236323

3	21751839	rs1080021	G	A	0.456598	0.543402	0.000366	1.189673

3	24759067	rs71328493	G	C	0.121505	0.878495	0.000213	1.301830

3	25519583	rs1864903	G	A	0.403176	0.596824	0.000366	1.057886

3	27188298	rs17317135	A	G	0.048133	0.951867	0.000064	0.653752

3	38736230	rs12639182	T	C	0.198184	0.801816	0.000395	0.806524

3	38759465	rs9990137	G	A	0.341528	0.658472	0.000148	0.765458

3	46628726	rs1829538	G	T	0.129451	0.870549	0.000257	0.757119

3	71238088	rs111323182	A	T	0.044740	0.955260	0.000270	0.902081

3	125649876	rs6438947	C	T	0.235079	0.764921	0.000126	1.208900

3	125837737	rs1868132	T	C	0.111588	0.888412	0.000102	1.421540

3	141408691	rs6440031	A	G	0.084557	0.915443	0.000071	0.730686

3	143909347	rs13089585	C	T	0.050473	0.949527	0.000302	0.600404

3	155736888	rs4680228	T	C	0.443082	0.556918	0.000157	1.248159

3	170214459	rs2008829	T	G	0.285307	0.714693	0.000376	1.093430

3	170266664	rs139670481	A	AAATT	0.141898	0.858102	0.000406	1.502990

3	170961913	rs115037737	A	G	0.057828	0.942172	0.000407	0.607395

3	173319456	rs6800283	C	T	0.089864	0.910136	0.000366	0.789994

3	195949310	rs35516030	A	AG	0.263765	0.736235	0.000375	1.315177

4	5820342	rs28426993	T	C	0.099661	0.900339	0.000114	0.575494

4	5821877	rs3774881	C	T	0.151611	0.848389	0.000046	0.647570

4	5821922	rs3774882	G	C	0.075890	0.924110	0.000035	0.573686

4	24316054	rs28735003	C	T	0.144258	0.855742	0.000295	0.658173

4	27383278	rs6810404	A	C	0.490985	0.509015	0.000099	0.776925

4	35945837	rs111866232	C	G	0.039927	0.960073	0.000157	0.541922

4	44418592	rs35540967	C	T	0.074931	0.925069	0.000029	1.777837

4	60147523	rs13126577	C	T	0.497288	0.502712	0.000293	1.301281

4	60236874	rs12498396	A	G	0.359748	0.640252	0.000292	0.875135

4	69705994	rs115162070	A	G	0.069851	0.930149	0.000004	0.566347

4	96348686	rs4277782	T	C	0.253317	0.746683	0.000314	1.099665

4	99439788	rs7668981	T	C	0.434271	0.565729	0.000273	0.750526

4	106909473	rs78699658	G	A	0.076853	0.923147	0.000213	0.769933

4	106943200	rs11729561	C	T	0.080734	0.919266	0.000104	0.776444

4	112613026	rs112641600	T	C	0.104676	0.895324	0.000058	0.646736

4	162009814	rs35850287	C	T	0.469632	0.530368	0.000330	0.786370

4	163076494	rs62331317	A	G	0.108942	0.891058	0.000113	1.821384

4	187239747	rs7687352	A	G	0.486412	0.513588	0.000288	1.144337

5	6471344	rs507971	C	T	0.210730	0.789270	0.000380	0.795710

5	59077872	rs62370540	C	T	0.105859	0.894141	0.000116	0.634414

5	59191612	rs159616	C	T	0.390352	0.609648	0.000259	0.881079

5	106396918	rs6864971	C	T	0.379226	0.620774	0.000373	1.309747

5	122559297	rs72787582	T	C	0.040661	0.959339	0.000117	0.495005

5	122832716	rs62377777	C	T	0.219393	0.780607	0.000065	0.697966

5	122958050	rs70988587	A	ATTC	0.192984	0.807016	0.000215	0.864676

5	123950404	rs4240376	T	G	0.201643	0.798357	0.000065	0.798894

5	124143238	rs71594388	T	C	0.074401	0.925599	0.000136	0.816311

5	135351183	rs72794907	A	G	0.311611	0.688389	0.000174	0.907778

5	135360738	rs35901765	T	C	0.455190	0.544810	0.000275	0.872551

5	135435801	rs7720483	T	C	0.474933	0.525067	0.000174	0.778409

5	142252549	rs10039856	T	C	0.097202	0.902798	0.000059	0.722279

5	159258092	rs78045322	C	G	0.058813	0.941187	0.000172	0.665840

5	161475413	rs17548653	T	C	0.051060	0.948940	0.000410	0.694777

5	173989338	rs2220543	A	T	0.289838	0.710162	0.000019	0.753959

5	176737309	rs7732626	G	A	0.075159	0.924841	0.000256	0.621750

5	180216905	rs10577599	C	CAT	0.060491	0.939509	0.000025	1.117455

5	180237828	rs113791144	T	C	0.066435	0.933565	0.000144	1.437219

6	6925195	rs6933436	C	A	0.283852	0.716148	0.000098	1.259040

6	12216966	rs10755709	G	A	0.300795	0.699205	0.000030	0.798524

6	18015447	rs140247774	T	C	0.066938	0.933062	0.000047	1.795184

6	18271083	rs10949488	T	C	0.058632	0.941368	0.000355	1.420327

6	18739469	rs2328283	C	A	0.439080	0.560920	0.000333	1.340607

6	20013937	rs13213659	G	A	0.356283	0.643717	0.000233	1.432682

6	20044587	rs594259	G	A	0.405796	0.594204	0.000375	1.066926

6	44184719	rs2297393	C	T	0.428032	0.571968	0.000409	0.802643

6	45704813	rs16873740	A	T	0.118789	0.881211	0.000030	1.293166

6	54128003	rs12193921	C	T	0.055659	0.944341	0.000184	1.567508

6	54353221	rs76378690	A	G	0.133523	0.866477	0.000206	0.703482

6	106326754	rs9386484	A	T	0.236292	0.763708	0.000006	0.700394

6	137545805	rs11759276	T	A	0.066107	0.933893	0.000183	0.633909

6	148522455	rs117687499	A	G	0.080216	0.919784	0.000262	0.638077

6	148557644	rs9390548	A	G	0.172941	0.827059	0.000149	0.785698

6	151433505	rs6928557	A	C	0.177199	0.822801	0.000121	0.662379

6	154813083	rs6935885	C	G	0.146378	0.853622	0.000306	1.227099

6	170445103	rs9366129	T	C	0.377194	0.622806	0.000133	1.169297

7	6687563	rs55944391	A	C	0.287309	0.712691	0.000395	1.259924

7	9397414	rs13238693	T	G	0.426972	0.573028	0.000286	1.212542

7	11301277	rs7807069	G	A	0.485509	0.514491	0.000271	0.780193

7	11362820	rs76731008	A	G	0.141480	0.858520	0.000277	1.500773

7	14862658	rs2109498	A	T	0.153107	0.846893	0.000419	0.865912

7	36662714	rs58016731	A	AGTCTT	0.095719	0.904281	0.000391	0.677730

7	45244259	rs1294888	T	C	0.263570	0.736430	0.000395	0.684875

7	52994281	rs7804465	C	T	0.374660	0.625340	0.000342	1.187107

7	99987236	rs7798226	G	T	0.386582	0.613418	0.000222	1.238744

7	105125204	rs111283303	A	G	0.157476	0.842524	0.000232	0.876698

7	114940068	rs7800941	A	G	0.161413	0.838587	0.000199	1.305293

7	138401542	rs3778698	T	C	0.254384	0.745616	0.000200	0.835206

7	154700565	rs882469	A	G	0.238270	0.761730	0.000222	0.861757

8	12850333	rs2947375	A	G	0.218314	0.781686	0.000162	0.682120

8	16790149	rs118072448	C	T	0.076838	0.923162	0.000024	0.567696

8	32590598	rs4351382	A	T	0.151498	0.848502	0.000273	1.643539

8	38821327	rs10808999	A	G	0.133668	0.866332	0.000088	0.781512

8	38897470	rs13282163	C	A	0.083088	0.916912	0.000074	0.697853

8	40181978	rs11779911	A	C	0.334674	0.665326	0.000084	0.759358

8	55488334	rs74458553	T	C	0.055479	0.944521	0.000254	0.734023

8	61303777	rs147353244	GA	G	0.126562	0.873438	0.000327	0.753803

8	74268198	rs2010843	T	C	0.468438	0.531562	0.000056	0.764239

8	143261211	rs72685583	C	T	0.068522	0.931478	0.000218	1.571550

9	4329170	rs3895472	T	C	0.073518	0.926482	0.000024	0.662754

9	21131627	rs12236000	C	G	0.076624	0.923376	0.000045	0.668847

9	23650820	rs4584238	G	C	0.319618	0.680382	0.000220	1.216505

9	35490636	rs10814241	G	C	0.093586	0.906414	0.000220	1.562150

9	75061917	rs11143296	T	C	0.451371	0.548629	0.000140	0.795898

9	75127404	rs35460846	A	AT	0.481894	0.518106	0.000082	1.428832

9	75136789	rs7022441	A	G	0.473267	0.526733	0.000343	0.709246

9	78174461	rs13298924	C	T	0.237835	0.762165	0.000210	1.184631

9	81158113	rs7027911	A	G	0.428022	0.571978	0.000091	1.184363

9	81158443	rs3009696	T	C	0.260891	0.739109	0.000130	1.413299

9	116468053	rs75260470	T	C	0.062434	0.937566	0.000402	0.657867

9	122045518	rs487545	C	T	0.108712	0.891288	0.000269	0.816792

9	138006474	rs61018036	A	G	0.178813	0.821187	0.000373	1.346902

9	138202418	rs7032559	T	C	0.368961	0.631039	0.000250	1.228892

9	139024232	rs7021573	A	G	0.438519	0.561481	0.000309	0.777563

9	139047766	rs10858230	A	G	0.201935	0.798065	0.000367	1.208077

9	140227646	rs11523787	T	C	0.469081	0.530919	0.000137	1.450898

10	3096047	rs12241312	C	A	0.348689	0.651311	0.000403	0.798146

10	6042472	rs17322780	G	A	0.096814	0.903186	0.000185	0.735578

10	6079344	rs11256442	T	C	0.287927	0.712073	0.000183	0.767768

10	6128547	rs7078273	T	G	0.405064	0.594936	0.000303	1.159191

10	8109274	rs520236	G	C	0.219884	0.780116	0.000349	0.756563

10	9030308	rs71481792	A	T	0.381127	0.618873	0.000022	1.192942

10	27204531	rs1815323	C	T	0.111020	0.888980	0.000276	0.660612

10	27929163	rs12774308	A	G	0.130100	0.869900	0.000354	0.718834

10	31913890	rs11008551	T	G	0.195413	0.804587	0.000366	1.331756

10	37277870	rs2091431	A	G	0.291382	0.708618	0.000094	0.746109

10	37370440	rs1794410	A	G	0.411272	0.588728	0.000118	0.857482

10	37454397	rs1892429	G	A	0.160418	0.839582	0.000034	0.745569

10	43563260	rs3026716	G	A	0.253344	0.746656	0.000246	1.269510

10	43942080	rs12355127	A	G	0.121879	0.878121	0.000182	1.223674

10	44015051	rs10793436	T	G	0.317755	0.682245	0.000030	0.691022

10	50140588	rs2940708	G	A	0.076009	0.923991	0.000404	0.667254

10	54088932	rs75242872	G	C	0.064881	0.935119	0.000117	0.492357

10	54100345	rs1441121	A	T	0.438240	0.561760	0.000032	0.821531

10	57401482	rs2463950	T	C	0.076162	0.923838	0.000366	1.827848

10	63303879	rs79189092	G	GA	0.170880	0.829120	0.000234	1.297045

10	63420128	rs11595927	T	C	0.290509	0.709491	0.000333	1.236769

10	68576077	rs12218365	C	T	0.138063	0.861937	0.000113	0.607017

10	72223789	rs10740349	C	G	0.082859	0.917141	0.000394	1.272646

10	72532238	rs2253801	C	T	0.423564	0.576436	0.000387	0.810933

10	90464714	rs303509	G	T	0.275512	0.724488	0.000118	0.899337

10	109569850	rs12570947	C	T	0.149219	0.850781	0.000269	1.403835

10	115225859	rs10430681	A	T	0.074066	0.925934	0.000303	0.678792

11	270987	rs7396066	C	T	0.187477	0.812523	0.000245	0.833428

11	2893867	rs10766439	A	G	0.362551	0.637449	0.000094	1.423212

11	2897875	rs4929952	T	C	0.202575	0.797425	0.000110	0.619703

11	2902105	rs3864883	T	C	0.111433	0.888567	0.000222	0.576303

11	10531548	rs142012992	C	CTTAG	0.325845	0.674155	0.000111	0.697824

11	69896252	rs4980753	A	G	0.376448	0.623552	0.000199	1.158722

11	117755082	rs491292	T	C	0.207093	0.792907	0.000246	1.437101

11	119248855	rs76560104	C	T	0.102744	0.897256	0.000279	1.664471

11	120784855	rs2852238	G	C	0.278141	0.721859	0.000274	1.343841

11	126061372	rs35747384	T	G	0.105597	0.894403	0.000237	0.743710

11	130323805	rs7109513	T	C	0.402800	0.597200	0.000256	0.770364

12	2144357	rs75442877	C	T	0.065950	0.934050	0.000407	0.685377

12	7606158	rs11611785	C	T	0.429591	0.570409	0.000234	1.060466

12	8760610	rs11613792	G	A	0.138548	0.861452	0.000003	0.809562

12	25159263	rs859134	A	G	0.496130	0.503870	0.000368	1.187565

12	25427178	rs140584644	T	C	0.253790	0.746210	0.000107	1.424311

12	41970164	rs970970	G	A	0.384510	0.615490	0.000405	1.165447

12	50375477	rs7979554	A	G	0.280549	0.719451	0.000158	1.204227

12	58267346	rs112976255	A	C	0.072297	0.927703	0.000259	0.688523

12	67821215	rs7955453	A	C	0.099287	0.900713	0.000403	1.272926

12	68010614	rs1904551	C	T	0.412363	0.587637	0.000272	0.880242

12	76404693	rs1433362	G	T	0.097993	0.902007	0.000196	0.868310

12	77114964	rs12827237	G	A	0.163516	0.836484	0.000227	0.712866

12	95127606	rs6538530	T	C	0.208557	0.791443	0.000207	1.316565

12	106624953	rs12823094	A	T	0.244075	0.755925	0.000063	1.166247

12	116784113	rs1732329	A	G	0.350353	0.649647	0.000287	1.418587

12	125537439	rs145578156	A	AATTTTTT	0.126772	0.873228	0.000402	1.346757

12	126551205	rs11058488	T	C	0.042023	0.957977	0.000239	1.581584

12	129563020	rs2002553	A	G	0.122964	0.877036	0.000387	0.901839

12	129567952	rs58003804	G	A	0.091258	0.908742	0.000265	0.798624

12	130242016	rs73436164	G	A	0.168624	0.831376	0.000389	0.655603

12	130254478	rs73160210	T	G	0.081785	0.918215	0.000267	1.744820

13	23658838	rs1984162	G	A	0.258794	0.741206	0.000103	1.255399

13	28230642	rs10712355	A	AT	0.211941	0.788059	0.000415	0.841463

13	71296869	rs2249209	A	G	0.409982	0.590018	0.000385	1.243197

13	74558505	rs12871414	T	C	0.265293	0.734707	0.000094	0.752194

13	74804797	rs2039342	T	C	0.110526	0.889474	0.000345	1.761241

13	98753461	rs545096	G	C	0.465548	0.534452	0.000349	1.268761

13	99778655	rs35784338	CT	C	0.199852	0.800148	0.000199	0.777428

13	101425653	rs9585503	G	T	0.079322	0.920678	0.000250	0.503729

13	102622688	rs7339161	G	T	0.306514	0.693486	0.000203	0.859200

14	35857405	rs61988300	G	A	0.189617	0.810383	0.000315	0.790426

14	41523312	rs11157189	G	A	0.498534	0.501466	0.000314	0.893382

14	65225452	rs58725048	C	G	0.056880	0.943120	0.000269	1.285578

14	72891494	rs2238191	A	C	0.428866	0.571134	0.000196	1.256746

14	72908102	rs2238187	G	A	0.352272	0.647728	0.000008	1.442188

14	72934229	rs12587980	T	C	0.374816	0.625184	0.000093	1.277281

14	76011690	rs2734265	G	A	0.480501	0.519499	0.000178	1.168686

14	97526363	rs75607541	A	T	0.050779	0.949221	0.000337	0.566281

15	27274425	rs149380649	C	CT	0.065191	0.934809	0.000112	0.738484

15	33407307	rs17816808	G	A	0.096916	0.903084	0.000295	1.377528

15	33908103	rs12593288	T	C	0.206252	0.793748	0.000023	0.839689

15	33914240	rs16973353	A	T	0.425332	0.574668	0.000307	1.140667

15	33916053	rs2229117	C	G	0.133455	0.866545	0.000056	0.753496

15	46666881	rs1994195	C	A	0.270838	0.729162	0.000295	0.880898

15	53279291	rs719715	G	A	0.181209	0.818791	0.000321	0.711192

15	81412674	rs2683240	C	T	0.237678	0.762322	0.000184	1.164944

15	89162709	rs112248718	A	G	0.105913	0.894087	0.000406	1.241798

15	89165665	rs73451724	A	G	0.038001	0.961999	0.000235	1.633115

16	4065412	rs12448453	G	A	0.079483	0.920517	0.000148	1.385477

16	5898969	rs11647387	C	A	0.250999	0.749001	0.000406	0.804925

16	6081670	rs8053942	T	C	0.473297	0.526703	0.000304	1.121533

16	6653119	rs12934582	C	T	0.096462	0.903538	0.000264	1.562484

16	27040235	rs2063839	A	G	0.077891	0.922109	0.000272	0.703891

16	31392047	rs11574646	T	C	0.219875	0.780125	0.000358	0.816824

16	31404502	rs2454907	A	G	0.149476	0.850524	0.000304	0.758433

16	78624025	rs72803978	G	A	0.065596	0.934404	0.000061	0.651614

16	78865144	rs68020681	G	T	0.093881	0.906119	0.000404	0.750446

16	84006469	rs2250573	A	C	0.112712	0.887288	0.000148	0.614458

16	85992829	rs11117428	C	T	0.233426	0.766574	0.000318	0.861041

17	3110572	rs34259120	C	A	0.446158	0.553842	0.000320	1.312096

17	9170408	rs34761447	T	C	0.093770	0.906230	0.000026	0.766580

17	15548222	rs55821658	T	C	0.054679	0.945321	0.000365	0.615661

17	18671675	rs55828488	A	G	0.419792	0.580208	0.000284	1.241867

17	29737612	rs178840	A	G	0.245600	0.754400	0.000075	0.704646

17	29740894	rs35054028	T	G	0.087438	0.912562	0.000250	0.638460

17	31676083	rs59341815	C	T	0.290909	0.709091	0.000294	1.271254

18	649311	rs9966612	A	G	0.283148	0.716852	0.000210	0.827402

18	3899729	rs2667396	C	T	0.468088	0.531912	0.000259	1.228547

18	9095227	rs16954792	C	T	0.153181	0.846819	0.000368	0.711941

18	10016417	rs618909	A	C	0.182016	0.817984	0.000193	1.375523

18	59747387	rs652473	C	T	0.491954	0.508046	0.000338	1.348178

18	67208392	rs12958013	C	T	0.135816	0.864184	0.000032	0.755900

18	67209524	rs34527658	AT	A	0.237574	0.762426	0.000391	0.839220

18	76506592	rs35409638	T	C	0.254573	0.745427	0.000390	1.430958

19	3058098	rs3217064	CT	C	0.211726	0.788274	0.000188	0.784584

19	6533402	rs3097296	T	C	0.364959	0.635041	0.000306	0.775131

19	15565046	rs9646651	A	G	0.074145	0.925855	0.000323	0.642400

19	32023957	rs8105499	A	C	0.304119	0.695881	0.000103	0.763075

19	44436733	rs8100011	G	A	0.475911	0.524089	0.000334	0.832135

19	44492164	rs60744406	A	G	0.415555	0.584445	0.000019	0.783341

19	50693096	rs648691	C	T	0.434267	0.565733	0.000229	0.865794

19	53333975	rs10411226	G	A	0.248705	0.751295	0.000092	1.246091

19	55500034	rs76616660	C	T	0.114844	0.885156	0.000312	0.714110

19	57133633	rs35011777	T	C	0.041190	0.958810	0.000365	2.311258

20	20344377	rs7270923	C	A	0.100555	0.899445	0.000159	1.632986

20	38792298	rs6016275	T	C	0.386489	0.613511	0.000192	1.294481

20	45355986	rs2076293	G	A	0.465760	0.534240	0.000217	1.088991

20	52985158	rs6097944	G	T	0.113465	0.886535	0.000284	0.798795

20	53043792	rs6023232	T	G	0.191508	0.808492	0.000186	0.862778

21	20402128	rs62216866	G	A	0.097434	0.902566	0.000367	0.853945

21	35423390	rs1986076	C	T	0.368991	0.631009	0.000297	0.895966

21	39962001	rs9789875	T	C	0.489381	0.510619	0.000299	0.852266

21	39963301	rs975846	A	G	0.323369	0.676631	0.000268	1.326863

21	41321695	rs11701006	A	G	0.233935	0.766065	0.000196	0.842328

21	43080428	rs2252109	A	T	0.481328	0.518672	0.000043	1.207556

21	43086264	rs2849697	T	C	0.458596	0.541404	0.000144	1.228411

21	46991937	rs76902403	A	G	0.100782	0.899218	0.000155	0.578181

22	22564734	rs5757427	A	T	0.351595	0.648405	0.000002	0.764406

22	22724951	rs7290963	T	G	0.448669	0.551331	0.000072	1.306623

22	24407483	rs11090305	C	T	0.186359	0.813641	0.000038	1.155463

22	44323597	rs139049	T	C	0.410270	0.589730	0.000306	1.377674

22	44341300	rs17494724	A	G	0.069631	0.930369	0.000157	1.803635

22	47986266	rs56813510	A	C	0.165988	0.834012	0.000189	0.838987

22	49677464	rs62220604	A	G	0.276036	0.723964	0.000036	0.908008

TABLE 3

Informative polymorphisms of the invention - 58 polymorphism panel.

					Frequency	Frequency	p-value for
Chromsome	Position	SNP ID	Allele 1	Allele 2	Allele 1	Allele 2	association	OR

1	2998313	rs12745140	A	G	0.088688543	0.911311457	0.0000317	2.2144
1	31624029	rs12083278	G	C	0.295029481	0.704970519	0.00000243	1.8349
1	36374101	rs2765013	T	C	0.086283231	0.913716769	0.000039	0.4296
1	63766718	rs112728381	T	C	0.310873133	0.689126867	0.0000252	0.6017
1	87628173	rs10873821	T	C	0.247508766	0.752491234	0.0000228	0.5706
2	36905013	rs6714112	A	C	0.138077241	0.861922759	0.00000781	0.457
2	217524986	rs2270360	C	A	0.265435195	0.734564805	0.0000245	0.5804
3	1093795	rs1504061	G	C	0.050105352	0.949894648	0.0000716	2.5193
3	27188298	rs17317135	A	G	0.048132554	0.951867446	0.0000641	0.3776
3	141408691	rs6440031	A	G	0.084557	0.915443	0.0000714	2.188
4	5821877	rs3774881	C	T	0.151610603	0.848389397	0.0000459	0.5358
4	5821922	rs3774882	G	C	0.075889692	0.924110308	0.0000349	0.4206
4	27383278	rs6810404	A	C	0.49098462	0.50901538	0.0000988	0.6344
4	44418592	rs35540967	C	T	0.074930921	0.925069079	0.0000289	2.4621
4	69705994	rs115162070	A	G	0.069850883	0.930149117	0.00000356	0.3716
4	112613026	rs112641600	T	C	0.10467637	0.89532363	0.000058	0.4607
5	122832716	rs62377777	C	T	0.219392587	0.780607413	0.0000653	0.5724
5	123950404	rs4240376	T	G	0.201642654	0.798357346	0.000065	0.5706
5	142252549	rs10039856	T	C	0.097201508	0.902798492	0.0000585	0.4621
5	173989338	rs2220543	A	T	0.28983847	0.71016153	0.0000187	0.5793
5	180237828	rs113791144	T	C	0.066435175	0.933564825	0.000144	2.2728
6	6925195	rs6933436	C	A	0.283851708	0.716148292	0.0000983	1.6177
6	12216966	rs10755709	G	A	0.300794985	0.699205015	0.00003	0.6017
6	18015447	rs140247774	T	C	0.066938299	0.933061701	0.000047	2.5961
6	45704813	rs16873740	A	T	0.118789191	0.881210809	0.0000296	2.0401
6	106326754	rs9386484	A	T	0.236291943	0.763708057	0.00000617	0.5385
8	16790149	rs118072448	C	T	0.076837933	0.923162067	0.0000235	0.4194
8	38821327	rs10808999	A	G	0.133667804	0.866332196	0.0000884	1.8927
8	38897470	rs13282163	C	A	0.083088415	0.916911585	0.0000739	0.4185
8	40181978	rs11779911	A	C	0.334673592	0.665326408	0.0000841	0.6126
8	74268198	rs2010843	T	C	0.468438061	0.531561939	0.0000556	1.5904
9	4329170	rs3895472	T	C	0.0735178	0.9264822	0.0000235	2.4181
9	21131627	rs12236000	C	G	0.076624262	0.923375738	0.0000452	0.409
9	81158113	rs7027911	A	G	0.428022077	0.571977923	0.0000905	0.6281
10	9030308	rs71481792	A	T	0.38112705	0.61887295	0.0000223	0.5863
10	37277870	rs2091431	A	G	0.29138208	0.70861792	0.0000935	1.6209
10	37454397	rs1892429	G	A	0.160418285	0.839581715	0.0000335	0.5273
10	44015051	rs10793436	T	G	0.317754779	0.682245221	0.0000302	0.5969
10	54100345	rs1441121	A	T	0.438240499	0.561759501	0.0000322	1.5904
11	2893867	rs10766439	A	G	0.362550753	0.637449247	0.0000937	0.6376
12	8760610	rs11613792	G	A	0.138547884	0.861452116	0.00000256	0.4686
12	106624953	rs12823094	A	T	0.244075133	0.755924867	0.0000633	1.7006
13	74558505	rs12871414	T	C	0.265293174	0.734706826	0.000094	0.6108
14	72908102	rs2238187	G	A	0.352271894	0.647728106	0.00000821	1.7023
14	72934229	rs12587980	T	C	0.374815784	0.625184216	0.0000933	1.573
15	33908103	rs12593288	T	C	0.206251661	0.793748339	0.000023	0.5599
15	33916053	rs2229117	C	G	0.133454893	0.866545107	0.0000561	0.5184
16	78624025	rs72803978	G	A	0.065595834	0.934404166	0.0000612	0.3727
17	9170408	rs34761447	T	C	0.093769913	0.906230087	0.0000262	0.4612
17	29737612	rs178840	A	G	0.245600067	0.754399933	0.0000753	0.5886
18	67208392	rs12958013	C	T	0.135815591	0.864184409	0.0000319	0.5148
19	44492164	rs60744406	A	G	0.415555399	0.584444601	0.000019	1.6389
19	53333975	rs10411226	G	A	0.248705364	0.751294636	0.0000923	0.5644
21	43080428	rs2252109	A	T	0.481327711	0.518672289	0.0000428	0.6269
22	22564734	rs5757427	A	T	0.351595363	0.648404637	0.00000237	1.804
22	22724951	rs7290963	T	G	0.448668942	0.551331058	0.0000716	1.5872
22	24407483	rs11090305	C	T	0.18635889	0.81364111	0.0000377	1.8386
22	49677464	rs62220604	A	G	0.276036142	0.723963858	0.0000355	0.5927

TABLE 4

Informative polymorphisms used in genetic risk assessment
of Example 5-64 polymorphism panel. All SNPs are from the
COVID-19 Host Genetics Initiative meta-analysis of hospitalisation
vs non-hospitalisation except for rs11385942 and rs657152,
which are from Ellinghaus et al. (2020).

Chromo-		Reference	Risk allele	Risk allele	Risk allele
some	ID	allele	(A1 allele)	odds ratio	frequency

1	rs12745140	G	A	2.21	0.11
1	rs12083278	G	C	1.83	0.70
1	rs2765013	T	C	2.33	0.92
1	rs2274122	G	A	1.78	0.80
1	rs10873821	T	C	1.75	0.77
2	rs6714112	A	C	2.19	0.86
2	rs2270360	C	A	1.72	0.71
3	rs1504061	C	G	2.52	0.06
3	rs17317135	A	G	2.65	0.94
3	rs1868132	C	T	1.97	0.10
3	rs6440031	A	G	2.19	0.89
4	rs3774881	C	T	1.87	0.85
4	rs3774882	G	C	2.38	0.92
4	rs6810404	A	C	1.58	0.51
4	rs35540967	T	C	2.46	0.07
4	rs115162070	A	G	2.69	0.92
4	rs11729561	C	T	2.25	0.92
4	rs112641600	T	C	2.17	0.90
5	rs62377777	C	T	1.75	0.79
5	rs4240376	T	G	1.75	0.80
5	rs10039856	T	C	2.16	0.91
5	rs2220543	A	T	1.73	0.71
5	rs113791144	C	T	2.27	0.06
6	rs6933436	A	C	1.62	0.28
6	rs10755709	G	A	1.66	0.69
6	rs140247774	C	T	2.60	0.06
6	rs16873740	T	A	2.04	0.12
6	rs9386484	A	T	1.86	0.75
8	rs118072448	C	T	2.38	0.91
8	rs10808999	A	G	1.89	0.86
8	rs13282163	C	A	2.39	0.93
8	rs11779911	A	C	1.63	0.66
8	rs2010843	T	C	1.59	0.55
9	rs3895472	T	C	2.42	0.91
9	rs12236000	C	G	2.44	0.93
9	rs7027911	G	A	1.59	0.44
10	rs71481792	T	A	1.71	0.38
10	rs2091431	A	G	1.62	0.71
10	rs1892429	G	A	1.90	0.79
10	rs10793436	T	G	1.68	0.68
10	rs1441121	A	T	1.59	0.56
11	rs10766439	G	A	1.57	0.39
12	rs11613792	G	A	2.13	0.84
12	rs12823094	T	A	1.70	0.26
13	rs1984162	A	G	1.65	0.26
13	rs12871414	T	C	1.64	0.72
14	rs2238187	A	G	1.70	0.36
14	rs12587980	C	T	1.54	0.39
15	rs12593288	T	C	1.79	0.78
15	rs2229117	C	G	1.93	0.87
16	rs72803978	G	A	2.68	0.94
17	rs34761447	T	C	2.17	0.89
17	rs178840	A	G	1.70	0.75
18	rs12958013	C	T	1.94	0.85
19	rs8105499	A	C	1.62	0.69
19	rs60744406	A	G	1.64	0.61
19	rs10411226	A	G	1.77	0.24
21	rs2252109	T	A	1.60	0.49
22	rs5757427	A	T	1.80	0.63
22	rs7290963	G	T	1.59	0.45
22	rs11090305	T	C	1.84	0.18
22	rs62220604	A	G	1.69	0.71
3	rs11385942	G	GA	1.77	0.09
9	rs657152	C	A	1.32	0.35

TABLE 5

New informative polymorphisms used in the development of the models described in Example 6.

			Reference	Effect	Frequency	Frequency	p-value for
Chromosome	Position	SNP ID	Allele	Allele	1	2	association	OR	95% CI

1	46618634	rs17102023	A	G	1	0	0.5	1.33	0.63, 2.81
1	150271556	rs115492982	G	A	1	0	0.01	2.46	1.23, 4.91
1	152684866	rs2224986	C	T	0.91	0.09	0.8	0.98	0.85, 1.14
1	192526317	rs74508649	C	T	1	0	0.9	1.04	0.47, 2.32
1	239197542	rs112317747	T	C	0.97	0.03	0.05	1.26	1.00, 1.58
2	79895332	rs183569214	G	A	1	0	0.7	0.72	0.15, 3.45
2	80029580	rs77764981	T	C	1	0	0.6	1.29	0.54, 3.10
2	182353446	rs2034831	A	C	0.94	0.06	0.02	1.22	1.03, 1.46
3	3184653	rs1705826	C	G	0.63	0.37	0.5	1.03	0.94, 1.12
3	45841938	rs35896106	C	T	0.92	0.08	0.04	1.17	1.01, 1.35
3	45900634	rs76374459	G	C	0.94	0.06	0.03	1.2	1.02, 1.41
3	45908514	rs35652899	C	G	0.93	0.07	0.04	1.17	1.00, 1.36
3	45916222	rs12639224	C	T	0.73	0.27	0.7	1.02	0.93, 1.12
3	45916786	rs34901975	G	A	0.89	0.11	0.09	1.12	0.98, 1.27
3	46018781	rs71615437	A	G	0.92	0.08	0.1	1.12	0.97, 1.29
3	46049765	rs13433997	T	C	0.88	0.12	0.1	1.1	0.97, 1.24
3	46180416	rs10510749	C	T	0.91	0.09	0.9	0.99	0.85, 1.15
3	46222037	rs115102354	A	G	0.95	0.05	0.7	0.96	0.79, 1.16
3	62936766	rs13062942	A	G	0.64	0.36	0.09	0.92	0.84, 1.01
3	148718087	rs76488148	G	T	0.96	0.04	0.03	1.25	1.02, 1.52
5	169590905	rs4478338	T	G	0.92	0.08	0.3	1.08	0.93, 1.25
5	171480160	rs111265173	C	T	1	0	1	0.97	0.35, 2.66
6	27604726	rs61611950	C	T	0.99	0.01	0.8	0.92	0.56, 1.51
7	152960930	rs6967210	T	C	0.99	0.01	0.3	1.17	0.86, 1.59
8	8730488	rs332040	G	A	0.53	0.47	0.9	1	0.92, 1.09
9	27121456	rs71480372	A	T	0.66	0.34	0.7	0.98	0.90, 1.08
9	29688719	rs74790577	A	T	1	0	0.9	1.05	0.27, 4.03
10	123000638	rs5016035	T	G	0.51	0.49	0.9	1	0.91, 1.10
12	56084466	rs7397549	T	C	0.59	0.41	0.9	0.99	0.90, 1.09
13	63178476	rs2649134	C	T	0.97	0.03	0.5	0.93	0.72, 1.19
14	77692036	rs144114696	G	A	1	0	0.3	2.53	0.51, 12.44
15	45858905	rs77055952	A	G	0.95	0.05	0.5	1.07	0.88, 1.29
15	48984345	rs74750712	T	G	1	0	0.4	1.33	0.65, 2.69
16	10579876	rs72779789	G	C	0.95	0.05	0.7	1.04	0.85, 1.26
16	49311043	rs145643452	G	A	0.99	0.01	0.9	1.03	0.61, 1.74
17	80443309	rs9890316	G	A	0.69	0.31	0.9	1.01	0.92, 1.10
18	30006171	rs142257532	T	C	0.97	0.03	1	1.01	0.78, 1.30
20	39389409	rs56259900	A	G	1	0	0.6	1.15	0.65, 2.04
20	60473717	rs76253189	C	G	0.99	0.01	1	1.01	0.72, 1.42
21	44424444	rs75994231	C	T	0.98	0.02	0.7	1.06	0.79, 1.43

TABLE 6

Surrogate markers for polymorphism provided in Table 3 (Part A) and additional polymorphisms used in Table 4 (Part B).
Part C provides surrogate markers for rs115492982.

		Coordinate
Primary SNP	Proxy SNP	(GRCh37/hg19)	Alleles	MAF	Distance	D′	R2	Correlated_Alleles

Part A

rs2765013	rs581459	chr1:36375110	(C/T)	0.0875	1009	1	1	C = C, T = T

rs2765013	rs2791961	chr1:36371419	(G/C)	0.0885	−2682	1	0.9877	C = G, T = C

rs2765013	rs2791962	chr1:36371168	(T/C)	0.0885	−2933	1	0.9877	C = T, T = C

rs2765013	rs794222	chr1:36369024	(T/C)	0.0885	−5077	1	0.9877	C = T, T = C

rs2765013	rs661233	chr1:36364716	(A/G)	0.0885	−9385	1	0.9877	C = A, T = G

rs2765013	rs654688	chr1:36384215	(T/C)	0.0885	10114	1	0.9877	C = T, T = C

rs2765013	rs647690	chr1:36363982	(G/C)	0.0885	−10119	1	0.9877	C = G, T = C

rs2765013	rs636832	chr1:36363475	(G/A)	0.0885	−10626	1	0.9877	C = G, T = A

rs2765013	rs620956	chr1:36362162	(T/C)	0.0885	−11939	1	0.9877	C = T, T = C

rs2765013	rs653417	chr1:36356842	(G/T)	0.0885	−17259	1	0.9877	C = G, T = T

rs2765013	rs2765012	chr1:36356457	(A/G)	0.0885	−17644	1	0.9877	C = A, T = G

rs2765013	rs11263830	chr1:36345798	(G/A)	0.0885	−28303	1	0.9877	C = G, T = A

rs2765013	rs811114	chr1:36345758	(G/A)	0.0885	−28343	1	0.9877	C = G, T = A

rs2765013	rs11263843	chr1:36405929	(G/A)	0.0885	31828	1	0.9877	C = G, T = A

rs2765013	rs12031138	chr1:36407806	(A/G)	0.0885	33705	1	0.9877	C = A, T = G

rs2765013	rs6665591	chr1:36411737	(A/G)	0.0885	37636	1	0.9877	C = A, T = G

rs2765013	rs12041193	chr1:36412760	(C/T)	0.0885	38659	1	0.9877	C = C, T = T

rs2765013	rs67641270	chr1:36413329	(T/C)	0.0885	39228	1	0.9877	C = T, T = C

rs2765013	rs142884229	chr1:36419258	(-/AGAGAATACAGGTGT)	0.0885	45157	1	0.9877	C = -, T = AGAGAATACAGGTGT

rs2765013	rs716926	chr1:36424476	(T/C)	0.0885	50375	1	0.9877	C = T, T = C

rs2765013	rs716925	chr1:36424517	(A/G)	0.0885	50416	1	0.9877	C = A, T = G

rs2765013	rs10796876	chr1:36424985	(A/G)	0.0885	50884	1	0.9877	C = A, T = G

rs2765013	rs60867325	chr1:36429590	(-/G)	0.0885	55489	1	0.9877	C = -, T = G

rs2765013	rs645864	chr1:36430941	(C/A)	0.0885	56840	1	0.9877	C = C, T = A

rs2765013	rs649152	chr1:36438649	(T/G)	0.0885	64548	1	0.9877	C = T, T = G

rs2765013	rs709309	chr1:36441148	(G/T)	0.0885	67047	1	0.9877	C = G, T = T

rs2765013	rs686650	chr1:36441613	(A/G)	0.0885	67512	1	0.9877	C = A, T = G

rs2765013	rs682686	chr1:36443778	(T/A)	0.0885	69677	1	0.9877	C = T, T = A

rs2765013	rs665521	chr1:36445837	(T/C)	0.0885	71736	1	0.9877	C = T, T = C

rs2765013	rs630364	chr1:36449304	(C/T)	0.0885	75203	1	0.9877	C = C, T = T

rs2765013	rs625306	chr1:36454016	(T/C)	0.0885	79915	1	0.9877	C = T, T = C

rs2765013	rs688833	chr1:36455600	(C/T)	0.0885	81499	1	0.9877	C = C, T = T

rs2765013	rs112607939	chr1:36460189	(-/CTAT)	0.0885	86088	1	0.9877	C = -, T = CTAT

rs2765013	rs688191	chr1:36460681	(G/A)	0.0885	86580	1	0.9877	C = G, T = A

rs2765013	rs7542179	chr1:36461122	(G/A)	0.0885	87021	1	0.9877	C = G, T = A

rs2765013	rs56198971	chr1:36403174	(T/C)	0.0895	29073	1	0.9756	C = T, T = C

rs2765013	rs644095	chr1:36363172	(C/T)	0.0875	−10929	0.9875	0.9752	C = C, T = T

rs2765013	rs111375913	chr1:36403173	(-/C)	0.0875	29072	0.9875	0.9752	C = -, T = C

rs2765013	rs645383	chr1:36373823	(C/G)	0.0855	−278	1	0.9751	C = C, T = G

rs2765013	rs35467166	chr1:36410818	(-/C)	0.0865	36717	0.9874	0.9628	C = -, T = C

rs2765013	rs663163	chr1:36390527	(A/G)	0.0915	16426	1	0.9524	C = A, T = G

rs2765013	rs685370	chr1:36443214	(A/G)	0.0915	69113	1	0.9524	C = A, T = G

rs2765013	rs614235	chr1:36450565	(G/A)	0.0944	76464	1	0.9193	C = G, T = A

rs2765013	rs201615643	chr1:36468742	(-/T)	0.0934	94641	0.9875	0.9069	C = -, T = T

rs2765013	rs683622	chr1:36443569	(A/G)	0.0755	69468	1	0.8525	C = A, T = G

rs2765013	rs631202	chr1:36449099	(G/C)	0.0755	74998	1	0.8525	C = G, T = C

rs2765013	rs645123	chr1:36458075	(T/C)	0.0755	83974	1	0.8525	C = T, T = C

rs2765013	rs72661616	chr1:36370512	(G/A)	0.0746	−3589	1	0.8404	C = G, T = A

rs2765013	rs55762724	chr1:36382702	(C/T)	0.0746	8601	1	0.8404	C = C, T = T

rs2765013	rs72661613	chr1:36364781	(A/G)	0.0746	−9320	1	0.8404	C = A, T = G

rs2765013	rs199596553	chr1:36357062	(T/-)	0.0746	−17039	1	0.8404	C = T, T = -

rs2765013	rs72661623	chr1:36398368	(G/A)	0.0746	24267	1	0.8404	C = G, T = A

rs2765013	rs116757422	chr1:36349560	(G/A)	0.0746	−24541	1	0.8404	C = G, T = A

rs2765013	rs72661625	chr1:36401003	(T/C)	0.0746	26902	1	0.8404	C = T, T = C

rs2765013	rs79303353	chr1:36402056	(G/A)	0.0746	27955	1	0.8404	C = G, T = A

rs2765013	rs72661628	chr1:36406547	(A/G)	0.0746	32446	1	0.8404	C = A, T = G

rs2765013	rs74879824	chr1:36413117	(T/A)	0.0746	39016	1	0.8404	C = T, T = A

rs2765013	rs72661631	chr1:36417453	(A/G)	0.0746	43352	1	0.8404	C = A, T = G

rs2765013	rs72661632	chr1:36420715	(G/A)	0.0746	46614	1	0.8404	C = G, T = A

rs2765013	rs72661636	chr1:36426483	(T/C)	0.0746	52382	1	0.8404	C = T, T = C

rs2765013	rs142370407	chr1:36430202	(C/G)	0.0746	56101	1	0.8404	C = C, T = G

rs2765013	rs584873	chr1:36431806	(C/A)	0.0746	57705	1	0.8404	C = C, T = A

rs2765013	rs142324044	chr1:36433070	(C/T)	0.0746	58969	1	0.8404	C = C, T = T

rs2765013	rs72661640	chr1:36447232	(T/G)	0.0746	73131	1	0.8404	C = T, T = G

rs2765013	rs72661641	chr1:36448130	(A/G)	0.0746	74029	1	0.8404	C = A, T = G

rs2765013	rs72661643	chr1:36451599	(A/G)	0.0746	77498	1	0.8404	C = A, T = G

rs2765013	rs72661644	chr1:36453894	(G/A)	0.0746	79793	1	0.8404	C = G, T = A

rs2765013	rs72661646	chr1:36454954	(A/T)	0.0746	80853	1	0.8404	C = A, T = T

rs2765013	rs72661655	chr1:36475401	(G/T)	0.0746	101300	1	0.8404	C = G, T = T

rs2765013	rs138030683	chr1:36353261	(-/T)	0.0755	−20840	0.9856	0.8281	C = -, T = T

rs2765013	rs148983758	chr1:36336826	(T/-)	0.0755	−37275	0.9856	0.8281	C = T, T = -

rs2765013	rs629773	chr1:36431561	(A/G)	0.1064	57460	1	0.8054	C = A, T = G

rs112728381	rs12732999	chr1:63766723	(G/A)	0.3241	5	1	0.9597	C = G, T = A

rs10873821	rs7550642	chr1:87629310	(G/A)	0.2227	1137	1	1	C = G, T = A

rs6714112	rs6716934	chr2:36912284	(A/T)	0.1282	7271	1	0.9646	C = A, A = T

rs1504061	rs1504061	chr3:1093795	(C/G)	0.0487	0	1	1	C = C, G = G

rs1504061	rs1312543601	chr3:1094816	(-/T)	0.0567	1021	1	0.8525	C = -, G = T

rs1504061	rs55975362	chr3:1103832	(G/C)	0.0577	10037	1	0.8369	C = G, G = C

rs1504061	rs72993835	chr3:1104218	(T/C)	0.0577	10423	1	0.8369	C = T, G = C

rs1504061	rs111403218	chr3:1104296	(C/T)	0.0577	10501	1	0.8369	C = C, G = T

rs1504061	rs142136066	chr3:1104395	(T/G)	0.0577	10600	1	0.8369	C = T, G = G

rs1504061	rs116727350	chr3:1104957	(G/A)	0.0577	11162	1	0.8369	C = G, G = A

rs1504061	rs72993828	chr3:1097969	(T/G)	0.0586	4174	1	0.8218	C = T, G = G

rs1504061	rs72993831	chr3:1099234	(A/G)	0.0586	5439	1	0.8218	C = A, G = G

rs1504061	rs72993838	chr3:1106164	(G/A)	0.0586	12369	1	0.8218	C = G, G = A

rs17317135	rs9863368	chr3:27253521	(T/C)	0.0567	65223	1	0.947	G = C, A = T

rs3774881	rs148165034	chr4:5822073	(AAC/-)	0.1412	196	1	0.9838	T = AAC, C = -

rs3774881	rs6846920	chr4:5827911	(A/G)	0.1471	6034	0.9104	0.8027	T = A, C = G

rs3774881	rs11318332	chr4:5827975	(A/-)	0.1451	6098	0.9025	0.8015	T = A, C = -

rs6810404	rs3069341	chr4:27384266	(AGC/-)	0.4791	988	1	0.9921	C = AGC, A = -

rs6810404	rs7659968	chr4:27383850	(G/A)	0.4831	572	1	0.9764	C = G, A = A

rs6810404	rs9291496	chr4:27370190	(A/G)	0.4851	−13088	0.9957	0.8523	C = A, A = G

rs35540967	rs17539340	chr4:44429579	(A/G)	0.0765	10987	1	1	T = A, C = G

rs112641600	rs116432808	chr4:112626500	(T/C)	0.0934	13474	0.9883	0.9767	C = T, T = C

rs112641600	rs72680134	chr4:112627204	(C/T)	0.0934	14178	0.9883	0.9767	C = C, T = T

rs112641600	rs72680150	chr4:112711302	(A/G)	0.0934	98276	0.9765	0.9536	C = A, T = G

rs112641600	rs147521731	chr4:112680269	(G/A)	0.0964	67243	0.9765	0.9209	C = G, T = A

rs62377777	rs72080072	chr5:122835924	(TATAAG/-)	0.2356	3208	1	0.9891	T = TATAAG, C = -

rs62377777	rs55893406	chr5:122837233	(T/A)	0.2346	4517	1	0.9836	T = T, C = A

rs62377777	rs55914925	chr5:122834333	(G/A)	0.2336	1617	1	0.9782	T = G, C = A

rs62377777	rs2036321	chr5:122838501	(C/T)	0.2336	5785	1	0.9782	T = C, C = T

rs62377777	rs2173683	chr5:122841745	(C/A)	0.2336	9029	1	0.9782	T = C, C = A

rs62377777	rs10593358	chr5:122845970	(CAAGC/-)	0.2336	13254	1	0.9782	T = CAAGC, C = -

rs62377777	rs1392437	chr5:122821063	(C/T)	0.2336	−11653	0.9888	0.9564	T = C, C = T

rs62377777	rs17164879	chr5:122863961	(C/T)	0.2237	31245	0.9942	0.9138	T = C, C = T

rs62377777	rs12519921	chr5:122830670	(G/A)	0.2078	−2046	1	0.8416	T = G, C = A

rs62377777	rs62377776	chr5:122814546	(T/G)	0.2107	−18170	0.9814	0.8254	T = T, C = G

rs62377777	rs12517980	chr5:122813888	(T/C)	0.2117	−18828	0.9754	0.8201	T = T, C = C

rs62377777	rs6595453	chr5:122814897	(T/C)	0.2117	−17819	0.9692	0.8097	T = T, C = C

rs4240376	rs10066524	chr5:123950880	(G/A)	0.2207	476	1	0.9429	G = G, T = A

rs4240376	rs10068590	chr5:123951944	(C/A)	0.1998	1540	0.9874	0.9117	G = C, T = A

rs4240376	rs12520962	chr5:123953224	(G/C)	0.2018	2820	0.9688	0.8886	G = G, T = C

rs4240376	rs4580771	chr5:123954814	(T/C)	0.2018	4410	0.9688	0.8886	G = T, T = C

rs4240376	rs6871217	chr5:123960447	(G/A)	0.1988	10043	0.9747	0.8828	G = G, T = A

rs4240376	rs10428701	chr5:123960974	(C/T)	0.1988	10570	0.9747	0.8828	G = C, T = T

rs4240376	rs12514836	chr5:123957328	(A/G)	0.2068	6924	0.933	0.8498	G = A, T = G

rs4240376	rs62372143	chr5:123957443	(A/G)	0.2068	7039	0.933	0.8498	G = A, T = G

rs4240376	rs62372144	chr5:123957610	(All)	0.2068	7206	0.933	0.8498	G = A, T = T

rs4240376	rs66500836	chr5:123957985	(C/G)	0.2068	7581	0.933	0.8498	G = C, T = G

rs4240376	rs6899090	chr5:123958033	(A/G)	0.2068	7629	0.933	0.8498	G = A, T = G

rs4240376	rs6859251	chr5:123958110	(G/A)	0.2068	7706	0.933	0.8498	G = G, T = A

rs4240376	rs6859613	chr5:123958332	(G/C)	0.2068	7928	0.933	0.8498	G = G, T = C

rs4240376	rs12513982	chr5:123959748	(T/A)	0.2068	9344	0.933	0.8498	G = T, T = A

rs4240376	rs373774956	chr5:123961537	(TTTGTTTG/-)	0.2068	11133	0.933	0.8498	G = TTTGTTTG, T = -

rs4240376	rs6893169	chr5:123960800	(T/A)	0.2058	10396	0.9327	0.844	G = T, T = A

rs4240376	rs11241757	chr5:123961500	(T/C)	0.2058	11096	0.9327	0.844	G = T, T = C

rs4240376	rs12516428	chr5:123959806	(A/G)	0.2048	9402	0.9323	0.8383	G = A, T = G

rs4240376	rs10052511	chr5:123963652	(C/T)	0.1889	13248	0.98	0.8375	G = C, T = T

rs4240376	rs71574121	chr5:123964265	(GA/-)	0.1849	13861	0.9796	0.8152	G = GA, T = -

rs2220543	rs7720656	chr5:173996282	(A/G)	0.3012	6944	0.9952	0.9672	T = G, A = A

rs2220543	rs1387768	chr5:173993166	(A/G)	0.2803	3828	1	0.9254	T = A, A = G

rs2220543	rs1387769	chr5:173993252	(C/A)	0.2803	3914	1	0.9254	T = C, A = A

rs2220543	rs4868427	chr5:173992056	(A/T)	0.2813	2718	0.995	0.9206	T = T, A = A

rs113791144	rs117101214	chr5:180237845	(C/T)	0.0835	17	1	1	C = C, T = T

rs113791144	rs147634845	chr5:180237902	(C/A)	0.0835	74	1	1	C = C, T = A

rs113791144	rs78102637	chr5:180238393	(G/A)	0.0835	565	1	1	C = G, T = A

rs113791144	rs11544558	chr5:180235737	(C/A)	0.0865	−2091	1	0.9624	C = C, T = A

rs113791144	rs75268547	chr5:180238308	(G/T)	0.0845	480	0.987	0.9617	C = G, T = T

rs113791144	rs112702671	chr5:180220930	(C/T)	0.0885	−16898	0.9869	0.9143	C = C, T = T

rs113791144	rs10577599	chr5:180216905	(AT/-)	0.0845	−20923	0.961	0.9116	C = AT, T = -

rs113791144	rs76809244	chr5:180216077	(C/G)	0.0855	−21751	0.9609	0.9	C = C, T = G

rs113791144	rs145796183	chr5:180238591	(1/-)	0.0686	763	1	0.8083	C = T, T = -

rs113791144	rs10040542	chr5:180242178	(C/T)	0.0686	4350	1	0.8083	C = C, T = T

rs10755709	rs7356945	chr6:12217422	(C/T)	0.3022	456	0.9525	0.8988	A = C, G = T

rs16873740	rs35926878	chr6:45705862	(G/A)	0.1183	1049	1	1	T = G, A = A

rs13282163	rs55750326	chr8:38949033	(C/-)	0.0875	51563	1	0.9877	A = C, C = -

rs12236000	rs10964856	chr9:21131785	(A/G)	0.0855	158	1	0.9873	G = A, C = G

rs2091431	rs2505150	chr10:37278930	(A/G)	0.3211	1060	1	1	A = A, G = G

rs2091431	rs10764117	chr10:37284272	(A/G)	0.325	6402	1	0.982	A = A, G = G

rs2091431	rs2505172	chr10:37286042	(C/A)	0.325	8172	1	0.982	A = C, G = A

rs2091431	rs2459442	chr10:37292750	(G/A)	0.3241	14880	0.9954	0.9774	A = A, G = G

rs2091431	rs2459421	chr10:37303511	(C/T)	0.3231	25641	0.9863	0.9639	A = T, G = C

rs2091431	rs138295864	chr10:37302778	(-/A)	0.3211	24908	0.9818	0.9639	A = A, G = -

rs2091431	rs35067257	chr10:37282717	(1/-)	0.336	4847	1	0.9346	A = T, G = -

rs2091431	rs1914151	chr10:37304072	(C/A)	0.338	26202	0.9766	0.8835	A = A, G = C

rs2091431	rs1914152	chr10:37304128	(A/G)	0.338	26258	0.9766	0.8835	A = G, G = A

rs2091431	rs2459426	chr10:37307089	(G/A)	0.337	29219	0.972	0.8791	A = A, G = G

rs2091431	rs2459429	chr10:37308202	(C/T)	0.337	30332	0.972	0.8791	A = T, G = C

rs2091431	rs2459433	chr10:37311324	(A/G)	0.337	33454	0.972	0.8791	A = G, G = A

rs2091431	rs2505166	chr10:37318114	(C/T)	0.337	40244	0.972	0.8791	A = T, G = C

rs2091431	rs11011000	chr10:37328362	(A/G)	0.337	50492	0.972	0.8791	A = G, G = A

rs2091431	rs2459440	chr10:37338386	(C/T)	0.337	60516	0.972	0.8791	A = T, G = C

rs2091431	rs2505174	chr10:37338614	(G/A)	0.337	60744	0.972	0.8791	A = A, G = G

rs2091431	rs2459441	chr10:37340271	(T/C)	0.337	62401	0.972	0.8791	A = C, G = T

rs2091431	rs34886927	chr10:37345130	(T/-)	0.337	67260	0.972	0.8791	A = -, G = T

rs2091431	rs10827752	chr10:37318895	(C/T)	0.338	41025	0.9719	0.8751	A = T, G = C

rs2091431	rs12221175	chr10:37327753	(C/T)	0.338	49883	0.9719	0.8751	A = T, G = C

rs2091431	rs2505164	chr10:37317344	(C/A)	0.339	39474	0.9719	0.8711	A = A, G = C

rs2091431	rs2459434	chr10:37311831	(G/T)	0.337	33961	0.9626	0.8623	A = T, G = G

rs1892429	rs1200880	chr10:37450374	(T/C)	0.2624	−4023	1	0.9	A = T, G = C

rs1892429	rs1767366	chr10:37494647	(G/A)	0.2624	40250	1	0.9	A = G, G = A

rs1892429	rs1933748	chr10:37498227	(C/T)	0.2624	43830	1	0.9	A = C, G = T

rs1892429	rs2490107	chr10:37484946	(G/A)	0.2634	30549	1	0.8954	A = G, G = A

rs1892429	rs1200876	chr10:37505141	(G/A)	0.2634	50744	1	0.8954	A = G, G = A

rs1892429	rs1148258	chr10:37506384	(G/A)	0.2634	51987	1	0.8954	A = G, G = A

rs1892429	rs138860607	chr10:37515709	(AGCAGCTATACCATTTTTCATT/-)	0.2634	61312	1	0.8954	A = AGCAGCTATACCATTTTTCATT, G = -

rs1892429	rs1200857	chr10:37521828	(A/C)	0.2634	67431	1	0.8954	A = A, G = C

rs1892429	rs1148264	chr10:37527168	(A/T)	0.2634	72771	1	0.8954	A = A, G = T

rs1892429	rs1767387	chr10:37536757	(C/T)	0.2634	82360	1	0.8954	A = C, G = T

rs1892429	rs1200875	chr10:37505192	(C/T)	0.2644	50795	1	0.8908	A = C, G = T

rs1892429	rs2765819	chr10:37525622	(G/T)	0.2644	71225	1	0.8908	A = G, G = T

rs1892429	rs1711240	chr10:37378880	(C/T)	0.2604	−75517	0.9446	0.8113	A = C, G = T

rs1441121	rs1441123	chr10:54101139	(T/G)	0.4334	794	1	0.996	A = T, T = G

rs1441121	rs73331255	chr10:54105039	(C/G)	0.4324	4694	1	0.9919	A = C, T = G

rs1441121	rs1372107	chr10:54114458	(G/C)	0.4404	14113	0.9959	0.9681	A = G, T = C

rs1441121	rs1441124	chr10:54114916	(G/A)	0.4404	14571	0.9959	0.9681	A = G, T = A

rs1441121	rs11001719	chr10:54108610	(C/T)	0.4394	8265	0.9918	0.9641	A = C, T = T

rs1441121	rs11814479	chr10:54108930	(T/G)	0.4394	8585	0.9918	0.9641	A = T, T = G

rs1441121	rs11001721	chr10:54109544	(A/G)	0.4394	9199	0.9918	0.9641	A = A, T = G

rs1441121	rs11001723	chr10:54110313	(C/T)	0.4394	9968	0.9918	0.9641	A = C, T = T

rs1441121	rs7079513	chr10:54110965	(TG)	0.4394	10620	0.9918	0.9641	A = T, T = G

rs1441121	rs7075951	chr10:54111095	(A/G)	0.4394	10750	0.9918	0.9641	A = A, T = G

rs1441121	rs7904997	chr10:54111864	(A/T)	0.4394	11519	0.9918	0.9641	A = A, T = T

rs1441121	rs10824390	chr10:54110416	(A/T)	0.4404	10071	0.9918	0.9602	A = A, T = T

rs1441121	rs894104	chr10:54106670	(T/C)	0.4384	6325	0.9878	0.9601	A = T, T = C

rs1441121	rs10762708	chr10:54108307	(C/G)	0.4384	7962	0.9878	0.9601	A = C, T = G

rs1441121	rs10762712	chr10:54112284	(T/C)	0.4364	11939	0.9797	0.9521	A = T, T = C

rs1441121	rs10740457	chr10:54112762	(A/G)	0.4364	12417	0.9797	0.9521	A = A, T = G

rs10766439	rs2078786	chr11:2896128	(A/G)	0.3678	2261	1	0.9872	A = A, G = G

rs10766439	rs11024404	chr11:2894452	(G/T)	0.3887	585	1	0.9034	A = G, G = T

rs10766439	rs148588273	chr11:2899462	(-/C)	0.3986	5595	0.9955	0.8587	A = -, G = C

rs10766439	rs10766443	chr11:2899586	(T/C)	0.3986	5719	0.9955	0.8587	A = T, G = C

rs10766439	rs10766442	chr11:2899538	(C/T)	0.3976	5671	0.991	0.8544	A = C, G = T

rs10766439	rs4929954	chr11:2900383	(C/G)	0.3966	6516	0.9865	0.8502	A = C, G = G

rs12823094	rs35769445	chr12:106625862	(G/C)	0.2396	909	0.9945	0.9837	T = G, A = C

rs2238187	rs2283380	chr14:72909738	(G/A)	0.3946	1636	0.924	0.8326	A = G, G = A

rs12587980	rs917428	chr14:72935767	(C/T)	0.4006	1538	1	0.9959	C = C, T = T

rs2229117	rs35242916	chr15:33917374	(C/T)	0.1332	1321	1	1	G = C, C = T

rs2229117	rs34638660	chr15:33919774	(C/T)	0.1332	3721	1	1	G = C, C = T

rs2229117	rs4780137	chr15:33922609	(T/A)	0.1332	6556	1	1	G = T, C = A

rs2229117	rs4780138	chr15:33922983	(G/A)	0.1332	6930	1	1	G = G, C = A

rs2229117	rs71462874	chr15:33924037	(G/A)	0.1332	7984	1	1	G = G, C = A

rs2229117	rs35203574	chr15:33928785	(A/G)	0.1342	12732	1	0.9915	G = A, C = G

rs2229117	rs36020093	chr15:33919750	(A/G)	0.1362	3697	1	0.9747	G = A, C = G

rs2229117	rs2291730	chr15:33923690	(C/T)	0.1362	7637	1	0.9747	G = C, C = T

rs2229117	rs3816940	chr15:33925062	(G/A)	0.1362	9009	1	0.9747	G = G, C = A

rs2229117	rs12901506	chr15:33929755	(G/C)	0.1511	13702	1	0.8634	G = G, C = C

rs2229117	rs71462875	chr15:33931313	(A/G)	0.1511	15260	1	0.8634	G = A, C = G

rs72803978	rs76614455	chr16:78633493	(G/A)	0.0527	9468	0.98	0.873	A = G, G = A

rs72803978	rs138270756	chr16:78640700	(-/AAT)	0.0626	16675	0.9448	0.8175	A = -, G = AAT

rs34761447	rs35985527	chr17:9172769	(G/A)	0.1044	2361	0.9884	0.8843	C = G, T = A

rs34761447	rs12952893	chr17:9176588	(G/C)	0.0994	6180	0.9306	0.8277	C = G, T = C

rs34761447	rs7215786	chr17:9172095	(T/C)	0.1113	1687	0.9883	0.8224	C = T, T = C

rs34761447	rs35880517	chr17:9174045	(G/A)	0.1004	3637	0.9305	0.8185	C = G, T = A

rs34761447	rs35306109	chr17:9174082	(A/G)	0.1004	3674	0.9305	0.8185	C = A, T = G

rs60744406	rs397964	chr19:44493969	(A/T)	0.4294	1805	1	1	A = T, G = A

rs10411226	rs1974832	chr19:53333465	(G/C)	0.2247	−510	0.9943	0.9886	G = G, A = C

rs5757427	rs2156880	chr22:22570271	(A/G)	0.3598	5537	1	0.9914	A = A, T = G

rs5757427	rs11376968	chr22:22567202	(-/A)	0.3549	2468	1	0.9871	A = -, T = A

rs5757427	rs2330040	chr22:22567762	(G/A)	0.3618	3028	1	0.9829	A = G, T = A

rs5757427	rs5750729	chr22:22566976	(A/G)	0.3549	2242	0.9869	0.9614	A = A, T = G

rs5757427	rs1007312	chr22:22569758	(A/C)	0.3539	5024	0.9869	0.9572	A = A, T = C

rs5757427	rs738876	chr22:22568531	(C/T)	0.3559	3797	0.9826	0.9572	A = C, T = T

rs5757427	rs5750739	chr22:22568820	(T/C)	0.3559	4086	0.9826	0.9572	A = T, T = C

rs5757427	rs6001415	chr22:22569640	(T/G)	0.3559	4906	0.9826	0.9572	A = T, T = G

rs5757427	rs5757477	chr22:22572904	(G/A)	0.3648	8170	0.9913	0.9534	A = G, T = A

rs5757427	rs111384644	chr22:22565774	(-/CAGG)	0.3569	1040	0.9783	0.953	A = -, T = CAGG

rs5757427	rs5757443	chr22:22566563	(T/G)	0.3579	1829	0.974	0.9488	A = T, T = G

rs5757427	rs738874	chr22:22567948	(A/G)	0.3579	3214	0.974	0.9488	A = A, T = G

rs5757427	rs738875	chr22:22568014	(G/T)	0.3579	3280	0.974	0.9488	A = G, T = T

rs5757427	rs5750741	chr22:22569448	(T/A)	0.3588	4714	0.974	0.9446	A = T, T = A

rs5757427	rs762464	chr22:22574458	(G/C)	0.3588	9724	0.9697	0.9362	A = G, T = C

rs5757427	rs5757469	chr22:22570658	(A/G)	0.3608	5924	0.9696	0.928	A = A, T = G

rs5757427	rs1029267	chr22:22571456	(T/A)	0.3608	6722	0.9696	0.928	A = T, T = A

rs5757427	rs1029270	chr22:22571681	(G/C)	0.3608	6947	0.9696	0.928	A = G, T = C

rs5757427	rs4599223	chr22:22571981	(C/T)	0.3608	7247	0.9696	0.928	A = C, T = T

rs5757427	rs5757486	chr22:22574248	(A/G)	0.3608	9514	0.9696	0.928	A = A, T = G

rs5757427	rs35158064	chr22:22572122	(T/-)	0.3618	7388	0.9695	0.9239	A = T, T = -

rs5757427	rs1023418	chr22:22572199	(T/C)	0.3618	7465	0.9695	0.9239	A = T, T = C

rs5757427	rs5750745	chr22:22572812	(C/T)	0.3618	8078	0.9695	0.9239	A = C, T = T

rs5757427	rs5750746	chr22:22572822	(G/A)	0.3618	8088	0.9695	0.9239	A = G, T = A

rs5757427	rs968897	chr22:22570821	(C/T)	0.3598	6087	0.9653	0.9238	A = C, T = T

rs5757427	rs5750749	chr22:22573209	(T/C)	0.3628	8475	0.9695	0.9198	A = T, T = C

rs5757427	rs5750750	chr22:22573365	(C/T)	0.3628	8631	0.9695	0.9198	A = C, T = T

rs5757427	rs5750751	chr22:22573414	(G/A)	0.3628	8680	0.9695	0.9198	A = G, T = A

rs5757427	rs5757482	chr22:22573837	(C/T)	0.3628	9103	0.9695	0.9198	A = C, T = T

rs5757427	rs5757483	chr22:22573878	(C/A)	0.3628	9144	0.9695	0.9198	A = C, T = A

rs5757427	rs5757484	chr22:22573973	(C/G)	0.3628	9239	0.9695	0.9198	A = C, T = G

rs5757427	rs5757485	chr22:22574035	(T/C)	0.3628	9301	0.9695	0.9198	A = T, T = C

rs5757427	rs5750752	chr22:22574105	(G/A)	0.3628	9371	0.9695	0.9198	A = G, T = A

rs5757427	rs762465	chr22:22574855	(C/T)	0.3618	10121	0.9652	0.9156	A = C, T = T

rs5757427	rs1029269	chr22:22571612	(G/A)	0.3887	6878	0.9682	0.8217	A = G, T = A

rs7290963	rs7287541	chr22:22725057	(T/G)	0.4433	106	1	0.992	G = T, T = G

rs11090305	rs9608231	chr22:24415817	(A/T)	0.2157	8334	0.9823	0.9426	T = T, C = A

rs11090305	rs6004044	chr22:24422166	(A/G)	0.2157	14683	0.9823	0.9426	T = G, C = A

rs11090305	rs873833	chr22:24427878	(G/A)	0.2157	20395	0.9823	0.9426	T = A, C = G

rs11090305	rs5996663	chr22:24429241	(C/T)	0.2157	21758	0.9823	0.9426	T = T, C = C

rs11090305	rs2282475	chr22:24438047	(A/G)	0.2157	30564	0.9823	0.9426	T = G, C = A

rs11090305	rs5751798	chr22:24443473	(T/C)	0.2157	35990	0.9823	0.9426	T = C, C = T

rs11090305	rs2070467	chr22:24452885	(A/G)	0.2157	45402	0.9823	0.9426	T = G, C = A

rs11090305	rs5760179	chr22:24411653	(C/T)	0.2147	4170	0.9822	0.9369	T = T, C = C

rs11090305	rs6004042	chr22:24420722	(C/T)	0.2147	13239	0.9822	0.9369	T = T, C = C

rs11090305	rs2267053	chr22:24457197	(C/T)	0.2147	49714	0.9822	0.9369	T = T, C = C

rs11090305	rs2051198	chr22:24465672	(A/G)	0.2147	58189	0.9822	0.9369	T = G, C = A

rs11090305	rs4822469	chr22:24425114	(C/G)	0.2157	17631	0.9764	0.9313	T = G, C = C

rs11090305	rs2283807	chr22:24472270	(A/G)	0.2157	64787	0.9764	0.9313	T = G, C = A

rs11090305	rs2000470	chr22:24488861	(C/T)	0.2157	81378	0.9764	0.9313	T = T, C = C

rs11090305	rs5751803	chr22:24489649	(T/C)	0.2157	82166	0.9764	0.9313	T = C, C = T

rs11090305	rs5760205	chr22:24490529	(C/T)	0.2157	83046	0.9764	0.9313	T = T, C = C

rs11090305	rs2236622	chr22:24492061	(T/C)	0.2157	84578	0.9764	0.9313	T = C, C = T

rs11090305	rs176156	chr22:24500866	(G/C)	0.2157	93383	0.9764	0.9313	T = G, C = C

rs11090305	rs112272	chr22:24510620	(C/T)	0.2157	103137	0.9764	0.9313	T = C, C = T

rs11090305	rs2267059	chr22:24525711	(T/C)	0.2157	118228	0.9764	0.9313	T = T, C = C

rs11090305	rs2003756	chr22:24527749	(T/C)	0.2157	120266	0.9764	0.9313	T = T, C = C

rs11090305	rs6519499	chr22:24532468	(T/C)	0.2157	124985	0.9764	0.9313	T = T, C = C

rs11090305	rs2001105	chr22:24535559	(T/C)	0.2157	128076	0.9764	0.9313	T = T, C = C

rs11090305	rs2267060	chr22:24535962	(G/A)	0.2157	128479	0.9764	0.9313	T = G, C = A

rs11090305	rs5760218	chr22:24541432	(T/C)	0.2157	133949	0.9764	0.9313	T = T, C = C

rs11090305	rs5760221	chr22:24542636	(T/A)	0.2157	135153	0.9764	0.9313	T = T, C = A

rs11090305	rs2267062	chr22:24544482	(G/T)	0.2157	136999	0.9764	0.9313	T = G, C = T

rs11090305	rs2267063	chr22:24544607	(T/C)	0.2157	137124	0.9764	0.9313	T = T, C = C

rs11090305	rs2267064	chr22:24544632	(T/G)	0.2157	137149	0.9764	0.9313	T = T, C = G

rs11090305	rs2267068	chr22:24550303	(T/C)	0.2157	142820	0.9764	0.9313	T = T, C = C

rs11090305	rs915595	chr22:24551909	(T/G)	0.2157	144426	0.9764	0.9313	T = T, C = G

rs11090305	rs879756	chr22:24552872	(C/A)	0.2157	145389	0.9764	0.9313	T = C, C = A

rs11090305	rs6519501	chr22:24556507	(T/C)	0.2157	149024	0.9764	0.9313	T = T, C = C

rs11090305	rs2267070	chr22:24558318	(T/C)	0.2157	150835	0.9764	0.9313	T = T, C = C

rs11090305	rs5996668	chr22:24575952	(T/C)	0.2157	168469	0.9764	0.9313	T = T, C = C

rs11090305	rs9624412	chr22:24585313	(A/G)	0.2157	177830	0.9764	0.9313	T = A, C = G

rs11090305	rs141628202	chr22:24474904	(ATC/-)	0.2167	67421	0.9706	0.9258	T = -, C = ATC

rs11090305	rs5844585	chr22:24483878	(T/-)	0.2167	76395	0.9706	0.9258	T = -, C = T

rs11090305	rs8137732	chr22:24567031	(A/G)	0.2167	159548	0.9706	0.9258	T = A, C = G

rs11090305	rs8137222	chr22:24576159	(G/A)	0.2167	168676	0.9706	0.9258	T = G, C = A

rs11090305	rs2070470	chr22:24583879	(T/C)	0.2167	176396	0.9706	0.9258	T = T, C = C

rs11090305	rs5760244	chr22:24584970	(G/A)	0.2167	177487	0.9706	0.9258	T = G, C = A

rs11090305	rs9624413	chr22:24585575	(T/C)	0.2167	178092	0.9706	0.9258	T = T, C = C

rs11090305	rs28687166	chr22:24585835	(T/C)	0.2167	178352	0.9706	0.9258	T = T, C = C

rs11090305	rs5751813	chr22:24586071	(T/C)	0.2167	178588	0.9706	0.9258	T = T, C = C

rs11090305	rs2267066	chr22:24546298	(G/A)	0.2147	138815	0.9763	0.9256	T = G, C = A

rs11090305	rs2236623	chr22:24578659	(A/G)	0.2177	171176	0.9649	0.9202	T = A, C = G

rs11090305	rs67342915	chr22:24598619	(G/-)	0.2127	191136	0.976	0.9143	T = G, C = -

rs11090305	rs4521150	chr22:24600438	(A/G)	0.2177	192955	0.959	0.9091	T = A, C = G

rs11090305	rs8138769	chr22:24591879	(A/G)	0.2117	184396	0.9759	0.9087	T = A, C = G

rs11090305	rs5760254	chr22:24602382	(A/C)	0.2187	194899	0.9534	0.9037	T = A, C = C

rs11090305	.	chr22:24417287	(-/G)	0.2286	9804	0.9589	0.8734	T = G, C = -

rs62220604	rs6009583	chr22:49677646	(C/T)	0.2465	182	0.989	0.8679	G = C, A = T

rs62220604	rs11703376	chr22:49678713	(C/T)	0.2485	1249	0.9781	0.858	G = C, A = T

rs62220604	rs8136272	chr22:49678782	(A/T)	0.2515	1318	0.9621	0.8436	G = A, A = T

Part B

rs2274122	rs679457	chr1:36496479	(A/G)	0.166	−53185	0.9781	0.8679	G = A, A = G

rs2274122	rs379507	chr1:36503907	(A/G)	0.166	−45757	0.9781	0.8679	G = A, A = G

rs2274122	rs491603	chr1:36532316	(T/C)	0.172	−17348	0.993	0.9333	G = T, A = C

rs1984162	rs1984163	chr13:23658864	(A/G)	0.2704	26	1	0.995	A = A, G = G

rs8105499	rs8106322	chr19:32024230	(A/G)	0.3519	273	0.995	0.8046	C = A, A = G

rs8105499	rs8106852	chr19:32024669	(A/G)	0.3211	712	0.9904	0.9153	C = A, A = G

rs8105499	rs8102936	chr19:32027330	(G/A)	0.336	3373	0.9853	0.8467	C = G, A = A

rs8105499	rs8103067	chr19:32027415	(G/A)	0.336	3458	0.9853	0.8467	C = G, A = A

rs8105499	rs139978707	chr19:32028824	(-/ACAC)	0.332	4867	0.9514	0.8036	C = -, A = ACAC

rs11385942	rs35896106	chr3:45841938	(C/T)	0.0855	−34521	0.9595	0.8624	- = C, A = T

rs11385942	rs13071258	chr3:45843242	(G/A)	0.0805	−33217	0.9866	0.9733	- = G, A = A

rs11385942	rs17763537	chr3:45843315	(C/T)	0.0805	−33144	0.9866	0.9733	- = C, A = T

rs11385942	rs34668658	chr3:45844198	(A/C)	0.0815	−32261	1	0.9867	- = A, A = C

rs11385942	rs17763742	chr3:45846769	(A/G)	0.0805	−29690	0.9866	0.9733	- = A, A = G

rs11385942	rs72893671	chr3:45850783	(T/A)	0.0875	−25676	1	0.9135	- = T, A = A

rs11385942	rs17713054	chr3:45859651	(G/A)	0.0805	−16808	1	1	- = G, A = A

rs11385942	rs13078854	chr3:45861932	(G/A)	0.0805	−14527	1	1	- = G, A = A

rs11385942	rs71325088	chr3:45862952	(T/C)	0.0805	−13507	1	1	- = T, A = C

rs11385942	rs10490770	chr3:45864732	(T/C)	0.0805	−11727	1	1	- = T, A = C

rs11385942	rs35624553	chr3:45867440	(A/G)	0.0805	−9019	1	1	- = A, A = G

rs11385942	rs71619611	chr3:45871139	(A/-)	0.0835	−5320	1	0.9612	- = A, A = -

rs11385942	rs67959919	chr3:45871908	(G/A)	0.0805	−4551	1	1	- = G, A = A

rs11385942	rs35508621	chr3:45880481	(T/C)	0.0805	4022	1	1	- = T, A = C

rs11385942	rs34288077	chr3:45888690	(A/G)	0.0795	12231	1	0.9866	- = A, A = G

rs11385942	rs35081325	chr3:45889921	(A/T)	0.0795	13462	1	0.9866	- = A, A = T

rs11385942	rs35731912	chr3:45889949	(C/T)	0.0795	13490	1	0.9866	- = C, A = T

rs11385942	rs34326463	chr3:45899651	(A/G)	0.0795	23192	1	0.9866	- = A, A = G

rs11385942	rs73064425	chr3:45901089	(C/T)	0.0795	24630	1	0.9866	- = C, A = T

rs11385942	rs13081482	chr3:45908116	(A/T)	0.0795	31657	1	0.9866	- = A, A = T

rs11385942	rs35652899	chr3:45908514	(C/G)	0.0775	32055	1	0.9598	- = C, A = G

rs11385942	rs35044562	chr3:45909024	(A/G)	0.0795	32565	1	0.9866	- = A, A = G

rs11385942	rs73064431	chr3:45909528	(C/T)	0.0885	33069	0.9865	0.878	- = C, A = T

rs11385942	rs13092887	chr3:45909644	(C/A)	0.0865	33185	0.9595	0.8515	- = C, A = A

rs11729561	rs11729561	chr4:106943200	(T/C)	0.0736	0	1	1	T = T, C = C

rs11729561	rs143299240	chr4:106952273	(-/T)	0.0736	9073	1	1	T = -, C = T

rs11729561	rs28472461	chr4:106956065	(C/T)	0.0736	12865	1	1	T = C, C = T

rs11729561	rs28709953	chr4:106958075	(C/A)	0.0736	14875	1	1	T = C, C = A

rs11729561	rs28663259	chr4:106958076	(C/T)	0.0736	14876	1	1	T = C, C = T

rs11729561	rs10023586	chr4:106973602	(A/G)	0.0746	30402	1	0.9856	T = A, C = G

rs11729561	rs79449940	chr4:106979613	(G/T)	0.0746	36413	1	0.9856	T = G, C = T

rs11729561	rs75853787	chr4:106981645	(C/T)	0.0736	38445	0.9854	0.971	T = C, C = T

rs11729561	rs7679603	chr4:106987746	(C/A)	0.0746	44546	1	0.9856	T = C, C = A

rs11729561	rs28783132	chr4:106994627	(T/C)	0.0746	51427	1	0.9856	T = T, C = C

rs11729561	rs11736679	chr4:106995182	(T/G)	0.0746	51982	1	0.9856	T = T, C = G

rs11729561	rs74725815	chr4:106998315	(G/A)	0.0746	55115	1	0.9856	T = G, C = A

rs11729561	rs28857517	chr4:107009841	(T/C)	0.0746	66641	1	0.9856	T = T, C = C

rs11729561	rs78336797	chr4:107010481	(G/A)	0.0746	67281	1	0.9856	T = G, C = A

rs11729561	rs77454815	chr4:107018793	(A/C)	0.0736	75593	0.9854	0.971	T = A, C = C

rs11729561	rs28597815	chr4:107020234	(T/C)	0.0736	77034	0.9854	0.971	T = T, C = C

rs11729561	rs28823294	chr4:107026693	(C/T)	0.0746	83493	1	0.9856	T = C, C = T

rs11729561	rs28786397	chr4:107032979	(T/G)	0.0746	89779	1	0.9856	T = T, C = G

rs11729561	rs28648796	chr4:107037155	(G/C)	0.0746	93955	1	0.9856	T = G, C = C

rs11729561	rs140517213	chr4:107038130	(C/-)	0.0765	94930	1	0.9579	T = C, C = -

rs11729561	rs28786712	chr4:107041195	(A/G)	0.0746	97995	1	0.9856	T = A, C = G

rs11729561	rs185825831	chr4:107041846	(C/A)	0.0746	98646	1	0.9856	T = C, C = A

rs11729561	rs28890246	chr4:107046012	(C/G)	0.0746	102812	1	0.9856	T = C, C = G

rs11729561	rs116513184	chr4:107052399	(A/G)	0.0785	109199	1	0.9317	T = A, C = G

rs11729561	rs10010622	chr4:107064347	(A/G)	0.0785	121147	1	0.9317	T = A, C = G

rs11729561	rs28843476	chr4:107074493	(G/A)	0.0785	131293	1	0.9317	T = G, C = A

rs11729561	rs28848568	chr4:107083274	(C/A)	0.0785	140074	1	0.9317	T = C, C = A

rs11729561	rs10010712	chr4:107087041	(G/A)	0.0785	143841	1	0.9317	T = G, C = A

rs11729561	rs28852980	chr4:107108590	(T/C)	0.0785	165390	1	0.9317	T = T, C = C

rs11729561	rs577231943	chr4:107118572	(A/G)	0.0785	175372	1	0.9317	T = A, C = G

rs11729561	rs74349962	chr4:107121289	(T/A)	0.0785	178089	1	0.9317	T = T, C = A

rs11729561	rs10026011	chr4:107125358	(A/C)	0.0785	182158	1	0.9317	T = A, C = C

rs11729561	rs28668834	chr4:107131465	(G/A)	0.0785	188265	1	0.9317	T = G, C = A

rs11729561	rs13258128	chr4:107136176	(G/A)	0.0785	192976	1	0.9317	T = G, C = A

rs11729561	rs191250332	chr4:107138855	(A/T)	0.0785	195655	1	0.9317	T = A, C = T

rs11729561	rs11729801	chr4:107158823	(C/T)	0.0785	215623	1	0.9317	T = C, C = T

rs11729561	rs76805843	chr4:107159508	(A/G)	0.0785	216308	1	0.9317	T = A, C = G

rs11729561	rs9996386	chr4:107163773	(T/C)	0.0785	220573	1	0.9317	T = T, C = C

rs11729561	rs10033060	chr4:107175191	(C/T)	0.0785	231991	1	0.9317	T = C, C = T

rs11729561	rs78279936	chr4:107179034	(T/A)	0.0785	235834	1	0.9317	T = T, C = A

rs11729561	rs10213435	chr4:107185588	(A/C)	0.0785	242388	1	0.9317	T = A, C = C

rs11729561	rs28432701	chr4:107206834	(G/A)	0.0785	263634	1	0.9317	T = G, C = A

rs11729561	rs28600674	chr4:107207836	(T/G)	0.0785	264636	1	0.9317	T = T, C = G

rs11729561	rs10009873	chr4:107222264	(C/T)	0.0785	279064	1	0.9317	T = C, C = T

rs11729561	rs7356173	chr4:107223219	(T/C)	0.0785	280019	1	0.9317	T = T, C = C

rs11729561	rs28408532	chr4:107228745	(G/A)	0.0775	285545	0.9854	0.9172	T = G, C = A

rs11729561	rs28722963	chr4:107232881	(T/C)	0.0785	289681	1	0.9317	T = T, C = C

rs11729561	rs11544776	chr4:107236833	(C/T)	0.0795	293633	1	0.919	T = C, C = T

rs11729561	rs143098221	chr4:107242218	(A/G)	0.0785	299018	0.9853	0.9046	T = A, C = G

rs11729561	rs9995260	chr4:107242748	(C/G)	0.0785	299548	0.9853	0.9046	T = C, C = G

rs11729561	rs114592099	chr4:107243136	(G/A)	0.0785	299936	0.9853	0.9046	T = G, C = A

rs11729561	rs28615207	chr4:107248029	(G/A)	0.0785	304829	0.9853	0.9046	T = G, C = A

rs11729561	rs6820647	chr4:107268203	(G/A)	0.0785	325003	0.9853	0.9046	T = G, C = A

rs11729561	rs76860372	chr4:107271232	(T/C)	0.0785	328032	0.9853	0.9046	T = T, C = C

rs11729561	rs148911649	chr4:107275349	(ATC/-)	0.0785	332149	0.9853	0.9046	T = ATC, C = -

rs11729561	rs7682001	chr4:107276380	(G/A)	0.0785	333180	0.9853	0.9046	T = G, C = A

rs11729561	rs75431821	chr4:107279518	(T/C)	0.0785	336318	0.9853	0.9046	T = T, C = C

rs11729561	rs146578076	chr4:107288993	(-/AAGT)	0.0775	345793	0.9707	0.8901	T = -, C = AAGT

rs657152	rs8176719	chr9:136132908	(-/C)	0.3946	−6357	0.9958	0.9713	C = -, A = C

rs657152	rs687621	chr9:136137065	(A/G)	0.3708	−2200	0.9955	0.8775	C = A, A = G

rs657152	rs687289	chr9:136137106	(G/A)	0.3718	−2159	0.9955	0.8812	C = G, A = A

rs657152	rs576123	chr9:136144308	(T/C)	0.3698	5043	0.9955	0.8737	C = T, A = C

rs657152	rs61457395	chr9:136145907	(-/A)	0.3708	6642	1	0.8854	C = -, A = A

rs657152	rs367689313	chr9:136145993	(AGAAGGGAAATTAATAAATATT/-)	0.3698	6728	1	0.8816	C = AGAAGGGAAATTAATAAATATT, A = -

rs657152	rs8176645	chr9:136149098	(T/A)	0.3966	9833		0.9876	C = T, A = A

Part C

rs115492982	rs7543314	chr1:150271247	(G/A)	0.002	−309	1	1	G = G, A = A

rs115492982	rs3738322	chr1:150272038	(G/A)	0.002	482	1	1	G = G, A = A

rs115492982	rs16835865	chr1:150270667	(C/T)	0.002	−889	1	1	G = C, A = T

rs115492982	rs73013119	chr1:150272447	(A/G)	0.002	891	1	1	G = A, A = G

rs115492982	rs112587175	chr1:150273621	(C/A)	0.002	2065	1	1	G = C, A = A

rs115492982	rs56965166	chr1:150269187	(A/T)	0.002	−2369	1	1	G = A, A = T

rs115492982	rs16830437	chr1:150266903	(T/C)	0.002	−4653	1	1	G = T, A = C

rs115492982	rs16835791	chr1:150265839	(A/C)	0.002	−5717	1	1	G = A, A = C

rs115492982	rs143549387	chr1:150277642	(A/-)	0.002	6086	1	1	G = A, A = -

rs115492982	rs16835782	chr1:150265360	(A/G)	0.002	−6196	1	1	G = A, A = G

rs115492982	rs369956581	chr1:150264667	(1/-)	0.002	−6889	1	1	G = T, A = -

rs115492982	rs73011400	chr1:150264215	(A/G)	0.002	−7341	1	1	G = A, A = G

rs115492982	rs16835911	chr1:150279333	(A/C)	0.002	7777	1	1	G = A, A = C

rs115492982	rs57361164	chr1:150261893	(A/G)	0.002	−9663	1	1	G = A, A = G

rs115492982	rs73013129	chr1:150281404	(T/C)	0.002	9848	1	1	G = T, A = C

rs115492982	rs112097040	chr1:150283854	(C/T)	0.002	12298	1	1	G = C, A = T

rs115492982	rs146795912	chr1:150285359	(G/A)	0.002	13803	1	1	G = G, A = A

rs115492982	rs113720135	chr1:150285600	(G/A)	0.002	14044	1	1	G = G, A = A

rs115492982	rs60531845	chr1:150285818	(C/T)	0.002	14262	1	1	G = C, A = T

rs115492982	rs3054393	chr1:150256412	(-/TTTATT)	0.002	−15144	1	1	G = -, A = TTTATT

rs115492982	rs16835708	chr1:150253872	(C/G)	0.002	−17684	1	1	G = C, A = G

rs115492982	rs112511224	chr1:150289455	(C/A)	0.002	17899	1	1	G = C, A = A

rs115492982	rs142046449	chr1:150290133	(C/T)	0.002	18577	1	1	G = C, A = T

rs115492982	rs16835699	chr1:150252247	(T/C)	0.002	−19309	1	1	G = T, A = C

rs115492982	rs58516261	chr1:150290969	(C/T)	0.002	19413	1	1	G = C, A = T

rs115492982	rs74953512	chr1:150251816	(T/A)	0.002	−19740	1	1	G = T, A = A

rs115492982	rs79484682	chr1:150292342	(C/T)	0.002	20786	1	1	G = C, A = T

rs115492982	rs73015063	chr1:150293416	(A/G)	0.002	21860	1	1	G = A, A = G

rs115492982	rs113579391	chr1:150247103	(C/T)	0.002	−24453	1	1	G = C, A = T

rs115492982	rs73011384	chr1:150246411	(C/T)	0.002	−25145	1	1	G = C, A = T

rs115492982	rs60758881	chr1:150297241	(T/C)	0.002	25685	1	1	G = T, A = C

rs115492982	rs114657335	chr1:150298649	(C/G)	0.002	27093	1	1	G = C, A = G

rs115492982	rs587687867	chr1:150244075	(G/A)	0.002	−27481	1	1	G = G, A = A

rs115492982	rs111644778	chr1:150243179	(C/T)	0.002	−28377	1	1	G = C, A = T

rs115492982	rs2275779	chr1:150300507	(A/G)	0.002	28951	1	1	G = A, A = G

rs115492982	rs4926420	chr1:150303244	(T/C)	0.002	31688	1	1	G = T, A = C

rs115492982	rs112431552	chr1:150303670	(A/G)	0.002	32114	1	1	G = A, A = G

rs115492982	rs112265199	chr1:150303734	(C/G)	0.002	32178	1	1	G = C, A = G

rs115492982	rs6700607	chr1:150304107	(TG)	0.002	32551	1	1	G = T, A = G

rs115492982	rs587595457	chr1:150237837	(T/C)	0.002	−33719	1	1	G = T, A = C

rs115492982	rs80215841	chr1:150306471	(A/G)	0.002	34915	1	1	G = A, A = G

rs115492982	rs60456922	chr1:150236472	(C/T)	0.002	−35084	1	1	G = C, A = T

rs115492982	rs6679726	chr1:150308908	(G/A)	0.002	37352	1	1	G = G, A = A

rs115492982	rs58373639	chr1:150309640	(A/T)	0.002	38084	1	1	G = A, A = T

rs115492982	rs6700009	chr1:150310159	(A/C)	0.002	38603	1	1	G = A, A = C

rs115492982	rs73015081	chr1:150313761	(T/C)	0.002	42205	1	1	G = T, A = C

rs115492982	rs373322282	chr1:150227390	(ATGGA/-)	0.002	−44166	1	1	G = ATGGA, A = -

rs115492982	rs16836130	chr1:150316265	(A/C)	0.002	44709	1	1	G = A, A = C

rs115492982	rs16836139	chr1:150318369	(G/A)	0.002	46813	1	1	G = G, A = A

rs115492982	rs111863435	chr1:150223285	(C/T)	0.002	−48271	1	1	G = C, A = T

rs115492982	rs57163995	chr1:150320622	(G/A)	0.002	49066	1	1	G = G, A = A

rs115492982	rs3737319	chr1:150321798	(T/G)	0.002	50242	1	1	G = T, A = G

rs115492982	rs111334066	chr1:150219183	(C/A)	0.002	−52373	1	1	G = C, A = A

rs115492982	rs116262820	chr1:150218023	(C/T)	0.002	−53533	1	1	G = C, A = T

rs115492982	rs587674887	chr1:150215634	(A/C)	0.002	−55922	1	1	G = A, A = C

rs115492982	rs2015955	chr1:150327788	(C/T)	0.002	56232	1	1	G = C, A = T

rs115492982	rs2015966	chr1:150327847	(G/A)	0.002	56291	1	1	G = G, A = A

rs115492982	rs111275178	chr1:150213525	(G/A)	0.002	−58031	1	1	G = G, A = A

rs115492982	rs73015095	chr1:150329821	(G/T)	0.002	58265	1	1	G = G, A = T

rs115492982	rs200378817	chr1:150329986	(-/TT)	0.002	58430	1	1	G = -, A = TT

rs115492982	rs73015096	chr1:150331437	(G/A)	0.002	59881	1	1	G = G, A = A

rs115492982	rs112896715	chr1:150333692	(G/A)	0.002	62136	1	1	G = G, A = A

rs115492982	rs16836442	chr1:150338613	(T/C)	0.002	67057	1	1	G = T, A = C

rs115492982	rs149553874	chr1:150341202	(A/G)	0.002	69646	1	1	G = A, A = G

rs115492982	rs73017006	chr1:150341765	(G/A)	0.002	70209	1	1	G = G, A = A

rs115492982	rs3850843	chr1:150348335	(A/G)	0.002	76779	1	1	G = A, A = G

rs115492982	rs148424403	chr1:150349418	(G/A)	0.002	77862	1	1	G = G, A = A

rs115492982	rs16836576	chr1:150351001	(T/C)	0.002	79445	1	1	G = T, A = C

rs115492982	rs73017073	chr1:150353003	(T/C)	0.002	81447	1	1	G = T, A = C

rs115492982	rs16836594	chr1:150354584	(T/C)	0.002	83028	1	1	G = T, A = C

rs115492982	rs16836601	chr1:150356343	(T/C)	0.002	84787	1	1	G = T, A = C

rs115492982	rs60459288	chr1:150356425	(A/G)	0.002	84869	1	1	G = A, A = G

rs115492982	rs145792768	chr1:150356925	(C/-)	0.002	85369	1	1	G = C, A = -

rs115492982	rs200485038	chr1:150360844	(TATACACA/-)	0.002	89288	1	1	G = TATACACA, A = -

rs115492982	rs145326563	chr1:150176573	(G/A)	0.002	−94983	1	1	G = G, A = A

rs115492982	rs59367061	chr1:150368788	(C/T)	0.002	97232	1	1	G = C, A = T

rs115492982	rs75909586	chr1:150174286	(T/C)	0.002	−97270	1	1	G = T, A = C

rs115492982	rs56909494	chr1:150368879	(G/T)	0.002	97323	1	1	G = G, A = T

rs115492982	rs56882505	chr1:150371505	(G/T)	0.002	99949	1	1	G = G, A = T

rs115492982	rs76711752	chr1:150371599	(T/C)	0.002	100043	1	1	G = T, A = C

rs115492982	rs112294023	chr1:150374142	(C/T)	0.002	102586	1	1	G = C, A = T

rs115492982	rs59639798	chr1:150374712	(G/A)	0.002	103156	1	1	G = G, A = A

rs115492982	rs146199040	chr1:150167946	(C/T)	0.002	−103610	1	1	G = C, A = T

rs115492982	rs73017086	chr1:150376628	(T/C)	0.002	105072	1	1	G = T, A = C

rs115492982	rs587746671	chr1:150166093	(G/A)	0.002	−105463	1	1	G = G, A = A

rs115492982	rs76176241	chr1:150379851	(G/T)	0.002	108295	1	1	G = G, A = T

rs115492982	rs16836786	chr1:150380364	(C/G)	0.002	108808	1	1	G = C, A = G

rs115492982	rs111982037	chr1:150380739	(C/G)	0.002	109183	1	1	G = C, A = G

rs115492982	rs80029546	chr1:150160082	(A/G)	0.002	−111474	1	1	G = A, A = G

rs115492982	rs145119256	chr1:150383919	(A/G)	0.002	112363	1	1	G = A, A = G

rs115492982	rs73017092	chr1:150384576	(G/T)	0.002	113020	1	1	G = G, A = T

rs115492982	rs4926430	chr1:150385500	(G/A)	0.002	113944	1	1	G = G, A = A

rs115492982	rs144073030	chr1:150385864	(G/A)	0.002	114308	1	1	G = G, A = A

rs115492982	rs6688983	chr1:150157150	(C/T)	0.002	−114406	1	1	G = C, A = T

rs115492982	rs111802234	chr1:150386950	(G/-)	0.002	115394	1	1	G = G, A = -

rs115492982	rs115006285	chr1:150155187	(C/T)	0.002	−116369	1	1	G = C, A = T

rs115492982	rs370281030	chr1:150388745	(TGA/-)	0.002	117189	1	1	G = TGA, A = -

rs115492982	rs59378360	chr1:150391946	(A/G)	0.002	120390	1	1	G = A, A = G

rs115492982	rs112490454	chr1:150392392	(A/G)	0.002	120836	1	1	G = A, A = G

rs115492982	rs80012313	chr1:150147374	(T/A)	0.002	−124182	1	1	G = T, A = A

rs115492982	rs113371939	chr1:150396417	(C/G)	0.002	124861	1	1	G = C, A = G

rs115492982	rs147729724	chr1:150397877	(T/C)	0.002	126321	1	1	G = T, A = C

rs115492982	rs59662772	chr1:150398202	(G/A)	0.002	126646	1	1	G = G, A = A

rs115492982	rs7550339	chr1:150140661	(C/T)	0.002	−130895	1	1	G = T, A = C

rs115492982	rs77778882	chr1:150140581	(A/G)	0.002	−130975	1	1	G = A, A = G

rs115492982	rs10788870	chr1:150140540	(A/C)	0.002	−131016	1	1	G = C, A = A

rs115492982	rs1382572	chr1:150139471	(T/C)	0.002	−132085	1	1	G = C, A = T

rs115492982	rs73017102	chr1:150403756	(G/A)	0.002	132200	1	1	G = G, A = A

rs115492982	rs112235324	chr1:150404205	(T/C)	0.002	132649	1	1	G = T, A = C

rs115492982	rs73020860	chr1:150134895	(G/A)	0.002	−136661	1	1	G = G, A = A

rs115492982	rs6685607	chr1:150134341	(G/A)	0.002	−137215	1	1	G = G, A = A

rs115492982	rs111400442	chr1:150410258	(G/A)	0.002	138702	1	1	G = G, A = A

rs115492982	rs113274217	chr1:150410365	(C/T)	0.002	138809	1	1	G = C, A = T

rs115492982	rs60527237	chr1:150131893	(C/T)	0.002	−139663	1	1	G = C, A = T

rs115492982	rs57971032	chr1:150411292	(G/A)	0.002	139736	1	1	G = G, A = A

rs115492982	rs113408614	chr1:150411507	(C/T)	0.002	139951	1	1	G = C, A = T

rs115492982	rs6681679	chr1:150130526	(C/T)	0.002	−141030	1	1	G = C, A = T

rs115492982	rs149608182	chr1:150416426	(G/A)	0.002	144870	1	1	G = G, A = A

rs115492982	rs149443445	chr1:150126326	(-/CT)	0.002	−145230	1	1	G = -, A = CT

rs115492982	rs16836943	chr1:150418796	(C/T)	0.002	147240	1	1	G = C, A = T

rs115492982	rs112272272	chr1:150419395	(T/C)	0.002	147839	1	1	G = T, A = C

rs115492982	rs73019017	chr1:150421654	(T/C)	0.002	150098	1	1	G = T, A = C

rs115492982	rs73019018	chr1:150421965	(T/C)	0.002	150409	1	1	G = T, A = C

rs115492982	rs73019020	chr1:150422271	(G/A)	0.002	150715	1	1	G = G, A = A

rs115492982	rs73019021	chr1:150422548	(T/A)	0.002	150992	1	1	G = T, A = A

rs115492982	rs6680391	chr1:150424182	(A/G)	0.002	152626	1	1	G = A, A = G

rs115492982	rs7532297	chr1:150117691	(A/G)	0.002	−153865	1	1	G = A, A = G

rs115492982	rs77553465	chr1:150427507	(G/A)	0.002	155951	1	1	G = G, A = A

rs115492982	rs7517537	chr1:150114083	(C/T)	0.002	−157473	1	1	G = C, A = T

rs115492982	rs371028395	chr1:150430107	(A/-)	0.002	158551	1	1	G = A, A = -

rs115492982	rs73019027	chr1:150430552	(C/T)	0.002	158996	1	1	G = C, A = T

rs115492982	rs113104968	chr1:150109281	(C/T)	0.002	−162275	1	1	G = C, A = T

rs115492982	rs12090508	chr1:150107793	(A/G)	0.002	−163763	1	1	G = A, A = G

rs115492982	rs57272513	chr1:150436496	(A/G)	0.002	164940	1	1	G = A, A = G

rs115492982	rs73019028	chr1:150437283	(G/C)	0.002	165727	1	1	G = G, A = C

rs115492982	rs111536367	chr1:150438467	(C/A)	0.002	166911	1	1	G = C, A = A

rs115492982	rs35766167	chr1:150103818	(1/-)	0.002	−167738	1	1	G = -, A = T

rs115492982	rs112640811	chr1:150097784	(G/A)	0.002	−173772	1	1	G = G, A = A

rs115492982	rs12082615	chr1:150097384	(A/C)	0.002	−174172	1	1	G = A, A = C

rs115492982	rs7530672	chr1:150096444	(G/A)	0.002	−175112	1	1	G = G, A = A

rs115492982	rs13057	chr1:150448688	(G/T)	0.002	177132	1	1	G = G, A = T

rs115492982	rs6677707	chr1:150092918	(A/G)	0.002	−178638	1	1	G = A, A = G

rs115492982	rs7533714	chr1:150092086	(T/C)	0.002	−179470	1	1	G = C, A = T

rs115492982	rs9727702	chr1:150090669	(G/A)	0.002	−180887	1	1	G = G, A = A

rs115492982	rs11205328	chr1:150087865	(T/C)	0.002	−183691	1	1	G = T, A = C

rs115492982	rs113887124	chr1:150456665	(G/A)	0.002	185109	1	1	G = G, A = A

rs115492982	rs3840448	chr1:150459893	(TGTT/-)	0.002	188337	1	1	G = TGIT, A = -

rs115492982	rs2275245	chr1:150460348	(C/T)	0.002	188792	1	1	G = C, A = T

rs115492982	rs3839012	chr1:150461761	(C/-)	0.002	190205	1	1	G = C, A = -

rs115492982	rs871527	chr1:150462088	(C/G)	0.002	190532	1	1	G = C, A = G

rs115492982	rs10624875	chr1:150463530	(-/AA)	0.002	191974	1	1	G = -, A = AA

rs115492982	rs142587704	chr1:150078791	(dc!-)	0.002	−192765	1	1	G = CTC, A = -

rs115492982	rs111842933	chr1:150465271	(G/A)	0.002	193715	1	1	G = G, A = A

rs115492982	rs143706301	chr1:150467213	(-/A)	0.002	195657	1	1	G = -, A = A

rs115492982	rs953127	chr1:150469256	(G/T)	0.002	197700	1	1	G = G, A = T

rs115492982	rs112820016	chr1:150071489	(C/T)	0.002	−200067	1	1	G = C, A = T

rs115492982	rs587637589	chr1:150476115	(TA/-)	0.002	204559	1	1	G = TA, A = -

rs115492982	rs28541919	chr1:150476117	(A/G)	0.002	204561	1	1	G = A, A = G

rs115492982	rs12058524	chr1:150066384	(C/T)	0.002	−205172	1	1	G = C, A = T

rs115492982	rs3834087	chr1:150478538	(GAG/-)	0.002	206982	1	1	G = GAG, A = -

rs115492982	rs111414303	chr1:150063936	(G/A)	0.002	−207620	1	1	G = G, A = A

rs115492982	rs73019054	chr1:150485599	(G/A)	0.002	214043	1	1	G = G, A = A

rs115492982	rs79595845	chr1:150055388	(T/A)	0.002	−216168	1	1	G = T, A = A

rs115492982	rs78312541	chr1:150487797	(C/A)	0.002	216241	1	1	G = C, A = A

rs115492982	rs58419446	chr1:150490370	(C/T)	0.002	218814	1	1	G = C, A = T

rs115492982	rs113872537	chr1:150493557	(G/A)	0.002	222001	1	1	G = G, A = A

rs115492982	rs79524321	chr1:150495269	(T/C)	0.002	223713	1	1	G = T, A = C

rs115492982	rs11205325	chr1:150044324	(G/A)	0.002	−227232	1	1	G = G, A = A

rs115492982	rs11205324	chr1:150041872	(G/T)	0.002	−229684	1	1	G = T, A = G

rs115492982	rs11205323	chr1:150041871	(C/A)	0.002	−229685	1	1	G = A, A = C

rs115492982	rs147106269	chr1:150502105	(G/A)	0.002	230549	1	1	G = G, A = A

rs115492982	rs9887866	chr1:150039267	(T/C)	0.002	−232289	1	1	G = C, A = T

rs115492982	rs77173601	chr1:150505070	(G/A)	0.002	233514	1	1	G = G, A = A

rs115492982	rs6657478	chr1:150507567	(A/G)	0.002	236011	1	1	G = A, A = G

rs115492982	rs78006356	chr1:150032621	(C/T)	0.002	−238935	1	1	G = C, A = T

rs115492982	rs113085079	chr1:150512512	(G/T)	0.002	240956	1	1	G = G, A = T

rs115492982	rs6687257	chr1:150029702	(T/C)	0.002	−241854	1	1	G = T, A = C

rs115492982	rs139447592	chr1:150513837	(G/T)	0.002	242281	1	1	G = G, A = T

rs115492982	rs145793287	chr1:150514145	(C/T)	0.002	242589	1	1	G = C, A = T

rs115492982	rs7514515	chr1:150517312	(G/C)	0.002	245756	1	1	G = G, A = C

rs115492982	rs75513680	chr1:150021709	(G/A)	0.002	−249847	1	1	G = G, A = A

rs115492982	rs57507911	chr1:150523982	(AG/-)	0.002	252426	1	1	G = AG, A = -

rs115492982	rs61684558	chr1:150524277	(G/A)	0.002	252721	1	1	G = G, A = A

rs115492982	rs144832337	chr1:150531320	(G/A)	0.002	259764	1	1	G = G, A = A

rs115492982	rs140930998	chr1:150011383	(G/A)	0.002	−260173	1	1	G = G, A = A

rs115492982	rs147310031	chr1:150011375	(G/A)	0.002	−260181	1	1	G = G, A = A

rs115492982	rs116614291	chr1:150531959	(G/A)	0.002	260403	1	1	G = G, A = A

rs115492982	rs79568347	chr1:150006818	(T/C)	0.002	−264738	1	1	G = T, A = C

rs115492982	rs145686348	chr1:150558152	(G/T)	0.002	286596	1	1	G = G, A = T

rs115492982	rs142892208	chr1:150560164	(A/-)	0.002	288608	1	1	G = A, A = -

rs115492982	rs6691535	chr1:150568894	(A/G)	0.002	297338	1	1	G = A, A = G

rs115492982	rs143004068	chr1:150582871	(G/A)	0.002	311315	1	1	G = G, A = A

rs115492982	rs74124941	chr1:150588009	(G/A)	0.002	316453	1	1	G = G, A = A

rs115492982	rs587647594	chr1:150601298	(G/T)	0.002	329742	1	1	G = G, A = T

rs115492982	rs74124944	chr1:150603752	(T/A)	0.002	332196	1	1	G = T, A = A

rs115492982	rs75508758	chr1:150604410	(A/G)	0.002	332854	1	1	G = A, A = G

rs115492982	rs145393663	chr1:150605772	(C/T)	0.002	334216	1	1	G = C, A = T

rs115492982	rs57025631	chr1:150607284	(G/T)	0.002	335728	1	1	G = G, A = T

rs115492982	rs74124966	chr1:150609082	(G/A)	0.002	337526	1	1	G = G, A = A

rs115492982	rs139726900	chr1:150612476	(G/A)	0.002	340920	1	1	G = G, A = A

rs115492982	rs1877469	chr1:150619602	(T/C)	0.002	348046	1	1	G = C, A = T

rs115492982	rs1151917	chr1:150622696	(T/A)	0.002	351140	1	1	G = T, A = A

rs115492982	rs1241578	chr1:150628365	(A/C)	0.002	356809	1	1	G = A, A = C

rs115492982	rs1241579	chr1:150630869	(T/C)	0.002	359313	1	1	G = T, A = C

rs115492982	rs1707158	chr1:150633833	(A/G)	0.002	362277	1	1	G = A, A = G

rs115492982	rs2458393	chr1:150634596	(G/A)	0.002	363040	1	1	G = G, A = A

rs115492982	rs1241575	chr1:150646372	(A/T)	0.002	374816	1	1	G = A, A = T

rs115492982	rs73008805	chr1:150676968	(T/C)	0.002	405412	1	1	G = T, A = C

rs115492982	rs57127659	chr1:150684117	(C/T)	0.002	412561	1	1	G = C, A = T

rs115492982	rs73008807	chr1:150687686	(G/T)	0.002	416130	1	1	G = G, A = T

rs115492982	rs587594230	chr1:150700505	(G/A)	0.002	428949	1	1	G = G, A = A

rs115492982	rs192267029	chr1:150702269	(G/C)	0.002	430713	1	1	G = G, A = C

rs115492982	rs113739463	chr1:150714363	(G/C)	0.002	442807	1	1	G = G, A = C

rs115492982	rs73008818	chr1:150718179	(G/A)	0.002	446623	1	1	G = G, A = A

rs115492982	rs112325265	chr1:150727987	(G/A)	0.002	456431	1	1	G = G, A = A

rs115492982	rs113422136	chr1:150733416	(G/A)	0.002	461860	1	1	G = G, A = A

rs115492982	rs28675769	chr1:150736807	(C/T)	0.002	465251	1	1	G = C, A = T

rs115492982	rs112049924	chr1:150748342	(T/C)	0.002	476786	1	1	G = T, A = C

rs115492982	rs112037529	chr1:150757290	(G/C)	0.002	485734	1	1	G = G, A = C

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 1 and Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 polymorphisms or at least 306 associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 1 or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 2 and Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 2 or Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50 or at least 60 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.
In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.
In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.
In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.
In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 19 or a polymorphism in linkage disequilibrium with one or more thereof.
In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 22 or a polymorphism in linkage disequilibrium with one or more thereof.
Polymorphisms in linkage disequilibrium with those specifically mentioned herein are easily identified by those of skill in the art. Table 6a provides examples of linked loci for the polymorphisms listed in Table 3. Table 6b provides examples of linked loci for the polymorphisms listed in Table 4 which are not provided in Table 6a. Such linked polymorphisms for the other polymorphisms listed in Table 1 can very easily be identified by the skilled person using the HAPMAP database.
Where relevant in each Table, the A1 or Allele 1 is the risk (minor allele) associated allele. The risk allele may be associated with a decreased or increased risk as described herein. As used herein, the terms “A1” and “Allele 1” are used interchangeably. As used herein, the terms “A2” and “Allele 2” are used interchangeably.
In an embodiment, if the method includes the analysis of rs11385942 and/or rs657152 the method further comprises detecting at least one other polymorphism provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith.
Calculating Composite Relative Risk “Genetic Risk”
An individual's “genetic risk” can be defined as the product of genotype relative risk values for each polymorphism assessed. A log-additive risk model can then be used to define three genotypes AA, AB and BB for a polymorphism having relative risk values of 1, OR, and OR², under a rare disease model, where OR is the previously reported disease odds ratio for the high-risk allele, B, vs the low-risk allele, A. If the B allele has frequency (p), then these genotypes have population frequencies of (1−p)², 2p(1−p), and p², assuming Hardy-Weinberg equilibrium. The genotype relative risk values for each polymorphism can then be scaled so that based on these frequencies the average relative risk in the population is 1. Specifically, given the unscaled population average relative risk for each SNP:
(μ)=(1−p)²+2p(1−p)OR+p ²OR²
Adjusted risk values 1/μ, OR/μ, and OR²/μ are used for AA, AB, and BB genotypes for each SNP. Missing genotypes are assigned a relative risk of 1. The following formula can be used to define the genetic risk:
SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, etc.
Similar calculations can be performed for non-SNP polymorphisms or a combination thereof.
An alternate method for calculating the composite risk is described in Mavaddat et al. (2015). In this example, the following formula is used;
PRS=β₁ x ₁+β₂ x ₂+ . . . β_κ x _κ+β_n x _n
where β_κ is the per-allele log odds ratio (OR) for the minor allele for SNP κ, and x_κ the number of alleles for the same SNP (0, 1 or 2), n is the total number of SNPs and PRS is the polygenic risk score (which can also be referred to as composite SNP risk). Similar calculations can be performed for non-SNP polymorphisms or a combination thereof.
In an alternate embodiment, the magnitude of effect of each risk allele is not used when calculating the genetic risk score. More specifically, allele counting as generally described in WO 2005/086770 is used. For example, in one embodiment if the subject was homozygous for the risk allele they were scored as 2, if they were heterozygous for the risk allele they were scored as 1, and if they were homozygous for the risk allele they were scored as 0. As the skilled person would appreciate, alternate values such as 1, 0.5 and 0 respectively, could be used.
In an embodiment, the percent of risk alleles present out of the total possible number of loci analysed is used to produce the genetic risk score. For example, in the 64 allele panel described in Example 5 the subject may have at most 128 risk alleles. If a subject had 64 out of these 128 alleles, they would have 50% of the total possible alleles which can be expressed as 0.5.
The genetic risk score can be expressed as:
ln_risk=−8.4953 (i.e. the model intercept)+0.1496×SNP %. Then, risk=exp(ln_risk).
In this example, the risk is the relative risk for severe disease (e.g. a person with risk=3.5 is at 3.5 times increased risk compared with a person with the average number of risk alleles). exp(β) is the odds ratio for an increase of 1% in risk alleles. So, exp(0.1496)=1.16, which means that risk increases by 16% for a 1% increase in SNP %. In an embodiment, the β coefficient (model intercept) is between −10.06391 to −6.926615, or −9.5 to −7.5, or −9 to −8. In an embodiment of the above formula, the adjustment of the starting ln(risk) for the percentage of risk alleles is 0.1237336 to 0.1755347, or 0.16 to 0.14.
In an embodiment, the genetic risk is the SNP Risk Factor (SNF). In one embodiment, SNF=Σ(No of Risk Alleles×SNP β coefficient).
The “risk” of a human subject developing a severe response to a Coronavirus infection can be provided as a relative risk (or risk ratio).
In an embodiment, the genetic risk assessment obtains the “relative risk” of a human subject developing a severe response to a Coronavirus infection. Relative risk (or risk ratio), measured as the incidence of a disease in individuals with a particular characteristic (or exposure) divided by the incidence of the disease in individuals without the characteristic, indicates whether that particular exposure increases or decreases risk. Relative risk is helpful to identify characteristics that are associated with a disease, but by itself is not particularly helpful in guiding screening decisions because the frequency of the risk (incidence) is cancelled out.
In an embodiment, a threshold value(s) is set for determining a particular action such as the need for routine diagnostic testing, the need for prophylactic anti-Coronavirus therapy, selection of a person for a vaccine or the need to administer an anti-Coronavirus therapy. For example, a score determined using a method of the invention is compared to a pre-determined threshold, and if the score is higher than the threshold a recommendation is made to take the pre-determined action. Methods of setting such thresholds have now become widely used in the art and are described in, for example, US 20140018258.

Clinical Risk Assessment

In an embodiment, the method further comprises performing a clinical risk assessment of the human subject; and combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection. The clinical risk assessment procedure can include obtaining clinical information from a human subject. In other embodiments, these details have already been determined (such as in the subject's medical records).
Examples of factors which can be used to produce the clinical risk assessment include, but are not limited to, obtaining information from the human on one or more of the following: age, family history of a severe response to a Coronavirus infection, race/ethnicity, gender, body mass index, total cholesterol level, systolic and/or diastolic blood pressure, smoking status, does the human have diabetes, does the human have a cardiovascular disease, is the subject on hypertension medication, loss of taste, loss of smell and white blood cell count.
In an embodiment, the clinical risk assessment is based only one or more or all of age, body mass index, loss of taste, loss of smell and smoking status.
In another embodiment, the clinical risk assessment is based only one or more or all of age, loss of taste, loss of smell and smoking status.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the clinical risk assessment at least includes age and gender.
The present inventors have also found that a severe response to a Coronavirus infection risk model that relies solely on clinical factors provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection. Such a test may be particularly useful in circumstances where a rapid decision needs to be made and/or when genetic testing is not readily available. Thus, in another aspect the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on two, three, four, five or more or all of age, gender, race/ethnicity, height, weight, blood type, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had liver disease, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the method comprises obtaining information from the subject on age and gender.
In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
Examples of respiratory diseases which are included in the test are chronic obstructive pulmonary disease, chronic bronchitis and emphysema.
The diabetes can be any type of diabetes.
In an embodiment, the clinical risk assessment is conducted using the following formula:
$\begin{matrix} \ln (r i s k) & = Model Intercept \\ + & OR if clinical factor one applies \\ + & OR if clinical factor two applies \\ + & OR if clinical factor three applies \\ \dots \\ + & OR if clinical factor n applies \end{matrix}$

Where OR=Odds Ratio.

In an embodiment, the clinical risk assessment is conducted using the following formula:
$\begin{matrix} \ln (r i s k) & = Model Intercept \\ + & OR if age group = 18 - 29 years or \\ + & OR if age group = 30 - 39 years or \\ + & OR if age group = 40 - 49 years or \\ + & OR if age group = 60 - 69 years or \\ + & OR if age group = 70 + year \\ + & OR if gender = male \\ + & OR if ethnicity = non -Caucasian \\ + & OR if ABO blood type = A or \\ + & OR if ABO blood type = B or \\ + & OR if ABO blood type = A B \\ + & OR if has / had autoimmune disease (namely, \\ rheumatoid arthritis, lupus, or psoriasis) = yes \\ + & OR if has / had cancer, haematological = yes \\ + & OR if has / had cancer, non - haematological = yes \\ + & OR if has / had diabetes = yes \\ + & OR if has / had hypertension = yes \\ + & OR if has / had repiratory disease (other than \\ asthma) = yes \end{matrix}$

Where OR=Odds Ratio.

Using the above formulae the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=
.
In one example, the clinical risk assessment is conducted using the following formula:
$\begin{matrix} \ln (r i s k) & = & - 0.2645 \\ + & - 1.3111 & if age group = 18 - 29 years \\ + & - 0.8348 & if age group = 30 - 39 years \\ + & - 0.4038 & if age group = 40 - 49 years \\ + & - 0.0973 & if age group = 60 - 69 years \\ + & 0.4419 & if age group = 70 + year \\ + & 0.0855 & if gender = male \\ + & 0.0404 & if ethnicity = non -Caucasian \\ + & - 0.0614 & if ABO blood type = A \\ + & 0.2039 & if ABO blood type = B \\ + & - 0.5541 & if ABO blood type = A B \\ + & 0.5424 & if has / had autoimmune disease \\ (namely, rheumatoid arthritis, lupus, or \\ psoriasis) = yes \\ + & 1.0104 & if has / had cancer, haematological = yes \\ + & 0.2436 & if has / had cancer, non - haematological = yes \\ + & 0.3863 & if has / had diabetes = yes \\ + & 0.3064 & if has / had hypertension = yes \\ + & 1.2642 & if has / had repiratory disease (other than \\ asthma) = yes \end{matrix}$
In an embodiment of the above formula, the starting ln(risk) (model intercept) is −0.5284 to 1.5509, or −0.16 to −0.36.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 18 to 29 is −1.5 to −1, or −1.4 to −1.2.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 30 to 39 is −1 to −0.7, or −0.9 to −0.8.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 40 to 49 is −0.6 to −0.2, or −0.45 to −0.35.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 60 to 69 is −0.4021263 to 0.2075385, or −0.19 to 0.09.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 70+ is 0.1504677 to 0.73339, or 0.34 to 0.54.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for males is −0.140599 to 0.3115929, or −0.3 to 0.19.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for non-Caucasians is −0.3029713 to 0.3837958, or −0.06 to 0.14.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for A blood type is −0.3018427 to 0.1791056, or −0.16 to 0.04.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for B blood type is −0.1817567 to 0.5895909, or 0.1 to 0.3.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for AB blood type is −1.172319 to 0.0641862, or −0.45 to −0.65.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, rheumatoid arthritis, lupus or psoriasis is −0.0309265 to 1.115784, or 0.44 to 0.64.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a haematological cancer is 0.1211918 to 1.899663, or 0.9 to 1.1.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a non-haematological cancer is −0.0625866 to 0.5498824, or 0.14 to 0.34.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, diabetes is 0.0624018 to 0.7101834, or 0.28 to 0.48.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, hypertension is 0.0504567 to 0.5623362, or 0.1 to 0.3.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a respiratory disease (excluding asthma) is 0.9775684 to 1.550944, or 1.16 to 1.36.
The present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment involves determining at least the age and sex of the subject and producing a score. In an embodiment, the method further comprises comparing the score to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.
In one embodiment, the subject is between 50 and 84 years of age and is asked their age and their sex.
In an embodiment, the method comprises determining the Log odds (LO). For example, the LO can be calculated using the formula:
LO=X+Σ Clinical β coefficients
In an embodiment, X is −2.25 to −1.25 or −2 or −1.5. In an embodiment, X is −1.749562.
In an embodiment, the relative risk is determined. In an embodiment, the relative risk is determined using the formula:
relative risk=e ^LO
In an embodiment, the probability is determined. In an embodiment, the probability is determined using the formula:
probability=e ^LO/(1+e ^LO)
“e” is the mathematical constant that is the base of the natural logarithm.
In an embodiment, the probability obtained by the above formula is multiplied by 100 to obtain a percent chance of a severe response to a Coronavirus infection such as hospitalisation being required.
In an embodiment, if the subject is between 50 and 64 years of age they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.
In an embodiment, if the subject is between 65 and 69 years of age they are assigned a β coefficient of 0 to 1, or 0.25 to 0.75 or 0.4694892.
In an embodiment, if the subject is between 70 and 74 years of age they are assigned a β coefficient of 0.5 to 1.5, or 0.75 to 1.25 or 1.006561.
In an embodiment, if the subject is between 75 and 79 years of age they are assigned a β coefficient of 0.9 to 1.9, or 1.15 to 1.65 or 1.435318.
In an embodiment, if the subject is between 80 and 84 years of age they are assigned a β coefficient of 1.1 to 2.1, or 1.35 to 1.85 or 1.599188.
In an embodiment, if the subject is female they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.
In an embodiment, if the subject is male they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.3911169.
In an embodiment, the last value provided above in each criteria is used.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, each of the above factors are assessed and

- LO=X+Σ Clinical β coefficients, where X is −1.8 to −0.8 or −1.6 or −1.15 or −1.36523;
- if the subject is between 50 and 69 years of age they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is between 70 and 74 years of age they are assigned a β coefficient of 0.1 to 1.1, or 0.35 to 0.85 or 0.5747727;
- if the subject is between 75 and 79 years of age they are assigned a β coefficient of 0.3 to 1.3, or −0.55 to 1.05 or 0.8243711;
- if the subject is between 18 and 29 years of age they are assigned a β coefficient of −2.3 to −0.3, or −0.18 to −0.8 or −1.3111;
- if the subject is between 30 and 39 years of age they are assigned a β coefficient of −1.8 to 0.2, or −1.23 to −0.3 or −0.8348;
- if the subject is between 40 and 49 years of age they are assigned a β coefficient of −1.4 to 0.6, or −0.9 to −0.1 or −0.4038;
- if the subject is between 80 and 84 years of age they are assigned a β coefficient of 0.5 to 1.5, or 0.25 to 1.25 or 1.013973;
- if the subject is female they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is male they are assigned a β coefficient of −0.25 to 0.75, or 0 to 0.5 or 0.2444891;
- if the subject is Caucasian they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is an ethnicity other than Caucasian they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or 0.29311;
- the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.1 to 2.1, or −1.35 to −1.85, or −1.602056 to provide the β coefficient to be assigned;
- if the subject has ever been diagnosed as having a cerebrovascular disease they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.4041337;
- if the subject has not ever been diagnosed as having a cerebrovascular disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a chronic kidney disease they are assigned a β coefficient of 0.2 to 1.2, or 0.55 to 0.95 or 0.6938494;
- if the subject has not ever been diagnosed as having a chronic kidney disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having diabetes they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.4297612;
- if the subject has not ever been diagnosed as having diabetes they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having haematological cancer they are assigned a β coefficient of 0.5 to 1.5, or 0.75 to 1.25 or 1.003877;
- if the subject has not ever been diagnosed as having haematological cancer they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having hypertension they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or 0.2922307;
- if the subject has not ever been diagnosed as having hypertension they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a non-haematological cancer they are assigned a β coefficient of −0.25 to 1, or 0 to 0.5 or 0.2558464;
- if the subject has not ever been diagnosed as having a non-haematological cancer they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a respiratory disease (other than asthma) they are assigned a β coefficient of 0.7 to 1.7, or 0.95 to 1.45 or 1.173753; and
- if the subject has ever been diagnosed as having a respiratory disease (other than asthma) they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, the last value provided above in each criteria is used.
In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, each of the above factors are assessed and

- LO=X+Σ Clinical β coefficients, where X is −2 to −1.5 or −1.75 or −1.25 or −1.469939;
- if the subject is between 50 and 64 years of age they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is between 65 and 69 years of age they are assigned a β coefficient of −0.3 to 0.7, or −0.05 to 0.45 or 0.1677566;
- if the subject is between 70 and 74 years of age they are assigned a β coefficient of 0.1 to 1.1, or 0.35 to 1.85 or 0.6352682;
- if the subject is between 75 and 79 years of age they are assigned a β coefficient of 0.4 to 1.4, or 0.65 to 1.15 or 0.8940548;
- if the subject is between 80 and 84 years of age they are assigned a β coefficient of 0.5 to 1.5, or 0.25 to 1.25 or 1.082477;
- if the subject is female they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is male they are assigned a β coefficient of −0.25 to 0.75, or 0 to 0.5 or 0.2418454;
- if the subject is Caucasian they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject is an ethnicity other than Caucasian they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or 0.2967777;
- if the subject has a blood type other than ABO they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has an ABO blood type they are assigned a β coefficient of −0.25 to 0.75, or 0 to 0.5 or −0.229737;
- the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.1 to 2.1, or −1.35 to −1.85, or −1.560943 to provide the β coefficient to be assigned;
- if the subject has ever been diagnosed as having a cerebrovascular disease they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.3950113;
- if the subject has not ever been diagnosed as having a cerebrovascular disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a chronic kidney disease they are assigned a β coefficient of 0.2 to 1.2, or 0.55 to 0.95 or 0.6650257;
- if the subject has not ever been diagnosed as having a chronic kidney disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having diabetes they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.4126633;
- if the subject has not ever been diagnosed as having diabetes they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having haematological cancer they are assigned a β coefficient of 0.5 to 1.5, or 0.75 to 1.25 or 1.001079;
- if the subject has not ever been diagnosed as having haematological cancer they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having hypertension they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or 0.2640989;
- if the subject has not ever been diagnosed as having hypertension they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having an immunocompromised disease they are assigned a β coefficient of 0.1 to 1.1, or 0.35 to 0.85 or 0.6033541;
- if the subject has not ever been diagnosed as having an immunocompromised disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having liver disease they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or 0.2301902;
- if the subject has not ever been diagnosed as having liver disease they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a non-haematological cancer they are assigned a β coefficient of −0.25 to 1, or 0 to 0.5 or 0.2381579;
- if the subject has not ever been diagnosed as having a non-haematological cancer they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;
- if the subject has ever been diagnosed as having a respiratory disease (other than asthma) they are assigned a β coefficient of 0.7 to 1.7, or 0.95 to 1.45 or 1.148496; and
- if the subject has ever been diagnosed as having a respiratory disease (other than asthma) they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, the last value provided above in each criteria is used.
In an embodiment, the subject's body mass index is determined using their height and weight.
In an embodiment, if any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.
In an embodiment, one or more or all of the clinical factors are self-assessed (self-reported). In an embodiment, the race/ethnicity is self-assessed (self-reported). In an embodiment, one or more or all of current or previous disease status, such as an autoimmune disease, an haematological cancer, an non-haematological cancer, diabetes, hypertension or a respiratory disease, is self-assessed (self-reported).
In an embodiment, the clinical assessment comprises determining the blood type of the subject. This will typically comprise obtaining a sample comprising blood from the subject. The detection method used can any be any suitable method known in the art. In embodiment, a genetic test as described in the Examples is used, preferably concurrently with a genetic analysis for assessing the risk of a human subject developing a severe response to a coronavirus infection.
For instance, ABO blood type can be imputed using three SNPs, namely rs505922, rs8176719 and rs8176746) in the ABO gene on chromosome 9q34.2. An rs8176719 deletion (or for those with no result for rs8176719, a T allele at rs505922) indicates haplotype O. At rs8176746, haplotype A is indicated by the presence of the G allele and haplotype B is indicated by the presence of the T allele (see Table 7).

TABLE 7

SNPS and ABO Imputation.

rs8176719	rs505922	rs8176746	Genotype	Phenotype

T/T			OO	O

TC/T		C/C (G/G)	AO	A

TC/T		C/A (G/T)	BO	B

TC/T		A/A (T/T)	BO	B

TC/TC		C/C (G/G)	AA	A

TC/TC		A/A (T/T)	BB	B

TC/TC		C/A (G/T)	AB	AB

missing	T/T		OO	O

missing	C/T	C/C (G/G)	AO	A

missing	C/T	C/A (G/T)	BO	B

missing	C/T	A/A (T/T	BO	B

missing	C/C	C/C (G/G)	AA	A

missing	C/C	A/A (T/T)	BB	B

missing	C/C	C/A (G/T)	AB	AB

In an embodiment, whether a subject has or has had (also referred to herein as “has ever been diagnosed”) with a particular disease state, the disease is classified using the international Classification of Disease (ICD) system. Thus,

- asthma is as per ICD9 (493*) and ICD10 (J45* and J46),
- an autoimmune (rheumatoid/lupus/psoriasis) is as per ICD9 (954, 696*, 7100, 714, 7140* and 7142* and ICD10 (J990, L40*, L41*, M05*-M07* and M32*),
- a haematological cancer is as per ICD9 (200*-208*) and ICD10 (C81*-C86*, C88* and C90*-C96*),
- a non-haematological cancer is as per ICD9 (140*-165*, 169*-175*, 179*-195* and 196*-199*) and ICD10 (C00*-C26*, C30*-C34*, C37*-058*, C60*-C80*, C97*),
- a cerebrovascular disease is as per ICD9 (430*-438*) and ICD10 (G46* and I60*-I69*),
- diabetes is as per ICD9 (250*) and ICD10 (E10*-E14*),
- heart disease is as per ICD9 (413*-416*, V422, V432-V434) and ICD10 (I20*-I25*, I48*, Z95*),
- hypertension is as per ICD9 (401*, 405*, 6420-6422) and ICD10 (I10*, I15*, O10*),
- an immunocompromised disease is as per ICD9 (V420, V421, V426, V427, V429, 042, 043, 044, 279, 2790*) and ICD10 (B20*-B24, D80*-D84*, Z940-Z944, Z949),
- a kidney disease is as per ICD9 (585*) and ICD10 (N18*),
- liver disease is as per ICD9 (571*) and ICD10 (K70*-K77*), and
- a respiratory disease (excluding asthma) is as per ICD9 (494*-496*, 500*, 501*-508*, 491*, 492*, 496*) and ICD10 (J60*-J70*, J80*-J82, J84*-J86*, J90-J96*, J98*, J41*-J44*).

Combined Clinical Assessment and Genetic Assessment

In an embodiment, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:
$\begin{matrix} \ln (r i s k) & = Model Intercept \\ + & OR x percentage of the number of risk alleles \\ + & OR if clinical factor one applies \\ + & OR if clinical factor two applies \\ + & OR if clinical factor three applies \\ \dots \\ + & OR if clinical factor n applies \end{matrix}$

Where OR=Odds Ratio.

In an embodiment, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:
$\begin{matrix} \ln (r i s k) & = Model Intercept \\ + & OR x percentage of the number of risk alleles \\ + & OR if age group = 18 - 29 years or \\ + & OR if age group = 30 - 39 years or \\ + & OR if age group = 40 - 49 years or \\ + & OR if age group = 60 - 69 years or \\ + & OR if age group = 70 + year \\ + & OR if gender = male \\ + & OR if ethnicity = non -Caucasian \\ + & OR if ABO blood type = A or \\ + & OR if ABO blood type = B or \\ + & OR if ABO blood type = A B \\ + & OR if has / had autoimmune disease (namely, \\ rheumatoid arthritis, lupus, or psoriasis) = yes \\ + & OR if has / had cancer, haematological = yes \\ + & OR if has / had cancer, non - haematological = yes \\ + & OR if has / had diabetes = yes \\ + & OR if has / had hypertension = yes \\ + & OR if has / had repiratory disease (other than \\ asthma) = yes \end{matrix}$

Where OR=Odds Ratio

Using the above formulae the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=
.
In one example, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:
$\begin{matrix} \ln (r i s k) & = & - 10.7657 \\ + & 0.1717 & x percentage of the number of risk alleles \\ + & - 1.3111 & if age group = 18 - 29 years \\ + & - 0.8348 & if age group = 30 - 39 years \\ + & - 0.4038 & if age group = 40 - 49 years \\ + & - 0.0600 & if age group = 60 - 69 years \\ + & 0.5325 & if age group = 70 + year \\ + & 0.1387 & if gender = male \\ + & 0.3542 & if ethnicity = non -Caucasian \\ + & - 0.2164 & if ABO blood type = A \\ + & - 0.1712 & if ABO blood type = B \\ + & - 0.8746 & if ABO blood type = A B \\ + & 0.7876 & if has / had autoimmune disease \\ (namely, rheumatoid arthritis, lupus, or \\ psoriasis) = yes \\ + & 1.0375 & if has / had cancer, haematological = yes \\ + & 0.3667 & if has / had cancer, non - haematological = \\ yes \\ + & 0.4890 & if has / had diabetes = yes \\ + & 0.3034 & if has / had hypertension = yes \\ + & 1.2331 & if has / had repiratory disease (other than \\ asthma) = yes \end{matrix}$
Using this formula the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=
.
In an embodiment of the above formula, the starting ln(risk) (model intercept) is −12.5559 to −8.9755, or −12 to −8, or −11 to −10.5.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for the percentage of risk alleles is 0.142 to 0.2006, or 0.16 to 0.18.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 18 to 29 is −1.5 to −1, or −1.4 to −1.2.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 30 to 39 is −1 to −0.7, or −0.9 to −0.8.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 40 to 49 is −0.6 to −0.2, or −0.45 to −0.35.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 60 to 69 is −0.3819 to 0.2619, or −0.1 to 0.1.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 70+ is 0.2213 to 0.8438, or 0.43 to 0.63.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for males is −0.1005 to 0.3779, or 0.03 to 0.23.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for non-Caucasians is −0.0084 to 0.7167, or 0.25 to 0.45.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for A blood type is −0.4726 to 0.0397, or −0.11 to −0.31.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for B blood type is −0.2348 to 0.5773, or 0.07 to 0.27.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for AB blood type is −1.5087 to −0.2404, or −0.77 to −0.97.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, rheumatoid arthritis, lupus or psoriasis is 0.1832 to 1.3920, or 0.68 to 0.88.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a haematological cancer is 0.0994 to 1.9756, or 0.93 to 1.13.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a non-haematological cancer is 0.0401 to 0.6933, or 0.26 to 0.46.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, diabetes is 0.1450 to 0.8330, or 0.39 to 0.59.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, hypertension is 0.0313 to 0.5756, or 0.2 to 0.4.
In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a respiratory disease (excluding asthma) is 0.9317 to 0.1535, or 1.13 to 1.33.
In an alternate embodiment, and as outlined above, the method comprises determining the Log odds (LO). For example, the LO can be calculated using the formula:
LO=X+SRF+Σ Clinical β coefficients
In an embodiment, the SRF is the SNP Risk Factor which is: (No of Risk Alleles×SNP β coefficient).
In an embodiment, the relative risk is determined. In an embodiment, the relative risk is determined using the formula:
relative risk=e^LO
In an embodiment, the probability is determined. In an embodiment, the probability is determined using the formula:
probability=e ^LO/(1+e ^LO)
“e” is the mathematical constant that is the base of the natural logarithm.
In an embodiment, the probability obtained by the above formula is multiplied by 100 to obtain a percent chance of a severe response to a Coronavirus infection such as hospitalisation being required.
In an embodiment, the genetic risk assessment involves the analysis of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792. In an embodiment, X is −1.8 to −0.8 or −1.6 or −1.15. In an embodiment, X is −1.36523. In an embodiment, the subject is assigned a β coefficient of −0.08 to 0.32, or 0.02 to 0.22 or 0.124239 for each G (risk) allele present at rs10755709. Thus, for example, if the subject is homozygous for the risk allele they can be assigned a β coefficient of 0.248478, if they are heterozygous can be assigned a β coefficient of 0.124239, and if they is homozygous for the non-risk allele (C at rs10755709) they can be assigned a β coefficient of 0.248478. In an embodiment, the subject is assigned a β coefficient of 0.07 to 0.47, or 0.17 to 0.37 or 0.2737487 for each C (risk) allele present at rs112317747. In an embodiment, the subject is assigned a β coefficient of −0.43 to −0.03, or −0.33 to −0.13 or −0.2362513 for each T (risk) allele present at rs112641600. In an embodiment, the subject is assigned a β coefficient of −0.4 to 0, or −0.3 to −0.1 or −0.1995879 for each C (risk) allele present at rs118072448. In an embodiment, the subject is assigned a β coefficient of 0.04 to 0.44, or 0.14 to 0.34 or 0.2371955 for each C (risk) allele present at rs2034831. In an embodiment, the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.1019074 for each A (risk) allele present at rs7027911. In an embodiment, the subject is assigned a β coefficient of −0.3 to 0.1, or −0.2 to 0 or −0.1058025 for each T (risk) allele present at rs71481792. In an embodiment, the Clinical β coefficients is determined as above such as factoring in β coefficients for each of age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
In an embodiment, the genetic risk assessment involves the analysis of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911, rs71481792, rs115492982 and rs1984162. In an embodiment, X is −2 to −1.5 or −1.75 or −1.25. In an embodiment, X is −1.469939. In an embodiment, the subject is assigned a β coefficient of −0.08 to 0.32, or 0.02 to 0.22 or 0.1231766 for each G (risk) allele present at rs10755709. Thus, for example, if the subject is homozygous for the risk allele they can be assigned a β coefficient of 0.2463532, if they are heterozygous can be assigned a β coefficient of 0.1231766, and if they is homozygous for the non-risk allele (C at rs10755709) they can be assigned a β coefficient of 0.248478. In an embodiment, the subject is assigned a β coefficient of 0.06 to 0.46, or 0.16 to 0.36 or 0.2576692 for each C (risk) allele present at rs112317747. In an embodiment, the subject is assigned a β coefficient of −0.43 to −0.03, or −0.33 to −0.13 or −0.2384001 for each T (risk) allele present at rs112641600. In an embodiment, the subject is assigned a β coefficient of −0.4 to 0, or −0.3 to −0.1 or −0.1965609 for each C (risk) allele present at rs118072448. In an embodiment, the subject is assigned a β coefficient of 0.04 to 0.44, or 0.14 to 0.34 or 0.2414792 for each C (risk) allele present at rs2034831. In an embodiment, the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.0998459 for each A (risk) allele present at rs7027911. In an embodiment, the subject is assigned a β coefficient of −0.3 to 0.1, or −0.2 to 0 or −0.1032044 for each T (risk) allele present at rs71481792. In an embodiment the subject is assigned a β coefficient of 0.21 to 0.61, or 0.31 to 0.51 or 0.4163575 for each A (risk) allele present at rs115492982. In an embodiment the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.1034362 for each A (risk) allele present at rs1984162. In an embodiment, the Clinical β coefficients is determined as above such as factoring in β coefficients for each of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).
Any of the above calculations can be performed for non-SNP polymorphisms or a combination thereof.
In another embodiment, when combining the clinical risk assessment with the genetic risk assessment to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:
[Risk (i.e. Clinical Evaluation×SNP risk)]=[Clinical Evaluation risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈,×SNP_Netc.
Where Clinical Evaluation is the risk provided by the clinical evaluation, and SNP₁to SNP_Nare the relative risk for the individual SNPs, each scaled to have a population average of 1 as outlined above. Because the SNP risk values have been “centred” to have a population average risk of 1, if one assumes independence among the SNPs, then the population average risk across all genotypes for the combined value is consistent with the underlying Clinical Evaluation risk estimate.
In an embodiment, the genetic risk assessment is combined with the clinical risk assessment to obtain the “relative risk” of a human subject developing a severe response to a Coronavirus infection.
A threshold(s) can be set as described above when genetic risk is assessed alone. In one example, the threshold could be set to be at least 5, at least 6, at least 7, at least 8, at least 9 or at least 10, when using the embodiment of the test described in Example 5. If set at 5 in this example, about 10% of the UK biobank population have a risk score over 5.0 resulting in the following performance characteristics for the test:

Sensitivity 38.41%

Specificity 93.79%

Positive predictive value 91.78%
Negative predictive value 45.76%
As the skilled person would understand, various different thresholds could be set altering performance depending on the level of risk the entity conducting the test is willing accept.
Depending upon the end-usage of the test, a threshold may be altered to the most appropriate values.

Marker Detection Strategies

Amplification primers for amplifying markers (e.g., marker loci) and suitable probes to detect such markers or to genotype a sample with respect to multiple marker alleles, can be used in the disclosure. For example, primer selection for long-range PCR is described in U.S. Ser. No. 10/042,406 and U.S. Ser. No. 10/236,480; for short-range PCR, U.S. Ser. No. 10/341,832 provides guidance with respect to primer selection. Also, there are publicly available programs such as “Oligo” available for primer design. With such available primer selection and design software, the publicly available human genome sequence and the polymorphism locations, one of skill can construct primers to amplify the polymorphisms to practice the disclosure. Further, it will be appreciated that the precise probe to be used for detection of a nucleic acid comprising a polymorphism (e.g., an amplicon comprising the polymorphism) can vary, e.g., any probe that can identify the region of a marker amplicon to be detected can be used in conjunction with the present disclosure. Further, the configuration of the detection probes can, of course, vary. Thus, the disclosure is not limited to the sequences recited herein.
Examples of primer pairs for detecting some of the SNP's disclosed herein include: rs11549298 (ACCTGGTATCAGTGAAGAGGATCAG (SEQ ID NO:1) and TCTTGATACAACTGTAAGAAGTGGT (SEQ ID NO:2)), rs112317747 (TATTTCTTTGTTGCCCTCTATCTCT (SEQ ID NO:3) and GAAAGAGATGGGTTGGCATTATTAT (SEQ ID NO:4)), rs2034831 (TAAAATTAGAACTGGAGGGCTGGGT (SEQ ID NO:5) and TGGCATTATAAACACTCACTGAAGT (SEQ ID NO: 6)), rs112641600 (AATGCCATCTGATGAGAGAAGTTTT (SEQ ID NO:7) and TACAGTTTTAAAAATGGGCGTTTCT (SEQ ID NO:8)), rs10755709 (TATAATAACACGTGGAAGTGAAAAT (SEQ ID NO:9) and TTGTTTGTATGTGTGAAATGATTCT (SEQ ID NO:10)), rs118072448 (AAGCAAACTATTCTTCAGGAATCCA (SEQ ID NO:11) and ATTTCTGCATTTCACTTTGTGTGGT (SEQ ID NO:12)), rs7027911 (GTAAATGCTGCTAACAGAGCTCTTT (SEQ ID NO:13) and GAAGAGAGTTTATTAGCAAGGCCTC (SEQ ID NO:14)), rs71481792 (CATTTGGGAAAAGCCACTGAATGGA (SEQ ID NO:15) and AGATTGACTAGCCGTTGAGAGTAGA (SEQ ID NO:16)), and rs1984162 (ACTGACTCCTGACACTCTTGAAGCG (SEQ ID NO:17) and GACTCTTCTCTGGCATCTTCTCATG (SEQ ID NO:18)).
Indeed, it will be appreciated that amplification is not a requirement for marker detection, for example one can directly detect unamplified genomic DNA simply by performing a Southern blot on a sample of genomic DNA.
Typically, molecular markers are detected by any established method available in the art, including, without limitation, allele specific hybridization (ASH), detection of extension, array hybridization (optionally including ASH), or other methods for detecting polymorphisms, amplified fragment length polymorphism (AFLP) detection, amplified variable sequence detection, randomly amplified polymorphic DNA (RAPD) detection, restriction fragment length polymorphism (RFLP) detection, self-sustained sequence replication detection, simple sequence repeat (SSR) detection, and single-strand conformation polymorphisms (SSCP) detection.
Some techniques for detecting genetic markers utilize hybridization of a probe nucleic acid to nucleic acids corresponding to the genetic marker (e.g., amplified nucleic acids produced using genomic DNA as a template). Hybridization formats, including, but not limited to: solution phase, solid phase, mixed phase, or in situ hybridization assays are useful for allele detection. An extensive guide to the hybridization of nucleic acids is found in Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes Elsevier, New York, as well as in Sambrook et al. (supra).
PCR detection using dual-labelled fluorogenic oligonucleotide probes, commonly referred to as “TaqMan™” probes, can also be performed according to the present disclosure. These probes are composed of short (e.g., 20-25 base) oligodeoxynucleotides that are labelled with two different fluorescent dyes. On the 5′ terminus of each probe is a reporter dye, and on the 3′ terminus of each probe a quenching dye is found. The oligonucleotide probe sequence is complementary to an internal target sequence present in a PCR amplicon. When the probe is intact, energy transfer occurs between the two fluorophores and emission from the reporter is quenched by the quencher by FRET. During the extension phase of PCR, the probe is cleaved by 5′ nuclease activity of the polymerase used in the reaction, thereby releasing the reporter from the oligonucleotide-quencher and producing an increase in reporter emission intensity. Accordingly, TaqMan™ probes are oligonucleotides that have a label and a quencher, where the label is released during amplification by the exonuclease action of the polymerase used in amplification. This provides a real time measure of amplification during synthesis. A variety of TaqMan™ reagents are commercially available, e.g., from Applied Biosystems (Division Headquarters in Foster City, Calif.) as well as from a variety of specialty vendors such as Biosearch Technologies (e.g., black hole quencher probes). Further details regarding dual-label probe strategies can be found, e.g., in WO 92/02638.
Other similar methods include e.g. fluorescence resonance energy transfer between two adjacently hybridized probes, e.g., using the “LightCycler®” format described in U.S. Pat. No. 6,174,670.
Array-based detection can be performed using commercially available arrays, e.g., from Affymetrix (Santa Clara, Calif.) or other manufacturers. Reviews regarding the operation of nucleic acid arrays include Sapolsky et al. (1999); Lockhart (1998); Fodor (1997a); Fodor (1997b) and Chee et al. (1996). Array based detection is one preferred method for identification markers of the disclosure in samples, due to the inherently high-throughput nature of array based detection.
The nucleic acid sample to be analysed is isolated, amplified and, typically, labelled with biotin and/or a fluorescent reporter group. The labelled nucleic acid sample is then incubated with the array using a fluidics station and hybridization oven. The array can be washed and or stained or counter-stained, as appropriate to the detection method. After hybridization, washing and staining, the array is inserted into a scanner, where patterns of hybridization are detected. The hybridization data are collected as light emitted from the fluorescent reporter groups already incorporated into the labelled nucleic acid, which is now bound to the probe array. Probes that most clearly match the labelled nucleic acid produce stronger signals than those that have mismatches. Since the sequence and position of each probe on the array are known, by complementarity, the identity of the nucleic acid sample applied to the probe array can be identified.
Markers and polymorphisms can also be detected using DNA sequencing. DNA sequencing methods are well known in the art and can be found for example in Ausubel et al, eds., Short Protocols in Molecular Biology, 3rd ed., Wiley, (1995) and Sambrook et al, Molecular Cloning, 2nd ed., Chap. 13, Cold Spring Harbor Laboratory Press, (1989). Sequencing can be carried out by any suitable method, for example, dideoxy sequencing, chemical sequencing, or variations thereof.
Suitable sequencing methods also include Second Generation, Third Generation, or Fourth Generation sequencing technologies, all referred to herein as “next generation sequencing”, including, but not limited to, pyrosequencing, sequencing-by-ligation, single molecule sequencing, sequence-by-synthesis (SBS), massive parallel clonal, massive parallel single molecule SBS, massive parallel single molecule real-time, massive parallel single molecule real-time nanopore technology, etc. A review of some such technologies can be found in (Morozova and Marra, 2008), herein incorporated by reference. Accordingly, in some embodiments, performing a genetic risk assessment as described herein involves detecting the at least two polymorphisms by DNA sequencing. In an embodiment, the at least two polymorphisms are detected by next generation sequencing.
Next generation sequencing (NGS) methods share the common feature of massively parallel, high-throughput strategies, with the goal of lower costs in comparison to older sequencing methods (see, Voelkerding et al., 2009; MacLean et al., 2009).
A number of such DNA sequencing techniques are known in the art, including fluorescence-based sequencing methodologies. In some embodiments, automated sequencing techniques are used. In some embodiments, parallel sequencing of partitioned amplicons is used (WO2006084132). In some embodiments, DNA sequencing is achieved by parallel oligonucleotide extension (See, e.g., U.S. Pat. Nos. 5,750,341 and 6,306,597). Additional examples of sequencing techniques include the Church polony technology (Mitra et al., 2003; Shendure et al., 2005; U.S. Pat. Nos. 6,432,36; 6,485,944; 6,511,803), the 454 picotiter pyrosequencing technology (Margulies et al., 2005; US 20050130173), the Solexa single base addition technology (Bennett et al., 2005; U.S. Pat. Nos. 6,787,308; 6,833,246), the Lynx massively parallel signature sequencing technology (Brenner et al., 2000; U.S. Pat. Nos. 5,695,934; 5,714,330), and the Adessi PCR colony technology (Adessi et al., 2000).

Correlating Markers to Phenotypes

These correlations can be performed by any method that can identify a relationship between an allele and a phenotype, or a combination of alleles and a combination of phenotypes. For example, alleles defined herein can be correlated with a severe response to Coronavirus infection phenotypes. The methods can involve referencing a look up table that comprises correlations between alleles of the polymorphism and the phenotype. The table can include data for multiple allele-phenotype relationships and can take account of additive or other higher order effects of multiple allele-phenotype relationships, e.g., through the use of statistical tools such as principle component analysis, heuristic algorithms, etc.
Correlation of a marker to a phenotype optionally includes performing one or more statistical tests for correlation. Many statistical tests are known, and most are computer-implemented for ease of analysis. A variety of statistical methods of determining associations/correlations between phenotypic traits and biological markers are known and can be applied to the present disclosure (Hartl et al., 1981). A variety of appropriate statistical models are described in Lynch and Walsh (1998). These models can, for example, provide for correlations between genotypic and phenotypic values, characterize the influence of a locus on a phenotype, sort out the relationship between environment and genotype, determine dominance or penetrance of genes, determine maternal and other epigenetic effects, determine principle components in an analysis (via principle component analysis, or “PCA”), and the like. The references cited in these texts provides considerable further detail on statistical models for correlating markers and phenotype.
In addition to standard statistical methods for determining correlation, other methods that determine correlations by pattern recognition and training, such as the use of genetic algorithms, can be used to determine correlations between markers and phenotypes. This is particularly useful when identifying higher order correlations between multiple alleles and multiple phenotypes. To illustrate, neural network approaches can be coupled to genetic algorithm-type programming for heuristic development of a structure-function data space model that determines correlations between genetic information and phenotypic outcomes.
In any case, essentially any statistical test can be applied in a computer implemented model, by standard programming methods, or using any of a variety of “off the shelf” software packages that perform such statistical analyses, including, for example, those noted above and those that are commercially available, e.g., from Partek Incorporated (St. Peters, Mo.; www.partek.com), e.g., that provide software for pattern recognition (e.g., which provide Partek Pro 2000 Pattern Recognition Software).
Systems for performing the above correlations are also a feature of the disclosure. Typically, the system will include system instructions that correlate the presence or absence of an allele (whether detected directly or, e.g., through expression levels) with a predicted phenotype.
Optionally, the system instructions can also include software that accepts diagnostic information associated with any detected allele information, e.g., a diagnosis that a subject with the relevant allele has a particular phenotype. This software can be heuristic in nature, using such inputted associations to improve the accuracy of the look up tables and/or interpretation of the look up tables by the system. A variety of such approaches, including neural networks, Markov modelling, and other statistical analysis are described above.

Polymorphic Profiling

The disclosure provides methods of determining the polymorphic profile of an individual at the polymorphisms outlined in the present disclosure (e.g. Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22) or polymorphisms in linkage disequilibrium with one or more thereof.
The polymorphic profile constitutes the polymorphic forms occupying the various polymorphic sites in an individual. In a diploid genome, two polymorphic forms, the same or different from each other, usually occupy each polymorphic site. Thus, the polymorphic profile at sites X and Y can be represented in the form X (x1, x1), and Y (y1, y2), wherein x1, x1 represents two copies of allele x1 occupying site X and y1, y2 represent heterozygous alleles occupying site Y.
The polymorphic profile of an individual can be scored by comparison with the polymorphic forms associated with resistance or susceptibility to a severe response to a Coronavirus infection occurring at each site. The comparison can be performed on at least, e.g., 1, 2, 5, 10, 25, 50, or all of the polymorphic sites, and optionally, others in linkage disequilibrium with them. The polymorphic sites can be analysed in combination with other polymorphic sites.
Polymorphic profiling is useful, for example, in selecting agents to affect treatment or prophylaxis of a severe response to a Coronavirus infection in a given individual. Individuals having similar polymorphic profiles are likely to respond to agents in a similar way.
Polymorphic profiling is also useful for stratifying individuals in clinical trials of agents being tested for capacity to treat a severe response to a Coronavirus infection or related conditions. Such trials are performed on treated or control populations having similar or identical polymorphic profiles (see EP 99965095.5), for example, a polymorphic profile indicating an individual has an increased risk of developing a severe response to a Coronavirus infection. Use of genetically matched populations eliminates or reduces variation in treatment outcome due to genetic factors, leading to a more accurate assessment of the efficacy of a potential drug.
Polymorphic profiling is also useful for excluding individuals with no predisposition to a severe response to a Coronavirus infection from clinical trials. Including such individuals in the trial increases the size of the population needed to achieve a statistically significant result. Individuals with no predisposition to a severe response to a Coronavirus infection can be identified by determining the numbers of resistances and susceptibility alleles in a polymorphic profile as described above. For example, if a subject is genotyped at ten sites of the disclosure associated with a severe response to a Coronavirus infection, twenty alleles are determined in total. If over 50% and alternatively over 60% or 75% percent of these are resistance genes, the individual is unlikely to develop a severe response to a Coronavirus infection and can be excluded from the trial.

Computer Implemented Method

The methods of the present disclosure may be implemented by a system such as a computer implemented method. For example, the system may be a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as “processor”) connected to a memory. The memory may be a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM. Software, that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the risk of a human subject developing a severe response to a Coronavirus infection; receiving data indicating the clinical risk assessment and the genetic risk assessment of the human subject developing a severe response to a Coronavirus infection, wherein the genetic risk was derived by detecting at least two polymorphisms known to be associated with a severe response to a Coronavirus infection; processing the data to combine the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection; outputting the risk of a human subject developing a severe response to a Coronavirus infection.
For example, the memory may comprise program code which when executed by the processor causes the system to determine at least two polymorphisms known to be associated with a severe response to a Coronavirus infection; process the data to combine the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection; report the risk of a human subject developing a severe response to a Coronavirus infection.
In another embodiment, the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information. For example, the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.
In an embodiment, the program code may causes the system to determine the “Polymorphism risk”.
In an embodiment, the program code may causes the system to determine CombinedClinical Risk×Genetic Risk (for example Polymorphism risk).
In an embodiment, the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network. For example, the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network. In other embodiments, the system may be isolated from direct user interaction.
In another embodiment, performing the methods of the present disclosure to assess the risk of a human subject developing a severe response to a Coronavirus infection, enables establishment of a diagnostic or prognostic rule based on the clinical risk assessment and the genetic risk assessment of the human subject developing a severe response to a Coronavirus infection. For example, the diagnostic or prognostic rule can be based on the Combined Clinical Risk×Genetic Risk score relative to a control, standard or threshold level of risk.
In another embodiment, the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a population of polymorphisms and disease status observed in training data (with known disease status) to infer relationships which are then used to determine the risk of a human subject developing a severe response to a Coronavirus infection in subjects with an unknown risk. An algorithm is employed which provides an risk of a human subject developing a severe response to a Coronavirus infection. The algorithm performs a multivariate or univariate analysis function.

Kits and Products

In an embodiment, the present disclosure provides a kit comprising at least two sets of primers for amplifying two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 2 and Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 4 or Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 3 or Table 6a, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising one or more or all of the polymorphisms provided in Table 19, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising one or more or all of the polymorphisms provided in Table 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
As would be appreciated by those of skill in the art, once a polymorphism is identified, primers can be designed to amplify the polymorphism as a matter of routine. Various software programs are freely available that can suggest suitable primers for amplifying polymorphisms of interest.
Again, it would be known to those of skill in the art that PCR primers of a PCR primer pair can be designed to specifically amplify a region of interest from human DNA. Each PCR primer of a PCR primer pair can be placed adjacent to a particular single-base variation on opposing sites of the DNA sequence variation. Furthermore, PCR primers can be designed to avoid any known DNA sequence variation and repetitive DNA sequences in their PCR primer binding sites.
The kit may further comprise other reagents required to perform an amplification reaction such as a buffer, nucleotides and/or a polymerase, as well as reagents for extracting nucleic acids from a sample.
Array based detection is one preferred method for assessing the polymorphisms of the disclosure in samples, due to the inherently high-throughput nature of array based detection. A variety of probe arrays have been described in the literature and can be used in the context of the present disclosure for detection of polymorphisms that can be correlated to a severe response to a Coronavirus infection. For example, DNA probe array chips are used in one embodiment of the disclosure. The recognition of sample DNA by the set of DNA probes takes place through DNA hybridization. When a DNA sample hybridizes with an array of DNA probes, the sample binds to those probes that are complementary to the sample DNA sequence. By evaluating to which probes the sample DNA for an individual hybridizes more strongly, it is possible to determine whether a known sequence of nucleic acid is present or not in the sample, thereby determining whether a marker found in the nucleic acid is present.
Thus, in another embodiment, the present disclosure provides a genetic array comprising at least two sets of probes for hybridising to two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of probes for hybridising a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of probes for hybridising a polymorphism selected from Table 2 and Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of probes for hybridising a polymorphism selected from Table 4 or Table 5, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of probes for hybridising a polymorphism selected from Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of probes for hybridising a polymorphism selected from Table 3 or Table 6a, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of probes for hybridising a polymorphism selected from Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprises a probe(s) for hybridising one or more or all of the polymorphisms provided in Table 19, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
In an embodiment, the kit comprising a probe(s) for hybridising one or more or all of the polymorphisms provided in in Table 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
Primers and probes for other polymorphisms can be included with the above exemplified kits. For example, primers and/or probes may be included for detecting a Coronavirus, such as a SARS-CoV-2 viral, infection.

EXAMPLES

Example 1—Polymorphisms Associated with Disease Severity in Covid-19 Infected Patients

Approximately 11 million SNP results were analysed. These were sorted by p-value, from lowest to highest and the top one million of these were utilised for further pruning. This equated to all variants p<0.0969. A p-value threshold of p<0.001 was then applied, as was a beta value window between −1 to 1 and an average pooled allele frequency of 0.01-0.99.
These were then further pruned for linkage disequilibrium using the online tool LDLink, snpclip (ldlink.nci.nih.gov) using the EUR populations as reference, set to threshold at R2 of <0.5. Non-single nucleotide variants were excluded if no linked surrogate/proxy SNP was available.
Informative polymorphisms derived from publicly available pooled genome-wide association study (GWAS) results from 716 cases (confirmed COVID-19 (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) diagnosis and hospitalised) and 616 controls (confirmed COVID-19 diagnosis and non-hospitalised are provided in Table 2.
Informative polymorphisms derived from 2,863 patients within the UK Biobank Study, of which 825 were hospitalized for severe response to the infection. GWAS results were sorted by p-value. A p-value threshold of p<0.00001 was applied, as was an allele frequency threshold set at a minor allele frequency >0.01. The identified polymorphisms are provided in Table 8.

TABLE 8

Informative polymorphisms derived from 2,863 patients within the UK Biobank Study.

Chromo-					Frequency 1	Frequency 2	p-value for
some	Position	SNP ID	Allele 1	Allele 2	Allele	Allele	association	OR

1	3680362	rs146866117	T	C	0.00042	0.99958	3.21516E−05	11.3818

1	10993680	rs75721992	C	T	0.00091	0.99909	8.03153E−09	18.9971

1	15698556	rs12562412	G	C	0.18020	0.81980	7.34872E−05	1.43641

1	15758944	rs117338853	A	G	0.00125	0.99875	9.31685E−05	9.83042

1	16109212	rs72647169	G	C	0.03574	0.96426	1.72286E−05	4.49562

1	17295659	rs199765517	T	C	0.00045	0.99955	6.47434E−05	10.3168

1	20072025	rs199727655	A	G	0.00003	0.99997	3.41489E−05	11.3655

1	22538788	rs78360109	A	G	0.00727	0.99273	1.32276E−06	3.74574

1	34829829	rs79955780	G	A	0.03884	0.96116	8.74145E−05	1.8346

1	38661814	rs61778695	C	A	0.03148	0.96852	4.43093E−05	1.93677

1	67804320	rs578200723	GTTA	G	0.00168	0.99832	0.000020482	5.75441

1	72488455	rs116544454	T	G	0.01396	0.98604	1.85829E−05	2.48638

1	109823418	rs144022094	T	C	0.00064	0.99936	3.57493E−05	6.50582

1	109933450	rs56072034	A	G	0.02556	0.97444	4.12342E−05	2.10457

1	168696733	rs76129265	T	G	0.05740	0.94260	7.51539E−05	1.68733

1	204092087	rs201772428	A	G	0.00036	0.99964	4.3011E−06	14.8225

1	206776460	rs35252702	A	G	0.00679	0.99321	1.56671E−07	8.81138

1	207610967	rs61821114	T	C	0.01504	0.98496	3.00757E−05	2.42913

1	210901242	rs1934624	A	T	0.02193	0.97807	5.48665E−05	2.16618

1	218938774	rs76354174	A	G	0.02281	0.97719	7.54499E−05	2.10557

1	230907829	rs143186556	A	G	0.00089	0.99911	5.46536E−05	7.7448

1	231133014	rs200114138	A	G	0.00101	0.99899	3.96515E−06	8.13897

2	22958939	rs59447738	G	T	0.10234	0.89766	8.21388E−05	1.5646

2	23005876	rs73918088	A	C	0.05530	0.94470	7.50922E−05	1.7204

2	25861939	rs189303418	T	C	0.00156	0.99844	0.000040202	5.43754

2	34108876	rs1718746	C	G	0.15379	0.84621	8.52475E−05	0.678928

2	34119632	rs1705143	A	G	0.15212	0.84788	8.08594E−05	0.676255

2	64630299	rs872241	G	A	0.35970	0.64030	7.17367E−05	1.37043

2	64641736	rs11676644	C	T	0.30746	0.69254	0.000032141	1.39753

2	115258481	rs56735442	A	G	0.02150	0.97850	3.59163E−05	2.25221

2	122952399	rs75945051	G	A	0.01208	0.98792	4.38486E−05	2.48027

2	126726522	rs76187206	C	G	0.00948	0.99052	5.74668E−05	2.79499

2	160721407	chr2	A	AC	0.00001	0.99999	4.45501E−05	10.9923
		160721407

2	179428061	rs11896637	T	C	0.00304	0.99696	1.81846E−06	11.2684

2	179441917	rs72646881	C	T	0.00324	0.99676	1.59099E−05	8.89845

2	179612315	rs145581345	C	T	0.00135	0.99865	8.45307E−05	5.91193

2	181910717	rs78593095	A	G	0.01388	0.98612	2.26398E−05	2.66342

2	191278341	rs6725814	G	A	0.37811	0.62189	1.88302E−05	0.714396

3	2748191	rs80225140	G	A	0.03080	0.96920	8.74289E−06	2.11655

3	34944013	rs17032477	G	A	0.01700	0.98300	3.63638E−05	2.54994

3	65100060	rs2128405	C	A	0.07271	0.92729	1.71219E−05	1.69956

3	70783491	rs6766000	T	C	0.13852	0.86148	2.00031E−05	1.53315

3	81810551	rs35196441	T	G	0.00100	0.99900	3.53506E−05	11.2502

3	154812638	rs2196521	G	A	0.12811	0.87189	3.73634E−05	0.662498

3	160379672	rs4679910	G	A	0.38677	0.61323	1.90222E−05	1.4334

3	171853417	rs73167212	G	A	0.11289	0.88711	0.00002702	1.5595

3	177796194	rs74911757	T	C	0.01229	0.98771	1.43291E−05	2.6372

4	39943691	rs115509062	G	C	0.03178	0.96822	6.54964E−05	1.90376

4	45117625	rs75072424	A	G	0.11765	0.88235	2.80438E−05	1.54717

4	69795670	rs144454074	A	G	0.00307	0.99693	4.19779E−05	4.35549

4	73555556	rs28616128	G	A	0.06206	0.93794	3.02015E−06	1.78187

4	75191300	rs115044024	C	T	0.01950	0.98050	4.15557E−05	2.18747

4	84216649	rs144185023	A	G	0.00166	0.99834	3.72469E−05	5.50931

4	87939942	rs76456240	C	T	0.02364	0.97636	9.48747E−05	2.09846

4	121958473	rs202221151	C	T	0.00031	0.99969	1.61977E−06	16.7318

4	142326546	rs76589765	G	A	0.01836	0.98164	4.20983E−06	2.39938

4	151724769	rs116015734	T	C	0.01283	0.98717	9.26679E−05	2.44853

4	153821201	rs62319956	C	A	0.00963	0.99037	2.01266E−06	3.02949

4	170238293	rs76519323	A	C	0.03375	0.96625	2.30315E−05	1.98212

4	170498270	rs74557505	C	T	0.03048	0.96952	6.57926E−05	2.01122

4	181162752	rs17069033	T	G	0.00223	0.99777	1.35337E−05	9.07875

4	185567903	rs4647611	C	G	0.00897	0.99103	2.72675E−05	6.07186

5	6826973	rs275444	C	G	0.12706	0.87294	3.53004E−05	0.530803

5	19993490	rs4466171	A	T	0.02086	0.97914	3.08251E−06	5.43862

5	43280480	rs55770078	A	G	0.00266	0.99734	1.16965E−05	4.82309

5	99815379	rs115319054	A	C	0.01396	0.98604	8.88589E−05	2.37898

5	133519273	rs79601653	G	A	0.05445	0.94555	9.89897E−05	1.68817

5	155405405	rs958444	T	C	0.04320	0.95680	2.01272E−05	0.540169

5	160448591	rs11749317	C	T	0.01465	0.98535	0.000080983	2.31323

6	2885791	rs318470	C	A	0.03985	0.96015	9.76222E−05	0.534104

6	16217762	rs149442766	A	G	0.00598	0.99402	3.42524E−05	3.20428

6	26408145	rs144114619	A	T	0.00298	0.99702	4.57681E−05	4.32309

6	33156845	rs41268014	G	C	0.00111	0.99889	1.61406E−05	7.06085

6	36976747	rs140572234	C	G	0.00360	0.99640	6.20165E−08	4.94704

6	36999980	rs114925152	G	C	0.01045	0.98955	0.000019845	2.8117

6	37139030	rs35760989	C	G	0.00316	0.99684	2.15792E−05	4.19015

6	101279459	rs9485415	T	C	0.05955	0.94045	2.95345E−05	1.79563

6	147885588	rs140643252	A	G	0.00080	0.99920	0.000080315	7.36219

7	21730647	rs7790948	G	T	0.14286	0.85714	3.13489E−05	1.51436

7	35720134	rs79496619	T	G	0.01734	0.98266	3.21712E−05	4.65324

7	38677134	rs78966608	A	C	0.06041	0.93959	7.06676E−05	1.69619

7	100483400	rs200675508	A	G	0.00165	0.99835	7.25535E−05	5.06219

7	104782689	rs55743527	G	T	0.00060	0.99940	1.36022E−06	11.6143

7	122122078	rs73431600	A	G	0.01909	0.98091	5.15467E−05	2.32846

7	122196872	rs73433754	A	C	0.01639	0.98361	3.48011E−05	2.47199

7	154926067	rs117164958	C	G	0.01908	0.98092	7.61301E−06	4.84386

7	158928541	rs13225056	A	G	0.02825	0.97175	1.53596E−05	2.04944

8	2044114	rs147776183	T	C	0.00025	0.99975	8.64871E−06	13.5674

8	9478274	rs78876374	T	C	0.05327	0.94673	8.85355E−05	1.69943

8	19218826	rs145746072	A	G	0.00063	0.99937	1.91735E−05	5.15347

8	20354600	rs13249119	G	A	0.12835	0.87165	1.33633E−05	1.57795

8	20447019	rs73626732	A	G	0.08816	0.91184	1.40229E−05	1.65522

8	20460661	rs35258926	G	T	0.08444	0.91556	9.04111E−06	1.68733

8	39799765	rs7845003	T	C	0.38763	0.61237	9.39614E−05	1.37383

8	125839433	rs118040942	T	C	0.02098	0.97902	0.000059504	2.11631

8	130677813	rs57557483	A	G	0.03911	0.96089	8.17573E−06	1.95465

8	130694201	rs75572486	A	G	0.03949	0.96051	2.53314E−05	1.8823

9	10276812	rs10959000	A	G	0.06996	0.93004	7.85152E−05	1.65945

9	22080363	rs16905613	G	A	0.00861	0.99139	5.78633E−06	3.06828

9	38669062	rs2993177	A	G	0.02733	0.97267	7.37838E−05	0.512648

9	79453107	rs7853555	T	C	0.39843	0.60157	5.25711E−05	0.726342

9	83032179	rs72744937	G	A	0.01117	0.98883	6.92799E−05	2.60258

9	100137855	rs200908751	A	G	0.00074	0.99926	6.57652E−05	7.53882

9	101874496	rs76094400	A	G	0.01217	0.98783	3.95481E−08	3.22873

9	125704675	rs76670825	A	G	0.01316	0.98684	5.01185E−05	2.47436

9	125903047	rs77089732	A	G	0.01284	0.98716	0.00005145	2.45902

9	128794405	rs62570501	T	C	0.05704	0.94296	7.35517E−05	1.70643

9	131771551	rs17455482	A	G	0.00240	0.99760	5.2118E−06	5.14273

10	67680203	rs41313840	G	A	0.00029	0.99971	7.19977E−08	11.5573

10	127258691	rs7084502	A	G	0.41534	0.58466	7.81329E−05	0.720431

10	131456440	rs79858702	T	C	0.00885	0.99115	0.000070971	2.83622

11	1795214	rs79194907	G	A	0.08673	0.91327	6.83674E−05	0.457071

11	5146083	rs12365149	T	C	0.08312	0.91688	3.11673E−05	0.427618

11	44547746	rs12275504	G	T	0.02803	0.97197	1.40655E−05	2.13659

11	46727333	rs149066130	A	G	0.00042	0.99958	7.98651E−05	10.0401

11	57982229	rs1966836	A	G	0.29483	0.70517	6.87681E−05	0.720184

11	65414949	rs199717374	T	C	0.00021	0.99979	1.05822E−05	9.34606

11	78904266	rs75970706	T	C	0.03718	0.96282	0.000040305	1.8614

11	94665002	rs76360689	A	G	0.00749	0.99251	1.05432E−06	3.44965

11	133713033	rs75786498	A	G	0.01903	0.98097	3.13102E−05	2.20737

12	2174748	rs117821007	C	T	0.01160	0.98840	4.86991E−05	2.65265

12	25681286	rs58907459	T	C	0.12688	0.87312	6.07118E−05	1.50994

12	57589676	rs143285614	A	G	0.00041	0.99959	2.69071E−05	11.6706

12	125302200	rs143093152	G	A	0.00050	0.99950	6.62377E−06	14.0613

13	39433574	rs138539682	A	G	0.00073	0.99927	3.32149E−05	8.17464

13	68302925	rs75022796	T	C	0.00560	0.99440	7.24669E−05	3.17997

14	52276643	rs117852779	C	T	0.02739	0.97261	2.89233E−06	2.17106

14	80405359	rs11159425	T	G	0.11363	0.88637	4.36025E−07	0.583

14	80570671	rs114463019	G	T	0.01076	0.98924	3.85403E−05	2.71426

14	87813714	rs28450466	A	G	0.37204	0.62796	9.45754E−05	1.35996

14	93016441	rs57851052	C	T	0.32996	0.67004	5.53318E−05	1.37778

14	104863663	rs80083325	A	G	0.05367	0.94633	0.000073746	1.71423

15	22845849	rs150408740	G	A	0.00068	0.99932	1.76793E−05	8.79206

15	34498314	rs75915717	T	C	0.08459	0.91541	1.0293E−06	1.74564

15	41047777	rs35673728	C	T	0.06056	0.93944	6.67895E−05	1.67388

15	41254865	rs12915860	C	A	0.06743	0.93257	1.01336E−05	1.72191

15	41712936	rs62001419	A	C	0.05640	0.94360	9.84979E−05	1.68361

15	52689631	rs1724577	T	G	0.00955	0.99045	2.42041E−05	0.197849

15	58047086	rs77910305	G	C	0.00656	0.99344	1.30369E−05	3.14637

15	65851028	rs200531541	A	G	0.00017	0.99983	3.83063E−06	13.9416

15	78471034	rs34921279	T	C	0.00117	0.99883	2.40367E−05	8.48175

15	84063245	rs12591031	G	A	0.21734	0.78266	1.16687E−05	1.46192

15	91452594	rs142925505	T	C	0.00205	0.99795	6.04339E−07	5.95363

16	49391921	rs62029091	A	G	0.11884	0.88116	7.11949E−05	1.52258

16	49394276	rs8057939	C	T	0.12559	0.87441	1.12522E−05	1.57483

16	60671279	rs118097562	T	A	0.00770	0.99230	1.90641E−05	6.63236

16	61851413	rs151208133	T	C	0.00057	0.99943	6.3915E−06	9.86885

16	81194912	rs11642802	C	A	0.26295	0.73705	4.32104E−06	1.45947

16	90075827	rs201800670	T	C	0.00044	0.99956	1.60067E−05	12.5559

17	1462712	rs73298816	G	A	0.29300	0.70700	5.44207E−05	0.682825

17	3844344	rs144535413	T	C	0.00487	0.99513	1.11488E−05	3.62812

17	36485146	rs147966258	A	G	0.00198	0.99802	1.64933E−05	5.06987

17	39240563	rs193005959	A	C	0.00837	0.99163	3.72836E−05	2.98372

17	55803083	rs72841559	C	G	0.01897	0.98103	2.26086E−05	2.2379

17	56329775	rs368901060	ACCAT	A	0.00219	0.99781	4.70507E−06	5.18629

17	63919929	rs7220318	G	A	0.00461	0.99539	2.39945E−05	10.9127

17	72890474	rs689992	T	A	0.26539	0.73461	8.97907E−05	1.39123

17	78215658	rs117140258	A	G	0.02691	0.97309	4.15567E−05	2.08397

18	4610215	rs76902871	G	T	0.01267	0.98733	2.95635E−05	2.56921

18	13501162	rs2298530	C	T	0.03718	0.96282	6.48889E−05	1.85512

18	14310187	rs117505121	G	A	0.01057	0.98943	3.59448E−05	2.71447

18	49288587	rs117781678	A	C	0.01002	0.98998	4.32852E−05	2.66495

18	76650871	rs7240086	G	A	0.43336	0.56664	6.95487E−06	1.42281

19	36018109	rs74726174	C	T	0.00069	0.99931	1.82372E−05	12.2885

19	38867031	rs200403794	A	G	0.00019	0.99981	8.36021E−05	10.0252

20	8782776	rs138434221	A	C	0.00031	0.99969	4.80296E−06	14.6291

20	15632993	rs6110707	C	T	0.17779	0.82221	1.31264E−05	1.50802

20	55021575	rs6069749	T	C	0.07845	0.92155	2.92257E−05	1.64645

20	55111747	rs6014757	A	G	0.15160	0.84840	6.44584E−05	1.47163

21	19045795	rs73200561	A	T	0.00879	0.99121	9.40896E−05	2.714

21	37444937	rs2230191	A	G	0.00079	0.99921	3.4537E−07	13.3335

22	28016883	rs1885362	A	C	0.15733	0.84267	4.65803E−05	1.70351

22	40056937	rs113038998	T	C	0.06752	0.93248	1.37758E−05	1.73209

22	44285118	rs117421847	A	G	0.01292	0.98708	6.60782E−05	2.4314

Example 2—Genetic Risk Assessment—108 Polymorphism Panel

SNP-based (relative) risk score was calculated using estimates of the odds ratio (OR) per allele and risk allele frequency (p) assuming independent and additive risks on the log OR scale. For each SNP, the unscaled population average risk was calculated as μ=(1−p)2+2p(1−p) OR+p2OR2. Adjusted risk values (with a population average risk equal to 1 were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes defined by number of risk alleles (0, 1, or 2). The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 108 SNPs (Tables 9 and 10).
Thus, a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization.

Example 3—Genetic Risk Assessment—58 Polymorphism Panel

The present inventors have found that a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization.
The model has been developed using 2,863 patients within the UK Biobank Study, of which 825 were hospitalized for severe response to the infection.
SNP-based (relative) risk score was calculated using estimates of the odds ratio (OR) per allele and risk allele frequency (p) assuming independent and additive risks on the log OR scale. For each SNP, the unscaled population average risk was calculated as μ=(1−p)2+2p(1−p) OR+p2OR2. Adjusted risk values (with a population average risk equal to 1 were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes defined by number of risk alleles (0, 1, or 2). The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 58 SNPs (Table 11). The 58 SNPs analysed are provided in Table 3.
Thus, a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization. Due to the higher OR, this panel performed better than the 108 SNP panel described in Example 2.

TABLE 9

Informative polymorphisms used in genetic risk assessment of Example 2.

					Frequency	Frequency	p-value for
Chromosome	Position	SNP ID	Allele 1	Allele 2	Allele 1	Allele 2	association	OR

1	15698556	rs12562412	G	C	0.180202869	0.819797131	7.34872E−05	1.43641
1	16109212	rs72647169	G	C	0.035742611	0.964257389	1.72286E−05	4.49562
1	34829829	rs79955780	G	A	0.038842122	0.961157878	8.74145E−05	1.8346
1	38661814	rs61778695	C	A	0.031483826	0.968516174	4.43093E−05	1.93677
1	72488455	rs116544454	T	G	0.013959529	0.986040471	1.85829E−05	2.48638
1	109933450	rs56072034	A	G	0.02556272	0.97443728	4.12342E−05	2.10457
1	168696733	rs76129265	T	G	0.05740251	0.94259749	7.51539E−05	1.68733
1	207610967	rs61821114	T	C	0.015043834	0.984956166	3.00757E−05	2.42913
1	210901242	rs1934624	A	T	0.021933325	0.978066675	5.48665E−05	2.16618
1	218938774	rs76354174	A	G	0.022811438	0.977188562	7.54499E−05	2.10557
2	22958939	rs59447738	G	T	0.102337052	0.897662948	8.21388E−05	1.5646
2	23005876	rs73918088	A	C	0.055296476	0.944703524	7.50922E−05	1.7204
2	34108876	rs1718746	C	G	0.153787681	0.846212319	8.52475E−05	0.678928
2	34119632	rs1705143	A	G	0.152124807	0.847875193	8.08594E−05	0.676255
2	64630299	rs872241	G	A	0.359695861	0.640304139	7.17367E−05	1.37043
2	64641736	rs11676644	C	T	0.30746047	0.69253953	0.000032141	1.39753
2	115258481	rs56735442	A	G	0.021502475	0.978497525	3.59163E−05	2.25221
2	122952399	rs75945051	G	A	0.012078152	0.987921848	4.38486E−05	2.48027
2	181910717	rs78593095	A	G	0.013875458	0.986124542	2.26398E−05	2.66342
2	191278341	rs6725814	G	A	0.378106477	0.621893523	1.88302E−05	0.714396
3	2748191	rs80225140	G	A	0.030803699	0.969196301	8.74289E−06	2.11655
3	34944013	rs17032477	G	A	0.017000358	0.982999642	3.63638E−05	2.54994
3	65100060	rs2128405	C	A	0.072712034	0.927287966	1.71219E−05	1.69956
3	70783491	rs6766000	T	C	0.138522268	0.861477732	2.00031E−05	1.53315
3	81810551	rs2196521	G	A	0.128112119	0.871887881	3.73634E−05	0.662498
3	160379672	rs4679910	G	A	0.386771125	0.613228875	1.90222E−05	1.4334
3	171853417	rs73167212	G	A	0.112891842	0.887108158	0.00002702	1.5595
3	177796194	rs74911757	T	C	0.012287422	0.987712578	1.43291E−05	2.6372
4	39943691	rs115509062	G	C	0.031782343	0.968217657	6.54964E−05	1.90376
4	45117625	rs75072424	A	G	0.117654783	0.882345217	2.80438E−05	1.54717
4	73555556	rs28616128	G	A	0.06205979	0.93794021	3.02015E−06	1.78187
4	75191300	rs115044024	C	T	0.019501076	0.980498924	4.15557E−05	2.18747
4	87939942	rs76456240	C	T	0.02364031	0.97635969	9.48747E−05	2.09846
4	142326546	rs76589765	G	A	0.018356247	0.981643753	4.20983E−06	2.39938
4	151724769	rs116015734	T	C	0.012831113	0.987168887	9.26679E−05	2.44853
4	170238293	rs76519323	A	C	0.033750916	0.966249084	2.30315E−05	1.98212
4	170498270	rs74557505	C	T	0.030475432	0.969524568	6.57926E−05	2.01122
5	6826973	rs275444	C	G	0.127064744	0.872935256	3.53004E−05	0.530803
5	19993490	rs4466171	A	T	0.020864374	0.979135626	3.08251E−06	5.43862
5	99815379	rs115319054	A	C	0.013956451	0.986043549	8.88589E−05	2.37898
5	133519273	rs79601653	G	A	0.054445035	0.945554965	9.89897E−05	1.68817
5	155405405	rs958444	T	C	0.043203962	0.956796038	2.01272E−05	0.540169
5	160448591	rs11749317	C	T	0.014652992	0.985347008	0.000080983	2.31323
6	2885791	rs318470	C	A	0.039851542	0.960148458	9.76222E−05	0.534104
6	36999980	rs114925152	G	C	0.010452207	0.989547793	0.000019845	2.8117
6	101279459	rs9485415	T	C	0.059550603	0.940449397	2.95345E−05	1.79563
7	21730647	rs7790948	G	T	0.142864617	0.857135383	3.13489E−05	1.51436
7	35720134	rs79496619	T	G	0.01734375	0.98265625	3.21712E−05	4.65324
7	38677134	rs78966608	A	C	0.060412303	0.939587697	7.06676E−05	1.69619
7	122122078	rs73431600	A	G	0.019090743	0.980909257	5.15467E−05	2.32846
7	122196872	rs73433754	A	C	0.016394855	0.983605145	3.48011E−05	2.47199
7	154926067	rs117164958	C	G	0.019077785	0.980922215	7.61301E−06	4.84386
7	158928541	rs13225056	A	G	0.028246298	0.971753702	1.53596E−05	2.04944
8	9478274	rs78876374	T	C	0.053269431	0.946730569	8.85355E−05	1.69943
8	20354600	rs13249119	G	A	0.12835319	0.87164681	1.33633E−05	1.57795
8	20447019	rs73626732	A	G	0.088162098	0.911837902	1.40229E−05	1.65522
8	20460661	rs35258926	G	T	0.084440378	0.915559622	9.04111E−06	1.68733
8	39799765	rs7845003	T	C	0.387630529	0.612369471	9.39614E−05	1.37383
8	125839433	rs118040942	T	C	0.020977249	0.979022751	0.000059504	2.11631
8	130677813	rs57557483	A	G	0.039111916	0.960888084	8.17573E−06	1.95465
8	130694201	rs75572486	A	G	0.039487371	0.960512629	2.53314E−05	1.8823
9	10276812	rs10959000	A	G	0.069955622	0.930044378	7.85152E−05	1.65945
9	38669062	rs2993177	A	G	0.027325082	0.972674918	7.37838E−05	0.512648
9	79453107	rs7853555	T	C	0.398429245	0.601570755	5.25711E−05	0.726342
9	83032179	rs72744937	G	A	0.011167213	0.988832787	6.92799E−05	2.60258
9	101874496	rs76094400	A	G	0.012165395	0.987834605	3.95481E−08	3.22873
9	125704675	rs76670825	A	G	0.013160405	0.986839595	5.01185E−05	2.47436
9	125903047	rs77089732	A	G	0.012843423	0.987156577	0.00005145	2.45902
9	128794405	rs62570501	T	C	0.057035262	0.942964738	7.35517E−05	1.70643
10	127258691	rs7084502	A	G	0.415343172	0.584656828	7.81329E−05	0.720431
11	1795214	rs79194907	G	A	0.086733113	0.913266887	6.83674E−05	0.457071
11	5146083	rs12365149	T	C	0.083117054	0.916882946	3.11673E−05	0.427618
11	44547746	rs12275504	G	T	0.028028822	0.971971178	1.40655E−05	2.13659
11	57982229	rs1966836	A	G	0.294826316	0.705173684	6.87681E−05	0.720184
11	78904266	rs75970706	T	C	0.037184377	0.962815623	0.000040305	1.8614
11	133713033	rs75786498	A	G	0.019027141	0.980972859	3.13102E−05	2.20737
12	2174748	rs117821007	C	T	0.011596011	0.988403989	4.86991E−05	2.65265
12	25681286	rs58907459	T	C	0.126878043	0.873121957	6.07118E−05	1.50994
14	52276643	rs117852779	C	T	0.027392806	0.972607194	2.89233E−06	2.17106
14	80405359	rs11159425	T	G	0.113626338	0.886373662	4.36025E−07	0.583
14	80570671	rs114463019	G	T	0.010759957	0.989240043	3.85403E−05	2.71426
14	87813714	rs28450466	A	G	0.372042781	0.627957219	9.45754E−05	1.35996
14	93016441	rs57851052	C	T	0.329959028	0.670040972	5.53318E−05	1.37778
14	104863663	rs80083325	A	G	0.053669505	0.946330495	0.000073746	1.71423
15	34498314	rs75915717	T	C	0.084593227	0.915406773	1.0293E−06	1.74564
15	41047777	rs35673728	C	T	0.060558997	0.939441003	6.67895E−05	1.67388
15	41254865	rs12915860	C	A	0.06742592	0.93257408	1.01336E−05	1.72191
15	41712936	rs62001419	A	C	0.056399246	0.943600754	9.84979E−05	1.68361
15	84063245	rs12591031	G	A	0.217339031	0.782660969	1.16687E−05	1.46192
16	49391921	rs62029091	A	G	0.118836542	0.881163458	7.11949E−05	1.52258
16	49394276	rs8057939	C	T	0.125591649	0.874408351	1.12522E−05	1.57483
16	81194912	rs11642802	C	A	0.262949597	0.737050403	4.32104E−06	1.45947
17	1462712	rs73298816	G	A	0.292998283	0.707001717	5.44207E−05	0.682825
17	55803083	rs72841559	C	G	0.018970721	0.981029279	2.26086E−05	2.2379
17	72890474	rs689992	T	A	0.265385949	0.734614051	8.97907E−05	1.39123
17	78215658	rs117140258	A	G	0.026907587	0.973092413	4.15567E−05	2.08397
18	4610215	rs76902871	G	T	0.012668006	0.987331994	2.95635E−05	2.56921
18	13501162	rs2298530	C	T	0.03717617	0.96282383	6.48889E−05	1.85512
18	14310187	rs117505121	G	A	0.010569152	0.989430848	3.59448E−05	2.71447
18	49288587	rs117781678	A	C	0.010023409	0.989976591	4.32852E−05	2.66495
18	76650871	rs7240086	G	A	0.433358842	0.566641158	6.95487E−06	1.42281
20	15632993	rs6110707	C	T	0.177787033	0.822212967	1.31264E−05	1.50802
20	55021575	rs6069749	T	C	0.078451567	0.921548433	2.92257E−05	1.64645
20	55111747	rs6014757	A	G	0.15160471	0.84839529	6.44584E−05	1.47163
22	28016883	rs1885362	A	C	0.157331933	0.842668067	4.65803E−05	1.70351
22	40056937	rs113038998	T	C	0.067518225	0.932481775	1.37758E−05	1.73209
22	44285118	rs117421847	A	G	0.012916257	0.987083743	6.60782E−05	2.4314

Example 4—Combining Genetic and Clinical Risk Assessment

The present specification provides methods for a Covid-19 risk model which combines a clinical risk assessment and a genetic risk assessment which can be used discriminate between cases with a severe response to Covid-19 infection, versus controls without a severe response.
The clinical risk factors incorporated into a combined model are assigned a relative risk, which indicates the magnitude of association with the severity of a Covid-19 infection, the clinical factors are combined with the polygenic risk score by multiplication. For example clinical risk factor A is assigned the relative risk RRa and clinical risk factor B is assigned the relative risk RRb. The full risk score is then calculated as Polygenic Risk Score×RRa×RRb=Combined Risk.

TABLE 10

Performance characteristics of a 108 SNP polygenic risk score
to discriminate between cases with a severe response to Covid-19
infection, versus controls without a severe response.

Number
of SNPs
in the			Z-			Area
polygenic	Odds	Standard	sta-		95% Conf.	of
risk score	Ratio	Error	tistic	P > z	interval	ROC

108	1.57	0.109307	6.51	0.00	1.371796-1.801599	0.66

TABLE 11

Performance characteristics of a 58 SNP polygenic risk score
to discriminate between cases with a severe response to Covid-19
infection, versus controls without a severe response.

Number
of SNPs
in the			Z-			Area
polygenic	Odds	Standard	sta-		95% Conf.	of
risk score	Ratio	Error	tistic	P > z	interval	ROC

58	5.49	0.6401827	14.61	0.00	4.369035-6.900403	0.87

Example 5—Combined Genetic and Clinical Risk Assessment—64 Polymorphism Panel

Data and Eligibility

The inventors extracted COVID-19 testing and hospital records from the UK Biobank COVID-19 data portal on 15 Sep. 2020. At the time of data extraction, primary care data was only available for just over half of the identified participants and was therefore not used in these analyses.
Eligible participants were those who had tested positive for COVID-19 and for whom SNP genotyping data and linked hospital records were available. Of the 18,221 participants with COVID-19 test results, 1,713 had tested positive and 1,582 of those had both SNP and hospital data available.

COVID-19 Severity

The inventors used source of test result as a proxy for severity of disease: outpatient representing non-severe disease and inpatient representing severe disease. For participants with multiple test results, the disease was considered to be severe if at least one result came from an inpatient setting.

Selection of SNPs for Risk of Severe COVID-19

The inventors identified 62 SNPs from the publicly available (release 2) results of the meta-analysis of non-hospitalised versus hospitalised cases of COVID-19 conducted by the COVID-19 Host Genetics Initiative consortium (COVID-19 Host Genetics Initiative (2020) and COVID-19 Host Genetics Initiative: results. 2020 accessed May 13, 2020, at www.covid19hg.org/results). ( ). P<0.0001 was used as the threshold for loci selection and variants that were associated with hospitalisation in only one of the five studies included in the meta-analysis were removed. Variants that had a minor allele frequency of <0.01 and beta coefficients from −1 to 1 were then discarded (Dayem et al., 2018). Linkage disequilibrium pruning was performed using an r²threshold of 0.5 against the 1000 Genomes European populations (CEU, TSI, FIN, GBR, IBS) representing the ethnicities of the submitted populations (Machiela et al., 2015). Where possible, SNP variants were chosen over insertion—deletion variants to facilitate laboratory validation testing.
The two lead SNPs from the loci found by Ellinghaus et al. (2020) that reached genome-wide significance were also included. Therefore, a panel of 64 SNPs for severe COVID-19 was used.

Genetic Risk Score

For the SNPs identified from the COVID-19 Host Genetics Initiative, the odds ratios for severe disease ranged from 1.5 to 2.7 (Table 4). While the inventors would normally construct a SNP relative risk score by using published odds ratios and allele frequencies to calculate adjusted risk values (with a population average of 1) for each SNP and then multiplying the risks for each SNP (Mealiffe et al., 2020), the size of the odds ratios for each SNP meant that this approach could result in relative risk SNP scores of several orders of magnitude. Therefore, to construct the SNP score for this study, the inventors calculated the percentage of risk alleles present in the genotyped SNPs for each participant as generally described in WO 2005/086770. More specifically, for each of the 64 SNPs, if the subject was homozygous for the risk allele they were scored as 2, if they were heterozygous for the risk allele they were scored as 1, and if they we homozygous for the risk allele they were scored as 0. The total number was then converted to a percentage for use in determining risk.
Percentage rather than a count was used because some of the eligible participants had missing data for some SNPs (9% had all SNPs genotyped, 82% were missing 1-5 SNPs and 9% were missing 6-15 SNPs).

Imputation of ABO Genotype

Blood type was imputed for genotyped UK Biobank participants using three SNPs (rs505922, rs8176719 and rs8176746) in the ABO gene on chromosome 9q34.2. A rs8176719 deletion (or for those with no result for rs8176719, a T allele at rs505922) was considered to indicate haplotype O. At rs8176746, haplotype A was indicated by the presence of the G allele and haplotype B was indicated by the presence of the T allele (Melzer et al., 2008; Wolpin et al., 2010).

Clinical Risk Factors

Risk factors for severe COVID-19 were identified from large epidemiological studies of electronic health records (Williamson et al., 2020; Petrilli et al., 2020) and advice posted on the Centers for Disease Control and Prevention website. Rare monogenic diseases (thalassemia, cystic fibrosis and sickle cell disease) were not considered in these analyses.
Age was classified as 50-59 years, 60-69 year and 70+ years. This was based on the participants' approximate age at the peak of the first wave of infections (April 2020) and was calculated using the participants' month and year of birth. Self-reported ethnicity was classified as white and other (including unknown). The Townsend deprivation score at baseline was classified into quintiles defined by the distribution of the score in the UK Biobank as a whole. Body mass index and smoking status were also obtained from the baseline assessment data. Body mass index was inverse transformed and then rescaled by multiplying by 10. Smoking status was defined as current versus past, never or unknown. The other clinical risk factors were extracted from hospital records by selecting records with ICD9 or ICD10 codes for the disease of interest.

Statistical Methods

Logistic regression was used to examine the association of risk factors with severity of COVID-19 disease. To develop the final model, the inventors began with a base model that included SNP score, age group and gender. They then included all of the candidate variables and used step-wise backwards selection to remove variables with p-values of >0.05. The final model was refined by considering the addition of the removed candidate variables one at a time. Model selection was informed by examination of the Akaike information criterion and the Bayesian information criterion, with a decrease of >2 indicating a statistically significant improvement.
Model calibration was assessed using the Pearson-Windmeijer goodness-of-fit test and model discrimination was measured using the area under the receiver operating characteristic curve (AUC). To compare the effect sizes of the variables in the final model, the inventors used the odds per adjusted standard deviation (Hopper, 2015) using dummy variables for age group and ABO blood type. The intercept and beta coefficients from the final model were used to calculate the COVID-19 risk score for all UK Biobank participants.
Stata (version 16.1) (StataCorp LLC: College Station, Tex., USA) was used for analyses; all statistical tests were two-sided, and p-values of less than 0.05 were considered nominally statistically significant.

Results

Of the 1,582 UK Biobank participants with a positive SARS-CoV-2 test result and hospital and SNP data available, 564 (35.7%) were from an outpatient setting and considered not to have severe disease (controls), while 1,018 (64.4%) were from an inpatient setting and considered to have severe disease (cases). Cases ranged in age from 51 to 82 years with a mean of 69.1 (standard deviation [SD]=8.8) years. Controls ranged in age from 50 to 82 years with a mean of 65.0 (SD=9.0) years. Mean body mass index was 29.0 kg/m2 (SD=5.4) for cases and 28.5 (SD=5.4) for controls. Body mass index was transformed to the inverse multiplied by 10 for all analyses and ranged from 0.2 to 0.6 for both cases and controls. The percentage of risk alleles in the SNP score ranged from 47.6 to 73.8 for cases and from 43.7 to 72.5 for controls. The distributions of the variables of interest for cases and controls and the unadjusted odd ratios and 95% confidence intervals (CI) are shown in Table 12.
The model selected included SNP score, age group, gender, ethnicity, ABO blood type, and a history of autoimmune disease (rheumatoid arthritis, lupus or psoriasis), haematological cancer, non-haematological cancer, diabetes, hypertension or respiratory disease (excluding asthma) and was a good fit to the data (Windmeijer's H=0.02, p=0.9) (Table 13). The SNP score was strongly associated with severity of disease, increasing risk by 19% per percentage increase in risk alleles. A negative impact of age was only evident in the group aged 70 years and over, and while gender was not statistically significant (p=0.3), it was retained because it was one of the three variables considered the base model to which other variables were added. Ethnicity showed a 43% increase in risk for non-whites but was only marginally statistically significant (p=0.06). The AB blood type was protective (p=0.007), but the protective effect of blood type A and the increased risk for blood type B were not statistically significant (p=0.1 and p=0.4, respectively).
The SNP score was, by far, the strongest predictor followed by respiratory disease and age 70 years or older.
The receiver operating characteristic curves for the final model and for alternative models with clinical factors only; SNP score only; and age and gender are shown in FIG. 1. The SNP score alone had an AUC of 0.680 (95% CI=0.652, 0.708). The model with age and gender had an AUC of 0.635 (95% CI=0.607, 0.662), while the model with clinical factors only had an AUC of 0.723 (95% CI=0.698, 0.749). Given that the minimum possible value for an AUC is 0.5, the model with clinical factors only was a 65% improvement over the model with age and gender (χ2=57.97, df=1, p<0.001). The combined model had an AUC of 0.786 (95% CI=0.763, 0.808) and was an 28% improvement over the model with clinical factors only (χ2=39.54, df=1, p<0.001) and a 111% improvement over the model with age and sex (χ2=113.67, df=1, p<0.001).

TABLE 12

Characteristics of cases and controls and unadjusted odds ratios for risk of severe COVID-19.

			Unadjusted	95% confidence
Variable	Cases	Controls	odds ratio	interval	p-value

Continuous variables		Mean (SD)	Mean (SD)

SNP score	% risk	62.1	(4.1)	59.3	(4.7)	1.16	1.13, 1.19	<0.001
	alleles
Inverse of body	10/BMI	0.36	(0.06)	0.36	(0.06)	0.15	0.03, 0.79	0.03
mass index (kg/m²)

Categorical variables		N (%)	N (%)

Age group	50-59	218	(21.4)	210	(37.2)	—
(years)	60-60	210	(20.6)	157	(27.8)	1.29	0.97, 1.71	0.08
	70+	590	(58.0)	197	(34.9)	2.89	2.25, 3.70	<0.001
Gender	Female	443	(43.5)	298	(52.8)	—
	Male	575	(56.5)	266	(47.2)	1.45	1.18, 1.79	<0.001
Ethnicity	White	888	(87.2)	489	(86.7)	—
	Other	123	(12.1)	73	(12.9)	0.93	0.68, 1.26	0.6
	Missing	7	(0.7)	2	(0.4)
Quintile of	1	134	(13.2)	84	(14.9)	—
Townsend	2	165	(16.2)	95	(16.8)	1.09	0.75, 1.58	0.7
deprivation	3	179	(17.6)	98	(17.4)	1.14	0.79, 1.65	0.5
index at baseline	4	215	(21.1)	124	(22.0)	1.09	0.77, 1.54	0.6
	5	325	(31.9)	162	(28.7)	1.26	0.90, 1.75	0.2
	Missing	0	(0.0)	1	(0.2)
ABO blood type	O	425	(41.8)	235	(41.7)	—
	A	450	(44.2)	249	(44.2)	1.00	0.80, 1.25	1.0
	B	113	(11.1)	55	(9.8)	1.14	0.79, 1.63	0.5
	AB	30	(3.0)	25	(4.4)	0.66	0.38, 1.15	0.1
Smoking status	Never/	882	(86.6)	499	(88.5)	—
at baseline	previous
	Current	124	(12.2)	60	(10.6)	1.17	0.84, 1.62	0.3
	Missing	12	(1.2)	5	(0.9)
Asthma	No	852	(83.7)	487	(86.4)	—
	Yes	166	(16.3)	77	(13.7)	1.23	0.92, 1.65	0.2
Autoimmune	No	947	(93.0)	547	(97.0)	—
(rheumatoid/	Yes	71	(7.0)	17	(3.0)	2.41	1.41, 4.14	0.001
lupus/psoriasis)
Cancer -	No	972	(95.5)	558	(98.9)	—
haematological	Yes	46	(4.5)	6	(1.1)	4.40	1.87, 10.37	0.001
Cancer - non-	No	799	(78.5)	486	(86.2)	—
haematological	Yes	219	(21.5)	78	(13.8)	1.71	1.29, 2.26	<0.001
Cerebro-	No	847	(83.2)	503	(89.2)	—
vascular	Yes	171	(16.8)	61	(10.8)	1.66	1.22, 2.28	0.001
disease
Diabetes	No	765	(75.2)	493	(87.4)	—
	Yes	253	(24.9)	71	(12.6)	2.30	1.72, 3.06	<0.001
Heart disease	No	633	(62.2)	437	(77.5)	—
	Yes	385	(37.8)	127	(22.5)	2.09	1.66, 2.65	<0.001
Hypertension	No	419	(41.2)	354	(62.8)	—
	Yes	599	(58.8)	210	(37.2)	2.41	1.95, 2.98	<0.001
Immuno-	No	1,001	(98.3)	560	(99.3)	—
compromised	Yes	17	(1.7)	4	(0.7)	2.38	0.80, 7.10	0.1
Kidney disease	No	859	(84.4)	521	(92.4)	—
	Yes	159	(15.6)	43	(7.6)	2.24	1.57, 3.19	<0.001
Liver disease	No	937	(92.0)	541	(95.9)	—
	Yes	81	(8.0)	23	(4.1)	2.03	1.26, 3.27	0.003
Respiratory	No	571	(56.1)	486	(86.2)	—
disease	Yes	447	(43.9)	78	(13.8)	4.88	3.73, 6.38	<0.001
(excluding
asthma)

TABLE 13

Final model for risk of severe COVID-19 given a positive test.

β

95% confidence

Variable	coefficient	interval	p-value

Model intercept		−10.7657	−12.5559, −8.9755	<0.001
SNP score	% risk	0.1717	0.1429, 0.2006	<0.001
	alleles
Age group	18-29	−1.3111
(years)*	30-39	−0.8348
	40-49	−0.4038
	50-59	—
	60-69	−0.0600	−0.3819, 0.2619	0.7
	70+	0.5325	0.2213, 0.8438	0.001
Gender	Female	—
	Male	0.1387	−0.1005, 0.3779	0.3
Ethnicity	White	—
	Other	0.3542	−0.0084, 0.7167	0.06
ABO blood type	O	—
	A	−0.2164	−0.4726, 0.0397	0.1
	B	0.1712	−0.2348, 0.5773	0.4
	AB	−0.8746	−1.5087, −0.2404	0.007
Autoimmune	No	—
disease	Yes	0.7876	0.1832, 1.3920	0.01
(rheumatoid
arthritis/
lupus/psoriasis)
Cancer -	No	—
haematological	Yes	1.0375	0.0994, 1.9756	0.03
Cancer - non-	No	—
haematological	Yes	0.3667	0.0401, 0.6933	0.03
Diabetes	No	—
	Yes	0.4890	0.1450, 0.8330	0.005
Hypertension	No	—
	Yes	0.3034	0.0313, 0.5756	0.03
Respiratory	No	—
disease	Yes	1.2331	0.9317, 0.1535	<0.001
(excluding
asthma)

*Note:
The β coefficient is the natural log of the odds ratio; estimates for the 18-29, 30-39 and 40-49 age groups are based on information on page 9 of the Centers for Disease Control COVIDView report for 1 Aug. 2020.

FIG. 2 illustrates the difference in the distributions of the COVID-19 risk scores in cases and controls. The median score was 3.35 for cases and 0.90 for controls. Fifteen percent of cases and 53% of controls had COVID-19 risk scores of less than 1, and 18% of cases and 25% of controls had scores ≥1 and <2. COVID-19 risk scores ≥2 were more common in cases than in controls, with 13% of cases and 9% of controls having scores ≥2 and <3, 8% of cases and 4% of controls having scores ≥3 and <4, and 38% of cases and 6% of controls having scores ≥4.
FIG. 3 shows that the distribution of the COVID-19 risk score in the whole UK Biobank is similar to that for the controls in FIG. 2b . The median risk score in the whole UK Biobank was 1.32. Thirty-eight percent of the UK Biobank have COVID-19 risk scores of less than 1, while 29% have scores ≥1 and <2, 13% have scores ≥2 and <3, 6% have scores ≥3 and <2, and 14% have scores of 4 or over.

Example 6—Combined Genetic and Clinical Risk Assessment—7 and 10 Polymorphism Panels

To further improve the method of the invention the inventors downloaded an updated results file on 8 Jan. 2021 from the UK Biobank. Eligible participants were active UK Biobank participants with a positive SARS-CoV-2 test result and who had SNP and hospital data available. Of the 47,990 UK Biobank participants with a SARS-CoV-2 test result available, 8,672 (18.1%) had a positive test result, and of these, 7,621 met the eligibility criteria.
The inventors used source of test result as a proxy for severity of disease, where inpatient results were considered severe disease (cases) and outpatient results were considered non-severe disease (controls). If a participant had more than one test result, they were classified as having severe disease if at least one of their results was from an inpatient setting. Of the 7,621 eligible participants, 2,205 were cases and 5,416 were controls.
The inventors identified a further 40 SNPs from the publicly available (release 4) results of the meta-analysis of non-hospitalised versus hospitalised cases of COVID-19 conducted by the COVID-19 Host Genetics Initiative consortium (COVID-19 Host Genetics Initiative (2020) and COVID-19 Host Genetics Initiative: results. 2020 accessed Jan. 7, 2020, at www.covid19hg.org/results). P<0.0001 was used as the threshold for loci selection and variants that were associated with hospitalisation in only one of the five studies included in the meta-analysis were removed. Variants that had a minor allele frequency of <0.01 and beta coefficients from −1 to 1 were then discarded (Dayem et al., 2018). Linkage disequilibrium pruning was performed using an r2 threshold of 0.5 against the 1000 Genomes European populations (CEU, TSI, FIN, GBR, IBS) representing the ethnicities of the submitted populations (Machiela et al., 2015). Where possible, SNP variants were chosen over insertion—deletion variants to facilitate laboratory validation testing. A further 12 SNPs were identified from publicly available meta-analysis of Covid-19 data (Pairo-Castineira et al., 2020).
The above identified SNPs were combined with the 64 identified in our original study to provide a test SNP panel of 116 SNPs.
To develop a new model to predict risk of severe COVID-19, the inventors used all of the available data and randomly divided it into a 70% training dataset and a 30% validation dataset (ensuring that it was balanced for origin of test result). Because the missing data is assumed to be missing at random (if not missing completely at random), a multiple imputation with 20 imputations was used to address the missing data for body mass index (linear regression) and the SNP data (predictive mean matching) for the development of the new model in the training dataset. To more closely reflect the availability of data in the real world, the inventors did not use imputed data in the validation dataset.
The clinical variables considered for inclusion in the new model were age, sex, BMI, ethnicity, ABO blood type and the following chronic health conditions: asthma, autoimmune disease (rheumatoid arthritis, lupus or psoriasis), haematological cancer, non-haematological cancer, cerebrovascular disease, diabetes, heart disease, hypertension, immunocompromised, kidney disease, liver disease and respiratory disease (excluding asthma). Dummy variables were used for the categorical classifications of age and ABO blood type.
The SNPs selected for the development of the new model came from three sources: (i) from Tables 2 to 4, (ii) the 40 SNPs newly selected from the (release 4) results of the COVID-19 Host Genetics Initiative meta-analysis of non-hospitalised versus hospitalised cases of COVID-191 2 and (iii) the 12 SNPs from the paper by Pairo-Castineira et al. (2020). The inventors used unadjusted logistic regression in the testing dataset to identify SNPS that were associated with risk of severe COVID-19 with P<0.05 (see Table 14).
Stata (version 16.1) was used for analyses; all statistical tests were two-sided and P<0.05 was considered nominally statistically significant.

TABLE 14

Informative polymorphisms assessed in Example 6.

		Position	Reference		Effect
Chr	SNP	(GRCh37)	Allele	Frequency	Allele	Frequency	OR	95% CI	P

1	rs10873821	87628173	C	0.75	T	0.25	0.92	0.84, 1.02	0.10
1	rs112317747	239197542	T	0.97	C	0.03	1.26	1.00, 1.58	0.05
1	rs115492982	150271556	G	1.00	A	0.00	2.46	1.23, 4.91	0.01
1	rs12083278	31624029	G	0.29	C	0.71	1.04	0.95, 1.15	0.40
1	rs12745140	2998313	G	0.91	A	0.09	0.90	0.77, 1.06	0.20
1	rs17102023	46618634	A	1.00	G	0.00	1.33	0.63, 2.81	0.50
1	rs2224986	152684866	C	0.91	T	0.09	0.98	0.85, 1.14	0.80
1	rs2274122	36549664	G	0.20	A	0.80	0.97	0.88, 1.07	0.50
1	rs2765013	36374101	C	0.91	T	0.09	1.01	0.96, 1.26	0.20
1	rs74508649	192526317	C	1.00	T	0.00	1.04	0.47, 2.32	0.90
2	rs183569214	79895332	G	1.00	A	0.00	0.72	0.15, 3.45	0.70
2	rs2034831	182353446	A	0.94	C	0.06	1.22	1.03, 1.46	0.02
2	rs2270360	217524986	A	0.74	C	0.26	0.94	0.85, 1.04	0.20
2	rs6714112	36905013	C	0.86	A	0.14	1.02	0.90, 1.16	0.70
2	rs77764981	80029580	T	1.00	C	0.00	1.29	0.54, 3.10	0.60
3	rs10510749	46180416	C	0.91	T	0.09	0.99	0.85, 1.15	0.90
3	rs11385942	45876459	G	0.92	GA	0.08	1.16	1.00, 1.34	0.05
3	rs115102354	46222037	A	0.95	G	0.05	0.96	0.79, 1.16	0.70
3	rs12639224	45916222	C	0.73	T	0.27	1.02	0.93, 1.12	0.70
3	rs13062942	62936766	A	0.64	G	0.36	0.92	0.84, 1.01	0.09
3	rs13433997	46049765	T	0.88	C	0.12	1.10	0.97, 1.24	0.10
3	rs1504061	1093795	C	0.95	G	0.05	1.13	0.94, 1.36	0.20
3	rs1705826	3184653	C	0.63	G	0.37	1.03	0.94, 1.12	0.50
3	rs17317135	27188298	G	0.95	A	0.05	0.89	0.73, 1.09	0.30
3	rs1868132	125837737	C	0.90	T	0.10	1.02	0.89, 1.17	0.80
3	rs34901975	45916786	G	0.89	A	0.11	1.12	0.98, 1.27	0.09
3	rs35652899	45908514	C	0.93	G	0.07	1.17	1.00, 1.36	0.04
3	rs35896106	45841938	C	0.92	T	0.08	1.17	1.01, 1.35	0.04
3	rs6440031	141408691	A	0.08	G	0.92	0.94	0.80, 1.11	0.50
3	rs71325088	45862952	T	0.92	C	0.08	1.15	0.99, 1.33	0.07
3	rs71615437	46018781	A	0.92	G	0.08	1.12	0.97, 1.29	0.10
3	rs73064425	45901089	C	0.92	T	0.08	1.15	0.99, 1.33	0.07
3	rs76374459	45900634	G	0.94	C	0.06	1.20	1.02, 1.41	0.03
3	rs76488148	148718087	G	0.96	T	0.04	1.25	1.02, 1.52	0.03
4	rs112641600	112613026	C	0.89	T	0.11	0.83	0.72, 0.96	0.01
4	rs115162070	69705994	G	0.93	A	0.07	0.90	0.75, 1.07	0.20
4	rs11729561	106943200	T	0.92	C	0.08	0.96	0.82, 1.12	0.60
4	rs35540967	44418592	T	0.93	C	0.07	1.02	0.87, 1.19	0.80
4	rs3774881	5821877	T	0.84	C	0.16	0.91	0.81, 1.02	0.10
4	rs3774882	5821922	C	0.92	G	0.08	0.90	0.77, 1.06	0.20
4	rs6810404	27383278	C	0.51	A	0.49	0.97	0.89, 1.05	0.50
5	rs10039856	142252549	C	0.90	T	0.10	1.10	0.96, 1.26	0.20
5	rs111265173	171480160	C	1.00	T	0.00	0.97	0.35, 2.66	1.00
5	rs113791144	180237828	C	0.93	T	0.07	0.97	0.82, 1.15	0.70
5	rs2220543	173989338	T	0.71	A	0.29	1.04	0.94, 1.14	0.50
5	rs4240376	123950404	G	0.80	T	0.20	0.99	0.89, 1.10	0.80
5	rs4478338	169590905	T	0.92	G	0.08	1.08	0.93, 1.25	0.30
5	rs62377777	122832716	T	0.79	C	0.21	0.96	0.87, 1.07	0.50
6	rs10755709	12216966	A	0.70	G	0.30	1.11	1.01, 1.21	0.03
6	rs140247774	18015447	C	0.93	T	0.07	0.93	0.78, 1.10	0.40
6	rs143334143	31121426	G	0.93	A	0.07	1.00	0.85, 1.18	1.00
6	rs16873740	45704813	T	0.88	A	0.12	1.16	1.03, 1.32	0.02
6	rs3131294	32180146	A	0.13	G	0.87	1.00	0.88, 1.13	1.00
6	rs61611950	27604726	C	0.99	T	0.01	0.92	0.56, 1.51	0.80
6	rs6933436	6925195	A	0.71	C	0.29	1.01	0.92, 1.11	0.90
6	rs9380142	29798794	G	0.30	A	0.70	1.08	0.99, 1.19	0.09
6	rs9386484	106326754	T	0.76	A	0.24	0.95	0.85, 1.05	0.30
7	rs6967210	152960930	T	0.99	C	0.01	1.17	0.86, 1.59	0.30
8	rs10808999	38821327	A	0.13	G	0.87	1.01	0.89, 1.14	0.90
8	rs11779911	40181978	C	0.67	A	0.33	0.99	0.90, 1.09	0.90
8	rs118072448	16790149	T	0.92	C	0.08	0.82	0.70, 0.97	0.02
8	rs13282163	38897470	A	0.92	C	0.08	0.93	0.80, 1.09	0.40
8	rs2010843	74268198	T	0.47	C	0.53	1.04	0.96, 1.13	0.40
8	rs332040	8730488	G	0.53	A	0.47	1.00	0.92, 1.09	0.90
9	rs12236000	21131627	G	0.92	C	0.08	0.95	0.81, 1.11	0.50
9	rs3895472	4329170	T	0.08	C	0.92	1.04	0.88, 1.22	0.70
9	rs657152	136139265	C	0.63	A	0.37	0.95	0.87, 1.03	0.20
9	rs7027911	81158113	G	0.57	A	0.43	1.11	1.01, 1.21	0.02
9	rs71480372	27121456	A	0.66	T	0.34	0.98	0.90, 1.08	0.70
9	rs74790577	29688719	A	1.00	T	0.00	1.05	0.27, 4.03	0.90
10	rs10793436	44015051	G	0.68	T	0.32	0.95	0.86, 1.04	0.20
10	rs1441121	54100345	T	0.57	A	0.43	0.95	0.87, 1.03	0.20
10	rs1892429	37454397	A	0.84	G	0.16	0.99	0.88, 1.11	0.80
10	rs2091431	37277870	A	0.28	G	0.72	1.03	0.94, 1.14	0.50
10	rs5016035	123000638	T	0.51	G	0.49	1.00	0.91, 1.10	0.90
10	rs71481792	9030308	A	0.38	T	0.62	0.89	0.82, 0.97	0.01
11	rs10766439	2893867	A	0.37	G	0.63	0.97	0.89, 1.05	0.40
12	rs10735079	113380008	G	0.36	A	0.64	0.98	0.90, 1.07	0.60
12	rs11613792	8760610	A	0.85	G	0.15	1.01	0.88, 1.14	0.90
12	rs12823094	106624953	T	0.76	A	0.24	1.08	0.98, 1.19	0.10
12	rs6489867	113363550	C	0.36	T	0.64	0.98	0.90, 1.07	0.70
12	rs7397549	56084466	T	0.59	C	0.41	0.99	0.90, 1.09	0.90
13	rs12871414	74558505	C	0.74	T	0.26	0.95	0.86, 1.05	0.30
13	rs1984162	23658838	A	0.75	G	0.25	1.10	1.00, 1.21	0.05
13	rs2649134	63178476	C	0.97	T	0.03	0.93	0.72, 1.19	0.50
14	rs12587980	72934229	C	0.63	T	0.37	1.03	0.95, 1.13	0.40
14	rs144114696	77692036	G	1.00	A	0.00	2.53	0.51, 12.44	0.30
14	rs2238187	72908102	A	0.65	G	0.35	1.07	0.97, 1.17	0.20
15	rs12593288	33908103	C	0.80	T	0.20	0.91	0.82, 1.01	0.08
15	rs2229117	33916053	G	0.86	C	0.14	0.90	0.80, 1.02	0.10
15	rs74750712	48984345	T	1.00	G	0.00	1.33	0.65, 2.69	0.40
15	rs77055952	45858905	A	0.95	G	0.05	1.07	0.88, 1.29	0.50
16	rs145643452	49311043	G	0.99	A	0.01	1.03	0.61, 1.74	0.90
16	rs72779789	10579876	G	0.95	C	0.05	1.04	0.85, 1.26	0.70
16	rs72803978	78624025	A	0.94	G	0.06	0.88	0.74, 1.05	0.20
17	rs178840	29737612	G	0.75	A	0.25	0.93	0.84, 1.03	0.20
17	rs34761447	9170408	C	0.90	T	0.10	1.02	0.89, 1.18	0.80
17	rs9890316	80443309	G	0.69	A	0.31	1.01	0.92, 1.10	0.90
18	rs12958013	67208392	T	0.86	C	0.14	1.08	0.96, 1.22	0.20
18	rs142257532	30006171	T	0.97	C	0.03	1.01	0.78, 1.30	1.00
19	rs10411226	53333975	G	0.25	A	0.75	1.04	0.94, 1.16	0.40
19	rs11085727	10466123	C	0.72	T	0.28	1.06	0.96, 1.16	0.20
19	rs2109069	4719443	G	0.68	A	0.32	1.01	0.92, 1.10	0.80
19	rs60744406	44492164	A	0.41	G	0.59	1.02	0.94, 1.11	0.60
19	rs74956615	10427721	T	0.95	A	0.05	1.05	0.87, 1.27	0.60
19	rs8105499	32023957	C	0.70	A	0.30	0.98	0.90, 1.08	0.70
20	rs56259900	39389409	A	1.00	G	0.00	1.15	0.65, 2.04	0.60
20	rs76253189	60473717	C	0.99	G	0.01	1.01	0.72, 1.42	1.00
21	rs13050728	34615210	C	0.68	T	0.32	1.08	0.99, 1.18	0.10
21	rs2236757	34624917	G	0.70	A	0.30	1.06	0.97, 1.16	0.20
21	rs2252109	43080428	A	0.48	T	0.52	0.98	0.90, 1.07	0.70
21	rs75994231	44424444	C	0.98	T	0.02	1.06	0.79, 1.43	0.70
22	rs11090305	24407483	T	0.80	C	0.20	1.06	0.96, 1.18	0.20
22	rs5757427	22564734	T	0.65	A	0.35	0.96	0.88, 1.05	0.40
22	rs62220604	49677464	G	0.73	A	0.27	0.97	0.88, 1.07	0.50
22	rs7290963	22724951	G	0.55	T	0.45	1.00	0.92, 1.09	1.00

Development of New Model

The inventors used multivariable logistic regression in the multiple imputation training dataset to develop the new model to predict risk of severe COVID-19. The inventors began with a model that included all the clinical variables and the SNPs with unadjusted associations with severe COVID-19 and used backwards stepwise selection to develop the most parsimonious model. For the removed variables a final determination was made on their inclusion or exclusion by adding them one at a time to the parsimonious model. To directly compare the effect sizes of the variables in the final model, regardless of the scale on which they were measured, the odds per adjusted standard deviation was used. The intercept and beta coefficients from the new model to calculate the COVID-19 risk score was used for all eligible UK Biobank participants.

Model Performance

The inventors assessed the performance of the new model in the imputed development dataset and in the non-imputed validation dataset. The association between the risk score and severe COVID-19 was assessed using logistic regression to estimate the odds ratio per quintile of risk score. It was assessed model discrimination using the area under the receiver operating characteristic curve (AUC). For models that showed good discrimination, calibration was assessed using logistic regression of the log of the risk score to estimate the intercept and the slope (beta coefficient). An intercept close to 0 indicated good calibration, while an intercept less than 0 indicated overall overestimation of risk and an intercept greater than 0 indicated overall underestimation of risk. A slope of close to 1 indicated good dispersion with a slope of less than 1 indicating over-dispersion and slope of greater than 1 indicating under-dispersion.
The best performing tests are detailed below.

Risk Models

Three models were developed for assessing the risk of a human subject developing a severe response to a Coronavirus infection. In particular, the methods can be used to determine the probability the subject would require hospitalisation if infected with a Coronavirus. The first model is based solely on sex and age (referred to herein as the “age and sex model”), the second model (referred to herein as the “full model”) includes numerous clinical factors and genetic factors, whereas the third model (referred to herein as the “expanded model”) includes additional clinical factors and genetic factors to those in the full model.

Age and Sex Model

Inputs of the age and sex model are provided in Table 15 and the β-coefficients provided in Table 16.

TABLE 15

Age and Sex Model Product Inputs

Clinical
Risk Factor	Input	Acceptance	TRF Question

Age (years)	Value	50-84	What is your age?
Gender	Male	Male	What is your gender?
	Female	Female

TABLE 16

Age and Sex Model Risk Factors

	Variable	Value	β coefficient

Age group	50-64	0
(years)	65-69	0.4694892
	70-74	1.006561
	75-79	1.435318
	80-84	1.599188
Gender	Female		0
	Male	0.3911169

The long odds is calculated using: Log odds (LO)=−1.749562+Σ Clinical β coefficients.
The age and sex relative risk=e^LO.
Age and sex probability=e^LO/(1+e^LO).
If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

Full Model

Inputs of the full model are provided in Table 17 and the β-coefficients provided in Tables 18 and 19.

TABLE 17

Full Model Product Inputs

Clinical
Risk Factor	Input	Acceptance	TRF Question

Age (years)	Value	50-84	What is your age?
Gender	Male	Male	What is your gender?
	Female	Female
Ethnicity	Caucasian	All	What is your ethnicity?
	Non-
	Caucasian
	Unknown
Height (m)	(m)	All	What is your height?
	Unknown
Weight (kg)	(kg)	All	What is your weight?
	Unknown
Cerebro-	No	All	Have you ever been diagnosed
vascular	Yes		with cerebrovascular disease?
disease	Unknown
Chronic kidney	No	All	Have you ever been diagnosed
disease	Yes		with chronic kidney disease?
	Unknown
Diabetes	No	All	Have you ever been diagnosed
	Yes		with any type of diabetes?
	Unknown
Haematological	No	All	Have you ever been diagnosed
cancer	Yes		with haematological cancer?
	Unknown
Hypertension	No	All	Have you ever been diagnosed
	Yes		with hypertension?
	Unknown
Non-	No	All	Have you ever been diagnosed
haematological	Yes		with another type of cancer?
cancer	Unknown
Respiratory	No	All	Have you ever been diagnosed
disease	Yes		with a respiratory disease
(excluding	Unknown		(excluding asthma)?
asthma)

TABLE 18

Full Model Clinical Risk Factors

Variable	Value	β coefficient

Age group (years)	50-59	0
	70-74	0.5747727
	75-79	0.8243711
	80-84	1.013973
Gender	Female		0
	Male	0.2444891
Ethnicity	Caucasian	0
	Other/Unknown	0.29311
Height (m)
Weight (kg)

$10 \times inverse BMI = \frac{10 \times m^{2}}{kg}$	$\frac{10 \times m^{2}}{kg}$	−1.602056

Cerebrovascular disease	No		0
	Yes	0.4041337
Chronic kidney disease	No		0
	Yes	0.6938494
Diabetes	No		0
	Yes	0.4297612
Haematological cancer	No		0
	Yes	1.003877
Hypertension	No		0
	Yes	0.2922307
Non-haematological cancer	No		0
	Yes	0.2558464
Respiratory disease	No	0
(excluding asthma)	Yes	1.173753

TABLE 19

Full Model SNP Risk Alleles

SNPs	Risk Allele	No of Risk Alleles	β coefficient

rs10755709

G

	0, 1, or 2	0.124239
rs112317747	C		0, 1, or 2	0.2737487
rs112641600	T		0, 1, or 2	−0.2362513
rs118072448	C		0, 1, or 2	−0.1995879
rs2034831	C		0, 1, or 2	0.2371955
rs7027911	A		0, 1, or 2	0.1019074
rs71481792	T		0, 1, or 2	−0.1058025

The SNP risk factor (SRF) is determined using: (SRF)=Σ (No of Risk Alleles×SNP β coefficient).
The long odds is calculated using: Log odds (LO)=−1.36523+SRF+Σ Clinical β coefficients.
The age and sex relative risk=e^LO.
Age and sex probability=e^LO/(1+e^LO).
If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

Expanded Model

Inputs of the expanded model are provided in Table 20 and the β-coefficients provided in Tables 21 and 22.

TABLE 20

Expanded Model Product Inputs

Clinical
Risk Factor	Input	Acceptance	TRF Question

Age (years)	Value	50-84	What is your age?
Gender	Male	Male	What is your gender?
	Female	Female
Ethnicity	Caucasian	All	What is your ethnicity?
	Non-
	Caucasian
	Unknown
Blood Type	O	All	What is your blood type?
	A
	B
	AB
	Unknown
Height (m)	(m)	All	What is your height?
	Unknown
Weight (kg)	(kg)	All	What is your weight?
	Unknown
Cerebro-	No	All	Have you ever been diagnosed
vascular	Yes		with cerebrovascular disease?
disease	Unknown
Chronic kidney	No	All	Have you ever been diagnosed
disease	Yes		with chronic kidney disease?
	Unknown
Diabetes	No	All	Have you ever been diagnosed
	Yes		with any type of diabetes?
	Unknown
Haematological	No	All	Have you ever been diagnosed
cancer	Yes		with haematological cancer?
	Unknown
Hypertension	No	All	Have you ever been diagnosed
	Yes		with hypertension?
	Unknown
Immuno-	No	All	Have you ever been diagnosed
compromised	Yes		with an immuno compromised
disease	Unknown		disease?
Liver disease	No	All	Have you ever been diagnosed
	Yes		with a liver disease?
	Unknown
Non-	No	All	Have you ever been diagnosed
haematological	Yes		with another type of cancer?
cancer	Unknown
Respiratory	No	All	Have you ever been diagnosed
disease	Yes		with a respiratory disease
(excluding	Unknown		(excluding asthma)?
asthma)

TABLE 21

Exapnded Model Clinical and SNP Risk Factors

	Variable	Value	β coefficient

Age group (years)	50-64	0
	65-69	0.1677566
	70-74	0.6352682
	75-79	0.8940548
	80-84	1.082477
Gender	Female		0
	Male	0.2418454
Ethnicity	Caucasian	0
	Other/Unknown	0.2967777
Blood Type	O	0
	A	0
	B	0
	AB	−0.229737
Height (m)
Weight (kg)

$10 \times inverse BMI = \frac{10 \times m^{2}}{kg}$	$\frac{10 \times m^{2}}{kg}$	−1.560943

Cerebrovascular disease	No		0
	Yes	0.3950113
Chronic kidney disease	No		0
	Yes	0.6650257
Diabetes	No		0
	Yes	0.4126633
Haematological cancer	No		0
	Yes	1.001079
Hypertension	No		0
	Yes	0.2640989
Immunocompromised disease	No		0
	Yes	0.6033541
Liver disease	No		0
	Yes	0.2301902
Non-haematological cancer	No		0
	Yes	0.2381579
Respiratory disease	No	0
(excluding asthma)	Yes	1.148496

TABLE 22

Expanded Model SNP Risk Alleles

SNPs	Risk Allele	No of Risk Alleles	β coefficient

rs10755709

G

	0, 1, or 2	0.1231766
rs112317747	C		0, 1, or 2	0.2576692
rs112641600	T		0, 1, or 2	−0.2384001
rs115492982	A		0, 1, or 2	0.4163575
rs118072448	C		0, 1, or 2	−0.1965609
rs1984162	A		0, 1, or 2	0.1034362
rs2034831	C		0, 1, or 2	0.2414792
rs7027911	A		0, 1, or 2	0.0998459
rs71481792	T		0, 1, or 2	−0.1032044

The SNP risk factor (SRF) is determined using: (SRF)=Σ (No of Risk Alleles×SNP β coefficient).
The long odds is calculated using: Log odds (LO)=−1.469939+SRF+Σ Clinical β coefficients.
The age and sex relative risk=e^LO.
Age and sex probability=e^LO/(1+e^LO).
If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

SUMMARY

In terms of discrimination between cases and controls, the age and sex model had an AUC of 0.671 (95% CI=0.646, 0.696) but the full model with an AUC of 0.732 (95% CI=0.708, 0.756) was a substantial improvement (χ2=41.23, df=1, P<0.001). The receiver operating characteristic curves for both models are shown in FIG. 4.
The models were well calibrated with no evidence of overall overestimation or underestimation for the age and sex model (α=−0.02; 95% CI=−0.18, 0.13; P=0.7) or the full model (α=−0.08; 95% CI=−0.21, 0.05; P=0.3). There was also no evidence of under or over dispersion for the age and sex model (β=0.96, 95% CI=0.81, 1.10, P=0.6) and for the full model (β=0.90, 95% CI=0.80, 1.00, P=0.06). Calibration plots for both models are shown in FIG. 5.
The inventors calculated the probability of severe COVID-19 for all UK Biobank participants who met our eligibility criteria for this study; the distributions are shown in FIG. 6. Using the age and sex model, the mean probability was 0.32 (SD=0.13) and ranged from a minimum of 0.15 to a maximum of 0.56. Using the full model, the mean probability was 0.27 (SD=0.16) and the range was from 0.04 to 0.98, a much wider range than for the age and sex model.
The expanded model provided a slight improvement in discrimination in this dataset (Table 23).

TABLE 23

Test Performances.

	Model	AUC	95% Confidence interval

Age and Sex	0.6755	0.65948-0.69160
Full Model	0.7512	0.73653-0.76673
Expanded Model	0.7524	0.73730-0.76749

Example 7—Combined Genetic and Clinical Risk Assessment—Under 50 Years of Age

The algorithm to calculate the risk of developing severe Covid-19 has been modified to enable a risk calculation to be provided for patients aged 18-85 years (previously 50-85 years). More specifically, the look-up tables providing the age-related risk values have been modified to include three additional values for the following age ranges: 18-29, 30-39, 40-49 (Tables 24).
For people aged under 50 years, the probability of severe disease is adjusted using data on risk of hospitalization due to Covid-19 which were obtained from the United States Centers for Disease Control and Prevention (www.cdc.gov).
The SNPs analysed, and the methods used for analysis, are the same as used in Example 6.

TABLE 24

Test Performances.

Variable	Value	β coefficient

Age group (years)	18-29	−1.3111
	30-39	−0.8348
	40-49	−0.4038
	50-69	0
	70-74	0.5747727
	75-79	0.8243711
	80-84	1.013973
Gender	Female		0
	Male	0.2444891
Ethnicity	Caucasian	0
	Other/Unknown	0.29311
Height (m)	Value	Used in in BMI calculation
Weight (kg)	Value	Used in in BMI calculation

$10 \times inverse BMI = \frac{10 \times m^{2}}{kg}$	$\frac{10 \times m^{2}}{kg}$	−1.602056

Cerebrovascular disease	No		0
	Yes	0.4041337
Chronic kidney disease	No		0
	Yes	0.6938494
Diabetes	No		0
	Yes	0.4297612
Haematological cancer	No		0
	Yes	1.003877
Hypertension	No		0
	Yes	0.2922307
Non-haematological cancer	No		0
	Yes	0.2558464
Respiratory disease	No	0
(excluding asthma)	Yes	1.173753

The present application claims priority from AU 2020901739 filed 27 May 2020, AU 2020902052 filed 19 Jun. 2020, AU 2020903536 filed 30 Sep. 2020, and AU 2021900392 filed 17 Feb. 2021, the entire contents of each of which are incorporated herein by reference.
It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.
All publications discussed and/or referenced herein are incorporated herein in their entirety.
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

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Claims

1-12. (canceled)

13. A method for determining the probability a human subject will develop a severe response to a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the method comprising:

i) performing a clinical risk assessment of the human subject wherein the clinical risk assessment comprises:

(a) obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma); and

(b) assigning a clinical β coefficient based on each piece of information obtained from the subject in step (i)(a);

ii) determining the Log Odds (LO) using the following formula:

LO=X+Σ Clinical β coefficients

wherein X is −1.8 to −0.8;

iii) determining the probability the subject will develop a severe response to a SARS-CoV-2 infection using the following formula:

e ^LO/(1+e ^LO), which is then multiplied by 100.

14. The method of claim 13, wherein in step (ii) X is −1.36523.

15. The method of claim 13, wherein the subject is between 18 and 84 years of age and in the clinical risk assessment

a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age;

b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age;

c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age;

d) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age;

e) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age;

f) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age;

g) a β coefficient of 0.2444891 is assigned if the subject is male;

h) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;

i) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m2) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;

j) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;

k) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;

l) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;

m) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;

n) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;

o) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and

p) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).

16. The method of claim 14, wherein the subject is between 18 and 84 years of age and in the clinical risk assessment

g) a β coefficient of 0.2444891 is assigned if the subject is male;

17. The method of claim 13, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.

18. The method of claim 14, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.

19. The method of claim 15, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.