KR20090105921A - Genetic analysis systems and methods - Google Patents

Abstract

The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Description

Genetic analysis system and method {GENETIC ANALYSIS SYSTEMS AND METHODS}

The present invention relates to genetic analysis systems and methods.

Due to the sequencing of the human genome and other recent advances in human genomics, it has been found that the similarity of any two human genome structures is greater than 99.9%. Due to the relatively small number of mutations that occur on DNA between individuals, differences in phenotypic traits arise, which are also associated with a number of human diseases, susceptibility to various diseases, and response to disease treatment. DNA variations between individuals occur at both coding and non-coding sites, including variations that occur at bases present at specific positions in the genomic DNA sequence, and insertion and deletion of DNA. Variations that occur at one base position in the genome are called Single Nucleotide Polymorphisms, or "SNPs."

SNPs are not relatively common in the human genome, but cause multiple DNA sequence mutations between individuals, as occurs approximately once every 1,200 base pairs in the human genome (International HapMap Project; www.hapmap. org)). As human genetic information becomes more available, the complexity of SNPs can also be understood. Furthermore, it has been found that the development of SNPs in the genome is correlated with the presence and / or susceptibility to various diseases and conditions.

Because of this correlation in human genetics and further developments in this area, problems related to medical care and personal health generally progress in a customized way, and patients are also able to Considering genomic information, you will make appropriate medical and other choices. Therefore, it is necessary to provide patients and caregivers with information specific to each individual's genome that will provide medical and other decisions appropriate for the individual.

Summary of the Invention

The present invention provides a method for assessing the correlation of an individual's genotype comprising the following steps: a) obtaining a genetic sample of the individual, b) generating a genomic profile for the individual, c) genomic profile of the individual Determining the correlation between the genotype and the phenotype of the individual by comparing the database of human genotypes and phenotypes with the currently identified database; d) determining the correlation between the individual genotype and the phenotype; Reporting to the care manager, e) updating the database of human genotype correlations with such additional correlations of human genotypes as additional correlations of human genotypes have been identified, f) Compare the genomic profile of the individual obtained from step c) or a portion thereof with additional correlations of the human genotype And by measuring an additional correlation of the object's genotype relationship, updating the relationship between the object genotype, and g) the step of reporting the result obtained from step f) to a care manager for the object or the object.

The present invention further provides a business method for assessing the correlation of a genotype of an individual, comprising the following steps: a) obtaining a genetic sample of the individual; b) forming a genomic profile for the individual; c) determining the individual's genotype correlations by comparing the individual's genomic profile with a database of human genotype correlations; d) providing the subject with a result of measuring a genotype correlation for a subject in a secure manner; e) updating the human genotype correlation as an additional correlation of the human genotype as the additional correlation of the human genotype is identified; f) updating the individual's genotype correlations by comparing the individual's genomic profile or a portion thereof with additional human genotype correlations and measuring the individual's additional genotype correlations; And g) providing a result of updating the correlation of the individual genotypes to a health care manager individual in charge of the individual.

Another aspect of the invention is directed to a method of forming a phenotype profile for an individual, comprising the following steps: a) providing a rule set comprising rules, wherein each of the The rule indicating a correlation between one or more genotypes and one or more phenotypes, b) providing a data set comprising genomic profiles for each of the plurality of individuals, wherein each genomic profile is a plurality of genotypes. C) periodically updating the rule set with one or more new rules, wherein the one or more new rules are genotype and phenotype (not originally correlated with each of the rules in the rule set). Indicating a correlation between; d) applying each new rule to the genomic profile of one or more of the individuals, correlating one or more genotypes of that individual with one or more phenotypes about the individual, and optionally, e) comprising the phenotype profile of the individual Steps to create a report.

The invention also relates to a set of rules comprising a) rules, each rule representing a correlation between one or more genotypes and one or more phenotypes; b) a cipher that periodically updates the rule set with one or more new rules, wherein the one or more new rules indicate a correlation between genotypes and phenotypes that were not originally correlated with each other in the rule set; c) a database comprising genomic profiles of a plurality of individuals; d) applying a rule set to an individual's genomic profile to determine a phenotype profile for that individual; And e) provide a system that includes a password to generate a report for each entity.

Another aspect of the invention relates to transmitting on a network in a secure manner or a non-secure manner, and to the methods and systems as described above.

Citations on Bibliography

All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application were specifically and separately cited for reference.

Brief description of the drawings

1 is a flow chart illustrating aspects of the method of the present invention.

2 shows an example of a genomic DNA quality control assay.

3 shows an example of a hybridization quality control measurement method.

Figure 4 shows a table of the results of predicting the correlation between each genotype and the test SNP obtained from the published literature, and the effect due to error. A to I show a correlation of single position genotype; J indicates genotype correlation between two positions; K represents a genotype correlation between three positions; L) represents an abbreviation index for ethnicity and country name used in A to K; M) stands for the abbreviation, permittivity, and reference index for permittivity for the short phenotype names A to K.

5A to 5J show tables of correlations between the results predicted for the effects and the respective genotypes.

6A to 6F show tables of prediction results regarding correlations and relative risks of respective genotypes.

7 illustrates a report.

8 schematically depicts a system for analysis and transmission of genomic and phenotype profiles on a network.

9 is a flowchart showing aspects of the business method of the present application.

10 shows the effect of a prevalence prediction on the prediction of relative risk. Assuming a Hardy-Weinberg Equilibrium, each graph corresponds to a different value for the frequency of expression of alleles in the population. Two black lines are for the odds ratios of 9 and 6, two red lines are for the 6 and 4 cases, and two blue lines are for the crossing ratios of 3 and 2.

11 shows the effect of allele frequency predictions on relative risk estimates. Each graph corresponds to different prevalence values in the population. Two black lines are the case where the crossing ratio is 9 and 6, two red lines are the case when the 6 and 4, and two blue lines are the case where the crossing ratio is 3 and 2.

12 shows the results of a pairwise comparison of absolute values for different models.

FIG. 13 shows pairwise comparison results of grade values (GCI scores) based on different models. Spearman correlations between different pairs are shown in Table 2.

14 shows the prevalence report results for the GCI score. The Spearman correlation between two of any prevalence figures is at least 0.99.

15 illustrates an example web page in a personalized portal.

FIG. 16 illustrates an example web page of a personalized portal that presents personal risk for prostate cancer.

17 shows an example web page in a personalized portal that presents the risk of an individual with Crohn's disease.

FIG. 18 shows a bar graph of the GCI score for multiple sclerosis based on a hap map using two SNPs.

FIG. 19 shows the results of measuring the lifetime risk of an individual in multiple sclerosis using GCI Plus. FIG.

20 shows a bar graph relating to GCI score for Crohn's disease.

21 shows a table of multiple position correlations.

Figure 22 shows a table of the correlation between SNPs and phenotypes.

23 shows a table relating to phenotype and prevalence.

FIG. 24 is a collection of abbreviations shown in FIGS. 21, 22, and 25.

Figure 25 shows a table of correlations between SNPs and phenotypes.

Detailed description of the invention

The present invention creates phenotype profiles based on stored genomic profiles of individuals or groups of individuals, and also easily generates original phenotype profiles and updated phenotype profiles based on stored genomic profiles. It provides a method and system for. Genomic profiles are generated by measuring genotypes from biological samples obtained from an individual. Biological samples obtained from the subjects can be any sample from which a genetic sample can be obtained. The sample may be derived from scraping a mouth with a swab, saliva, blood, hair or any other type of tissue sample. The genotype can then be determined from the biological sample. The genotype can be any genetic variant or biological marker, eg, single nucleotide polymorphism (SNP), monomorphic or genomic sequence. The genotype may be the entire genome sequence of the individual. Genotypes can be obtained from high throughput assays that generate thousands or millions of data points, for example, microarray assays for most or all of the known SNPs. In other embodiments, genotypes may also be determined by high throughput sequencing.

Genotypes form a genomic profile for an individual. Genomic profiles are stored digitally and can be easily accessed at any point in time to obtain a phenotype profile. Phenotype profiles are generated by applying rules that correlate or associate genotypes with phenotypes. Rules can be established based on scientific findings that explain the correlation between genotypes and phenotypes. Correlations may be supplemented or substantiated by a committee of one or more experts. By applying these rules to an individual's genomic profile, the association between the individual's genotype and phenotype can be determined. In this way the phenotype profile for the individual will be measured. The measurement result may be in a positive association between the subject's genotype and the given phenotype, so that the individual will have or will express the phenotype. Alternatively, the individual will not have or have a predetermined phenotype. In other embodiments, the result of the measurement can be a risk factor, prediction or probability that the individual will have or develop a phenotype.

Measurements can be made based on a number of rules, for example, a number of rules can be applied to a genomic profile to determine the association of a particular phenotype with an individual's genotype. Measurement results may also include factors specific to the individual, such as ethnicity, sex, lifestyle (eg, eating habits and exercise habits), age, environment (eg, location of residence), family history, individual history, And other known phenotypes. Specific factors can be incorporated by modifying them by including them in existing rules. Alternatively, separate rules can be formed by such factors, and can also be applied to the phenotypic measurement results for an individual after applying existing rules.

Phenotypes can include any measurable trait or characteristic, such as a response to sensitivity or drug treatment to any disease. Other phenotypes that may be included are physical and mental traits such as height, weight, hair color, eye color, sensitivity to sunburn, size, memory, intelligence, optimistic levels and overall disease predisposition. Phenotypes may also include genetic comparisons to other individuals or organisms. For example, an individual may be interested in the similarity between his genomic profile and the celebrity's genomic profile. In addition, their genomic profiles may be compared to other organisms such as bacteria, plants or other animals.

In addition, the collection of correlation phenotypes measured for an individual includes a phenotype profile for that individual. Phenotype profiles are available by on-line portal. Alternatively, at any point in time, the phenotype profile may be provided in a report sheet, and may also be provided in a report sheet upon further update. The phenotype profile may also be provided by the on-line portal. The on-line portal can optionally be a secure on-line portal. A phenotype profile is a subscriber, that is, only a person who forms a rule about the correlation between the phenotype and the genotype, determines the individual's genomic profile, and applies the rule to the genomic profile to form the individual's phenotype profile. You can get it. In addition, non-subscribers may access, provided they are able to obtain their phenotype profile and / or report about it within a limited scope, or may have an initial report or a phenotype profile formed, but with an updated report You will only be able to get this if you apply. Health care managers and health care providers, such as caregivers, specialists, and genetic counselors, may also obtain phenotype profiles.

In another aspect of the invention, genomic profiles are formed for subscribers and non-subscribers and can be stored digitally, but phenotype profiles and reports are only available to subscribers. In other variations, subscribers and non-subscribers may obtain genotype and phenotype profiles, while non-subscribers may receive limited availability or receive a limited range of reports, while subscribers may be intact. You can get it and get all the reports. In other embodiments, both the subscriber and the non-subscriber can both get full access initially or get all the initial reports, but only the subscriber will receive updated reports based on their genomic profile stored. Can be.

In another aspect of the invention, information that associates multiple genetic markers with one or more diseases or conditions is integrated and analyzed to obtain a Genetic Composite Index (GCI) score. This score incorporates known risk factors and other information and assumptions such as the frequency of expression of alleles and the prevalence of disease. The GCI can be used to quantitatively predict the outcome of associating a complex effect with a set of genetic markers with a disease or condition. The GCI score is reliable (ie firm) for those who are not trained in the field of genetics, as a result of comparing each risk for their disease with the relevant groups based on current scientific research. It can be used to provide a sense that is easy to understand and / or intuitive. The GCI score can be used to generate a GCI Plus score. The GCI Plus score may include all GCI assumptions, such as the risk of a condition (eg, lifetime risk), age prevalence, and / or age incidence. The lifetime risk for an individual can then be calculated as a GCI Plus score, where the GCI Plus score is proportional to the individual's GCI score divided by the average GCI score. The average GCI score can be measured from a group of people with similar lineages, such as Caucasians, Asians, East Indians, or other groups of people with a common lineage. These groups may include 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 or 60 or more people. In some embodiments, the mean can be measured from 75, 80, 95 or 100 or more people. The GCI Plus score can be determined by measuring the GCI score for an individual, dividing this GCI score by the average relative risk, and then multiplying by the lifetime risk for the condition or phenotype. For example, by using the data obtained from FIGS. 22 and / or 25 together with the information of FIG. 24, for example, the GCI plus score of FIG. 19 can be calculated.

The present invention includes the use of the GCI scores presented herein, and one of ordinary skill in the art will readily appreciate that instead of the GCI scores described herein, the GCI Plus scores or modified values thereof may be used.

In one embodiment, the GCI score can be scored for each of the subject disease or condition. Such GCI scores can be collected to form a risk profile for the individual. The GCI score can be stored digitally, so that it can be easily obtained at any point in time to form a risk profile. The risk profile can be updated by a wide range of disease groups, such as cancer, heart disease, metabolic disease, psychiatric disease, bone disease or age-specific onset disease. Extensive disease groups may also be divided into subcategories. For example, in a broad group of cancers, for example, cancer, subcategories of cancer may include, for example, type (sarcoma, carcinoma, or leukemia, etc.) or tissue specificity (nerve, breast, ovary, testis, prostate, bone, lymph). Section, pancreas, esophagus, stomach, liver, brain, lung, kidney, etc.).

In another embodiment, a GCI score is scored for an individual to make it easier for the individual to understand information about the risk of developing one or more diseases or conditions or susceptibility to the disease or condition. In one embodiment, multiple GCI scores are scored for different diseases or conditions. In other embodiments, one or more GCI scores can be obtained through the on-line portal. Alternatively, one or more GCI scores may be provided in the form of a report sheet, which is subsequently updated as a form of report sheet. In one embodiment, the subscriber, ie the individual applying for service, will receive one or more GCI scores. In alternative embodiments, non-subscribers may also be available, in which case these non-subscribers have limited access to one or more of their GCI scores, or one or more of their GCI scores. You can have an initial written report for the report, but the updated report will be available only to the person who signed up for the subscription. In other embodiments, health care managers and health care providers, such as caregivers, specialists, and genetic counselors, may also obtain one or more of an individual's GCI score.

There may also be a basic subscription model. At base subscription, a phenotype profile can be provided if the subscriber can choose to apply all existing rules to his genomic profile, or to apply a subset of existing rules to his genomic profile. For example, the primary subscriber may choose to apply only the rules for phenotypes of diseases that may be brought to action. Basic subscriptions may exhibit different levels within the subscription groups. For example, such different levels may vary depending on the number of phenotypes the subscriber wants to correlate with his or her genomic profile, or the number of people who can obtain the subscriber's own phenotype profile. Another level of basic enrollment may be incorporating factors specific to an individual, such as already known phenotypes, such as age, gender or medical history, factors specific to their phenotype profile. Another level of base enrollment may enable an individual to obtain one or more GCI scores for a disease or condition. Modifications to this level also indicate that if any change occurs in one or more GCI scores by modifying the assay used to generate one or more GCI scores, the subject may have one or more GCI scores for the disease or condition that will occur. You can also specify to update automatically. In some embodiments, the subject may be notified that it has been automatically updated by email, voice message, text message, postal delivery or fax.

Subscribers may also receive a report containing a phenotype profile and information about the phenotype, eg, genetic and medical information about the phenotype. For example, the report may include the prevalence of phenotypes in the population, the genetic variants used for correlation, the molecular mechanisms expressing the phenotype, the treatment for the phenotype, options for the selection of therapies according to the phenotype, and protective measures. In another embodiment, the report may also include similarities between the information, for example, the genotype of the individual and the genotype of another individual, such as a celebrity or other celebrity. Information on similarity may include, but is not limited to,% homology, number of identical variants, and phenotypes that may be similar. Such a report may also include one or more GCI scores.

The report may also link to other sites that contain additional information about the phenotype, or, if you apply for the report on-line, on-line social gatherings of people with the same phenotype or one or more similar phenotypes and their Link to a bulletin board, link to an on-line genetic counselor or specialist, or link to schedule telephone counseling or visit counseling with a genetic counselor or specialist. If the report is in the form of a sheet of paper, the information may be the website address of the aforementioned link, or the telephone number and address of the genetic counselor or specialist. The subscriber can also choose which phenotypes to include in his phenotype profile and which information to include in his report. Phenotype profiles and reports may also be obtained by an individual's healthcare manager or healthcare provider, such as a caregiver, specialist, psychiatrist, psychologist, clinician or genetic counselor. The subscriber can choose whether to make the phenotype profile and reports, or portions thereof, available to the health care manager or health care provider of such an individual.

The invention may also include an advanced level of subscription. The high level of subscription gives the subscriber the opportunity to create an initial phenotype profile and report, then digitally maintain the subscriber's genomic profile, and create a report that includes an update correlation derived from the phenotype profile and the latest research results. In another embodiment, subscribers have the opportunity to create a report that includes an updated correlation from the risk profile and the latest study results. When research reveals new correlations between genotypes and phenotypes, diseases, or conditions, new rules based on these new correlations will be formed, and these newly defined rules will already be stored and stored. Can be applied to a genomic profile. New rules can correlate with genotypes not originally associated with any phenotype, correlate new phenotypes with genotypes, modify existing correlations, or newly discovered associations between genotypes and diseases or conditions A basis for adjusting GCI scores based on relationships can be provided. Subscribers may be informed of new correlations via email or other electronic means, and if the phenotype is the purpose, the subscribers may choose to update their phenotype profile with the new correlation. Subscribers can select a subscription condition to pay for a renewal for a specified period of time (eg, 3 months, 6 months or 1 year), each renewal, or multiple, or indefinitely. At other subscription levels, whenever a new rule is formed based on a new correlation, instead of allowing the entity to choose when to update the entity's phenotype profile or risk profile, the subscriber may choose to update their phenotype profile or risk profile. It can be updated automatically.

In another aspect of subscription, subscribers may entrust non-subscribers to services that generate rules relating to the correlation between phenotype and genotype, measure the genomic profile of the individual, and apply this rule to the genomic profile to You can create phenotype profiles. The consignor entrusted by the subscriber may be entitled to a discount on the cost of the subscription to the service, or to renew existing subscription conditions. Entrusted entities can get information freely for a limited time, or can get a discounted subscription price.

Phenotype profiles and reports, and risk profiles and reports, can be prepared for human and non-human entities. For example, the subjects may include other mammals such as cattle, horses, sheep, dogs, or cats. The term subscriber, as used herein, refers to an individual who is the person who purchased or paid for one or more services. Services may include, but are not limited to, one or more of the following: making the subscriber's or other individuals, for example, measuring the genomic profile of the subscriber's child or pet, generating a phenotype profile To update the phenotype profile, and to generate a report based on the genomic and phenotype profile.

In another aspect of the present invention, it is possible to collect from such an individual through a "field-deployed" mechanism to obtain a phenotype profile for the individual. In a preferred embodiment, the individual may exhibit an initial phenotype profile formed based on genetic information. For example, an initial phenotype profile is formed that includes risk factors for different phenotypes and suggested therapeutic or prophylactic measures. For example, the profile may include drug information and / or suggestions regarding dietary changes or modes of exercise that may be used for any condition. The individual may choose to discuss the phenotype profile by consulting or contacting a specialist or genetic counselor via a web portal or telephone. The individual may decide to take courses of any action, such as taking specific medicines or changing their eating habits.

The subject may then submit a biological sample to evaluate changes in his or her physical condition and possible changes in risk factors. An individual may exhibit a change measured by submitting a biological sample directly to an organ that forms a genomic profile and a phenotype profile (or an organ that handles a substance that forms a gene profile and a phenotype profile). Alternatively, an individual may use a "on-site" mechanism, in which the individual sends his saliva, blood or other biological sample to a detection device at home, where it can be analyzed by a third party. The data can then be sent for integration into other phenotype profiles. For example, an individual may receive an initial written phenotype report based on genetic data indicating an individual with increased lifetime risk for myocardial infarction (MI). This report may also provide suggestions for prevention measures, such as cholesterol-lowering drugs and dietary changes, which reduce the risk of MI. The individual may choose to contact a genetic counselor or specialist to discuss reports and preventive measures, and may also decide to change eating habits. After a renewal of dietary habits over a period of time, each individual can have his or her dedicated specialist measure cholesterol levels. New information (cholesterol levels), along with new information used to form new phenotype profiles for individuals, including new risk factors for myocardial infarction and / or other conditions (eg, the Internet). Can be sent to the object.

Individuals may also measure individual responses to particular medications by utilizing "on-site" or direct mechanisms. For example, an individual may exhibit a measured drug response, and the information obtained may be used to determine a more effective treatment. Measurable information includes metabolite levels, glucose levels, ion (eg, calcium, sodium, potassium, and iron) levels, vitamins, blood cell counts, body mass index (BMI), protein levels, transcript levels, heart rate, and more. Including but not limited to, such information may be measured by methods that can be readily implemented, and may also be divided into algorithms that incorporate an initial genomic profile that measures a modified overall risk prediction score.

The term "biological sample" refers to any biological sample that can be separated from an individual, for example, a sample from which genetic material can be separated. As used herein, the term "genetic sample" refers to DNA and / or RNA obtained or derived from an individual.

As used herein, the term "genome" refers to the complete complement of chromosomal DNA found in the nucleus of human cells. The term "genomic DNA" refers to one or more chromosomal DNA molecules or portions of chromosomal DNA molecules that exist in nature in the nucleus of a human cell.

The term "genomic profile" refers to a set of information about a gene of an individual, such as the presence or absence of a particular SNP or whether a mutation occurs. Genomic profiles include the genotype of an individual. The genomic profile may also be the substantially complete genomic sequence of the individual. In some embodiments, the genomic profile may be at least 60%, at least 80%, or at least 95% of an individual's complete genomic sequence. The genomic profile may be about 100% of the complete genomic sequence of the individual. In the context of genomic profiles, “part thereof” means genomic profile for a subset of genomic profiles of the entire genome.

The term "genotype" refers to the specific genetic makeup of an individual's DNA. Genotypes may include genetic variants and markers of an individual. Genetic markers and variants may include nucleotide repeats, nucleotide insertions, nucleotide deletions, chromosomal translocations, chromosomal duplications or copy number variations. The copy number variation may include microsatelite repeats, nucleotide repeats, centriole repeats, or telomere repeats. Genotypes may also be SNPs, haplotypes or diplotypes. Haplotype may mean a position or allele. Haplotypes are also statistically related and may refer to a single nucleotide polymorphism (SNP) set present on a single chromosome. The diploid is a set of haploids.

The term single nucleotide polymorphism or “SNP” refers to a specific location on a chromosome that exhibits diversity, for example, at least 1%, with respect to the identity of nitrogen-containing bases present at any of the positions of the human population. For example, if one individual may have adenosine (A) at a specific nucleotide position of a given gene, the other individual may have cytosine (C), guanine (G) or thymine (T) at this position As a result, the SNP is present at this specific position.

As used herein, the term “SNP genomic profile” refers to the base content of a given individual DNA at an SNP position that is entirely present in the entire genomic DNA sequence of an individual. "SNP profile" may mean an entire genome profile, or it may mean a more localized SNP profile that may be associated with, for example, a particular gene or set of genes.

The term "phenotype" is used to denote a quantitative trait or characteristic of an individual. Phenotypes include, but are not limited to, medical and non-medical conditions. Medical conditions include diseases and disorders. Phenotypes also include physical traits such as hair color, physiological traits such as spirometry, mental traits such as memory retention, emotional traits such as the ability to control anger, race eg ethnic It may include, but is not limited to, a background, a family, for example, a point from which an individual originated, and an age, such as age expectancy or age at which a different phenotype begins to develop. Phenotypes are also contemplated as being the case where a single heredity, ie, one gene can be correlated with a phenotype, or where a multifactor inheritance, ie, when more than one gene can be correlated with a phenotype.

The term "rule" is used to define a correlation between genotype and phenotype. This rule can define correlations by numerical values, such as percentages, risk factors, or confidence scores. Rules can incorporate multiple genotype and phenotype correlations. A "rule set" includes one or more rules. A "new rule" may be a rule that does not currently exist, indicating a correlation between genotype and phenotype. New rules can also be correlated with genotypes that are not correlated with the phenotype. The new rule can also correlate genotypes that have already been correlated with phenotypes and phenotypes that were not originally correlated. The "new rule" may also be an existing rule modified by other factors, such as other rules. Existing rules can be modified through known characteristics of the individual, such as ethnicity, pedigree, habitat, gender, age, family history or other previously identified phenotypes.

As used herein, “genotype correlation” is used to refer to the occurrence of a genotype of an individual, such as any mutation or mutation, and a phenotype, such as a particular disease, condition, physical condition, and / or mental state. It means that there is a possibility of a postmark being created for. The frequency with which any phenotype is observed in the presence of specific genotypes determines the degree of correlation of the genotypes or the likelihood of expression of a particular phenotype. For example, as described in detail herein, SNPs that produce apolipoprotein E4 subtypes are associated with the formation of premature onset of Alzheimer's disease. Genotype correlations may also mean non-prestige correlations or negative correlations for phenotypes. Genotype correlations may also represent predictive results for an individual to express a phenotype or to be predisposed to express a phenotype. Genotype correlations can be indicated by numerical values such as percentages, relative risk factors, predictive results for effects, or confidence scores.

The term "phenotype profile" means a collection of multiple phenotypes correlated with an individual's genotype or genotypes. The phenotype profile may comprise information formed by applying one or more rules to a genomic profile, or to information about the correlation of genotypes applied to the genomic profile. Phenotypic profiles can be obtained by applying the rule that multiple genotypes and phenotypes are correlated. Probabilities or predictions can be expressed as percentages, numerical risk factors or numerical confidence intervals. Probabilities can also be expressed as high, medium or low. The phenotype profile may also indicate whether the phenotype is expressed or the risk of expression of the phenotype. For example, the phenotype profile may indicate whether blue eyes are present or whether the risk of developing diabetes is high. The phenotype profile may also indicate an expected prognosis, therapeutic efficacy, or response to treatment of a medical condition.

The term risk profile refers to a collection of GCI scores for one or more diseases or conditions. GCI scores are based on analysis of associations between genotypes of individuals with one or more diseases or conditions. In addition, the risk profile may represent GCI scores classified into disease categories. In addition, the risk profile may indicate information about predicting how the GCI score changes as the age of a particular individual or various risk factors are adjusted. For example, the GCI score for a particular disease may take into account the effects of a change in the ingested food or the effects of warped prevention measures (non-smoking, drug use, dual radical mastectomy, hysterectomy). GCI scores can be displayed as measured values, graphical display, auditory response, or any combination of the foregoing.

As used herein, the term “on-line portal” refers to an object readily available to an individual by using computer and internet websites, telephones or similar means of obtaining information. Means a source of information. The on-line portal may be a secure website. The website may provide links to other secure websites and non-secure websites, for example, to share a phenotype profile of a secure website and an entity, or a non-secure website, for example a specific phenotype. It can provide a link to the bulletin board of an object.

Unless otherwise stated, conventional techniques and knowledge of molecular biology, cell biology, biochemistry, and immunology known to those skilled in the art can be utilized in practicing the present invention. Such conventional techniques include nucleic acid separation, polymer array synthesis, hybridization, ligation, and methods for detecting hybridization using labels. Embodiments of suitable techniques are illustrated and cited herein. However, other conventional methods in the same range can also be used. Other conventional techniques and knowledge can be found in standard laboratory manuals and textbooks [e.g., Genome Analysis: A Laboratory Manual Series (Vols. I-IV), PCR Primer: A Laboratory Manual, Molecular Cloning: A Laboratory Manual. All published by the Cold Spring Harbor Laboratory Press); Stryer, L. (1995) Biochemistry (4th Ed.) Freeman, New York; Gait, "Oligonucleotide Synthesis: A Practical Approach" 1984, IRL Press, London, Nelson and Cox (2000); Lehninger, Principles of Biochemistry 3rd Ed., W.H. Freeman Pub., New York, N. Y .; And Berg et al. (2002) Biochemistry, 5th Ed., W.H. Freeman Pub., New York, N. Y .; These documents are incorporated by reference in their entirety herein for all purposes.

The methods of the present invention comprise analyzing the genomic profile of an individual to provide the individual with molecular information related to the phenotype. As described herein, an individual provides a genetic sample in which his genomic profile has been formed. Data on the genomic profile of an individual raises the question of genotype correlations by comparing profiles against databases of established and proven human genotype correlations. The above established and validated database of genotype correlations is based on the facts further determined and supplemented by mutually reviewed literature and by a committee of one or more practitioners within a specific field, for example, genetics, epidemiology or statistics. You can get it. In a preferred embodiment, the rules are made based on complementary genotype correlations and applied to the individual's genomic profile to form a phenotype profile. The results of analyzing the individual's genomic profile and phenotype profile, along with interpretation and supplemental information, are provided to the individual's healthcare manager individual, and as a result, are individually authorized to select the individual's healthcare as desired.

The method of the present invention is shown in FIG. 1, where a genomic profile of an individual is initially formed. The genomic profile of an individual will include information about the gene of the individual based on the genetic variation or marker. Genetic variation is the genotype that makes up the genomic profile. Such genetic variations or markers include single nucleotide polymorphisms, single and / or multiple nucleotide repeats, single and / or multiple nucleotide deletions, hypersatellite repeats (usually a few including 5 to 1,000 repeat units). Nucleotide repeats), di-nucleotide repeats, tri-nucleotide repeats, sequence rearrangements (including translocations and overlapping portions), and copy number variations (deletions and additions of specific positions), etc. no. Other genetic variations include chromosomal duplications and translocations, as well as isotope and telomere repeats.

Genotypes may also include haplotypes and diplotypes. In some embodiments, the genomic profile may have more than 100,000, 300,000, 500,000, or 1,000,000 genotypes. In some embodiments, the genomic profile can be substantially the complete genomic sequence of the individual. In other embodiments, the genomic profile is at least 60%, at least 80% or at least 95% of an individual's complete genomic sequence. The genomic profile may be about 100% of the complete genomic sequence of the individual. Gene samples that include a target include, but are not limited to, non-amplified genomic DNA or RNA samples, or amplified DNA (or cDNA). The target may be a specific site of genomic DNA comprising a genetic marker with a specific purpose.

In step 102 of FIG. 1, the genetic sample of the individual is separated from the biological sample of the individual. Such biological samples include, but are not limited to, blood, hair, skin, saliva, semen, urine, fecal material, sweat, buccal tissue and various body tissues. In some embodiments, the tissue sample may be collected directly by the subject, for example, the buccal sample may be obtained by the subject stealing the interior of his fire with a swab. Other samples such as saliva, semen, urine, feces or sweat may also be supplied by the subject itself. Other biological samples may be taken by a health care professional, such as a hematologist, nurse or specialist. For example, a blood sample can be collected from an individual by a nurse. Tissue biopsies can be performed by a health care professional, and kits for efficiently obtaining samples are also available to health care professionals. To remove small amounts of tissue or bodily fluid samples, small envelopes can be removed or needles can be used.

In some embodiments, a kit is provided to a subject equipped with a sample collection container for holding a biological sample of the subject. The kit may also provide instructions for the individual, that is, instructions that allow the user to collect his or her own sample (which indicates how much hair, urine, sweat or saliva should be collected). The kit may also include instructions to allow the individual to collect a tissue sample by a health care professional. The kit can also include a location where a sample can be collected by a third party, for example, the kit can be provided to a medical facility that collects a sample from an individual. The kit may also provide a recoverable packaging unit for the sample to be sent to the sample processing facility when the genetic material is separated from the biological sample in step 104.

Gene samples such as DNA or RNA can be isolated from biological samples according to any of several well-known biochemical and molecular biological methods (eg, Sambrook et al., Molecular Cloning: A Laboratory Manual (Cold). Spring Harbor Laboratory, New York (1989). Several kits and reagents for separating DNA or RNA from biological samples are commercially available (eg, DNA Genotek, Gentra Systems, Qiagen, Ambiion). And commercially available from other suppliers]. Buccal sample kits are also readily available on the market, such as MasterAmp ™ Buccal Swab DNA extraction kit (Epicentre Biotechnologies) and kits for extracting DNA from blood samples. There is an Extract-N-Amp ™ (Sigma Aldrich). DNA derived from other tissues can be obtained by digesting the tissue with proteolytic enzymes and heat, centrifuging the sample, extracting the unwanted material with phenol-chloroform, and then leaving the DNA in the aqueous phase. . The DNA can then be further separated by ethanol precipitation.

In a preferred embodiment, the genomic DNA is isolated from saliva. For example, using DNA self-collection kit technology (DNA Genotek), an individual collects saliva samples for clinical processing. The sample can be conveniently stored and left at room temperature. After sending the sample to a suitable laboratory for processing, typically at 50 ° C., using a reagent provided by a collection kit supplier for at least 1 hour, the sample is thermally denatured and digested with proteolytic enzymes to separate DNA. . The sample is then centrifuged and the supernatant is precipitated with ethanol. The resulting DNA pellet is suspended in a buffer suitable for subsequent analysis.

In other embodiments, RNA can be used as a genetic sample. In particular, expressed genetic modifications can be identified from mRNA. The term "messenger RNA" or "mRNA" includes pro-mRNA transcript (s), processing intermediates of transcripts, mature mRNA (s) for translation, and transcripts of genes or genes, or mRNA transcript (s). Nucleic acids derived from, but are not limited thereto. Transcript processing may include splicing, editing, and degradation. As used herein, a nucleic acid derived from an mRNA transcript refers to a nucleic acid in which the synthesis of an mRNA transcript or a dependent sequence thereof is ultimately used as a template. Therefore, cDNA reverse-transcribed from mRNA, DNA amplified from cDNA, RNA transcribed from amplified DNA, and the like are all derived from mRNA transcripts. RNA can be isolated from any of several body tissues using methods known in the art, such as the separation of RNA from unfractionated whole blood using PAXgene ™ Blood RNA Systems (PreAnalytiX). Can be. Typically, mRNA will be used to reverse transcribe cDNA, which will then be used or amplified for genetic modification analysis.

Prior to performing genomic profile analysis, genetic samples will typically be amplified from cDNA reverse transcribed from DNA or RNA. DNA can be amplified by a number of methods (mostly using PCR). See, eg, PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, N. Y., 1992); PCR Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic Press, San Diego, Calif, 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); And US Pat. Nos. 4,683,202, 4,683,195, 4,800,159, 4,965,188, and 5,333,675; Each of which is hereby incorporated by reference in its entirety for all purposes.

Other suitable amplification methods include ligase chain reaction (LCR) [eg, Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988) and Barringer et al. Gene 89: 117 (1990 )], Transcriptional amplification [Kwoh et al., Proc. Natl. Acad. Sci. USA 86: 1173-1177 (1989) and WO 88/10315, freestanding sequence replication [Guatelli et al., Proc. Nat. Acad. Sci. USA, 87: 1874-1878 (1990) and WO 90/06995, selective amplification of target polynucleotide sequences [US Pat. No. 6,410,276], polymerase chain reaction (CP-PCR) primed with consensus sequences (US Pat. 4,437,975), randomly primed polymerase chain reaction (AP-PCR) (US Pat. Nos. 5,413,909 and 5,861,245), nucleic acid-based sequence amplification (NABSA), rolling circle amplification (RCA) ), Multiple substitution amplification (MDA) (US Pat. Nos. 6,124,120 and 6,323,009) and circle-to-circle amplification (C2CA) [Dahl et al. Proc. Natl. Acad. Sci 101: 4548-4553 (2004); US Pat. Nos. 5,409,818, 5,554,517 and 6,063,603; Each of which is incorporated herein by reference. Other amplification methods that can be used are described in US Pat. Nos. 5,242,794, 5,494,810, 5,409,818, 4,988,617, 6,063,603, and 5,554,517 and US Patent Application Nos. ; Each of which is incorporated herein by reference.

In step 106 a genomic profile is generated using any of several methods. Several methods of identifying genetic modifications are known in the art, such as DNA sequencing by any of several methods, PCR-based methods, fragment length polymorphism assays (limited fragment length polymorphism (RFLP), cleavage) Fragment length polymorphism (CFLP)), hybridization methods using allele specific oligonucleotides as templates (e.g., TaqMan PCR method, invader method, DNA chip method), primer extension reaction Methods and mass spectral analysis (MALDI-TOF / MS method) and the like.

In one embodiment, high density DNA arrays are used for SNP identification and profile generation. Such arrays include Affymetrix and Illumina (Affymetrix GeneChip® 500K Assay Manual (Affymetrix, Santa Clara, CA; cited for reference); Commercially available from Sentrix® humanHap650Y Genotyping Bead Chips (Illumina, San Diego, Calif.).

For example, SNP profiles can be generated by genotyping over 900,000 SNPs using Affymetrix Genome Wide Human SNP Array 6.0. Alternatively, more than 500,000 SNPs through whole-genomic sampling assays can be determined using an Affymetrix GeneChip Human Mapping 500K Array Set. In such assays, a subset of the human genome is amplified via a single primer amplification reaction using adapter-ligated human genomic DNA digested with restriction enzymes. As shown in FIG. 2, the concentration of ligation DNA can then be measured. The amplified DNA is then fragmented, the quality of the sample is measured, and step 106 is continued. If the sample meets the PCR and fragmentation standards, the sample is denatured and labeled and then hybridized to a microarray containing small DNA probes at specific locations on the coated quartz surface. The amount of label hybridized to each probe, which is a function of the amplified DNA sequence, is monitored to obtain information about the sequence, which results in analyzing the SNP genotype.

According to the manufacturer's instructions, the Affymetrix Gene Chip 500K assay is performed. In short, the isolated genomic DNA is first digested with NspI or StyI restriction endonucleases. The digested DNA is then ligated with NspI or StyI adapter oligonucleotides (ie, oligonucleotides annealed to either NspI or StyI restriction DNA, respectively). The ligated adapter-containing DNA is then amplified by PCR to obtain amplified DNA fragments (identified by gel electrophoresis) that are about 200 to 1100 base pairs. Purify and quantify PCR products that meet amplification standards for fragmentation. PCR products are fragmented with DNaseI for optimal DNA chip hybridization. After fragmentation, the DNA fragments were less than 250 base pairs in size, on average about 180 base pairs (identified by gel electrophoresis). Subsequently, using the terminal deoxynucleotidyl transferase, samples meeting the fragmentation standard are labeled with the biotin compound. The labeled fragments are then denatured and then hybridized to the true chip 250K array. After hybridization, the array is stained, followed by a three-step process: streptavidin phycoerythrin (SAPE) staining → antibody amplification using biotinylated anti-streptavidin antibody (goat) → streptavi Scanning is done with a process consisting of a final staining process using Dean Picoerythrin (SAPE). After labeling, the array is immersed in array holding buffer and then scanned with a scanner such as the Affymetrix GeneChip Scanner 3000.

As shown in Fig. 3, according to the manufacturer's instructions, the data obtained after scanning with the Affymetrix Gene Chip Human Mapping 500K array set is analyzed. In short, raw data is obtained using GeneChip Operating Software (GCOS). This data can also be obtained using the Affymetrix GeneChip Command Console ™. The raw data is thus obtained and analyzed by GeneChip Genotyping Analysis Software (GTYPE). For the purposes of the present invention, samples with a GTYPE call rate of less than 80% are excluded. The sample is then analyzed by BRLMM and / or SNiPer algorithm analysis. Samples with less than 95% BRLMM call rate or samples with less than 98% SNiPer call rate are excluded. Finally, a combined assay is performed and samples with SNiPer quality index less than 0.45 and / or samples with Hardy-Weinberg p-values less than 0.00001.

As an alternative to or in addition to DNA microarray assays, genetic variations such as SNPs and mutations can be detected by DNA sequencing. DNA sequencing may also be used to sequence substantial portions, or the entire genomic sequence of an individual.

Typically, general DNA sequencing is based on polyacrylamide gel fractionation, which analyzes a population of chain-terminated fragments [Sanger et al., Proc. Natl. Acad. Sci. USA 74: 5463-5467 (1977). Alternative methods for increasing the speed and ease of DNA sequencing have been developed and are still being developed. For example, high throughput and single molecule sequencing platforms are commercially available or under development. (454 Life Sciences; Branford, CT) (Margulies et al., Nature (2005) 437: 376-380 ( 2005)); Solexa (Hayward, Calif.); Helicos BioSciences Corporation (Cambridge, MA) (US Patent Application No. 11/167046 (23 June 2005)), and Li-Cor Biosciences (Lincoln, NE) (US Patent Application No. 11/118031 (April 29, 2005)).

After generating the genomic profile of the individual in step 106, step 108 stores the profile digitally, where the profile can be stored digitally in a secure manner. The genomic profile can be encoded in a computer readable format, which will be stored as part of the data set, and also stored as a database, where the genomic profile can be "banked" and then obtained again. . The data set includes a plurality of data points, where each data point is associated with an entity. Each data point may hold a number of data elements. One data element is a unique identifier that is used to identify an individual's genomic profile. This may be a bar code. Other data elements include genotype information such as SNPs or nucleotide sequences of an individual's genome. Data elements corresponding to genotype information may also be included within this data point. For example, if genotyping information includes SNPs identified by microarray analysis, other data elements may include microarray SNP identification numbers, SNP rs numbers, and polymorphic nucleotides. Other data elements may be the chromosomal location of genotype information, quality metrics of this data, raw data files, data images, and deduced intensity scores.

Specific factors related to individuals, for example, physical data, medical data, ethnicity, household, habitat, gender, age, family history, known phenotypes, demographic data, exposure data, lifestyle data , Behavioral data, and other known phenotypes may also be incorporated as data elements. For example, examples of such factors include those relating to an individual, such as place of birth, parents and / or grandparents, household with cousin, residence, ancestor's residence, environmental conditions, known health conditions, known drug interactions. , Family health conditions, lifestyle conditions, eating habits, exercise habits, marital status, and physical measurements such as weight, height, cholesterol levels, heart rate, blood pressure, glucose levels, and other measurements known in the art. It may include, but is not limited thereto. The factors for relatives or ancestors of the aforementioned individuals, for example parents and grandparents, may also be incorporated as data elements and used to determine the risk of developing a phenotype or condition in the individual.

Specific factors may be obtained from questionnaires or from the individual's health care manager. The information obtained from the "accumulated" profile can then be obtained and used as desired. For example, in the initial evaluation of an individual's genotype correlations, global information about the individual (typically, SNPs or other genomic sequences that are present or derived from the entire genome) is analyzed for genotype correlations. Will be. In subsequent assays, either whole information, or portions thereof, can be obtained from the stored or accumulated genomic profile, as desired or suitably.

Database comparison of genome profile and genotype correlation

In step 110, genotype correlations may be obtained from the scientific literature. Genotype correlations for genetic variation are determined by analyzing the presence of one or more expressing traits of interest and the population of individuals tested for the genotype profile. Then, the alleles in which each gene mutation or polymorphism exists in the profile are examined, and as a result, it is possible to confirm whether or not a specific allele is associated with the target trait. Correlations can be analyzed by statistically standard methods, and correlations between statistically significant genetic variations and phenotypic characteristics are recorded. For example, it can be seen that there is a correlation between the presence of polymorph A in allele A1 and the occurrence of heart disease. As a further example, the presence of polymorph A in allele A1 and polymorph B in allele B1 correlates with an increased risk of developing cancer. The results of the analysis are published in the mutually reviewed literature, can be substantiated by other research groups, and / or can be verified, analyzed by experts such as geneticists, statisticians, epidemiologists and specialists, It may be supplemented.

4, 5 and 6 show examples of correlations between genotypes and phenotypes that can be based on rules to be applied to genomic profiles. For example, in Figures 4A and 4B, each column corresponds to a phenotype / position / ethnicity, where Figures 4C-4I contain additional information regarding the correlation of each column. For example, in FIG. 4A, the abbreviated phenotype name BC is an index for the abbreviated phenotype name, as shown in FIG. 4M, and is an abbreviation for breast cancer. In the column BC_4 (general name for location), the gene LSP1 is correlated with breast cancer. The functional SNP that has been disclosed or found to be correlated is rs3817198 (see FIG. 4C), wherein the published risk allele is C and the non-risk allele is T. Published SNPs and alleles are identified through published literature such as seminar publications (see FIGS. 4E-4G). In the example of LSP1 in FIG. 4E, the seminar publication is Easton et al., Nature 447: 713-720 (2007). 22 and 25 further list information about correlation. Correlations in FIGS. 22 and 25 can be used to calculate the risk of developing a condition or phenotype in an individual, for example, to calculate a GCI score or a GCI plus score. The GCI score or the GCI plus score may also incorporate information such as the prevalence of the condition (see, eg, FIG. 23).

Alternatively, correlations can be generated from stored genomic profiles. For example, an individual representing a stored genomic profile may also have known phenotypic information that has been stored. By analyzing stored genome profiles and known phenotypes, genotype correlations can be determined. For example, 250 individuals representing stored genomic profiles can store information that they had previously been diagnosed with diabetes. Their genomic profile is analyzed and the results compared to a control group of individuals who do not develop diabetes. It is then found that individuals who have previously been diagnosed with diabetes have a higher rate of mutation of certain genes than controls, and that genotypes between such gene variants and diabetes may be correlated.

In step 112, rules are determined based on the proven correlation of genetic variants to specific phenotypes. For example, the rules can be determined based on the correlated genotypes and phenotypes listed in Table 1. Correlation-based rules incorporate other factors, such as gender (eg, FIG. 4) or ethnicity (FIGS. 4 and 5), to predict the effect (eg, FIGS. 4 and 5). You can make it work. Other measurements obtained from these rules can be used to predict whether the relative risk will increase (FIG. 6). Predictions of the effects and whether the predicted relative risk increases can be grasped from published literature or calculated from published literature. Alternatively, the rules may be based on correlations between stored genomic profiles and previously known phenotypes. In some embodiments, the rule is based on the correlations shown in FIGS. 22 and 25.

In a preferred embodiment, the genetic variant will be SNP. SNPs are generated at one location, and individuals with specific SNP alleles at one location often have specific SNP alleles at other locations. The correlation of alleles and SNPs in individuals that predispose to diseases and conditions is formed through linkage disequilibrium, in which case non-random associations of alleles at two or more locations are present. In either population, occurs somewhat more frequently than expected associations from random formation through recombination.

Other genetic markers or variants, such as nucleotide repeats or inserts, may also be in an imbalanced association with genetic markers that are believed to be associated with specific phenotypes. For example, nucleotide insertions are correlated with phenotypes, and SNPs are in an unbalanced association with nucleotide insertions. Rules are formed based on the correlation between SNPs and phenotypes. Rules based on the correlation between nucleotide insertion and phenotype can also be formed. Either rule or both rules can be applied to the genomic profile, i.e., when an SNP and any risk factor can be combined to increase the risk, the presence of one SNP means that any risk factor Other SNPs may form other risk factors.

Through linkage disequilibrium, disease predisposition alleles are cosegregated with a combination of specific alleles of SNPs or specific alleles of SNPs. Particular combinations of SNP alleles present along the chromosome are called haploids, and DNA sites with SNP alleles together may be called haploid blocks. Haploid blocks may consist of one SNP, whereas haploid blocks typically represent a contiguous series of two or more SNPs, with low haploid diversity between individuals and generally low recombination frequency. The haplotype can be identified by identifying one or more SNPs present in the haplotype block. Therefore, SNP profiles can typically be used to identify haploid blocks without necessarily identifying all SNPs present in a given haploid block.

The genotype correlation between SNP haploid pattern and disease, condition or physical condition is increasingly known. For a given disease, the haploid pattern of the group of people known to have developed the disease is compared to the haploid pattern of the group of people who have not developed the disease. By analyzing a large number of individuals, the frequency of expression of polymorphisms in a population can be measured, and such incidence or genotype can be associated with a particular phenotype, eg, disease or condition. Examples of known SNP-disease correlations include polymorphisms in complement factor H in age-related macular degeneration (Klein et al., Science: 308: 385-389 (2005)) and the INSIG2 gene associated with obesity Variants which exist adjacent to (Herbert et al., Science: 312: 279-283 (2006)). Other known SNP correlations include polymorphisms in 9p21 sites including CDKN2A and B, for example rs10757274, rs2383206, rs13333040, rs2383207, and rs1O116277 correlated with myocardial infarction [Helgadottir et al., Science 316: 1491 -1493 (2007); McPherson et al., Science 316: 1488-1491 (2007)].

The SNP may be functional or non-functional. For example, functional SNPs express phenotypes by affecting cellular function, while non-functional SNPs may be silent in function but in associative imbalance with functional SNPs. The SNPs may also be the same or not the same. Identical SNPs are SNPs that produce identical polypeptide sequences due to different forms, which are non-functional SNPs. If the SNPs produce different polypeptides, the SNPs may not be identical and may or may not be functional. SNPs or other genetic markers used to identify haplotypes in diplotypes consisting of two or more haplotypes may also be used to correlate phenotypes associated with diplotypes. Information about an individual's haplotype, diplotype, and SNP profile may be within the individual's genomic profile.

In a preferred embodiment, for a rule formed based on a genetic marker that is in associative imbalance with other genetic markers correlated with the phenotype, the genetic marker is generally used to determine an r ² or D 'score, i.e., associative imbalance in the art. The score used may be at least 0.5. In a preferred embodiment, the score is at least 0.6, 0.7, 0.8, 0.90, 0.95 or 0.99. As a result, in the present invention, the genetic marker used to correlate the phenotype with the genomic profile of the individual may be the same as or different from the functional SNP or the published SNP correlated with the phenotype. For example, when using BC_4, the test SNP and the published SNP are the same, which is the same for the test risk, and also the nonrisk allele is the same with the published risk and non-risk alleles 4a and 4c). However, in the correlation of BC_5, CASP8 and this to breast cancer, the test SNP is different from its functional SNP or published SNP, which is a test risk allele and a non-risk allele for published and non-risk alleles. The same is true for traits. Test alleles and published alleles are relatively biased with the (+) chain of the genome, from which it is possible to infer homozygous risk genotypes or non-risk genotypes, thus allowing for the addition of an individual, e. The rule to be applied can be formed. In some embodiments, test SNPs cannot be identified, but by using published SNP information, allelic differences or SNPs can be identified based on other assays such as TaqMan. For example, AMD_5 of FIG. 25A, the published SNP, is rs1061170, but no test SNP was identified. The test SNPs can be identified by LD assay using published SNPs. Alternatively, instead of being unable to use the test SNP, a Taqman or other comparative assay will be used to evaluate the genome of the individual having the test SNP.

The test SNP may be a "DIRECT" or "TAG" SNP (FIGS. 4E-4G and 5). The DIRECT SNP is the same test SNP as the published or functional SNP, for example SNP for BC_4. DIRECT SNP may also be used for FGFR2 correlation with breast cancer, using SNP rs1073640 from Europeans and Asians, in which case the allele is A and the other allele is G [Easton et al., Nature 447: 1087-1093 (2007). Another SNP for FGFR2 correlations for published and functional breast cancer is rs1219648, which can also be found in Europeans and Asians [Hunter et al., Nat. Genet. 39: 870-874 (2007). If the test SNP is different from the functional or published SNP, the Tag SNP is the same as in the case of BC_5. Tag SNPs also include SNPs for other gene variants such as CAMTA1 (rs4908449), 9p21 (rs10757274, rs2383206, rs13333040, rs2383207, rs10116277), COL1A1 (rs1800012), FVL (rs6025), HLA-DQA1 (rs2588331) rs4988889 , eNOS (rs1799983), MTHFR (rs1801133) and APC (rs28933380) can also be used.

Databases of SNPs can be found, for example, in the International HapMap Project [www.hapmap.org, The International HapMap Consortium, Nature 426: 789-796 (2003), and The International HapMap Consortium, Nature 437: 1299-1320 (2005). ], Publicly available from the Human Gene Mutation Database (HGMD) Public Database (see www.hgmd.org), and the Single Nucleotide Polymorphism Database (dbSNP) (www.ncbi.nlm.nih.gov/SNP/). . Such a database can provide SNP diploids or allow the determination of SNP diploid patterns. Therefore, this SNP database allows the observation of the genetic risk factors inherent in a wide range of diseases and conditions, such as cancer, inflammatory diseases, cardiovascular diseases, neurodegenerative diseases and infectious diseases. A disease or condition can be brought against it, where the treatments and treatments associated with the disease or condition are currently being performed. Therapies may include prophylactic therapies and therapies to alleviate symptoms and diseases, such as lifestyle changes and the like.

Many other phenotypes can also be observed, such as physical traits, physiological traits, mental traits, emotional traits, ethnicity, family and age. Physical traits may include height, hair color, eye color, body, or traits such as physical strength, persistence and agility. Mental traits may include intelligence, memory or learning ability. Ethnicity and ancestry can include identifying ancestors or nations, ie, analyzing ancestors from which an individual originated. Age can be measured by the actual age of the individual or by the age at which the genetic population of the individual is associated with the entire population. For example, even if the actual age of an individual is 38 years old, the genetic characteristics of the individual may determine memory capacity, or the average physical health age may be 28 years old. Other age-dependent traits may determine the longevity of either individual.

Other phenotypes may also include non-medical conditions, eg, “fun” phenotypes. Such phenotypes may include results compared to well-known entities such as foreign celebrities, politicians, celebrities, inventors, athletes, musicians, artists, businessmen, and infamous figures such as criminals. . Other “interesting” phenotypes may include the results of comparisons with other organisms such as bacteria, insects, plants or animals other than humans. For example, an individual may be interested in how a person's genomic profile compares to the pet dog he is raising or a former president.

In step 114, rules are applied to the stored genomic profile to form the phenotype profile of step 116. For example, the information in FIGS. 4-6 may form the basis of rules or tests for applying the genomic profile of an individual. The rule may include test SNPs and alleles, and the effect estimates of FIG. 4, wherein the unit for the effect predictions (UNIT) is a unit of the effect prediction, for example, OR, or crossover ratio (95% confidence interval). ) Or units of average. In a preferred embodiment, the predictive effect may be genotype risk, eg, risk for homozygotes (homoz or RR), risk heterozygotes (heteroz or RN), and non-risk homozygotes (homoz or NN) (FIG. 4C). To 4 g). In other embodiments, the effect prediction may be a carrier risk, ie, RR or RN vs NN. In another embodiment, the effect estimate is an allele, an allele risk, eg, R vs. Can be based on N. In addition, there may be genotyping effect estimates (eg, RRRR and RRNN in nine possible genotype combinations for two positional effect predictions) at two positions (FIG. 4J) or three positions (FIG. 4K). . The frequency of expression of test SNPs in the public hap map is also shown in FIGS. 4H and 4I.

In other embodiments, the information obtained from FIGS. 21-23 and / or 25 can be used to generate information that applies to the genomic profile of an individual. For example, the information can be used to generate a GCI score or a GCI plus score in an individual (eg, FIG. 19). The score may also be used to generate genetic risk for one or more conditions in an individual's phenotype profile, eg, information regarding predicted lifetime risk (eg, FIG. 15). In this way, one can calculate lifetime predicted risk or relative risk for one or more phenotypes or conditions as listed in FIG. 22 or 25. Risk for one condition can be known based on one or more SNPs. For example, the predicted risk for a phenotype or condition can be based on 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more SNPs, where SNPs can be published SNPs, test SNPs, or both (see, eg, FIG. 25).

The predicted risk for either condition can be based on the SNPs listed in FIG. 22 or 25. In some embodiments, the risk for either condition may be based on one or more SNPs. For example, individual risk assessment for Alzheimer's disease (AD), colorectal cancer (CRC), osteoarthritis (OA), or exfoliative glaucoma (XFG) can be based on 1 SNP [eg, rs4420638 for AD. Rs6983267 for CRC, rs4911178 for OA, and rs2165241 for XFG. For other conditions, such as obesity (BMIOB), Graves' disease (GD), or hemochromatosis (HEM), the predicted risk of an individual may be based on one or more SNPs (eg, rs9939609 for BMIOB). And / or rs9291171; For GD DRB1 * 0301 DQA1 * O5O1 and / or rs3087243; Rs1800562 and / or rs129128 for HEM. Conditions For example, in myocardial infarction (MI), multiple sclerosis (MS) or psoriasis (PS), one, two or three SNPs can be used to assess an individual's risk for any particular condition [eg , Rs1866389, rs1333049 and / or rs6922269 for MI; For MS rs6897932, rs12722489 and / or DRB1 * 1501; Rs6859018, rs11209026 and / or HLAC * 0602 for PS. In predicting individual risk of lower extremity anxiety syndrome (RLS) or celiac disease (CelD), one, two, three or four SNPs (e.g., rs6904723, rs2300478, rs1O26732 and / or rs9296249 in CelD; rs6840978, rs11571315, rs2187668 and / or DQA1 * 0301 DQB1 * 0302) may be used. In prostate cancer (PC) or lupus disease (SLE), one, two, three, four, or five SNPs (e.g., rs4242384, rs6983267, rs16901979 for PC) in order to predict an individual's risk for PC or SLE. rs12576511, rs10954213, rs2004640, DRB1 * 0301 and / or DRB1 * 1501 may be used for the SLE. In predicting the lifetime risk of macular degeneration (AMD) or rheumatoid arthritis (RA) in any individual, one, two, three, four, five, or six SNPs may be used (eg, rs10737680 for AMD). , rs10490924, rs541862, rs2230199, rs1061170 and / or rs9332739; Rs6679677, rs11203367, rs6457617, DRB * 0101, DRB1 * 0401 and / or DRB1 * 0404 for RA]. In predicting the lifetime risk of any individual for breast cancer (BC), 1, 2, 3, 4, 5, 6 or 7 SNPs can be used [eg, rs3803662, rs2981582, rs4700485, rs3817198, rs17468277 , rs6721996 and / or rs3803662]. In predicting the lifetime risk of an individual for Crohn's disease (CD) or type 2 diabetes (T2D), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11 SNPs [For example, rs2066845, rs5743293, rs10883365, rs17234657, rs10210302, rs9858542, rs11805303, rs1000113, rs17221417, rs2542151 and / or rs10761659 for CD; Rs13266634, rs4506565, rs10012946, rs7756992, rs1O811661, rs12288738, rs8050136, rs1111875, rs4402960, rs5215, and / or rs1801282 for T2D. In some embodiments, the SNPs used as the basis for measuring risk may be as described above, or an association imbalance with the SNPs as listed in FIGS. 22 or 25.

The phenotype profile of an individual may include multiple phenotypes. In particular, when assessing the risk of a disease or other condition of a patient, eg, drug reactivity, e.g., metabolism, efficacy and / or safety, by a method of the invention, a symptomatic, symptomatic or asymptomatic Depending on whether the individual possesses an allele having a predisposition to one or more diseases / conditions, prognostic or diagnostic analysis of multiple and unrelated diseases and conditions may be performed. Therefore, these methods generally assess an individual's susceptibility to a disease or condition without making any predictions to test for that particular disease or condition. For example, the methods of the present invention allow to assess an individual's susceptibility to any of several of the conditions listed in Table 1, FIG. 4, 5 or 6, based on the genomic profile of the individual. In addition, the methods of the present invention allow to assess a subject's predicted lifetime risk or relative risk to one or more phenotypes or conditions (see, eg, FIG. 22 or 25).

Through the evaluation, information is preferably provided for at least two of these conditions, more preferably at least three, four, five, ten, twenty, fifty, one hundred or more of these conditions. In a preferred embodiment, the phenotype profile is generated by applying at least 20 rules on the genomic profile of the individual. In other embodiments, at least 50 rules are applied to an individual's genomic profile. One rule for a phenotype can be applied to a phenotype with a single gene. One or more rules may also be applied to one phenotype, for example, a phenotype by multiple genes or a phenotype by a single gene, in which case multiple gene variants in one gene affect the likelihood of exhibiting the phenotype.

After initially screening the genomic profile of an individual patient, when additional nucleotide variants are known, the correlation of the genotype of either individual is updated (or compared to such additional nucleotide variants, eg, SNPs). Can be updated). For example, step 110 may be performed on a regular basis (e.g. daily, weekly or monthly) by one or more persons skilled in the arts of genetics (i.e., the person who read the scientific paper on the correlation of new genotypes). Can be implemented. The new genotype correlations can then be further demonstrated by a committee of one or more of those skilled in the art. Thereafter, step 112 may also be updated periodically with new rules based on newly proven correlations.

The new rule may include genotypes or phenotypes except for existing rules. For example, genotypes not correlated with any phenotype are understood to be correlated with new or existing phenotypes. The new rule may also be for correlations between phenotypes that were not originally correlated with genotypes. New rules may also be determined for genotypes and phenotypes that include existing rules. For example, there are rules based on the correlation between genotype A and phenotype A. New research shows that genotype B correlates with phenotype A and that new rules are formed based on this correlation. Another example is phenotype B, which has been found to be associated with genotype A, so that new rules can be formed.

Rules can also be formed through facts based on known correlations that are not first identified in published scientific literature. For example, genotype C can be considered to be correlated with phenotype C. Other documents report that genotype D correlates with phenotype D. Phenotypes C and D are related symptoms, for example, phenotype C expresses shortness of breath and phenotype D expresses small spirometry. The correlation between genotype C and phenotype D, or genotype D with phenotype C, is a statistical method using genotypes C and D and the currently stored genomic profiles of individuals with phenotypes C and D, but for further study. Can be identified and verified. New rules can then be formed based on newly identified and proven correlations. In other embodiments, studying stored genomic profiles with respect to a number of individuals exhibiting a particular phenotype or related phenotype can determine genotypes common to the individual and correlate them. New rules can be formed based on this correlation.

Existing rules may be modified to form other rules. For example, the correlation between genotype and phenotype can be measured in part by known characteristics of the individual, such as ethnicity, pedigree, topography, sex, age, family history, or any phenotype known about the individual. . When a rule based on the characteristics of the known entity is formed and incorporated into an existing rule, a modified rule is formed. Selecting the correction rule to be applied will depend on the specific individual parameters of the entity. For example, the rule may be based on the probability of an individual having a phenotype E of 35% when the individual has genotype E. However, if the individual has a certain ethnicity, the probability is 5%. Based on this result, a new rule is formed, and this rule can be applied to an entity having a specific ethnicity. Alternatively, existing rules of 35% measure may be applied, followed by other rules based on ethnicity for such phenotypes. Rules based on the characteristics of known individuals can be measured from scientific literature or can be determined based on the results of studying stored genomic profiles. As new rules may be developed or applied regularly (eg, more than once a year), new rules may be added and applied to the genomic profile in step 114.

As technology advances, the SNP genomic profile can be analyzed more precisely, so information about the risk of developing disease in an individual can also be expanded. As mentioned above, the initial SNP genomic profile can be readily formed using microarray technology to scan 500,000 SNPs. Given the properties of haploid blocks, the numbers correspond to each profile of every SNP in the individual's genome. Nevertheless, about 10 million SNPs are generally expected to form within the human genome [International Hap Map Project; www.hapmap.org]. As technological advances evolve, for example, microarrays containing 1,000,000, 1,500,000, 2,000,000, 3,000,000 or more SNPs, or whole genome sequencing methods, SNPs can be brought to a more precise and practical level at a more practical and cost-effective level. This allows for analysis, and more specifically, SNP genomic profiles. Similarly, computer analysis can be further developed by analyzing the master database for SNP-disease correlation with cost-effective assays for more precise SNP genome profiles and updates.

After generating the phenotype profile in step 116, the subscriber or his healthcare manager can obtain the genomic profile or phenotype profile through an on-line portal or website as in step 118. Reports including phenotype profiles and other information related to phenotype profiles and genomic profiles may also be provided to the subscriber or their healthcare manager, as in steps 120 and 122. The report can be printed, stored on the subscriber's computer, or confirmed on-line.

An exemplary on-line report is shown in FIG. The subscriber may be selected to display one phenotype or one or more phenotypes. The subscriber may also have different viewing options, such as, for example, a "Quick View" option as shown in FIG. The phenotype may be a medical condition and the different treatment methods and symptoms in the instant report may be linked to other web pages that include additional information about the treatment method. For example, by clicking on a drug, you will be taken to a website that contains information about the drug's dosage, cost, side effects, and efficacy. You can also compare how you use the drug with other therapies. The website may also include a link to the drug manufacturer's website. The other link may provide an option for the subscriber to form a pharmaceutical genomic profile that includes information such as predictive response to the drug based on the subscriber's genomic profile. Alternatives to drug administration include, for example, precautions such as links to fitness and weight loss, links to dietary supplements and diet plans, and nearby health clubs, health clinics, health and preventive health care ( Links to wellness providers, day spas, and the like may also be provided. Instructional videos and informational videos, actionable treatments, actionable therapies, and summary information on general recommendations may also be provided.

The on-line report may also provide a schedule for scheduling an individual specialist or genetic counseling, or a link to contact an on-line genetic counselor or specialist, while at the same time providing more information about the subscriber's phenotype profile. There is also an opportunity to request information. Links to on-line genetic counseling and specialist questions may also be provided on the on-line report.

Reports may also be reviewed in other formats, such as in a comprehensive way of looking at one phenotype, in which case more details are provided for each category. For example, more detailed statistical data on the likelihood of a subscriber expressing a phenotype, more information about a typical symptom or phenotype, for example illustrative symptoms for a medical condition, or a range of non-medical physical conditions, for example. For example, there may be height, or more information about genes and gene variants, for example, population incidence, for example, in the world, or in other countries, or in a population of different ages or genders. . For example, FIG. 15 summarizes the predicted lifetime risk for multiple conditions. The individual may look for more information about a particular condition, such as prostate cancer (FIG. 16) or Crohn's disease (FIG. 17).

In other embodiments, the report may consist of a similarity of the “interesting” phenotype, eg, the genomic profile of a celebrity, eg, Albert Einstein, with the genomic profile of an individual. The report may indicate the percent similarity between the genomic profile of an individual and the genomic profile of Einstein, and may further indicate the expected IQ of Einstein and the expected IQ of the individual. Additional information may include how the genome profile of the entire population and the IQ of these members are compared with the genome profile and IQ of either individual and Einstein.

In other embodiments, the report may represent all phenotypes correlated with the subscriber's genomic profile. In another embodiment, the report can only show phenotypes that are positively correlated with the genomic profile of the individual. In other forms, the individual may choose to represent any subgroup of phenotypes, for example, medical phenotypes, or medical phenotypes that may be brought to litigation. For example, the phenotypes that can be filed and their correlation genotypes include Crohn's disease (correlated with IL23R and CARD15), type 1 diabetes (correlated with HLA-DR / DQ), Lupus disease (with HLA-DRB1). Correlated), psoriasis (correlated with HLA-C), multiple sclerosis (correlated with HLA-DQA1), Graves' disease (correlated with HLA-DRB1), rheumatoid arthritis (correlated with HLA-DRB1), second Type diabetes mellitus (correlated with TCF7L2), breast cancer (correlated with BRCA2), colon cancer (correlated with APC), transient memory (correlated with KIBRA), and osteoporosis (correlated with COL1A1). The individual may also be selected to show only subcategories of phenotypes in the report, such as inflammatory diseases for medical conditions, or physical traits for non-medical conditions. In some embodiments, the individual may choose to represent all conditions, and the predicted risk may highlight these conditions (eg, FIGS. 15A and 15D), highlight only those with increased risk (FIG. 15B), Or by highlighting only the risk-reduced condition (FIG. 15C), it can be calculated for either individual.

Information submitted by an entity and sent back to the entity is security and confidential, and obtaining such information can be controlled by the entity. Information derived from complex genomic profiles may be supplied to an individual as data on authorized regulatory agencies, easy to understand, medically relevant and / or powerful influences. Information may also be of general interest and may not be medically relevant. Information can be securely delivered to the entity by some means, for example, by a portal interface and / or by mail. More preferably, the information is provided securely (in the manner chosen by the entity) to the entity by the portal interface, in which case the entity takes a secure and confidential way of obtaining it. Such an interface is preferably provided on-line, via an Internet website, or alternatively, via a telephone or other means that are personal, secure and readily available. Genomic profiles, phenotype profiles, and reports are provided to individuals or their health care managers by data transfer over the network.

Therefore, FIG. 18 is a block diagram illustrating representative logical devices from which phenotype profiles and reports can be created. Fig. 18 receives and stores genome profiles, analyzes genotype correlations, generates rules based on the results of genotype correlations, and applies these rules to genome profiles to generate phenotype profiles and reports. A computer system (or digital device) 800 is shown. The computer system 800 can be understood as a logical device capable of reading instructions from the medium 811 and / or network port 805, which can be arbitrarily connected to the server 809 to which the medium 812 is fixed. have. The system shown in FIG. 8 includes a CPU 801, a disk drive 803, any input device such as a keyboard 815 and / or mouse 816, and any monitor 807. Data communication may reach a server 809 in a local or remote storage location, via a designated communication medium. The communication medium may comprise any means for transmitting and / or receiving data. For example, the communication medium may be a network connection, a wireless connection or an internet connection. Such a connection may provide communication via the World Wide Web. It will be appreciated that data related to the present invention may be transmitted via a network or access means for receipt and / or review by a party 822. Receiving party 822 may be, but is not limited to, an entity, subscriber, health care provider, or health care manager. In one embodiment, the computer-readable medium includes a medium suitable for transmitting the results of analysis of a biological sample or genotype correlation. The medium may comprise results relating to the phenotype profile of the subject individual, where such results are obtained using the methods disclosed herein.

The personal portal will preferably be used as the primary interface with the individual for receiving and evaluating genomic data. The portal will allow the entity to track the sample processing from the data collected through the tests and the results. By accessing the portal, an individual introduces a relative risk for common genetic disease, based on its genomic profile. Subscribers can select which rules to apply to their genomic profile via the portal.

In one embodiment, one or more web pages will list a phenotype list, and the box next to each phenotype is a box that allows the subscriber to select which of his phenotype profiles to include. The phenotype can be linked to information about the phenotype, which helps the subscriber make a wise choice for the phenotype that he wishes to include in his phenotype profile. The web page may also inform whether a phenotype organized by disease group, for example, an actionable disease or not. For example, the subscriber may select only phenotypes for filing a lawsuit, such as HLA-DQA1 and celiac disease. The subscriber may also choose to display a pre or post treatment method for the phenotype. For example, an individual may select a phenotype that may be sued for a symptomatic treatment method (eg, for celiac disease, a symptomatic treatment with a meal that does not contain gluten). As another example, for Alzheimer's disease, prophylactic treatment methods such as statin treatment, exercise, vitamin intake and mental activity recommendation. Another example is thrombosis, in which the method of treating prognosis is to avoid taking oral contraceptives and avoid sitting for a long time. An example of a phenotype that will perform an approved post-treatment method is wet AMD, which correlates with CFH, in which case an individual may apply laser therapy to his or her condition.

Phenotypes can also be organized by type or group, for example, neurological, cardiovascular, endocrine system, immune disease or condition. Phenotypes may also be classified into medical phenotypes and non-medical phenotypes. Phenotypes can also be classified by physical traits, physiological traits, mental traits or emotional traits on a webpage. The webpage may further provide a screening method for selecting any phenotype group by selecting one box. For all phenotypes, only medically relevant phenotypes, only non-medically related phenotypes, phenotypes that can only be sued, phenotypes that can not be sued, different disease groups or "interesting" phenotypes An example is the screening method. “Interesting” phenotypes can include results of comparisons with celebrities or other prominent individuals, or with other animals or other organisms. A list of genomic profiles useful for comparison may also be provided by the subscriber on a screening webpage to compare the subscriber's genomic profile.

The on-line portal may also provide a search engine that helps subscribers search the portal, search for specific phenotypes, or search for specific terms or information identified by phenotype profiles or reports. . Links to enable partner services and product delivery may also be provided by the portal. Additional links may also be provided to social gatherings, bulletin boards, and chat rooms of entities with common or similar phenotypes. The on-line portal can also provide links to other sites that have more information about phenotypes in the subscriber's phenotype profile. The on-line portal may also provide services that allow subscribers to share their phenotype profiles and reports with friends, family, or health care managers. Subscribers can choose which phenotypes to show in their phenotype profile that they wish to share with their friends, family or health care manager.

Phenotypic profiles and reports provide correlated genotypes for individuals. The genotype correlations provided to an individual can be used to determine an individual's health care practices and to make lifestyle choices. If a method of treatment is observed to have a strong correlation between useful disease and genetic variants, the method of detection of genetic variants may help to determine the treatment of the disease and / or to initiate monitoring of the subject. If a statistically significant correlation exists but is not considered to be a strong correlation, the individual may review the information with a dedicated professional physician, thereby deciding to take appropriate and useful measures. Potential measures that could have been useful to an individual from the point of view of specific genotype correlations include the implementation of a treatment method, monitoring the potential need for a treatment method or the effectiveness of the treatment method, eating habits, exercise and other personal habits / behaviors. It includes changing the way of life in Esau. For example, a phenotype that can bring a lawsuit, for example, celiac disease, can take a proliferative treatment through diet that does not contain gluten. Similarly, genotype correlation information can be applied through pharmacogenetic methods to predict the response that may occur, for example, the efficacy or safety associated with a particular drug treatment, and subjects may be subject to specific drug or You will be treated with this medication.

Subscribers can choose to provide their genomic profile and phenotype profile to their healthcare manager, eg, specialist or genetic counselor. Genomic and phenotype profiles can be obtained directly from a health care manager, a member can print out a copy and provide it to the health care manager, or link the health care manager to an on-line portal, e.g., an on-line report. You can send it directly.

If such relevant information is communicated, the patient will be able to take action with his or her specialist. In particular, through links to medical information and personal portals, and the ability to relate the patient's genomic information to the patient's own medical record, it may be valid if the patient and his dedicated physician discuss with each other. Medical information may include preventive and preventive health care information. The information provided to an individual patient by the present invention will allow the patient to make a smart choice about his or her health care. In this way, patients will be able to make choices that can help prevent and / or delay disease that makes their personal genomic profile (genetic DNA) more similar. In addition, patients will be able to use therapies to tailor their specific medical needs to their individual. Individuals will also have the right to obtain their own genotype data, which will provide information that will help them improve the disease and help their specialists formulate treatment strategies.

Information on genotype correlations may also be used along with the results of genetic counseling to advise couples considering pregnancy and potential genetic significance for mothers, parents and / or children. Genetic counselors can provide information and support to subscribers with phenotype profiles with increased risk for specific conditions or diseases. They can explain information about the disease, analyze genetic patterns and risks of recurrence, and review options that may apply to you. Genetic counselors may also provide assistive counseling, such as inviting subscribers to join a community or city support service. Genetic counseling can be included with specific subscription plans. In some embodiments, genetic counseling can be scheduled within 24 hours of request and executed at a specific time, such as, for example, evening, Saturday, Sunday, and / or holiday.

The portal of the entity will also facilitate the delivery of additional information that is superior to the initial screening results. The individual will be informed of new scientific findings related to his genetic profile, e.g., information about new treatment methods or prevention strategies for his current or potential condition. New discoveries can also be communicated to the individual's own healthcare manager. In a preferred embodiment, the subscriber or his or her health care provider, by e-mail, is informed of the newly identified study results regarding the correlation and phenotype of the new genotype in the subscriber's phenotype profile. In another embodiment, an e-mail regarding an "interesting" phenotype is sent to the subscriber, e.g., the e-mail is sent to the subscriber, indicating that the subscriber's own genomic profile is 77% identical to that of Abraham Lincoln. It can also inform you of additional information available through the on-line portal.

The invention also forms new rules, modifies rules, integrates them, updates the ruleset periodically with new rules, keeps the genomic profile database safe, and applies the rules to genomic profiles. It also provides a computer code system that lets you measure profiles and generate reports. The computer may provide the subscriber with new or modified correlations, new rules or modifications, for example, with new preventive and preventive information, information about new therapies in ameliorating the disease, or new treatment options that are feasible. Encrypt rules to be notified and notification of new or revised reports.

Business way

The present invention provides a business method for evaluating the genotype correlation of an individual based on a comparison of a patient's genomic profile against a clinically derived database of established and medically relevant nucleotide variants. do. The present invention also provides a business method of utilizing an individual's genomic profile, which has been stored to evaluate new correlations that were not originally known, to update the phenotype profile for the individual without further submission of biological samples. Provide additionally. 9 is a flowchart illustrating such a business method.

When an individual initially requests and purchases a personalized genomic profile for genotype correlations for a number of factors, such as common human diseases, conditions, and physical conditions, the revenue stream for the target business method is partially As formed in step 101. Such requests and purchases may be made through any number of sources, such as on-line web portals, on-line health services, and specialists dedicated to individuals, or similar sources of personal medical attention. In alternative embodiments, the genomic profile can be provided free of charge, and the inlet stream is formed at a later stage, eg, step 103.

The subscriber, that is, the customer, requests the purchase of a phenotype profile. In response to the request and purchase, the customer is provided with a collection kit for the biological sample used to isolate the genetic sample in step 103. When the request is made on-line or over the phone, or when the request is made by another source for which the customer cannot physically obtain the collection kit, the collection kit will be shipped promptly, for example, in the same or next day delivery. Provided by the service. The collection kit includes a container to hold the sample and packaging to quickly deliver the sample to the laboratory for genomic profile formation. The kit may also include instructions to send the sample to a sample processing facility or laboratory and instructions to obtain the individual's genomic and phenotypic profiles through an on-line portal.

As mentioned above, genomic DNA can be obtained from any of many types of biological samples. Preferably, genomic DNA is isolated from saliva using a commercially available collection kit, eg, a collection kit commercially available from DNA Genotek. Using saliva and these kits, a customer can conveniently place a saliva sample in a container with a collection kit and then seal the container so that the sample can be collected so that it does not leak out. In addition, saliva samples can be stored and shipped at room temperature.

After placing the biological sample in a collection container or sample container, the customer will send the sample to the laboratory for step 105 to proceed. Typically, the customer can use the packaging provided in the collection kit to ship / ship this sample to the lab by express delivery, such as a courier service delivered the same or next day.

Laboratories that process samples and form genomic profiles can comply with the guidelines and conditions of appropriate government agencies. For example, in the United States, treatment laboratories may be established by one or more federal agencies, such as the Food and Drug Administration (FDA) or Geriatric Health Insurance and National Medical Assistance Centers (CMS), and / or one or more state agencies. Can be regulated. In the United States, clinical laboratories may be accredited or recognized under the Clinical Laboratory Improvement Amendments (CLIA) (1988).

In step 107, the laboratory treats the sample as described above to isolate a genetic sample of DNA or RNA. Thereafter, in step 109, an analysis and genomic profile of the separated genetic sample are formed. Preferably, genomic SNP profiles are formed. As mentioned above, several methods can be used to form the SNP profile. Preferably, high density arrays, such as commercially available platforms (Affymetrics or Illumina), are used for SNP identification and profile formation. For example, the SNP profile can be formed using the Affymetrix Gene Chip Assay, as described above. As technology advances, there may be other technicians who can form high density SNP profiles. In other embodiments, the genomic profile for the subscriber will be the genomic sequence of the subscriber.

After forming the genomic profile of the individual, the genotype data is encoded and entered into step 111, which is then accumulated or stored in a security database in step 113, in which case the information is stored as a future reference. The information related to the genomic profile may be confidential, in which case the genomic profile and such proprietary information is obtained by the individual and / or the specialist in charge of the individual. In addition, for example, a subscriber may allow access to family and individual genetic counselors.

The database or storage device may reside on the site of the treatment laboratory. Alternatively, the database may be in a separate storage location. In such a scenario, genomic profile data generated by the processing laboratory may be introduced to a separate facility including a database in step 111.

After the genomic profile of the individual is formed, the genetic variation of the individual is compared with a clinically formed database of established and medically relevant genetic variants (step 115). Alternatively, genotype correlations may not be medically relevant, but genotype correlations, eg, physical traits, eg, eye color, or “interesting” phenotypes, eg, genomic profile similarity with celebrities It can be integrated into a database on.

Medically relevant SNPs could be established through scientific and related literature. Non-SNP gene variants may also be established to correlate with the phenotype. In general, the correlation of SNPs for a given disease is established by comparing the haploid pattern of the group of people who have not developed the disease with the haploid pattern of the group of people known to have developed the disease. By analyzing a large number of individuals, the frequency of expression of polymorphisms in a population can be measured, and the frequency of expression of such genotypes can then be associated with certain phenotypes, such as diseases or conditions. Alternatively, the phenotype can be a non-medical condition.

Related SNPs and non-SNP gene variants may also be determined by analyzing the genomic profile of the stored individual, rather than by the available published literature. Individuals whose genomic profiles have been stored can reveal phenotypes that were previously measured. Analyzes of the genotypes and published phenotypes of an individual can be compared to those of an individual that does not represent a phenotype, to determine correlations that can then be applied to other genomic profiles. Individuals measuring genomic profiles can fill in the questionnaire about phenotypes previously measured. The questionnaire may include questions about medical and non-medical conditions, for example, diseases previously diagnosed, family history of the medical condition, lifestyle, physical traits, mental traits, age, social life, and the environment.

In one embodiment, an individual can assess the genomic profile free of charge when completing and submitting a questionnaire. In some embodiments, this questionnaire will be filled out periodically by the individual so that the individual's own phenotype profile and corresponding report will be freely available. In another embodiment, the individual filling out the questionnaire may be authorized to raise the subscription level so that they can get more information than the previous level of subscription, or they can subscribe or renew at a lower cost.

In step 121, all the information accumulated in the database on the medically relevant genetic variants is first examined and supervised in step 119 as to whether it is endorsed by a suitable government authority, as well as the study of scientific accuracy and importance. Is approved by a clinical advisory body. For example, in the United States, FDA may oversee the approval of algorithms used to validate genetic variants (typically SNPs, transcriptional levels or mutations) correlation data. In step 123, the scientific literature and other related sources are monitored for the correlation of additional genetic variants-diseases or conditions, which are then validated for accuracy and importance, as well as review and approval by government authorities, to ensure that these additional phenotypes The correlation is added to the master database in step 125.

It would be advantageous to have a database of approved and validated medically relevant genetic variants that can perform genetic risk-assessment for a number of diseases or conditions, along with profiles of individuals that are prevalent in the genome. After an individual's genomic profile has been edited, the individual's genotype correlations are based on a database of human nucleotide variants that correlate the individual's nucleotide (gene) variants or markers with a particular phenotype, such as a disease, condition or physical condition. It can be measured by comparing with. By comparing the master database of the genome profile and the genotype correlation of the individual, the individual can know whether or not it is positive or negative for the gene risk factor and to what extent. The individual will receive relative risk and / or predisposition data for a wide range of scientifically proven conditions (eg, Alzheimer's disease, cardiovascular disease, blood clot formation). For example, genotype correlations in Table 1 may be included. In addition, the correlation of SNP disease in the database may include, but is not limited to, the correlation shown in FIG. 4. Other correlations shown in FIGS. 5 and 6 may also be included. Therefore, the business method of the present invention provides a result of analyzing the risks for a number of diseases and conditions, in which case no prediction is needed as to what the multiple diseases and conditions may include. .

In other embodiments, a genotype correlation paired with the profile of an individual that is widely spread in the genome includes a non-medically related phenotype, such as an "interesting" phenotype or physical trait, such as hair color. In a preferred embodiment, the rule or rule set is applied to an individual's genomic profile or SNP profile, as described above. Applying rules to genomic profiles forms phenotype profiles for individuals.

Therefore, the master database on human genotype correlations expands with additional genotype correlations as new correlations are discovered and demonstrated. As appropriate or appropriate, the appropriate information may be taken from the genomic profile of the individual stored in the database and updated. For example, known new genotype correlations can be based on specific gene variants. Then, by searching for and comparing only the parts of the gene in the individual's entire genome profile, it can be determined whether the individual can be sensitive to the correlation of the new genotype.

The results of the questions about the genome are preferably analyzed and interpreted and provided to the individual in an understandable form. In step 117, the results of the initial screening are then presented to the patient in a secure and confidential form by mail or via an on-line portal interface, as described above.

The report may include genomic information about the phenotype profile and phenotypes in the phenotype profile, for example, basic genetic information about genes included in different populations or statistical data about genetic variants within the population. Other information based on phenotypic profiles that may be included in the report include preventive strategies, preventive health care information, therapies, awareness of symptoms, early detection techniques, intervention plans, and phenotypic sophisticated identification methods and subclassification results. After initially screening the genomic profile of an individual, the update may or may be made in a controlled and excised manner.

As new genotype correlations are expressed, validated and approved, the genome profile of an individual can be updated or updated, as well as updating the master database. New rules based on new genotype correlations can be applied to the initial genomic profile to provide an updated phenotype profile. The updated genotype correlation profile can be formed by comparing the relevant portion of the individual's genomic profile with the new genotype correlation in step 127. For example, if a new genotype correlation is found based on mutations occurring in a particular gene, the genetic portion of the individual's genomic profile can be analyzed for the new genotype correlation. In this case, one or more new rules may be applied to form an updated phenotype profile, rather than the entire set of rules including already applied rules. In step 129 the correlation of the updated genotype of the individual is provided in a secure manner.

The initial updated phenotype profile may be a service provided to the subscriber or customer. The combination of genomic profile analysis results and combinations can result in variable levels of subscription. Similarly, the level of subscription can be variable, allowing an individual to select a certain amount of service, i.e., a service that they wish to receive along with information about their genotype correlations. Therefore, the level of service provided will depend on the level of service subscription purchased by the entity.

The level of subscription at the time a subscriber subscribes may include a genomic profile and an initial phenotype profile. This may be the default subscription level. For a basic subscription level, that level may vary depending on the service level. For example, some levels of accession could provide reference and other service options to genetic counselors and specialists with specific expertise in treating or preventing certain diseases. Genetic counseling results can be obtained online or by telephone. In other embodiments, the subscription cost may depend on the number of phenotypes that an individual chooses for its phenotype profile. Another option may be whether or not the subscriber will receive on-line gene counseling.

In another scenario, it was possible to provide early genotype correlations that are prevalent in the genome while maintaining the genomic profile of the individual in the database; Such a database can be secured when selected by an individual. After completing the initial analysis, further analysis results and additional results can be received by requesting an individual and paying an additional fee. This can be received when the subscription level is the highest level.

In one embodiment of the business method of the present invention, the risk of the individual is updated and corresponding information may be provided to the individual based on the subscription. Renewal can be useful for subscribers who subscribe at the highest level. By subscribing to a genotype correlation analysis service, updates may be made to a subset or specific category of new genotype correlations, depending on the individual's preferences. For example, an individual may want to know only genotype correlations that can tell what is known as a treatment or prevention method. In order to help determine whether an individual will perform further analysis, the individual may be provided with information regarding the correlation of additional genotypes made available. Such information may be conveniently sent by mail or e-mail to the subscriber.

At the highest subscription, an additional level of service may be provided, in addition to the services mentioned at the time of the basic subscription, for example. Other subscription models may be provided at the highest level. For example, at the highest level, subscribers can be provided with unlimited renewal results and reports. As new correlations and rules are measured, the subscriber's profile may be updated. At this level, the subscriber may also allow for a large number of individuals to obtain, for example, family members and health managers. Subscribers may also grant unlimited access to on-line genetic counselors and specialists.

When subscribed at the highest level, the next level of subscription may provide a more limited aspect, for example, to renew a limited number of times. Subscribers may update their genomic profile for a limited number of times within the subscription period, for example four times a year. At other subscription levels, the subscriber can store the updated genomic profile once a week, once a month or once a year. In other embodiments, the subscriber may only have a limited number of phenotypes, so that he or she may choose to update his genomic profile.

Personal portals will also conveniently allow any individual to remain subscribed to a risk, keep the correlation and information up to date, or request an update on risk assessment and information. Will let you. As mentioned above, when variable levels of subscription are provided, an individual can choose between different levels of genotype correlation results and whether or not to renew, and levels of subscription may be selected differently by themselves through the subscriber's own personal portal. Can be.

Any of these subscription options will affect the entry stream to the business method of the present invention. The entry stream for the business method of the present invention will also be added by adding new customers and subscribers, in which case the new genomic profile is added to the database.

Table 1a-1s: Representative genes with genetic variation correlated with phenotype

Genetic Composite Index Index ; GCI )

The cause of many conditions or diseases lies in both genetic and environmental factors. In recent years, the development of genotyping techniques has provided the opportunity to identify new associations between disease markers and genetic markers expressed across the entire genome. Indeed, many recent studies have identified this association, suggesting that certain alleles or genotypes correlate with increased risk for disease. Some of the studies being done include collecting the patient and control sets and analyzing the allele distribution of the genetic markers between the two populations. In some of these findings, the association between specific genetic markers and disease is a measure of the results of separation from other genetic markers, which are treated as background but do not explain the results of statistical analysis.

Genetic markers and variants may include SNPs, nucleotide repeats, nucleotide insertions, nucleotide deletions, chromosomal translocations, chromosomal duplications, or copy number variations. Copy number variations may include hypersatellite repeats, nucleotide repeats, centriole repeats, or telomere repeats.

In one aspect of the invention, information about the association of multiple genetic markers with one or more diseases or conditions is integrated and analyzed to derive a GCI score. This GCI score, based on current scientific research, provides a reliable (ie robust), understandable, and / or intuitive sensation for comparing each disease risk with the relevant population. It can be used to provide people who are not familiar with it. In one embodiment, the method of generating a robust GCI score for multiple effects at different locations is based on the risk of the individual reported for each location studied. For example, after identifying a subject disease or disorder, sources of information, such as databases, patent publications, and scientific literature, may ask questions about information related to one or more gene positions and how the disease relates to such a condition. Throws. These sources of information are assisted and evaluated using quality standards. In some embodiments, the evaluation method comprises a plurality of steps. In other embodiments, the information source is evaluated against a number of quality criteria. Information from information sources is used to identify relative risks or odds ratios for one or more gene positions for each subject disease or condition.

In alternative embodiments, the relative risk (RR) or crossover ratio (OR) for one or more gene positions cannot be obtained from a useful source of information. The RR is then (1) OR reported for multiple alleles present at the same location, (2) frequency of expression of alleles derived from a data set, eg, a hap map data set, and / or (3 Calculations are made using sources that are useful for inducing RRs of all alleles of interest (eg, CDC, National Health Statistics Center, etc.). In one embodiment, the ORs of multiple alleles at the same location are predicted separately or independently. In a preferred embodiment, the ORs of multiple alleles present at the same location are integrated, as a result of which the dependencies between the ORs of the different alleles are calculated. In some embodiments, using established disease models (including but not limited to models such as multiplication, addition, Harvard-correction, and dominant effects), a median score representing an individual's risk is generated according to the selected model. do.

In another embodiment, methods are used for analyzing multiple models for a subject disease or condition and correlating results obtained from different models; This minimizes possible errors that can be introduced by selecting a particular disease model. This method minimizes the impact of reasonable errors in the prevalence, allele frequency, and estimates of OR obtained from sources of information on the calculation of relative risk. Due to the "linearity" of the RR or the monotonous nature of the predicted prevalence effect on this RR, even if the prevalence of the final ranking score is incorrectly predicted, there is little or no effect; However, the same model applies uniformly to all the entities for which the report is created.

In another embodiment, a method of considering environmental / behavioral / population statistical data as an additional “location” is used. In related embodiments, such data may be obtained from sources of information such as medical or scientific literature or databases (eg, associations with smoking w / lung cancer, or insurance industry risk assessment results). In one embodiment, the GCI score is scored for one or more complex diseases. Complex diseases can be affected by a number of genes, environmental factors, and their interactions. Many of the interactions that may occur need to be analyzed when research on complications is conducted. In one embodiment, a method such as Bonferroni calibration is used to calibrate a number of comparison results. In alternative embodiments, the Sims' test is used to adjust the overall level of significance (known as the "familywise error rate") when the tests are independent or exhibit certain types of dependencies. Sarkar S. (1998), Some probability inequalities for ordered MTP2 random variables: a proof of Simes conjecture, Ann Stat 26: 494-504. The Signs test negates the general null hypothesis that all K test-specific null hypotheses are true if p (k) ≦ αk / K , where any k is 1, ... K. [Simes RJ (1986) An improved Bonferroni procedure for multiple tests of significance, Biometrika 73: 751-754.

In other embodiments that can be used for multiple genetic analysis and multiple environmental factor analysis, the false-discovery rate, i.e., the predicted maintenance of the negated hypothesis negated by errors, is controlled. This method of research is particularly useful when a certain percentage of the null hypothesis can be assumed error, as in microarray studies. See Devlin et al. 2003, Analysis of multilocus models of association. Genet Epidemiol 25: 36-47] reported that Benjaminini and Hochberg, who control the rate of coming when testing multiple genes x gene interactions that can occur in a multilocus association study, A variation of the step-up procedure is proposed. How the mini Provenza and hocheu bug resigned's test [that is, k ^* = when set to maxk, p (k) and ≤αk / K, k for the _{p (1), ... p (} k) ^* Negate the null hypothesis]. Indeed, the Benjamini and Hotzberg methods revert to the Simims test when all null hypotheses are true [Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29: 1165-1188.

In some embodiments, an individual is ranked relative to a group of individuals based on a median score, resulting in a final ranking score that can be expressed as a ranking within the population (eg, 99 ^th bag). Quantile or 99 ^th , 98 ^th , 97 ^th , 96 ^th , 95 ^th , 94 ^th , 93 ^rd , 92 ^nd , 91 ^st , 90 ^th , 89 ^th , 88 ^th , 87 ^th , 86 ^th , 85 ^th , 84 ^th , 83 ^rd , 82 ^nd , 81 ^st , 80 ^th , 79 ^th , 78 ^th , 77 ^th , 76 ^th , 75 ^th , 74 ^th , 73 ^rd , 72 ^nd , 71 ^st , 70 ^th , 69 ^th , 65 ^th , 60 ^th , 55 ^th , 50 ^th , 45 ^th , 40 ^th , 40 ^th , 35 ^th , 30 ^th , 25 ^th , 20 ^th , 15 ^th , 10 ^th , 5 ^th or 0 ^th percentile]. In other embodiments, the ranking is in a range, for example, 100 ^{th to} 95 ^th percentile, 95 ^{th to} 85 ^th percentile, 85 ^th to 60 ^th percentile, or 100 ^{th to} 0 ^th percentile It can be a score that can be displayed as any range within. In another embodiment, an individual is ranked as a quartile, for example, up to 75 ^th quartile, or at least 25 ^th quartile. In another embodiment, the individual is ranked against the average or median score of the population.

In one embodiment, the population compared to the individual includes a large number of people of various geographical and ethnic backgrounds, for example, people around the world. In other embodiments, the population compared to the subject may be characterized by a condition (eg symptomatic, asymptomatic) according to a particular terrain, family, ethnicity, gender, age (fetal, newborn, child, youth, adolescent, adult, elderly). Visibility, early onset and late onset). In some embodiments, the population to which the individual is compared is derived from published information and / or information reported to private sources of information.

In one embodiment, the GCI score or GCI plus score of the individual is visualized through the display. In some embodiments, screens (eg, computer monitors or television screens) are used to visualize object portals that contain displays, eg, related information. In another embodiment, the display is a fixed display, such as a printed page. In one embodiment, the display may include, but is not limited to, one or more of the following: bins (eg, 1-5, 6-10, 11-15, 16) -20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, 66-70, 71-75, 76-80 , 81-85, 86-90, 91-95, 96-100, color gradient or grayscale gradient, thermometer, gauge, pie chart, histogram or bar graph. For example, FIGS. 18 and 19 are different displays for MS, and FIG. 20 is for Crohn's disease. In another embodiment, the thermometer is used to indicate the GCI score and the prevalence of the disease / condition. In another embodiment, the thermometer displays varying levels with reported GCI scores, for example, in FIGS. 15-17, the color corresponds to the risk. The thermometer may show colorimetric changes as the GCI score increases (eg, gradually changing from a low GCI score (blue) to a high GCI score (red)). In a related embodiment, the thermometer shows both colorimetric changes that change as the level of risk and the rank of risk increase according to the reported GCI score.

In alternative embodiments, the individual's GCI score is communicated to the individual by using auditory feedback. In one embodiment, auditory feedback is spoken to indicate that the risk rank is high or low. In other embodiments, auditory feedback is described in detail with respect to results of comparison with specific GCI scores, eg, mean or median GCI scores for numbers, percentiles, ranges, quartiles, or populations. In one embodiment, the auditory feedback is actually delivered by a person personally or via a remote communication device such as a telephone (terrestrial line, mobile phone or satellite phone), or through a personal portal. In another embodiment, auditory feedback is delivered by an automated system, eg, a computer. In one embodiment, auditory feedback is delivered as part of an interactive voice response (IVR) system, a technology that allows a computer to sense voice and adjust sound quality using a conventional telephone call. In other embodiments, the individual can interact with the central server via the IVR system. The IVR system can react with pre-recorded audio or powered audio to interact with the entities and provide them with auditory feedback regarding their risk ranking. In one example, either entity may dial a number that is answered by the IVR system. After optionally introducing an identification code, security code, or proceeding with a running speech recognition protocol, the IVR system requires the entity to select an option from a menu, such as a touch button or voice menu. One of these options can give the entity its own risk ranking.

In another embodiment, an individual's GCI score is visualized using a display and delivered using auditory feedback, eg, a personal portal. Such combinations may include a visual display of the GCI score and auditory feedback, discussing the correlation of the GCI score to the individual's overall health and recommending applicable preventive measures.

In one example, GCI scores are scored using a multistep method. Initially, for each condition to be studied, the relative risk is calculated from the odds ratios for each of the genetic markers. For each prevalence value (p = 0.01, 0.02,..., 0.5), the GCI score of the hap map CEU population is calculated based on the prevalence and the frequency of hap map allele expression. If the GCI score is not variable when the prevalence is variable, the only assumption considered is the multiplication model. Otherwise, the model is sensitive to prevalence. When randomly combining no-call values, the relative risk and score distribution in the hap map population are obtained. For each new individual, the score of the individual is compared with the hap map distribution, and the resulting score corresponds to the rank of the individual in this population. Since assumptions are made in this process, the accuracy of the reported scores may be low. Cohorts will be divided into quartiles (3-6 bins), and the reported bins will be in the case of low ranking. The number of bins can be different for different diseases, based on several considerations, for example, score accuracy for each disease. If there is an association between the scores of different hap map entities, the average rank will be used.

In one embodiment, the higher the GCI score, the greater the risk of developing or being diagnosed with any one condition or disease. In another embodiment, a mathematical model is used to infer the GCI score. In some embodiments, the GCI score is based on a mathematical model that describes the incomplete nature of the information that was latent with respect to the population and / or disease or condition. In some embodiments, the mathematical model includes one or more inferences as part of the basis for calculating the GCI scores, wherein the assumptions include, but are not limited to: the assumption that a ratio of odds ratio is given; The assumption that the prevalence of the condition is known; The assumption that the frequency of expression of genotypes in a population is known; And the assumption that the client belongs to the same household background as the hap map and the population used in the study; Assume that the integration risk is the product of different risk factors for individual genetic markers. In some embodiments, the GCI may also include the assumption that the genotype's multi-genic expression frequency is the product of the alleles of each SNP or the expression frequency of individual gene markers (eg, different SNPs or gene markers Independent throughout the group].

Multiplicative Model

와 같이 정의한다. 이와 유사하게, r _i r _i 유전자형의 상대적 위험도를

와 같이 정의한다. 승법 모델 하에서, 유전자형 (g₁,...g_k)인 개체의 위험도는

라고 가정한다. 승법 모델은 환자군-대조군 연구(Case-Control Study)를 촉진하거나, 또는 가시화할 목적으로, 문헌에서 이미 사용되고 있다.In one embodiment, the GCI score is calculated on the assumption that the risk that affects the set of genetic markers is the product of the risks by the individual genetic markers. This means that different genetic markers independently cause other genetic markers to indicate the risk of disease. Officially, there are k gene markers comprising risk alleles r ₁ ,... R _k , and non-risk alleles n ₁ , ... n _k . In SNP i , three possible genotype values are represented by r _i r _i , n _i r _i and n _i n _i . According to the number of risk alleles present at position i , the genotype information of the individual can be represented as a vector ( g ₁ , ..., g _k ), where g _i can be 0, 1 or 2 . Here, the relative risk of the heterozygous genotype present at position i, as compared to the homozygous non-risk allele at the same position, is denoted λ ⁱ ₁ . In other words

It is defined as Similarly, the relative risk of the r _i r _i genotype

It is defined as Under the multiplication model, the risk for individuals of genotype (g ₁ , ... g _k ) is

Assume that Multiplicative models have already been used in the literature for the purpose of facilitating or visualizing Case-Control Studies.

Relative Risk Prediction

In other embodiments, the relative risks for the different genetic markers are known, and multiplication models can also be used to assess such risks. However, in some embodiments, including studies of associations, no relative risk is reported in the study design. In some patient-control studies, relative risks cannot be calculated directly from the data without further assumptions. It is customary to report the relative risk of genotypes instead of reporting the relative risk, in which case the relative risk is the probability of having the disease by the risk genotype ( r _i r _i or n _i r _i ). Large risk is the probability of not having a disease caused by the genotype. In summary,

Additional assumptions may be required to find the relative risk from the odds ratio. _Assume that the frequency of expression of alleles in the entire population, i.e., a = f _nini , b = f _niri and c = f _riri, is known or predicted (this is a current dataset containing 120 chromosomes, e.g., a Hapmap dataset Can be predicted from, and / or the prevalence of the disease, ie p = p (D), is known. From the three equations above, the following equation can be derived:

By defining the relative risk after dividing by the factor pP (D / n _i n _i ), the first equation can be rewritten as:

Therefore, the last two equations can be rewritten as:

When a is 1 (frequency of non-risk alleles is 1), simultaneous equation 1 is equivalent to the formula of Zhang and Yu in the literature [Zhang J and Yu K., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA, 280: 1690-1, 1998; Itself as a reference]. In contrast to intestinal and significant formulas, some embodiments of the invention contemplate the frequency of expression of alleles in a population that may affect relative risk. Some embodiments take into account interdependencies of relative risks. This is in contrast to when each of the relative risks is calculated independently.

The simultaneous equation 1 can be rewritten as two quadratic equations, in which case there are about four possible solutions. A descending algorithm can be used to solve this equation, in which case the starting point is set to an intersection ratio, for example λ ⁱ ₁ = OR ⁱ ₁ and λ ⁱ ₂ = OR ⁱ ₂ .

E.g:

Finding a solution to this equation is equivalent to finding the minimum of the function g (λ ₁ , λ ₂ ) = f ₁ (λ ₁ , λ ₂ ) ² + f ₂ (λ ₁ , λ ₂ ) ² .

therefore,

In this example, set x ₀ to OR ₁ and y ₀ to OR ₂ to start solving. We will set the numerical value [epsilon] = 10 ^-10 as the tolerance constant through the algorithm. Repeat symbol, we

Defined as Since then, the inventor

Set.

The above steps are repeated until "g (x _i y _i ) <tolerance", in which case the tolerance is set to 10 ^-7 of the supply codes.

In this example, the above equations provide the correct solution for the different values of a, b, c, p, OR ₁ and OR ₂ (FIG. 10).

Confirmation of relative risk estimates

이다. 만일

이면, λ_i는 역시 선형 함수에 근접하게 된다. 위험도-대립 형질의 발현 반도가 낮으면, λ₁ 및 λ₂는 함수 1/p의 거동에 근접할 것이다. 제한적으로, c가 0일 경우,

이다. 이는 곧, 위험도-대립 형질 발현 반도가 높아지면, 유병율의 부정확한 예측치는 결과로 얻어진 상대적 위험도에 거의 영향을 미치지 않을 것이라는 것을 나타낸다. 또한, 위험도-대립 형질 발현 빈도가 낮은 경우, 만일 유병율 수치 p'가 αp이면, 이는 정확한 유병율 p에 대해 치환되고, 이후, 결과로 얻어진 상대적 위험도는 거의 팩터 1/α만큼 떨어질 것이다. 이는 도 11의 섹션 (c)와 섹션 (d)에서 살펴볼 수 있다. 고 위험도-대립 형질 발현 반도에 있어서, 2개의 그래프는 거의 같은 반면, 낮은 대립 형질 발현 빈도에 대한 상대적 위험도 수치의 차이에 있어서 편차는 더욱 심해지며, 이때의 편차는 팩터 2 미만이다.In some embodiments, the effect of different parameters (prevalence, allele frequency and odds ratio error) on the prediction of relative risk is measured. To determine the effect of allele frequency and predicted prevalence on relative risk values, the relative risks derived from different sets of crossover ratios and different allele frequency values are calculated (under HWE), and the results of these calculations are calculated on the prevalence values. Graph is plotted (range = 0 to 1) (Fig. 10). In addition, for fixed values of prevalence, the resulting relative risk can be plotted as a risk-allele frequency function graph (FIG. 11). p = 0, λ ₁ = OR ₁ and λ ₂ = OR ₂ , and p = 1, λ ₁ = λ ₂ = 0. This can be calculated directly from the equation above. In addition, in some embodiments, when the risk allele frequency is high, λ ₁ approaches the linear function and λ ₂ approaches the concave function according to the second derivative. Restriction, when c is 1, λ ₂ = OR ₂ + p (1-OR ₂ ),

to be. if

Λ _i is also close to the linear function. If the expression half-risk of the risk-allele is low, λ ₁ and λ ₂ will approximate the behavior of function 1 / p . If c is zero,

to be. This, in turn, indicates that as the risk-allele expression peninsula increases, an inaccurate estimate of prevalence will have little impact on the resulting relative risk. Also, if the risk-allele frequency is low, if the prevalence value p ' is αp , it will be substituted for the correct prevalence p, and the resulting relative risk will then drop by approximately factor 1 / α. This can be seen in sections (c) and (d) of FIG. 11. For the high risk-allergic peninsula, the two graphs are nearly identical, while the deviation becomes more severe in the difference in relative risk values for low allele frequency, with the deviation being less than factor 2.

Calculation of the GI Score

In one embodiment, the gene synthesis index is calculated using a reference set representing the population of interest. This set of criteria can be one of the populations in the hap map or other genotype dataset.

In this embodiment, the GCI is calculated as follows. For each of the k risk locations, the relative risk is calculated from the odds ratio using simultaneous equation 1. The multiplication score is then calculated for each individual in the reference set. The GCI of an individual with a multiplier score s is the proportion of all individuals in the reference dataset (in this case s' ≦ s). For example, if the multiplicative score of 50% of the subjects in the reference set is less than s, the final GCI score of the subject will be 0.5.

Other models

In one embodiment, a multiplication model is used. In alternative embodiments, other models can be used to measure the GCI score. Other suitable models include, but are not limited to:

인 것으로 가정할 수 있다. Additive Model: Under the additive model, the risk of individuals of genotype (g ₁ , ... g _k ) is

Can be assumed to be.

인 것으로 가정할 수 있다. Generalized Additive Model: Under the generalized additive model, there exists a function f, in which the risk of an individual with a genotype (g ₁ , ..., g _k )

Can be assumed to be.

Harvard Modified Score (Het): This score is obtained from GA Colditz et al., Which is a score applied to genetic markers [Harvard report on cancer prevention volume 4: Harvard cancer risk index. Cancer Causes and Controls, 11: 477-488, 2000; Hereby incorporated by reference in its entirety]. Although the function f is operated according to the odds ratio value instead of the relative risk, the Het score is essentially a generalized additive score. This can be useful when it is difficult to predict relative risk. To define a function f, the intermediate function g is defined as follows:

을 계산하는데, 여기서 pⁱ _het는 기준 집단에 널리 퍼져 존재하는 SNPi 중 이형 접합 개체의 발현 빈도이다. 이후, 함수 f를 f(x) = g(x)/het로 정의하고, 하버드 수정 스코어(Het)는 간단하게

으로 정의한다.next,

Where p ⁱ _het is the frequency of expression of heterozygous individuals in SNPi that are prevalent in the reference population. Then, we define the function f as f (x) = g (x) / het, and the Harvard correction score (Het) is simply

It is defined as

[식중, pⁱ _hom은 동형 접합 위험도-대립 형질을 가지는 개체의 발현 빈도임]를 대입한다. Harvard Correction Score (Hom): This score is similar to the Het score, except that

_Wherein p ⁱ _hom is the frequency of expression of individuals with homozygous risk-alleles.

이다. Maximum-Odds Ratio: In this model, one of the genetic markers (gene markers with the maximum cross ratio) is assumed to be the minimum value associated with the overall panel integration risk. Expressed as a formula, the scores of individuals with genotypes (g ₁ , ... g _k ) are

to be.

Comparison between scores

In one example, the GCI score was calculated based on multiple models that are prevalent in the Hapmap CEU population, in which 10 SNPs are associated with T2D. Related SNPs are rs7754840, rs4506565, rs7756992, rs10811661, rs12804210, rs8050136, ra11 11875, rs4402960, rs5215, rs1801282. For each of these SNPs, crossover ratios for possible genotypes have been reported in the literature. The CEU population consists of 30 mother-father-child trios. Sixty of the parents in this group were used to avoid function dependencies. One of the 10 SNPs was no-call and one of the individuals was excluded, resulting in a set of 59 individuals. GCI rank for each individual was calculated using several different models.

For such datasets, different models yield highly correlated results (FIGS. 12 and 13). The spearman correlation was calculated for each pair of models (Table 2), whereby the correlation coefficient of the multiplicative model and additive model was 0.97, so the GCI score was either additive or multiplicative model. The use of one would be confirmed. Similarly, the correlation between the Harvard Correction Score and the Multiplication Model was 0.83, and the correlation coefficient between the Harvard Score and the additive model was 0.7. However, using the maximum crossover ratio as the gene score, a two minute score limited to one SNP was obtained. Presumably, it can be seen from these results that the score ranking provides a deterministic structure with minimal model dependence.

Table 2: Spearman Correlation for Score Distribution on CEU Data Between Model Pairs

Multiplication Additive Harv-het Harv-hom MAX OR Multiplication One 0.97 0.83 0.83 0.42 Additive 0.97 One 0.7 0.7 0.6 Harv-het 0.83 0.7 One One 0 Harv-hom 0.83 0.7 One One 0 MAX OR 0.42 0.6 0 0 One

As the prevalence of T2D changed, the effect of that change on the distribution was measured. The prevalence value between 0.001 and 0.512 was variable (FIG. 14). In the case of T2D, it was found that the order of the individuals was the same due to the different prevalence values (Spearman correlation> 0.99). As a result, an artificially fixed prevalence value of 0.01 could be assumed.

Extend the model to any number of variables

In other embodiments, the model can be extended to situations in which any number of variables can be applied. The above considerations deal with the case where there are three variables (nn, nr, rr) that can be created. In general, when multiple SNP associations are known, any number of variables can be found within a population. For example, nine variables may exist when the interaction between two genetic markers is associated with a condition. This results in eight different ratios of cross ratios.

이고,

이다: To generalize the initial formula, there may be k + 1 variables (a ₀ , ... a _k ), in which case the expression frequency is f ₀ , f ₁ , ... f _k , and the measured crossover ratio is It can be assumed that 1, OR ₁ , ..., OR _k , and that the unknown relative risk values are 1, λ ₁ , ..., λ _k . In addition, all relative risks and crossover ratios are measured in relation to a ₀ ,

ego,

to be:

를 기초로 함.this is

Based on.

OR _i is measured by the equation:

이면, 그 결과는 다음의 등식에 의해 얻어진다:As well as

If so, the result is obtained by the following equation:

therefore,

or

to be.

에 가장 근접한 해법을 찾기 위해서는 내림 차순이 적용될 수 있다. The latter equation is an equation involving one variable (C). This equation can lead to a number of different solutions (essentially up to k + 1 different solutions). Standard optimizer, for example

Descending order can be applied to find the solution closest to.

The present invention utilizes a definite scoring structure to quantify risk factors. As different genetic models can exhibit different scores, the results obtained therefrom are generally correlated. Therefore, the quantification of risk factors generally does not depend on the usage model.

Prediction of Relative Risk Patient-Control Study

Also provided by the present invention are methods for predicting the relative risk obtained from the odds ratios for a number of alleles in a patient-control study. In contrast to the above research methods, the method considers the dependence between the frequency of allele expression, the prevalence of the disease, and the relative risk of different alleles. The results of the simulated patient-control study were found to be very accurate.

Way

When testing whether a specific SNP is associated with disease D, R and N represent the risk alleles and non-risk alleles of this particular SNP. P (RR / D), P (RN / D) and P (NN / D) represent the likelihood of being affected by a disease. In this case, individuals are homozygous for risk alleles, For traits they are heterozygous or homozygous, respectively. f _RR , f _RN and f _NN are used to indicate the frequency of expression of the three genotypes in the population. Using this definition, the relative risk is defined as:

In the patient-control study, the values P (RR / D), P (RR / ~ D), ie the frequency of expression of RR between the patient group and the control group, and P (RN / D), P (RN / ~ D), P (NN / D) and P (NN / ~ D), ie the frequency of expression of RN and NN between patient and control group can be predicted. To predict relative risk, the following Bayes law can be used:

Therefore, if the frequency of expression of genotypes is known, this expression frequency can be used to calculate the relative risk. The frequency of expression of the genotypes in a population cannot be calculated from the patient-control study itself, since this frequency of expression is dependent on disease prevalence in the population. In particular, if the prevalence of the disease is p (D), the following function is established:

When p (D) is sufficiently small, the frequency of expression of the genotypes can be approximated by the frequency of expression of the genotypes in the control group, but the predictions will not be accurate when the prevalence is high. However, if a reference dataset (eg, a hap map [site]) is given, one can predict the frequency of genotype expression based on the reference dataset.

Most recent studies do not use reference datasets to predict relative risks, and only crossover ratios are given. The odds ratio can be defined as:

Generally it is not necessary to predict the frequency of expression of alleles in a population, so the odds ratio is usually advantageous; What is needed to calculate this crossover ratio is the frequency of expression of the genotypes in the patient and control groups.

In some cases, genotyping data itself is not useful, but summary data such as odds ratios are useful. Such is the case when the meta-analysis is performed based on results obtained from prior patient-control studies. In this case, the method of finding the relative risk from the odds ratio is demonstrated. The following equation is used:

p (D) = f _RR P (D / RR) + f _RN P (D / RN) + f _NN P (D / NN)

If we divide this equation by P (D / NN), we get:

Thus, the ratio of ratios can be described in the following way:

According to a similar calculation method, the following simultaneous equations can be obtained.

If the odds ratio, frequency of genotype expression in the population, and disease prevalence are known, the relative risk can be obtained by solving this system of equations.

Since there are two factorization methods, there can be up to four solutions. However, as shown below, there is usually one possible solution to this equation.

when f _NN is 1, simultaneous equation 1 is equivalent to the formula of Zhang and Yu; The frequency of expression of alleles in the population is considered. In addition, the method of the present invention takes into account the fact that the two relative risks are dependent on each other, while the previous method proposes to calculate each of the relative risks independently.

Relative risk for multi-allele location:

If multiple markers or other multiple allelic variants are considered, the calculation method is a bit complicated. a ₀ , a ₁ , ... a _k are denoted as k + 1 alleles that may be present, in which case a ₀ is a non-risk allele. Assume f ₀ , f ₁ , f ₂ , ..., f _k , the allele expression frequencies for k + 1 alleles that may exist in the population. For allele i, the relative risk and crossover ratio are defined as follows:

The following equations indicate disease prevalence:

Therefore, dividing both sides of the equation by p (D / a ₀ ) yields the following equation:

이 구하여진다.As a result

Is obtained.

로 놓았을 때,

가 구하여진다. 그러므로, C에 관한 정의에 의하면 다음과 같은 식이 구하여진다:

When set to

Is obtained. Therefore, the definition of C gives the following equation:

에 의해 결정되므로, 답은 이진 탐색법(binary search)을 이용하여 구하는 것이 용이하다. The equation is a polynomial with one variable C. Once C is determined, the relative risk is determined. Since the polynomial is (k + 1), it is expected that almost k + 1 answers will be obtained. However, since the right side of the equation is a function of C, the order of the equation decreases. Therefore, in this case, only one answer may exist. In this case, the answer is C = 1

As determined by, the answer is easy to find using binary search.

Confirmation of relative risk estimates

The effect of each of the different parameters (prevalence, allele frequency, and crossover error) on the prediction of relative risk was measured. In order to determine the influence of allele expression frequency and predicted prevalence on relative risk values, the relative risk is calculated from a set of different cross ratio values, different allele expression frequencies (under HWE), and then the prevalence values (0 to 1). The graph was created as a result of the calculation.

In addition, a function graph of the risk-allele expression frequency was prepared using the resulting relative risk in the established value of the prevalence. To be clear, in all cases, if p (D) = 0, then λ _RR = OR _RR and λ _RN = OR _RN , and if p (D) = 1, then λ _RR = λ _RN = 0. This can be calculated directly from equation 1. In addition, if the frequency of risk allele expression is high, λ _RR is close to linear behavior, and λ _RN is close to a concave function with a second oil derivative. If the risk-allele frequency is low, λ _RR and λ _RN are close to the behavior of function 1 / p (D). This means that in the high risk-allele frequency, mispredictions of prevalence will have little impact on the resulting relative risk.

The following examples illustrate and illustrate the invention. The scope of the invention is not limited by this embodiment.

Example I

Formation and Analysis of SNP Profiles

The analyte was placed, for example, in a sample tube in a kit commercially available from DNA Genotek, which contained a saliva sample (about 4 mL) from which genomic DNA was to be extracted. The saliva sample is sent to a CLIA certified laboratory for processing and analysis. Typically, the samples are transported overnight to the facility by mail in a shipping container conveniently provided to the subject in the collection kit.

In a preferred embodiment, the genomic DNA is isolated from saliva. For example, an individual collects about 4 ml of saliva samples for clinical processing using DNA self-collection kit technology (DNA Genotech). This sample is sent to a suitable laboratory for processing and is typically digested with thermal denaturation and proteolytic enzymes at 50 ° C. for at least 1 hour using reagents supplied by a collection kit supplier to separate DNA. The sample is then centrifuged and the supernatant is precipitated with ethanol. DNA pellets are suspended in a buffer suitable for subsequent analysis.

According to well known methods and / or methods suggested by the collection kit manufacturer, the genomic DNA of an individual is isolated from saliva samples. Generally, the sample is first denatured by heat and digested with proteolytic enzymes. The sample is then centrifuged and the supernatant collected. The supernatant is then precipitated with ethanol to obtain pellets containing about 5-16 ng of genomic DNA. The DNA pellet is suspended in 10 mM Tris pH 7.6 and 1 mM EDTA (TE). Genomic DNA is hybridized to commercially available high density SNP arrays, eg, arrays available from Affymetrix or Illumina, according to the instruments and instructions provided by the array manufacturer to form SNP profiles. Store the SNP profile of an entity in a security database or vault.

The patient's data structure is queried for risk-distributing SNPs by comparing them with established and medically relevant SNPs of clinically relevant SNPs that are correlated with a given disease or condition. The database contains information about the statistical correlation of specific SNPs and SNP haplotypes with specific diseases or conditions. For example, as shown in Example III, polymorphisms in the apolipoprotein E gene produce different subtypes of the protein, which correlate with the statistical likelihood of ongoing Alzheimer's disease. As another example, individuals with variants of the blood clot protein factor V, known as Factor V Leiden, have an increased tendency to blood clots. Table 1 shows a number of genes in which SNPs are associated with a disease or phenotype of a disease. Such information in the database is approved by research / clinical advisory bodies for scientific accuracy and importance and may be reviewed under the supervision of government agencies. The database is constantly updated as more SNP-disease correlations are revealed from the scientific community.

Analyze the patient's SNP profile in an on-line portal or by mail to provide the patient with security. The patient is provided with descriptive and supplemental information, for example, information informing about Factor V Leiden in Example IV. For example, by obtaining the SNP profile information of an individual while maintaining security through an on-line portal, consultation with the patient's specialist will be facilitated, and the patient will also have the right to select the appropriate medicine for the individual. Will give.

Example II

Update of Genotype Correlation

Following an initial request for measurement of the subject's genotype correlations, a genomic profile is formed and genotype correlations are revealed and the results are reported to the subject as described in Example I. After the initial measurement of the correlation of a genotype of an individual, the later updated correlation can be determined or determined as additional genotype correlations are known. Subscribers reach the highest level of subscription and genotype profile, and the results are stored in a secure database. The updated correlation is formed based on the stored genotype profile.

For example, early genotype correlations, for example, according to the correlation described in Example I above, a particular subject does not possess ApoE4 and therefore does not have a predisposition to premature onset of Alzheimer's disease, It can be seen that it does not have the factor V Leiden. After this initial measurement step, new correlations could be known and demonstrated, in which case the polymorphism in a given gene (assuming gene XYZ) is correlated with a given condition (assuming condition 321). As such, new genotype correlations are added to the master database on human genotype correlations. The correlation for the particular individual is then updated by first retrieving the relevant gene XYZ data from the genomic profile of the particular individual stored in the security database. The relevant gene XYZ data of a particular object is compared with the update master database information for gene XYZ. By such comparison, the susceptibility or genetic predisposition to condition 321 of a particular individual is measured. This comparison result is added to the genotype correlation of a particular individual. An updated result is provided to the particular individual, with descriptions and complementary information, as to whether the individual is susceptible to, or genetically predisposed to, the condition 321.

Example III

Correlation between ApoE4 Location and Alzheimer's Disease

The risk for Alzheimer's disease (AD) appears to be correlated with polymorphisms in the apolipoprotein E (APOE) gene, resulting in three subtypes of APOE: ApoE2, ApoE3 and ApoE4. The subtypes differ from each other by one or two amino acids present at residues 112 and 158 in the APOE protein. ApoE2 comprises 112/158 cys / cys; ApoE3 comprises 112/158 cys / arg; ApoE4 contains 112/158 arg / arg. As shown in Table 3, the risk of early onset of Alzheimer's disease increases with the number of APOE ε4 gene copies. Similarly, as shown in Table 3, the relative risk of AD increases with the number of APOE ε4 gene copies.

Prevalence of AD Risk Alleles (Corder et al., Science: 261: 921-3, 1993) APOE ε4 copy Prevalence (%) Alzheimer's Risk (%) Onset age 0 73 20 84 One 24 47 75 2  3 91 68

Relative risk of AD with ApoE4 (Farrer et al., JAMA: 278: 1349-56, 1997) APOE genotype Crossover ε2ε2  0.6 ε2ε3  0.6 ε3ε3  1.0 ε2ε4  2.6 ε3ε4  3.2 ε4ε4 14.9

Example IV

Information for patients with factor V Leiden positive

The information below exemplifies information that could be provided to an individual having a genomic SNP profile showing the presence of a gene for Factor V Leiden. The entity may be a primary subscriber whose information may be provided in the initial report.

What is the factor V Leiden?

The factor V Leiden is not a disease, but it means that there is a specific gene inherited from the parent of one individual. Factor V Leiden is a variant of protein factor V (5) required for blood clot formation. People who lack factor V tend to not stop bleeding well, while those who carry factor V Leiden have an increased tendency to blood clots.

People carrying the factor V Leiden gene are five times more likely to develop clot (thrombosis) than other individuals in the population. However, many people with this gene will never form blood clots. In the United Kingdom and the United States, 5% of the population carries one or more genes for factor V Leiden, which is actually much higher than the number of people who will develop thrombosis.

How do you get the argument V Leiden?

The gene for factor V is inherited from the parent of either individual. With all the inherited traits, one gene is inherited from the mother and the other gene from wealth. Thus, that is, two normal genes or one factor V Leiden gene and one normal gene or two factor V Leiden genes can be inherited. The risk of developing thrombosis is slightly higher with one factor V Leiden gene, but significantly higher with two genes.

What are the symptoms from factor V Leiden?

If no clot is formed, there are no signs (thrombosis).

What are the danger signs?

The most common problem is the formation of blood clots in the legs. This problem is manifested by swelling of the legs, pain or redness. In rare cases, blood clots form in the lungs (pulmonary embolism), which can make breathing difficult. Depending on the size of the blood clot, several types of patients may appear, ranging from patients who are unaware of their symptoms to patients suffering from severe breathing difficulties.

More rarely, blood clots can also occur in the arm or other part of the body. Since these clots are formed in veins that send blood to the heart but not to arteries (blood vessels that send blood out of the heart), Factor V Leiden does not increase the risk of developing coronary thrombosis.

What can you do to prevent blood clots?

Factor V Leiden only slightly increases the risk of clot formation, and the majority of people with this condition will not experience thrombosis. There are many things you can do to prevent blood clots from forming. Do not stand or sit in the same position for a long time. When traveling long distances, it is important to exercise regularly-because the blood must not be "stopped". Weight gain or smoking will also significantly increase the risk of blood clots. Women carrying the factor V Leiden gene should not take birth control pills because they will significantly increase the risk of developing thrombosis. Women who carry the factor V Leiden gene should also consult their doctor before pregnancy because pregnancy may increase the risk of thrombosis.

How can a doctor know if his patient has factor V Leiden?

The factor V Leiden gene can be found in blood samples.

Blood clots on the legs or arms can usually be confirmed by ultrasound.

Blood clots can also be detected by X-ray after administration of certain substances into the blood to allow blood clots to form. Pulmonary blood clots are hard to find, but doctors will typically use radioactive materials to test the distribution of blood flow in the lungs and the distribution of air to the lungs. The two patterns must match, but a mismatch indicates that a blood clot exists.

How is Factor V Leiden Treated?

A person with factor V Leiden does not need to start treatment unless his blood starts to harden, in which case doctors use blood thinning medications (anticoagulants), such as warfarin (e.g. , Marevan, or heparin may be prescribed to prevent further clotting. The treatment will generally last 3 to 6 months, but once the blood clots have formed, you can take the medicine for a longer time. In some cases, the medication course can last indefinitely; In very rare cases, blood clots may need to be removed surgically.

How is Factor V Leiden Therapy performed during pregnancy?

Women who carry two genes for factor V Leiden will need to receive heparin anticoagulant therapy during pregnancy. The same is true for women who have previously had a blood clot or have a blood clot family history and carry only one factor V Leiden gene.

All women who carry the gene for factor V Leiden may need to wear special tights that prevent blood clots from forming during the remainder of their pregnancy. After birth, anticoagulant heparin may be prescribed.

Prognosis

The risk of developing clots increases with age, but a survey of older people 100 years or older carrying this gene showed that only a small number of people suffer from thrombosis. The National Society for Genetic Counselors (NSGC) can provide a list of genetic counselors in the field that subscribers would like to know by the method of the present invention and information about whether to be a family history. Search the online database at www.nsgc.org/consumer.

While preferred embodiments of the invention have been described and disclosed herein, those skilled in the art will recognize that such embodiments are provided solely for purposes of illustration. Those skilled in the art will make various modifications, changes and substitutions to the present invention without departing from the scope of the invention. It should be understood that many alternatives to the embodiments of the invention disclosed herein can be used to practice the invention. The following claims limit the scope of the invention and will encompass methods and structures falling within such claims and their equivalents.

Claims (131)

A method of assessing a genotype correlation of an individual, a) obtaining a genetic sample of said individual; b) forming a genomic profile for said individual; c) comparing the individual's genomic profile with an existing database of correlations between the phenotype and the human genotype to determine a correlation between the phenotype and the individual genotype; d) reporting the result obtained from step c) to the individual or the health care manager of the individual; e) updating the database of human genotype correlations with additional human genotype correlations as additional human genotype correlations are identified; f) updating the genotype correlation of the individual by comparing the genomic profile of the individual or part thereof in step c) with the correlation of the additional human genotype and measuring the correlation of the additional genotype of the individual step; And g) reporting the result obtained from step f) to the individual or the health care manager of the individual. How to include. The method of claim 1, wherein said genetic sample is obtained from a third party. The method of claim 1, wherein forming the genomic profile is performed by a third party. The method of claim 1, wherein the result is based on a GCI score or a GCI plus score. The method of claim 1, wherein said reporting step comprises transmitting said results over a network. The method of claim 1, wherein reporting the results is through an on-line portal. The method of claim 1, wherein reporting the results is by paper or e-mail. The method of claim 1, wherein said reporting step comprises reporting the result in a secure manner. The method of claim 1, wherein said reporting step comprises reporting said result in a non-secure manner. The method of claim 1, wherein the genomic profile of the individual is stored in a security database or vault. The method of claim 1, wherein the subject is a subscriber. The method of claim 1, wherein the individual is not a subscriber. The method of claim 1, wherein the genetic sample is DNA. The method of claim 1, wherein the genetic sample is RNA. The method of claim 1, wherein the genomic profile is a single nucleotide polymorphism genomic profile, the database of correlations between human genotypes is a single nucleotide polymorphism correlation in human, and wherein the correlation of the additional human genotype is a single nucleotide polymorphism correlation. How. The method of claim 1, wherein the genomic profile comprises a cleavage, insertion, deletion or repeat and the database of correlations between human genotypes is a correlation of a cleavage, insertion, deletion or repeat of a human. Wherein said additional human genotype correlation is with respect to a cleavage, insertion, deletion or repeat. The method of claim 1, wherein the genomic profile has the entire genome of the individual. The method of claim 1, wherein the method comprises assessing a correlation of two or more genotypes. The method of claim 1, wherein the method comprises assessing a correlation of at least 10 genotypes. The method of claim 1, wherein the database of correlations between human genotypes comprises genetic variants of one or more genes listed in Table 1 and phenotypes correlated with the genetic variants. The method of claim 1, wherein the database of correlations between human genotypes comprises genetic variants of one or more genes listed in FIGS. 4, 5, 6, 22 or 25 and phenotypes correlated with the genetic variants. The method of claim 1, wherein the database of correlations of human genotypes comprises genetic variants measured from the genomic profile of the individual, and previously measured phenotypes published by the individual. The method of claim 1, wherein the database of correlations between human genotypes comprises single nucleotide polymorphisms in the genes listed in Table 1 or FIGS. 4, 5, 6, 22 or 25, and phenotypes correlated with the single nucleotide polymorphisms. How to be. The method of claim 1, wherein the genetic sample is derived from a biological sample selected from the group consisting of blood, hair color, skin, saliva, semen, urine, feces, sweat and buccal samples. The method of claim 15, wherein said genotype correlation is a correlation of single nucleotide polymorphism to disease and condition. The method of claim 15, wherein said genotype correlation is a correlation of single nucleotide polymorphism to a phenotype that is not a medical condition. The method of claim 1, wherein the genomic profile is formed using high density DNA microarrays. The method of claim 1, wherein the genomic profile is formed using genomic DNA sequencing. The method of claim 24, wherein the genetic sample is genomic DNA and the biological sample is saliva. a) providing a rule set comprising rules, each rule representing a correlation between one or more genotypes and one or more phenotypes; b) providing a data set comprising a genomic profile for each of the plurality of individuals, wherein each genomic profile comprises a plurality of genotypes; c) periodically updating the rule set with one or more new rules, wherein the one or more new rules indicate a correlation between phenotypes and genotypes previously uncorrelated with each other in the rule set; And d) applying each new rule to at least one genomic profile of said individuals, correlating at least one phenotype with at least one genotype for said individual How to include. 32. The method of claim 30, further comprising: e) generating a report comprising the phenotype profile of the individual. The method of claim 30, wherein after step b): i) applying the rules of the rule set to the genomic profile of the individual to determine a set of phenotype profiles for the individual; And ii) creating a report containing an initial phenotype profile of said individual How to further include. 33. The method of claim 31 or 32, wherein providing the report comprises sending the report over a network. 33. The method of claim 31 or 32, wherein the report is provided in a secure manner. 33. The method of claim 31 or 32, wherein the report is provided in a non-secure manner. 33. The method of claim 31 or 32, wherein the report is provided by an on-line portal. 33. The method of claim 31 or 32, wherein the report is provided by paper or e-mail. 31. The method of claim 30, wherein the new rule correlates a phenotype with a non-correlated genotype. 31. The method of claim 30, wherein the new rule correlates a genotype correlated with a previously unrelated phenotype in the rule set. 33. The method of claim 30, wherein the new rule modifies any of the rule sets. 31. The method of claim 30, wherein said new rule is formed by correlation of a genotype derived from said individual's genomic profile with a previously measured phenotype of said individual. 31. The method of claim 30, wherein said rule correlates a plurality of genotypes and phenotypes. 31. The method of claim 30, wherein applying the new rule comprises at least partially measuring the phenotype profile based on ethnicity, ancestry, topography, gender, age, family history and characteristics of the subject selected from previously measured phenotypes. Further comprising a step. 31. The method of claim 30, wherein the genotype comprises nucleotide repeats, nucleotide insertions, nucleotide deletions, chromosomal translocations, chromosomal duplications, or copy number variations. 45. The method of claim 44, wherein said copy number variation is a microsatelite repeat, a nucleotide repeat, a centriole repeat, or a telomere repeat. 31. The method of claim 30, wherein said genotype comprises single nucleotide polymorphism. 31. The method of claim 30, wherein the genotype comprises haplotypes and diplotypes. The method of claim 30, wherein the genotype comprises a genetic marker that is in linkage disequilibrium with a single nucleotide polymorphism correlated with a phenotype. 31. The method of claim 30, wherein said phenotype profile indicates the presence of said quantitative trait or risk of developing said quantitative trait. The method of claim 30, wherein the phenotype profile indicates that an individual with any genotype has or is likely to have any phenotype. 51. The method of claim 50, wherein the likelihood is based on a GCI score or a GCI plus score. 51. The method of claim 50, wherein the likelihood is a predicted lifetime risk. 31. The method of claim 30, wherein the correlation is curated. 33. The method of claim 30, wherein the rule set includes 20 or more rules. 33. The method of claim 30, wherein the rule set includes at least 50 rules. 31. The method of claim 30, wherein said rule set includes rules based on the correlation of genotypes in Table 1. 31. The method of claim 30, wherein the rule set comprises a rule based on the correlation of the genotypes in FIGS. 4, 5, 6, 22 or 25. The method of claim 30, wherein said phenotype comprises a quantitative trait. 59. The method of claim 58, wherein the quantitative trait comprises a medical condition. 60. The method of claim 59, wherein the phenotype profile indicates the presence of the medical condition, the risk of developing the medical condition, the prognosis of the medical condition, the efficacy of treatment for the medical condition, or the response to the treatment of the medical condition. How to be. 59. The method of claim 58, wherein the quantitative trait comprises a phenotype that is not a medical condition. 59. The method of claim 58, wherein the quantitative trait is selected from the group consisting of physical traits, physiological traits, mental traits, emotional traits, ethnicity, family or age. The method of claim 30, wherein the subject is a human. The method of claim 30, wherein the subject is non-human. 31. The method of claim 30, wherein the subject is a subscriber. 31. The method of claim 30, wherein the subject is not a subscriber. The method of claim 30, wherein said genomic profile comprises at least 100,000 genotypes. 31. The method of claim 30, wherein said genomic profile comprises at least 400,000 genotypes. The method of claim 30, wherein said genomic profile comprises at least 900,000 genotypes. The method of claim 30, wherein said genomic profile comprises at least 1,000,000 genotypes. The method of claim 30, wherein said genomic profile comprises a substantially complete whole genome sequence. 31. The method of claim 30, wherein the data set comprises a plurality of data points, wherein each data point is associated with an entity and includes a plurality of data elements, wherein the data element is a unique identifier of the subject, genotype information, micro Array SNP identification number, SNP rs number, chromosome location, polymorphic nucleotides, quality metrics, raw data file, image, extracted intensity score, physical data, medical data, ethnicity, ancestry, And one or more elements selected from topography, gender, age, family history, known phenotypes, demographic data, exposure data, lifestyle data, and behavioral data. 31. The method of claim 30, wherein the periodically updating and applying step is performed at least once a year. 33. The method of claim 30, wherein providing the data set Obtaining a genomic profile for each of the plurality of individuals, by (i) performing a genetic analysis on the genetic sample obtained from the individual, and (ii) encoding the analysis in a computer readable format. Which method. The method of claim 30, wherein said phenotype profile comprises a phenotype of a single gene. The method of claim 30, wherein said phenotype profile comprises phenotypes of multiple genes. 31. The method of claim 30, wherein the report comprises an initial phenotype profile. 31. The method of claim 30, wherein the report comprises an updated phenotype profile. 31. The method of claim 30, wherein the report is selected from one or more of prevention strategies, wellness information, therapy, awareness of symptoms, early detection techniques, intervention plans, and sophisticated identification methods and subclassifications of phenotypes in the phenotype profile. Further comprising information about said phenotype of the phenotype profile. The method of claim 30, e) adding a new genomic profile of the new individual to the data set of the individual; f) applying the rule set to the genomic profile of the new individual; And g) creating an initial report of phenotype profiles for the new entity How to further include. The method of claim 30, e) adding a new genomic profile of said individual; f) applying the rule set to a new genomic profile of the individual; And g) creating a new report of phenotype profiles for the individual How to include. a) a set of rules comprising rules, each rule representing a correlation between one or more genotypes and one or more phenotypes; b) code for periodically updating the rule set with one or more new rules, wherein the one or more new rules indicate a correlation between phenotypes and genotypes previously uncorrelated with each other in the rule set; c) a database comprising genomic profiles for the plurality of individuals; d) code for applying the rule set to the genomic profile of the individual to determine a phenotype profile for the individual; And e) code to generate a report for each object System comprising a. 83. The system of claim 82, wherein said report is transmitted over a network. 83. The system of claim 82, wherein said report is provided in a secure manner. 83. The system of claim 82, wherein said report is provided in a non-secure manner. 83. The system of claim 82, wherein said report is provided via an on-line portal. 83. The system of claim 82, wherein said report is provided via paper or e-mail. 83. The system of claim 82, further comprising code for notifying the subject of a new or modified correlation. 83. The system of claim 82, further comprising code for notifying the individual of new or modified rules that can be applied to the individual's genomic profile. 83. The system of claim 82, further comprising code for notifying the individual of new or modified prevention and health care information for the phenotype in the phenotype profile of the individual. a) one or more sample collection containers; b) instructions for obtaining a sample from the subject; c) instructions for accessing a genomic profile of said individual obtained from said sample through an on-line portal; d) instructions for accessing the phenotype profile of the subject obtained from the sample through an on-line portal; And e) packaging for shipping the sample collection container to the sample processing facility. Kit comprising a. An on-line portal that includes a website from which an entity can access the phenotype profile, wherein the website is defined by the entity. a) selecting rules to apply to the genomic profile of the individual; b) reviewing initial and updated reports on the website; c) printing an initial report and an updated report from the website; d) storing the initial and updated reports obtained from the website on the entity's computer; e) obtaining preventive and healthcare information about the phenotype profile of said individual; f) counseling with an on-line or telephone genetic counselor; g) extract information and share it with specialists / genetic counselors; And / or h) access to partner services and product applications; An online portal that allows one or more of the following. 93. The on-line portal of claim 92, wherein said information is transmitted over a network. 95. The on-line portal of claim 92, wherein said website is secure. 95. The on-line portal of claim 92, wherein said website is not secure. 93. The on-line portal of claim 92, wherein the entity is provided with one or more options regarding the security level of the entity or one or more portions thereof. 95. The on-line portal of claim 92, wherein said phenotype profile comprises an actionable medical condition. 93. The on-line portal of claim 92, wherein said phenotype profile comprises a medical condition currently without prophylactic treatment or therapy. 93. The on-line portal of claim 92, wherein said phenotype profile comprises a non-medical condition. As a method of assessing the risk of developing a condition in an individual, a) obtaining the genotype of the individual; b) measuring a GCI score or a GCI plus score from said genotype; c) generating a report based on the GCI score or GCI plus score; And d) providing the report to the subject or the health care manager of the subject How to include. As a method of assessing the risk of developing a condition in an individual, a) obtaining the genotype of the individual; b) forming a genomic profile for said individual; c) determining the risk of developing a condition in the individual from a database of correlations between said genomic profile and genotype; d) generating a report from step c); e) obtaining new information from the entity; f) incorporating said new information to determine a new risk of developing a condition; g) generating a report from step f); And h) providing the report to the subject or the health care manager of the subject How to include. As a method of assessing the risk of developing a condition in an individual, a) obtaining the genotype of the individual; b) forming a genomic profile for said individual; c) determining a risk of developing a condition in the individual from a database relating to the genomic profile and genotype correlations, wherein the risk is based on one or more SNPs; d) generating a report from step c); And e) providing the report to the subject or the health care manager of the subject How to include. 102. The method of any one of claims 100-102, wherein the genotype of the subject is obtained directly from the subject. 102. The method of any one of claims 100-102, wherein the genotype of the individual is obtained from a third party. 103. The method of any one of claims 100-102, wherein the providing step is via transmission over a network. 102. The method of claim 101, wherein said new information is obtained from a biological sample of said individual. 102. The method of claim 101, wherein said new information is obtained from an individual's physical measurements. 103. The method of claim 101 or 102, wherein said risk is derived from a GCI score or a GCI plus score. 109. The method of claim 100 or 108, wherein the GCI score or GCI plus score consolidates the pedigree of the individual. 109. The method of claim 100 or 108, wherein the GCI score or GCI plus score incorporates the sex of the individual. 109. The method of claim 100 or 108, wherein the GCI score or GCI plus score incorporates a factor specific for the subject and the factor is not derived from the genotype. 112. The method of claim 111, wherein said factor is Of the individual, place of birth, parents and / or grandparents, relatives, residence, ancestor residence, environmental conditions, known health conditions, known drug interactions, family health status, lifestyle status, diet, exercise habits, marital status , And physical measurements. 112. The method of claim 107 or 112, wherein the individual's physical measurements include blood pressure, heart rate, glucose levels, metabolite levels, ionic levels, weight, height, cholesterol levels, vitamin levels, blood cell counts, body mass index (BMI), The protein level and the transcript level. As a method of assessing the risk of developing a condition in an individual, a) obtaining the genotype of the individual; b) forming a genomic profile for said individual; c) measuring the risk of developing Alzheimer's disease (AD), colorectal cancer (CRC), osteoarthritis (OA), or exfoliative glaucoma (XFG) in an individual, wherein the risk is rs4420638 for AD, rs6983267 for CRC, and OA. Measuring based on rs4911178 for X and rs2165241 for XFG; d) generating a report from step c); And e) providing the report to an individual or a health care manager of the individual. How to include. 107. The method of claim 102, wherein the risk is measured from at least 3, 4, 5, 6, 7, 8, 9, 10 or 11 SNPs. 107. The method of claim 102, wherein the risk is measured from two or more SNPs. 116. The method of claim 116, wherein the risk is measured for obesity (BMIOB) and at least one of the two or more SNPs is rs9939609 or rs9291171. 117. The method of claim 116, wherein the risk is measured for Graves' disease (GD), and at least one of the two or more SNPs is rs3087243, DRB1 * 0301 DQA1 * 0501, or an imbalance associated with DRB1 * 0301 DQA1 * 0501. How to be. 116. The method of claim 116, wherein the risk is measured for hemochromatosis (HEM) and at least one of the two or more SNPs is rs1800562 or rs129128. 116. The method of claim 116, wherein the risk is measured for myocardial infarction (MI) and at least one of the two or more SNPs is rs1866389, rs1333049 or rs6922269. 116. The method of claim 116, wherein the risk is measured for multiple sclerosis (MS) and at least one of the two or more SNPs is rs6897932, rs12722489 or DRB1 * 1501. 117. The method of claim 116, wherein the risk is measured for psoriasis (PS) and at least one of the two or more SNPs is rs6859018, rs11209026 or HLAC * 0602. 117. The method of claim 116, wherein the risk is measured for Lower Anxiety Syndrome (RLS) and at least one of the two or more SNPs is rs6904723, rs2300478, rs1026732 or rs9296249. 116. The method of claim 116, wherein the risk is measured for celiac disease (CelD) and at least one of the two or more SNPs is rs6840978, rs11571315, rs2187668 or DQA1 * 0301 or DQB1 * 0302. 117. The method of claim 116, wherein the risk is measured for prostate cancer (PC) and at least one of the two or more SNPs is rs4242384, rs6983267, rs16901979, rs17765344 or rs4430796. 117. The method of claim 116, wherein the risk is measured for lupus (SLE) and at least one of the two or more SNPs is rs12531711, rs10954213, rs2004640, DRB1 * 0301, or DRB1 * 1501. 117. The method of claim 116, wherein the risk is measured for macular degeneration (AMD) and at least one of the two or more SNPs is rs10737680, rs10490924, rs541862, rs2230199, rs1061170 or rs9332739. 117. The method of claim 116, wherein the risk is measured for rheumatoid arthritis (RA) and at least one of the two or more SNPs is rs6679677, rs11203367, rs6457617, DRB * 0101, DRB1 * 0401 or DRB1 * 0404 . 116. The method of claim 116, wherein the risk is measured for breast cancer (BC) and at least one of the two or more SNPs is rs3803662, rs2981582, rs4700485, rs3817198, rs17468277, rs6721996 or rs3803662. 116. The method of claim 116, wherein the risk is measured for Crohn's disease (CD), and at least one of the two or more SNPs is rs2066845, rs5743293, rs1O883365, rs17234657, rs10210302, rs9858542, rs11805303, rs1OOOO113, rs17221417, rs2542151659 or rsO How. 117. The method of claim 116, wherein the risk is measured for type 2 diabetes (T2D), and at least one of the two or more SNPs is rs13266634, rs4506565, rs10012946, rs7756992, rs10811661, rs12288738, rs8050136, rs1111875, rs4402960, rs5215 or the method is rs1801282.

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