KR20190077997A - Method for disease and phenotype risk score calculation - Google Patents

Abstract

The present invention relates to an apparatus and method for predicting disease and phenotypic risk, which can increase accuracy of prediction of disease and phenotypic risk by appropriately providing a weight for each marker to a group to be predicted with respect to genetic risk and predicting the genetic risk for various disease and phenotypic genetic markers developed for races and ethnicities around the world. Further, to increase accuracy of prediction of non-disease phenotype through genetic testing, the accuracy of prediction of the disease and phenotypic risk can be improved by distinguishing and simultaneously considering increase and decrease effects influencing phenotype and predicting the genetic risk. Further, environmental factors affecting the occurrence of a disease are collected through clinical research without being measured through medical equipment, and the collected environmental factors are integrated with genetic factors to predict the genetic risk, thereby increasing the accuracy of prediction of the disease and phenotypic risk.

Description

[0001] METHOD FOR DISEASE AND PHENOTYPE RISK SCORE CALCULATION [0002]

The present invention relates to an apparatus and method for predicting diseases and phenotypic risk, and in predicting the occurrence of diseases and phenotypes using previously reported disease and phenotype related gene markers, 1) , 2) the increase / decrease effect of genetic markers on phenotype, and 3) an apparatus and method for predicting diseases and phenotypic risk predictions of diseases and phenotypes in consideration of environmental factors.

Genome-wide association studies (GWAS, etc.) have revealed various disease-related or phenotypic mutation mutation markers. For example, a variety of disease-related or phenotypic mutation mutation markers have been identified by genome-wide association studies (GWAS, etc.) of mutations of BRCA1 and BRCA2 genes ought. For example, mutations in the BRCA1 and BRCA2 genes have been reported to increase the likelihood of developing breast cancer and other diseases.

Therefore, various methods for predicting the risk of developing a disease or phenotype using a variety of genetic markers reported through existing studies have been reported. Domestic and foreign genetic testing agencies have also used genetic markers It checks the risk.

Genetic risk prediction methods currently in use include genetic markers that have been reported statistically significant in relation to a specific disease and are used to decode or genotype the gene location of the sample under test to identify several risk alleles And the method of calculating the risk by weighting the risk factors by the effective size reported by each genetic marker.

However, since the genome sequences possessed by various races and populations are population stratification, genetic marker mutations affecting diseases and phenotypes also have different mutation rates by race and population. Therefore, there is a problem that the valid gene markers of each group are different, for example, a specific gene marker has a statistically significant meaning only in a specific group.

In addition, genetic markers related to diseases are mostly identified with genetic markers that increase disease incidence and significance. However, in case of non-disease phenotype (intelligence, height, weight, blood cholesterol concentration, etc.) And genetic markers have been developed. For example, Chan Y et al . Am. J. Hum. Genet. In the 2015 paper, we developed a genetic marker with significance for the sitting height ratio, which was developed at the same time as a genetic marker associated with a large sitting key and a genetic marker associated with a small sitting key. To predict non-disease phenotypes in view of this increase / decrease effect, the commonly used method is to predict the genetic risk by one-way reference, using only the marker for one effect (increase effect). Another method is to predict the genetic risk in one direction (increasing direction), but not in the reverse direction (decreasing direction) It is a method to use as a genetic marker to predict genetic risk by replacing other normal alleles with increasing effects. However, this method has the problem of predicting the result in only one direction selected for the non-disease phenotype, and the other normal alleles of the allele affecting in one direction (increase) necessarily have the opposite direction There is a problem in that it may be inaccurate to predict the phenotype.

In addition, except for diseases that occur 100% by specific genetic influences, most diseases are affected by environmental factors such as lifestyle factors as well as genetic factors. For example, when smoking and drinking, or exposure to radiation, the likelihood of developing a particular cancer is increased, either separately or together with the genetic characteristics that it possesses. In recent years, studies have been published on the prediction / diagnosis of diseases that considers both genetic and non-genetic factors. Goldstein BA et al . Front. Genet. In the 2014 paper, the clinical risk score and the genetic risk score of the coronary heart disease were measured using the log-link function method. Respectively. However, this method has already been established because the Framingham Risk Score (considering gender, age, cholesterol level, blood pressure, diabetes, smoking status, etc.) has been established to measure and judge the clinical risk for coronary heart disease , The numbers required for calculating the Framingham Risk Score also require information measured by medical equipment such as health screening / medical examinations.

Korean Registered Patent No. 10-1582723 (registered on Dec. 29, 2015)

DISCLOSURE OF THE INVENTION The present invention has been made to solve the above problems, and it is an object of the present invention to provide a method and a kit for genetic markers, which are developed for racial / ethnic groups around the world, And to provide a device and a method for predicting a disease and phenotype risk.

In order to increase the accuracy of non-disease phenotyping prediction through genetic testing, the present invention provides a device and method for predicting a genetic risk by classifying and simultaneously considering an increasing effect and a decreasing effect on a phenotype, .

In addition, environmental factors affecting disease outbreaks are not collected through medical equipment but collected through Clinical Research, and diseases and diseases that predict genetic risk by integrating collected environmental factors with genetic factors And to provide a phenotype risk prediction apparatus and method.

In order to accomplish the above object, the present invention provides a genetic risk prediction apparatus comprising: a genetic marker prediction unit for obtaining genetic marker information on a disease from an external institution server and acquiring user genotype information from a user terminal; part; The genetic marker information is compared with the user genotype information to detect the number of risk factors of the user for the disease, the frequency value of the risk factor is detected in the group, and the effective size of the risk factor and the number of the risk factor of the user are used A detector for detecting an effective size of a genotype combination; And a calculation unit for calculating a genetic risk.

The calculation unit can calculate the genetic risk by considering the allele frequency in the population using the equation Σw _i * F _i * R _i where w is the effective size for the risk factor, F is the weight value, R Represents the number of risk factors retained by the user.

Also, the calculating unit may calculate the genetic risk by considering the allele frequency in the population using the equation ΠW _i * F _i , where W represents the effective size of the genotype combination, and F represents the weight value.

Here, the weight value F represents a value obtained by converting the risk factor frequency value of the group by the detection unit. When the risk factor frequency value within the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, If the risk factor frequency value is greater than 0.5, the weight value is 1 minus the risk factor frequency value in the population.

The detecting unit distinguishes the genetic marker information obtained from the obtaining unit with the genetic marker having the increasing effect and the genetic marker having the decreasing effect and the genetic marker having the incremental increasing effect and the genetic marker having the decreasing effect respectively, And the number of users holding the increase influence risk factor and the number of the decrease influence risk factor hold are detected.

The calculating unit can calculate the genetic risk considering the increase and decrease effects on the phenotype using the equation Σw _inc * R _{inc -} Σ w _dec * R _dec , where w _inc is the genetic marker , W _dec is the effective size for the risk factor of the genetic marker that affects the decrease, R _inc is the number of the increase risk factor holding factor of the user, and R _dec is the number of the risk factor holding factor for the decrease of the user .

The calculator may also calculate the genetic risk taking into account the increase and decrease effects on the phenotype using the equation Πw _inc * R _inc / Π w _dec * R _dec , where w _inc is the increase effect effective size, w _dec for the risk factors of genetic markers is the effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec may retain the user's reduced influence risk factors . In the above equation, when the number of users holding the increase risk factor is 0, the effective size of the risk marker of the genetic marker having an increasing influence multiplied by the number of risk factor holders of the user is 1, When the factor holding factor is zero, the effective magnitude of the risk factor of the genetic marker that has a decreasing influence is multiplied by the number of the risk factor holders whose decrease influence factor of the user is 1.

The acquisition unit acquires the ratio of the genetic factor and the environmental factor to the disease from the external institution server and acquires the clinical questionnaire information of the user from the user terminal, and the detector acquires the clinical questionnaire information from the user terminal It detects the increasing risk per factor.

The calculation unit can calculate the integrated risk considering the genetic factors and the environmental factors using the equation GRS * (e ₁ * e ₂ * ... * e _n ), where GRS is calculated using genetic factors And e represents an increased risk of environmental factors affecting the disease.

Also, the calculating unit may calculate the integrated risk considering the genetic factor and the environmental factor using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ) P is the rate at which the genetic factor affects the disease, and Pe is the rate at which the environmental factor affects the disease. .

In order to accomplish the above object, a disease and phenotypic risk prediction method according to the present invention is a genetic risk prediction method, comprising: obtaining genetic marker information on a disease from an external institution server and acquiring user genotype information from a user terminal; ; The genetic marker information is compared with the user genotype information to detect the number of risk factors of the user for the disease, the frequency value of the risk factor is detected in the group, and the effective size of the risk factor and the number of the risk factor of the user are used Detecting an effective size of a genotype combination; And calculating a genetic risk.

The genetic risk may be a genetic risk, taking into account the frequency of alleles in the population calculated using the equation Σ w _i * F _i * R _i where w is the effective size for the risk factor, F is the weight value , R represents the number of risk factors retained by the user.

The genetic risk may also be a genetic risk, taking into account the frequency of alleles in the population, calculated using the equation ΠW _i * F _i , where W is the effective size of the genotype combination, F is the weight value .

In this case, the weight value (F) represents a value obtained by converting the risk factor frequency value in the group by the detection unit. When the risk factor frequency value in the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, If the factor frequency value is greater than 0.5, the weight value is a value obtained by subtracting the risk factor frequency value in the group from 1.

In the detection step, the genetic marker information obtained in the acquisition step is distinguished from the genetic marker having the increasing effect and the genetic marker having the decreasing effect, and the genetic marker having the incremental increasing effect and the genetic marker having the decreasing effect are identified as the user genetic information And a step of detecting the increase influence risk factor holding number and the decrease influence risk factor hold number of the user.

The genetic risk may be a genetic risk taking into account the increasing and decreasing effects of phenotypes calculated using the formula Σw _inc * R _{inc -} Σ w _dec * R _dec , where w _inc is the increase effect effective size, w _dec for the risk factors of genetic markers is the effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec may retain the user's reduced influence risk factors .

In addition, genetic risk is, it may be a genetic risk Considering increasing and decreasing effect on the phenotype which is calculated using equation Πw _inc * R _inc / _dec Πw _dec * R, wherein, w _inc increased impact effective size of the risk factors for on genetic markers, w _dec is the effective size of the risk factors for genetic markers on the reduced effect, R _inc is held increase the user's influence risk factor, R _dec user of the reduced impact risk factors Quot; In the above equation, when the number of users holding the increase risk factor is 0, the effective size of the risk marker of the genetic marker having an increasing influence multiplied by the number of risk factor holders of the user is 1, When the factor holding factor is zero, the effective magnitude of the risk factor of the genetic marker that has a decreasing influence is multiplied by the number of the risk factor holders whose decrease influence factor of the user is 1.

The acquisition step may further include acquiring a ratio of genetic and environmental factors to the disease from the external agency server and acquiring the user's clinical questionnaire information from the user terminal, And detecting an increasing risk level for each environmental factor using the questionnaire information.

The genetic risk may be an integrated risk taking into account the genetic and environmental factors calculated using the equation GRS * (e ₁ * e ₂ * ... * e _n ), where GRS uses genetic factors And e represents the increased risk of environmental factors affecting the disease.

The genetic risk may also be an integrated risk taking into account the genetic and environmental factors calculated using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ) Is the genetic risk calculated using genetic factors, e is the risk of increasing environmental factors affecting the disease, Pg is the rate of genetic factors affecting the disease, Pe is the environmental factor Indicating the rate of influence.

According to the present invention, genetic markers of various diseases and phenotypes developed for racial / ethnic groups around the world can be genetically predicted by genetic risk prediction by assigning marker-specific weights to groups to be tested for genetic risk, The accuracy of risk prediction can be improved.

In order to increase the accuracy of non-disease phenotyping prediction through genetic testing, the effect of increasing and predicting the phenotypic risk can be improved by predicting the genetic risk by simultaneously considering the increasing and decreasing effects on the phenotype .

In addition, by collecting environmental factors that affect disease outbreaks in Clinical Research, and by integrating the collected environmental factors with genetic factors to predict genetic risk without measuring through medical equipment, disease and phenotype The accuracy of risk prediction can be improved.

1 is a system configuration diagram including a genetic risk prediction apparatus according to the present invention.
FIG. 2 is a block diagram showing the configuration of the genetic risk prediction apparatus shown in FIG. 1 in more detail.
FIG. 3 is a flowchart for explaining a disease and phenotypic risk prediction method according to the first embodiment of the present invention.
4 is a flowchart for explaining a disease and phenotype risk prediction method according to a second embodiment of the present invention.
FIG. 5 is a flowchart for explaining a disease and phenotypic risk prediction method according to a third embodiment of the present invention.

Best Mode for Carrying Out the Invention Hereinafter, preferred embodiments of a disease and phenotype risk prediction apparatus and method according to the present invention will be described in detail with reference to the accompanying drawings.

First, a genetic risk prediction apparatus according to the present invention will be described with reference to FIGS. 1 and 2. FIG.

1 is a system configuration diagram including a genetic risk prediction apparatus according to the present invention.

1, a genetic risk prediction apparatus (hereinafter, referred to as a 'risk prediction apparatus') 100 according to the present invention includes an external institution server 200 and a user terminal 300 via a network 400, .

The risk prediction apparatus 100 includes various data provided from the external institution server 200, for example, pre-established disease-related statistical information, pre-built genetic marker information, various data provided from the user terminal 300, For example, the genetic risk of the disease is calculated using the genetic information of the user.

The external institution server 200 refers to a server operated by an external organization such as a medical service provider such as a hospital, a medical state institution such as the Ministry of Health and Welfare, and a medical public institution such as a health insurance examination and evaluation institution. The external institution server 200 can provide the user's medical care information, pre-established disease-related statistical information, genetic marker information about the pre-established disease, and the like to the risk prediction apparatus 100 through the network 400.

Herein, the medical care information of the user is information indicating the history that the user has been treated at the medical service provider, and includes information such as the name of the user, the user identification information (for example, resident registration number and the like), the identification information of the medical service provider, The date of the visit, the content of the treatment (eg, the patient's disease, etc.).

The statistical information related to the disease refers to various statistical information related to the disease that has been established for the purpose of establishing the health care policy in the medical state institution or the medical public institution. For example, disease-related statistical information may include age-related disease-related statistical data, sex-related disease-related statistical data, occupational disease-related statistical data, and regional disease-

In addition, genetic marker information is genetic information having disease-related factors causing gene mutation, and is information that is in contrast with user genetic information in order to predict the risk related to the user's disease. Genetic marker information can include gene mutation sites, alleles, disease risk factors, and valid markers for risk factors.

The user terminal 300 may be connected to the risk prediction apparatus 100 via the network 400 to exchange various data.

That is, the user terminal 300 can provide the user's personal information, the user's genetic information, etc. to the risk prediction apparatus 100 through the network 400. Here, the personal information of the user may include a name of the user, identification information (e.g., resident registration number) of the user, gender of the user, age of the user, residence of the user, occupation of the user, . Genetic information of the user is information for comparing with disease risk factors causing gene mutation. Adenine (A), thymine (T), cytosine (C) , And guanine (G). For example, AT, CG, AA, CC, TT, and GG.

The user terminal 300 may display information provided from the risk prediction apparatus 100 through the network 400. [

The user terminal 300 may be a desktop computer, a notebook computer, a workstation, a palmtop computer, a UMPC (Ultra Mobile Personal Computer), a tablet PC, a personal digital assistant (PDA) A smart phone, a mobile phone, or the like, and a terminal equipped with a microprocessor and having computing capability.

The network 400 may include a data network including a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), and the Internet as well as a broadcasting network, a telephone network, All communication methods are available.

FIG. 2 is a block diagram showing the configuration of the genetic risk prediction apparatus shown in FIG. 1 in more detail.

2, the risk prediction apparatus 100 may include an acquisition unit 120, a detection unit 140, and a calculation unit 160. [ The risk prediction apparatus 100 can predict a genetic risk according to three embodiments, and the three embodiments can be simultaneously or selectively integrated.

According to a first embodiment of the present invention, the risk prediction apparatus 100 is adapted to predict a genetic marker related to diseases and phenotypes previously reported to racial / ethnic groups around the world, frequency) information, weights each marker appropriately for the group and predicts genetic risk.

According to the first embodiment of the present invention, the obtaining unit 120 obtains genetic marker information on the disease from the external institution server 200, and obtains the user genotype information from the user terminal 300.

Here, the genetic marker information is information including gene mutation positions, alleles, disease risk factors, effective size expressed as an odds ratio with respect to risk factors, etc., and the user's genotype information is a disease risk factor And is genetic information consisting of adenine (A), thymine (T), cytosine (C), and guanine (G)

According to the first embodiment of the present invention, the detector 140 compares the genetic marker information with the user genotype information to detect the number of the risk factors possessed by the user for the disease, to detect the frequency value of the risk factors in the population, The effective size of the genotype combination is detected using the effective size of the risk factors and the number of risk factors of the user.

Here, the risk factor frequency value in the group refers to the frequency of the risk factor within the group to which the user belongs, and is used as a weight for each genetic marker. At this time, the risk factor frequency value in the group becomes closer to 0.5 and has a higher weight value.

The effective size of the genotype combination is determined by the effective size of the risk factors and the number of risk factors of the subject.

In addition, the detector 140 converts the detected risk factor frequency value into a weight value. In this case, when the risk factor frequency value of the group is 0.5 or less, Value and the population risk factor frequency value is greater than 0.5, the value converted to the weight value is 1 minus the risk factor frequency value in the population.

According to the first embodiment of the present invention, the calculating unit 160 calculates the genetic risk of the user's disease.

At this time, the genetic risk can be calculated using the formula Σ w _i * F _i * R _i or Σlog (w _i ) * R _i , where w is the effective size for the risk factor, R is the frequency of the risk factor in the population converted into the value, and R is the number of the risk factor retained by the user.

Also, the genetic risk may be calculated using the equation ΠW _i * F _i , where W is the effective size of the genotype combination, and F is the frequency value of the risk factor in the population converted to a weight value .

According to the second embodiment of the present invention, in order to increase the accuracy of non-disease phenotype prediction through genetic testing, the risk prediction apparatus 100 classifies the increase and decrease effects on the phenotype, do.

According to the second embodiment of the present invention, the obtaining unit 120 obtains genetic marker information on the disease from the external institution server 200, and obtains the user genotype information from the user terminal 300.

In this case, the genetic marker information is information including the gene mutation position, disease risk factor, effective size expressed by beta coefficient for the risk factor, etc., and the genotype information of the user is compared with the disease risk factor causing the gene mutation Is genetic information composed of adenine (A), thymine (T), cytosine (C), and guanine (G) constituting the DNA base sequence.

According to the second embodiment of the present invention, the detection unit 140 distinguishes statistically significant genetic marker information associated with a particular phenotype from genetic markers with increasing affects and genetic markers with decreasing affects, Genomic markers and genetic markers that have a decreasing effect are compared with user genotype information, respectively, to detect how many users have increased and decreased risk factors, respectively.

According to the second embodiment of the present invention, the calculation unit 160 calculates the genetic risk for the phenotype.

At this time, the genetic risk for the phenotype can be calculated using the equation Σw _inc * R _{inc -} Σw _dec * R _dec , where w _inc is the effective size of the risk marker of the increasingly influencing genetic marker , w _dec refers to the effective size of the risk marker of the genetic marker that affects the decrease, R _inc refers to the number of users who have an increased influence risk factor, and R _dec refers to the number of users who have a decrease risk factor for the user.

Here, the number of possession of the increasing influence risk factor and the number of the risk influence reduction factor of the user are one of 0, 1, and 2.

The genetic risk for the phenotype may also be calculated using the equation Πw _inc * R _inc / Π w _dec * R _dec , where w _inc is the effective size of the risk marker of the increasingly affecting genetic marker , W _dec is the effective size of the risk marker of the genetic marker that has a decreasing effect, R _inc is the number of users holding the increasing influence risk factor, and R _dec is the number of the user holding the decrease influence risk factor.

Here, the number of possession of the increasing influence risk factor and the number of the decrease influence risk factor holding of the user are one of 1 and 2. If the number of users with increasing risk factors and the number of risk factors with decreasing risk factors is 0, the effective size is multiplied by the number of risk factors.

In other words, when the number of users holding the increase risk factor is 0, the effective size of the risk marker of the genetic marker that has an increasing influence multiplied by the number of the risk factor holders of the user increases by 1, If the number is zero, the effective size of the risk marker for the genetic marker that has a decreasing effect multiplies by the number of retention risk factors for the user is 1.

According to a third embodiment of the present invention, the risk prediction apparatus 100 predicts a genetic risk by simultaneously considering environmental factors as well as genetic factors.

According to the third embodiment of the present invention, the obtaining unit 120 obtains genetic marker information on a disease from the external institution server 200, a disease occurrence rate of a genetic factor and an environmental factor, The user genotype information and the clinical questionnaire information are obtained.

Genetic marker information is genetic information with disease-related factors that cause gene mutation, and is information that is in contrast with user genetic information to predict a user's disease-related risk. Genetic marker information can include gene mutation sites, alleles, disease risk factors, and valid markers for risk factors.

The genotype information of the user is information to compare with the disease risk factors causing the gene mutation. The information of adenine (A), thymine (T), cytosine (C), guanine ) Of genetic information.

Clinical questionnaire information is questionnaire information related to the user 's lifestyle, and clinical questionnaire information can be used to identify the environmental factors that affect disease occurrence.

According to the third embodiment of the present invention, the detector 140 calculates the genetic risk using the genetic marker information and the user genotype information, and detects the increasing risk of each environmental factor using the clinical questionnaire information.

The increasing risk by environmental factors indicates the risk of disease due to environmental factors (for example, smoking, drinking, etc.).

According to the third embodiment of the present invention, the calculation unit 160 calculates a genetic / environmental factor integration risk.

At this time, the genetic / environmental factor integration risk can be calculated using the equation GRS * (e ₁ * e ₂ * ... * e _n ), where GRS is the genetic risk and e is the disease- And the risk of increasing environmental factors.

GRS is a value computed in a variety of ways using genetic factors (e.g., risk factors, etc.) and does not limit the method of calculating genetic risk in the third embodiment according to the present invention.

Also, the genetic / environmental factor integrated risk may be calculated using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ), where GRS is the genetic risk and e Pg is the rate at which the genetic factor affects the disease, and Pe is the rate at which the environmental factor affects the disease.

The method of predicting diseases and phenotypes according to the present invention will now be described in detail with reference to FIGS. 3 to 5. FIG. The risk prediction method according to the present invention can predict a genetic risk according to three embodiments, and the three embodiments can be integrated simultaneously or selectively.

FIG. 3 is a flowchart for explaining a disease and phenotypic risk prediction method according to the first embodiment of the present invention. According to the first embodiment of the present invention, the genetic markers associated with diseases and phenotypes reported to racial / ethnic groups all over the world are analyzed by considering allele frequency information in the population to be tested for genetic risk Genetic risk is predicted by assigning marker-specific weights to groups.

According to the first embodiment of the present invention, gene marker information on a disease is obtained from the external institution server 200 and user genotype information is obtained from the user terminal 300 (S10).

Then, the genetic marker information and the user genotype information are compared with each other to detect the number of risk factors of the user for the disease, the frequency value of the risk factor is detected in the group, and the effective size of the risk factor and the number The effective size of the genotype combination is detected (S12).

As shown in Table 1 below, it can be seen that the mutation risk factor of the gene 4q23 is C. In comparison with the genotypes of subjects 1, 2 and 3, it can be seen that the number of risk factors of the subject 1 is 0, that of the subject 2 is 1, and that of the subject 3 is 0.

The effective size of the Zino type combination of the subjects can be known through the detected risk factor holding number. As shown in Table 1, the effective size of the 4q23 gene for risk factor C is 1.66, and the number of risk factors for the risk of the subjects is 0, 1, and 0, respectively. When the effective size of the Zino type combination of the subjects is calculated using this, the effective size of the Zino type combination of the subject 1 is 1., the effective size of the Zino type combination of the subject 2 is 1.66, the effective size of the Zino type combination of the subject 3 is 1 to be.

At this time, when the risk factor holding number is 0, the effective size of the genotype combination is 1.

Then, the detected in-group risk factor frequency value is converted into a weight value (S14). In this case, when the risk factor frequency value in the group is less than 0.5, the value converted to the weight value is equal to the risk factor frequency value in the group, and when the risk factor frequency value in the group is more than 0.5, It is the same as subtracting the risk factor frequency value in the group. As shown in Table 1, since the frequency of the mutation risk factor C in the locus 4q23 of the gene is 0.21 and not more than 0.5, the value converted to the weight is 0.21.

Thereafter, the genetic risk is calculated using the equation Σw _i * F _i * R _i or Σlog (w _i ) * R _i or the equation ΠW _i * F _i (S16).

For example, when the values shown in Table 1 are applied to the mathematical formula Σ w _i * F _i * R _i to calculate the genetic risk, the genetic risk of the subject 1 is (1.66 * 0.21 * 0) + (1.47 0.24 * 0) + (1.85 * 0.12 * 0) = 0 and the genetic risk of the subject 2 is 1.66 * 0.21 * + 1.47 * 0.24 * , The genetic risk of the subject 3 is (1.66 * 0.21 * 0) + (1.47 * 0.24 * 2) + (1.85 * 0.12 * 0) = 0.7056.

Further, haebomyeon calculating a genetic risk by applying the values shown in Table 1, the expression ΠW _i - F _i, genetic risk of the subject 1 is (1 - 0.21) * (1 * 0.24) * (1 * 0.12) = 0.006048 and the genetic risk of the subject 2 was (1.66 * 0.21) * (1 * 0.24) * (1.85 * 0.12) = 0.01857 and the genetic risk of the subject 3 was (1 * 0.21) 0.24) * (1 * 0.12) = 0.01306.

gene Marker  Information The subject's retained genotype Zino type  By combination
Effective size Gene mutation
location
Allele
danger
factor
available
size Risk Factor Frequency in the Population
Subject 1
Subject 2
Subject 3
Subject 1
Subject 2
Subject 3 4q23 C / T C 1.66 0.21 TT CT TT One 1.66 One 10q23 G / A G 1.47 0.24 AA AA GG One One 2.16 12q24 A / G A 1.85 0.12 GG AG GG One 1.85 One

4 is a flowchart for explaining a disease and phenotype risk prediction method according to a second embodiment of the present invention. According to the second embodiment of the present invention, in order to increase the accuracy of non-disease phenotype prediction through genetic testing, the genetic risk is predicted by distinguishing and simultaneously considering the increase effect and the decrease effect on the phenotype.

According to the second embodiment of the present invention, genetic marker information on the disease is obtained from the external institution server 200 and user genotype information is obtained from the user terminal 300 (S20).

Thereafter, the genetic marker information is distinguished from the genetic marker having the increasing effect and the genetic marker having the decreasing effect (S22), and the genetic marker having the incremental increasing effect and the genetic marker having the decreasing effect are compared with the user genetic information, respectively S24), the number of users having increased risk factors and the number of risk factors having decreased risk factors are detected (S26).

As shown in Table 2 below, the genetic markers that have an increasing influence are rs10495928, rs1800562, rs10224002, rs16926246, rs4671393, rs9399137, and the genetic markers that have a decreasing effect are rs6013509, rs570013781, and rs5030868. For example, the disease risk factor for the gene marker rs10495928, which has an increasing effect, is G, and comparing it with the genotype GT of the subject shows that the subject has one risk factor that has an increasing influence.

Then, the genetic risk for the phenotype is calculated using the equation Σw _inc * R _{inc -} Σ w _dec * R _dec or the equation Πw _inc * R _inc / Π w _dec * R _dec (S28).

For example, if we calculate the genetic risk for a phenotype by applying the values shown in [Table 2] to the equation Σw _inc * R _{inc -} Σ w _dec * R _dec ,

(0.06 * 1 + 0.16 * 2 + 0.07 * 1 + 0.11 * 1 + 0.58 * 0 + 0.42 * 2) - (0.06 * 1 + 0.20 * 2 + 0.13 * 1) = 0.81.

Further, haebomyeon calculate the genetic risk for the phenotype by applying the values shown in Table 2 in equation _{Πw inc * R inc / Πw dec} * R dec, [(0.06 * 1) * (0.16 * 2) (0.07 * 1) * (0.11 * 1) * 1 * (0.42 * 2)] / [(0.06 * 1) * (0.20 * 2) * (0.13 * 1)] = 0.040. Here, when the number of risk factors held by the user is 0, that is, when the user does not have a risk factor that increases or does not have a risk factor that affects the decrease, do.

gene
Mutation location Reported
Risk factor
Effective size Increase, Decrease
effect The genotype of the subject Increase impact Number of risk factors Number of risk factors rs10495928 G 0.06 g // dl increase GT One - rs1800562 A 0.16 g // dl increase AA 2 - rs10224002 G 0.07 g // dl increase GA One - rs16926246 T 0.11 g // dl increase TA One - rs4671393 A 0.58 g // dl increase TT 0 - rs9399137 C 0.42 g // dl increase CC 2 - rs6013509 A 0.06 g // dl decrease AT - One rs570013781 A 0.20 g // dl decrease AA - 2 rs5030868 A 0.13 g // dl decrease AG - One Sum - - 7 4

FIG. 5 is a flowchart for explaining a disease and phenotypic risk prediction method according to a third embodiment of the present invention. According to a third embodiment of the present invention, genetic risk is predicted by simultaneously considering environmental factors as well as genetic factors.

According to the third embodiment of the present invention, genetic marker information on diseases and a ratio of genetic factors and environmental factors to diseases are obtained from the external institution server 200, and user genotype information Clinical questionnaire information is obtained (S30).

Then, genetic risk is calculated using genetic marker information and user genotype information, and an increasing risk of each environmental factor is detected using clinical questionnaire information (S32).

As shown in Table 3 below, the increased risk for smoking is 1.8 and the increased risk for drinking is 1.5.

Then, the equation _{GRS * (e 1 * e 2} * ... * e n) or formula Pg * GRS + Pe * (e 1 * e 2 * ... * e n) genetic / environmental factors integrated risk using (S34).

In this case, the genetic risk (GRS) is a value calculated by various methods using a genetic factor (for example, a risk factor, etc.). In the third embodiment according to the present invention, However, for the sake of explanation, it is exemplified that the calculation is performed using the formula? W _i * F _i in the first embodiment. (1 * 0.24) * (1 * 0.12) = 0.006048 and the GRS value of the subject 2 is (1.66 * 0.21) * (1 * 0.24) * (1.85 * 0.12) = 0.01857 and the GRS value of the subject 3 is (1 * 0.21) * (2.16 * 0.24) * (1 * 0.12) = 0.01306.

The GRS values of the subjects and the values shown in Table 3 are calculated by applying the equation GRS * (e ₁ * e ₂ * ... * e _n ) to the subject's genetic / environmental factor risk of 0.006048 * 1.8 * 1.5) = 0.01633, the risk of genetic / environmental factor integration for subject 2 is 0.01857 * (1 * 1) = 0.01857 and that for subject 3 is 0.01306 * (1 * 1.5) = 0.01960.

Here, if the user answers 'No' to the question item, the increase risk per factor is calculated as 1.

Also, the genetic / environmental factor integration risk may be calculated using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ). Here, Pg is the ratio of genetic factors to the disease and phenotype, and Pe is the ratio of environmental factors to the disease and phenotype.

Using the formula Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ), the genetic / environmental The combined risk of genetic / environmental factors of subject 1 was 0.1 * 0.006048 + 0.9 * (1.8 * 1.5) = 2.4306, and the risk of genetic / environmental factors of subject 2 was 0.1 * 0.01857 + 0.9 * (1 * 1) = 0.9019, and the risk of genetic / environmental factor integration of Subject 3 is 0.1 * 0.01306 + 0.9 * (1 * 1.5) = 1.3513.

Here, if the user answers 'No' to the question item, the increase risk per factor is calculated as 1.

Environmental factor Increase by factor
Risk Subject 1 Subject 2 Subject 3 smoking 1.8 Yeah no no Drinking 1.5 Yeah no Yeah

The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer is stored. Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and a carrier wave Transmission). In addition, the computer-readable recording medium may be distributed to computer devices connected to a wired / wireless communication network, and a computer-readable code may be stored and executed in a distributed manner.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be possible. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as being included in the scope of the present invention.

100: Risk prediction device 120:
140: Detection unit 140:
200: external institution server 300: user terminal
400: Network

Claims (20)

As a genetic risk prediction device,
An acquiring unit acquiring genetic marker information on a disease from an external institution server and acquiring user genotype information from a user terminal;
The genetic marker information is compared with the user genotype information to detect the number of risk factors of the user for the disease, the frequency value of the risk factor is detected in the group, and the effective size of the risk factor and the number of the risk factor of the user are used A detector for detecting an effective size of a genotype combination; And
A calculation unit for calculating a genetic risk;
And a genetic risk prediction device. The method according to claim 1,
The calculating unit calculates,
Using genetic algorithm Σw _i * F _i * R _i , genetic risk is calculated considering allele frequencies in the population,
Where w is the effective size for the risk factor, F is the weight value, R is the number of risk factors retained by the user,
The weight value represents a value obtained by converting the risk factor frequency value of the group by the detection unit. When the risk factor frequency value within the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, Is greater than 0.5, the weight value is a value obtained by subtracting the risk factor frequency value in the population from 1. The method according to claim 1,
The calculating unit calculates,
The genetic risk of allele frequencies in the population is calculated using the equation ΠW _i * F _i ,
In this case, W represents the effective size of the genotype combination, F represents the weight value,
The weight value represents a value obtained by converting the risk factor frequency value of the group by the detection unit. When the risk factor frequency value within the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, Is greater than 0.5, the weight value is a value obtained by subtracting the risk factor frequency value in the population from 1. The method according to claim 1,
Wherein:
The genetic marker information obtained from the obtaining section is distinguished from the genetic marker having the increasing effect and the genetic marker having the decreasing effect, and the genetic marker having the incremental increasing effect and the genetic marker having the decreasing effect are compared with the user genetic information, respectively A genetic risk prediction device for detecting a user's increasing influence risk factor holding number and reducing influence risk factor holding number. 5. The method of claim 4,
The calculating unit calculates,
Using the equation Σw _inc * R _{inc -} Σ w _dec * R _dec , the genetic risk is calculated considering the increase and decrease effects on the phenotype,
In this case, w _inc is effective size, w _dec for the risk factors for genetic markers on the increased effect is effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec Is a genetic risk prediction device, which is a population of risk factors for the reduction of users. 5. The method of claim 4,
The calculating unit calculates,
Using the equation Πw _inc * R _inc / Πw _dec * R _dec , genetic risk is calculated taking into account the increase and decrease effects on the phenotype,
In this case, w _inc is effective size, w _dec for the risk factors for genetic markers on the increased effect is effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec Is a genetic risk prediction device, which is a population of risk factors for the reduction of users. The method according to claim 6,
If the increase factor of the risk factor of the user is 0, the effective size of the risk marker of the genetic marker with the increasing effect multiplied by the increase factor of the risk factor of the user is 1,
Wherein the effective magnitude of the risk factor of the genetic marker which has a decreasing influence and the number of the risk factor retaining number of the user is 1 when the risk factor holding factor of the user is 0, . The method according to claim 1,
Wherein the obtaining unit comprises:
Obtains the ratio of genetic and environmental factors for the disease from the external agency server, acquires the user's clinical questionnaire information from the user terminal,
Wherein:
And an increase risk of each environmental factor is detected using clinical questionnaire information obtained from the acquisition unit. 9. The method of claim 8,
The calculating unit calculates,
Using the formula GRS * (e ₁ * e ₂ * ... * e _n ), we calculate the integrated risk considering genetic and environmental factors,
GRS is a genetic risk calculated using genetic factors, and e is an increased risk factor for each environmental factor affecting the disease. 9. The method of claim 8,
The calculating unit calculates,
Using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n ), we calculate the integrated risk considering genetic and environmental factors,
Here, GRS is the genetic risk calculated using genetic factors, e is the risk of increasing environmental factors affecting the disease, Pg is the rate at which the genetic factor affects the disease, Pe is the environmental factor A genetic risk predictor, a rate that affects this disease. As a genetic risk prediction method,
Acquiring genetic marker information on a disease from an external institution server and obtaining user genotype information from a user terminal;
The genetic marker information is compared with the user genotype information to detect the number of risk factors of the user for the disease, the frequency value of the risk factor is detected in the group, and the effective size of the risk factor and the number of the risk factor of the user are used Detecting an effective size of a genotype combination; And
Calculating a genetic risk;
Gt; genetic < / RTI > risk prediction method. 12. The method of claim 11,
The genetic risk,
Is a genetic risk that accounts for the frequency of alleles in a population calculated using the equation Σ w _i * F _i * R _i ,
Where w is the effective size for the risk factor, F is the weight value, R is the number of risk factors retained by the user,
The weight value represents a value obtained by converting the risk factor frequency value of the group by the detection unit. When the risk factor frequency value within the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, Is greater than 0.5, the weight value is a value obtained by subtracting the risk factor frequency value in the population from 1. 12. The method of claim 11,
The genetic risk,
Is a genetic risk that accounts for the frequency of alleles in the population, calculated using the equation ΠW _i * F _i ,
In this case, W represents the effective size of the genotype combination, F represents the weight value,
The weight value represents a value obtained by converting the risk factor frequency value of the group by the detection unit. When the risk factor frequency value within the group is 0.5 or less, the weight value is equal to the risk factor frequency value in the group, Is greater than 0.5, the weight value is a value obtained by subtracting the risk factor frequency value in the population from 1. 12. The method of claim 11,
Wherein the detecting step comprises:
The genetic marker information obtained in the obtaining step is distinguished by a genetic marker having an increasing effect and a genetic marker having a decreasing effect, and a genetic marker having a distinct increasing effect and a genetic marker having a decreasing effect are compared with user genetic information, respectively Further comprising the step of detecting the number of users having increased risk factors and the number of users having reduced risk factors. 15. The method of claim 14,
The genetic risk,
It is a genetic risk taking into account the increase and decrease effects of phenotypes calculated using the formula Σw _inc * R _{inc -} Σ w _dec * R _dec ,
In this case, w _inc is effective size, w _dec for the risk factors for genetic markers on the increased effect is effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec A method for predicting genetic risk, which is the number of risk factors retained by a user. 15. The method of claim 14,
The genetic risk,
Is a genetic risk taking into account the increase and decrease effects of phenotypes calculated using the formula Πw _inc * R _inc / Πw _dec * R _dec ,
In this case, w _inc is effective size, w _dec for the risk factors for genetic markers on the increased effect is effective size, R _inc for the risk factors for genetic markers on the reducing effect is retained increase the user's influence risk factor, R _dec A method for predicting genetic risk, which is the number of risk factors retained by a user. 17. The method of claim 16,
If the increase factor of the risk factor of the user is 0, the effective size of the risk marker of the genetic marker with the increasing effect multiplied by the increase factor of the risk factor of the user is 1,
Wherein the effective magnitude of the risk marker of the genetic marker which has a decreasing influence and the number of the risk factor retaining number of the user is 1 when the risk factor holding factor of the user is 0, . 12. The method of claim 11,
The acquiring step includes:
Obtaining a ratio of a genetic factor and an environmental factor for a disease from an external institution server and obtaining a user's clinical questionnaire information from a user terminal,
Wherein the detecting step comprises:
Further comprising the step of detecting an increasing risk of each environmental factor using clinical questionnaire information obtained in the obtaining step. 19. The method of claim 18,
The genetic risk,
It is an integrated risk taking into account the genetic and environmental factors calculated using the formula GRS * (e ₁ * e ₂ * ... * e _n )
Here, GRS is a genetic risk calculated using genetic factors, and e is a risk of increasing environmental factors affecting disease. 19. The method of claim 18,
The genetic risk,
Is an integrated risk taking into account the genetic and environmental factors calculated using the equation Pg * GRS + Pe * (e ₁ * e ₂ * ... * e _n )
Here, GRS is the genetic risk calculated using genetic factors, e is the risk of increasing environmental factors affecting the disease, Pg is the rate at which the genetic factor affects the disease, Pe is the environmental factor A method of predicting genetic risk, a rate that affects this disease.

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