TWI669618B - A method and an apparatus forpredicting disease, and a method for calculating the weighted score of risk of occurrence of eachdisease - Google Patents

Abstract

The invention relates to a method and a device for predicting diseases and calculating each A method of weighting scores for the risk of developing a disease. A system and apparatus for disease-related gene analysis using a single nucleotide polymorphism according to the present invention includes the following steps: (1) a database of disease and drug reaction related databases, a research database, and a gene database by an improved algorithm Deriving an objective and specific single nucleotide polymorphism-disease association; and (2) analyzing the complexity of a particular disease-associated single nucleotide polymorphism derived in step (1), thereby calculating the The risk of the disease. Therefore, the system and device according to the present invention are expected to have an effect of improving the accuracy of disease prediction results.

Description

Methods and devices for predicting disease and calculating the occurrence of each disease Weighted score method

The present invention relates to a system and apparatus for disease-related gene analysis using single nucleotide polymorphisms.

Single nucleotide polymorphism (SNP) is a common DNA sequence variation occurring on a single base in a single chromosome region, and there are about 3 million single nucleotide polymorphisms in the human genome. Single nucleotide polymorphisms occur frequently, are stable, and are distributed throughout the genome, and thus individual genetic diversity occurs. Such differences in single nucleotide polymorphisms make differences in susceptibility to different diseases. Therefore, in recent years, "genetic analysis technologies for predicting diseases" have been developed, and the genetic analysis techniques for predicting diseases include: selecting humans based on single nucleotide polymorphism information Appropriate genes involved in the disease (disease candidate genes); identification of variants of the gene; And statistical analysis of the relationship between genes and diseases (US2008-0020484, KR1483284, etc.). The disease and drug response predictive genetic analysis service is designed to analyze the association with disease and drug response based on individual characteristics before examining, treating, and prescribing high-risk families and relatives who have developed certain genetic diseases. Genetic variation to predict the likelihood and prognosis of disease occurrence.

Therefore, the accuracy of the "repository on the relationship between disease and genetic information" is very important for accurately predicting disease.

Traditional genetic analysis systems for predicting disease and drug response include a series of procedures, such as pre-consultation, triage, individual genomic variant identification (experimental), and outcome reporting, depending on the requester's request and the type of suspected disease, and Collection and application of information associated with it. However, in such systems, reliability issues arise due to the lack of accurate identification of widely published material and the lack of discussion of objective and detailed reports to be submitted to the requester in the future.

The present invention relates to a system and apparatus for disease-related gene analysis using single nucleotide polymorphisms. The system according to the present invention contemplates the use of improved algorithms to provide a highly accurate predictive effect of the disease.

The present invention has been made to solve the above problems occurring in the prior art, and is related to a system and apparatus for disease-related gene analysis using single nucleotide polymorphism.

However, the technical object to be achieved by the present invention is not limited to the above technical purpose, and other objects not mentioned above can be clearly understood from the following description by those skilled in the art.

The present invention provides a method of calculating a weighted score for the risk of occurrence of each disease, comprising the steps of: (a) estimating the relative risk of genotype of each gene; (b) generalizing the relative risk of genotype of each gene in the population (c) calculating a score for the relative risk of each gene; (d) calculating an average score for the relative risk of all genes; (e) calculating a score for the genotype of the subject; and (f) calculating the subject The relative risk of the genotype.

In an embodiment of the invention, the relative risk in step (a) is calculated by the odds ratio / ((1 prevailing rate) + (prevailing rate * odds ratio)).

In an embodiment of the invention, the relative risk in the population in step (b) is calculated by the genotype frequency corresponding to the relative risk*.

In an embodiment of the invention, the fraction of the relative risk in step (c) is calculated as the sum of the scores of the relative risks of the genotype of each gene.

In an embodiment of the invention, the average score of the relative risk in step (d) is calculated as the product of the scores of the relative risks of each gene.

In an embodiment of the invention, the score in step (e) is calculated as the product of the relative risk of the genotype of each gene of the subject.

In an embodiment of the invention, the relative risk in step (f) is calculated by the score in step (e) / the average score of the relative risk in step (d).

The invention provides a method for predicting a disease comprising the steps of: (a) extracting DNA from a requested sample; (b) extracting genetic information from the DNA; (c) by using the genetic information from the first database The disease-single nucleotide polymorphism association results to the third database are compared to measure the risk of a particular disease; (d) when two or more single nucleotide polymorphisms associated with the particular disease are associated When the sex exists in the genetic information of the requested sample, the relative risk of each genotype is calculated by applying the above-mentioned weighted score calculated by the method of calculating the weighted score of each disease occurrence risk; (e) The relative risk (%) and incidence (%) of the sample requested; and (f) determining the risk of developing the sample of the request.

In an embodiment of the invention, the first database in step (c) is a disease and drug reaction related database, and the disease and drug reaction related database includes a specific single nucleotide polymorphism Information about the symptoms of the specific disease, the type of prescription drug, the concentration of the prescribed drug, the frequency of the drug prescription, the cycle of the drug prescription, and side effects.

In an embodiment of the invention, the second database in step (c) is a research database containing research papers on specific diseases associated with specific single nucleotide polymorphisms. It also contains the PubMed identifier, research objects, research methods, research cycles, research results, journal information, and research reproducibility information.

In an embodiment of the present invention, the third database in step (c) is a gene database containing a chromosome number of a specific single nucleotide polymorphism associated with a specific disease, Locus and dual gene information.

In an embodiment of the invention, the relative risk in step (d) is 1 or greater than 1 ( 1), the relative risk (%) in step (e) is calculated by (subject's average risk score -1) * 100, and the relative risk in step (d) is less than 1 (<1) The relative risk (%) in step (e) is calculated by (1 - average risk score of the subject) * 100.

In an embodiment of the invention, the incidence rate (%) in step (e) is calculated by the relative risk* prevalence rate in step (d).

In an embodiment of the invention, the determining in step (f) comprises when the relative risk (%) in step (e) is 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) in step (e) is greater than 1 (>1), it is judged as warning or attention.

The present invention provides an apparatus for predicting a disease comprising: (a) an extracting unit configured to extract DNA from a requested sample; (b) an input unit configured to extract genetic information from the DNA; (c) compare a unit configured to measure a risk of a particular disease by comparing the genetic information to a disease-single nucleotide polymorphism associated with the first to third databases; (d) an arithmetic unit And configured to calculate each disease occurrence by applying the above-described calculation when two or more single nucleotide polymorphisms associated with the specific disease are present in the genetic information of the requested sample The weighted score method of the risk calculates the relative risk of each genotype; (e) the calculation unit configured to calculate the relative risk (%) and incidence (%) of the requested sample; f) a judging unit configured to determine a risk of occurrence of the disease of the requested sample.

In an embodiment of the invention, (c) the first database in the comparison unit is a disease and drug reaction related database, and the disease and drug reaction related database contains polymorphisms with specific single nucleotides. Information on the symptoms of a particular disease associated with sex, the type of prescription drug, the concentration of the prescribed drug, the frequency of drug prescription, the prescription period of the drug, and side effects.

In an embodiment of the invention, (c) the second database in the comparison unit is a research database containing studies on specific diseases associated with a particular single nucleotide polymorphism. The paper also includes the PubMed identifier, research objects, research methods, research cycles, research results, journal information, and research reproducibility information.

In an embodiment of the invention, the third database in the (c) comparison unit is a gene database containing the number of chromosomes of a particular single nucleotide polymorphism associated with a particular disease. , loci and dual gene information.

In an embodiment of the invention, (d) the relative risk in the arithmetic unit is 1 or greater than 1 ( 1), (e) The relative risk (%) in the calculation unit is calculated by (subject's average risk score -1) * 100, and when (d) the relative risk in the arithmetic unit is less than 1 (< 1), (e) The relative risk (%) in the calculation unit is calculated by (1 - average risk score of the subject) * 100.

In an embodiment of the invention, the occurrence rate (%) in (e) the calculation unit is calculated by (d) the relative risk* prevalence rate in the arithmetic unit.

In an embodiment of the invention, (f) the determination in the determining unit includes when the relative risk (%) in the (e) calculating unit is 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) in the (e) calculation unit is greater than 1 (>1), it is judged as warning or caution.

A system and apparatus for disease-related gene analysis using a single nucleotide polymorphism according to the present invention includes the following steps: (1) a database of disease and drug reaction related databases, a research database, and a gene database by an improved algorithm Deriving an objective and specific single nucleotide polymorphism-disease association; and (2) analyzing the complexity of a particular disease-associated single nucleotide polymorphism derived in step (1), thereby calculating the The risk of the disease. Therefore, the system and device according to the present invention are expected to have an effect of improving the accuracy of disease prediction results.

1 is a schematic diagram showing a method of predicting a specific disease using a single nucleotide polymorphism according to an embodiment of the present invention.

2 is a schematic diagram showing a method of predicting susceptibility to a particular drug using a single nucleotide polymorphism, in accordance with one embodiment of the present invention.

3 is a schematic diagram showing a method of predicting side effects of a specific drug using a single nucleotide polymorphism according to an embodiment of the present invention.

4 is a schematic diagram showing a method of applying a weighted score according to each genotype and predicting the risk of developing a disease according to an embodiment of the present invention.

FIG. 5 is a view specifically showing the relative wind of a disease occurring in a subject according to the present invention. Schematic diagram of the steps of risk (%) and incidence (%).

In the following, various embodiments set forth herein will be set forth with reference to the drawings. In the following description, numerous specific details are set forth, such as a particular configuration, However, some embodiments may be practiced without one or more of these specific details or in combination with other known methods and configurations. In other instances, well known processes and fabrication techniques have not been described in detail in order not to unnecessarily obscure the invention. References to "an embodiment" or "an embodiment" or "an embodiment" or "an embodiment" or "an embodiment" or "an" Therefore, the phrase "in one embodiment" or "an embodiment" or "an" In addition, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

Unless otherwise indicated in the specification, all scientific terms and technical terms used in the specification have the same meaning as commonly understood by those skilled in the art.

In one embodiment of the invention, "genetic information" refers broadly to all information encoded in the nucleotide sequence of DNA. In the present invention, "genetic information" includes individual single nucleotide polymorphism information.

In one embodiment of the invention, "single nucleotide polymorphism (single) Nucleotide polymorphism (SNP)" is a common DNA sequence variation that occurs on a single base in a single chromosomal region. There are about 3 million single nucleotide polymorphisms in the human gene, and there is a single nucleotide polymorphism every about 500 to 1000 bases. In these single nucleotide polymorphisms, approximately 200,000 single nucleotide polymorphisms are assumed to be encoded as single nucleotide polymorphisms (cSNPs) present in genes encoding proteins. Single nucleotide polymorphisms occur frequently, are stable, and are distributed throughout the gene, and thus individual genetic diversity occurs. That is, any person has adenine (A) at a specific site of the DNA strand, and any person has cytosine (C) at the specific site. These small differences (single nucleotide polymorphisms) can alter the function of each gene, and these small differences interact to produce different types of humans and make differences in susceptibility to different diseases. That is, if the genetic difference between a hepatitis patient and a person who does not have hepatitis can be found, the cause of the change in hepatitis susceptibility can be found. As a result, the ultimate goal of human genetic research is to use this genetic difference to develop drugs for the prevention or treatment of hepatitis. Therefore, large global pharmaceutical companies and genetic research institutions believe that single nucleotide polymorphisms can provide information about the source of new drug development. Based on this belief, they formed the SNC Alliance (TSC) and focused on single nucleotide polymorphism research to promote the dream of healthy longevity, which is the eternal ideal of human beings. However, even if a large number of single nucleotide polymorphisms have been developed, the single nucleotide polymorphism alone is not meaningful in itself. That is, if there is no object to compare and analyze single nucleotide polymorphisms, the single nucleotide polymorphisms are useless. As a result, domestic pharmaceutical companies and research institutions have worked hard to ensure that many of the details (such as DNA and clinical data) of diseases such as heart disease, dementia, and AIDS (patients) are established and established. A database of which SNPs are associated with which diseases.

In one embodiment of the invention, "a database associated with disease and drug response" means a database that measures the risk of a particular disease by comparing the results of the disease-single nucleotide polymorphism association, and includes Information about the symptoms of a particular disease associated with a particular single nucleotide polymorphism, the type of prescription drug, the concentration of a prescription drug, the frequency of a drug prescription, the duration of a drug prescription, and side effects. The "database related to disease and drug reaction" in the present invention as a database may include diseases and drugs provided by the Food and Drug Administration (FDA), medical institutions, and health check centers. Information, and may include information on the age and gender of a particular individual and family history information of his/her spouse, children, parents, cousins, and the like.

In one embodiment of the invention, "research database" means a database that measures the risk of a particular disease by comparing disease-single nucleotide polymorphism association results, and the research data may include, but is not limited to, clinical Or academic papers. If the data in the database is derived from the paper, the database should contain the PubMed identifier (PMID) of the paper, the research object, the research method, the research period, the research results, the journal information, and the reproducibility information of the study, and The age and gender information of the individual who is the subject of the study, as well as family history information of his/her spouse, child, parent, cousin, etc., may be included.

In one embodiment of the invention, a "gene database" means a database that measures the risk of a particular disease by comparing the results of a disease-single nucleotide polymorphism association, wherein the database may contain and More specific single nucleotides associated with the disease State genetic information, including chromosome number, locus and dual gene information. In particular, in the data stored in the genetic database, the ethnic information of the individual to be analyzed may be an important factor, but is not limited thereto.

In one embodiment of the invention, the "winning ratio" is an estimate of the relative risk estimated from a case-control study. The relative risk is a value estimated from the Cohort study and is defined as the ratio of the probability of occurrence of any event in the presence of a risk factor to the probability of occurrence of the event in the absence of a risk factor. In generational studies, risk factors are pre-set, and the occurrence of events is observed over time, and thus the relative risk of correlating events with risk factors is reliable. However, in case-control studies falling within the technical field of the present invention, it is judged whether there is a risk factor or no risk factor after grouping according to whether an event occurs, and for this reason, the relative risk is not significant, and thus Use the odds ratio as an estimate of relative risk.

In one embodiment of the invention, "algorithm" refers to a program that is programmed by a computer to solve a particular problem. When the desired result is naturally obtained by mechanical processing in a predetermined order, the predetermined order is referred to as an algorithm for the purpose. Usually, the algorithm program known by the algorithm can be converted and processed by a computer program. The algorithms in this specification compare the genetic information of a particular individual with the information stored in the disease and drug response-related databases, research databases, and gene databases to derive the probability (risk) of a particular disease, or to derive A drug candidate that is highly sensitive to a particular disease, or a drug candidate that has a high risk of side effects, is not limited thereto.

In one embodiment of the present invention, "target disease" means a disease in which the risk and incidence can be predicted by the algorithm of the present invention. In the present invention, the target diseases can be broadly classified into chronic diseases, cancer diseases, drug-responsive diseases, and other diseases. Specifically, chronic diseases include, but are not limited to, type 1 diabetes, type 2 diabetes, hepatitis C, Kawasaki disease, ankylosing spondylitis, psoriasis, tuberculosis, hypertension, osteoarthritis, osteoporosis Symptoms, coronary artery disease, ulcerative colitis, narcolepsy, glaucoma, cerebral aneurysm, stroke, polycystic ovary syndrome, multiple sclerosis, gallstone, Lughih's disease ), lupus, rheumatoid arthritis, rheumatic heart disease, chronic kidney disease, knee osteoarthritis, pathological myopia (high myopia), Behcet's disease, cataract, vitiligo, obesity, non-alcohol Fatty liver, myocardial infarction, atrial fibrillation, aspirin allergic chronic urticaria, atopic dermatitis, allergic food allergy, gestational diabetes, pregnancy addiction, triglyceride levels, asthma, disc herniation, dementia, gram Crohn's disease, gout, Parkinson's disease, obstructive pulmonary disease, chronic sebaceous gland, coronary heart disease, migraine and macular degeneration; cancer Diseases include, but are not limited to, liver cancer, thyroid cancer, testicular cancer, oral cancer, acute myeloid leukemia, ovarian cancer, biliary tract cancer, colorectal cancer, head and neck cancer, diffuse gastric cancer, bladder cancer, childhood leukemia, esophageal cancer, kidney Cancer, stomach cancer, breast cancer, cervical cancer, endometrial cancer, prostate cancer, pancreatic cancer, lung cancer and skin cancer; drug-responsive diseases including but not limited to methamphetamine-induced psychosis, angiotensin-converting enzyme inhibitor sensitivity Sex, warfarin drug sensitivity, and propofol anesthesia sensitivity; and other diseases including but not limited to ADHD, Panic disorder, nicotine addiction, alcohol dependence, bipolar disorder, depression, autism, and schizophrenia.

In addition, the target diseases can be classified according to whether the prevalence rate can be calculated. Specifically, among the above-mentioned target diseases, type 1 diabetes, type 2 diabetes, hepatitis C, Kawasaki disease, ankylosing spondylitis, psoriasis, tuberculosis, hypertension, osteoarthritis, osteoporosis, coronary artery disease Ulcerative colitis, narcolepsy, glaucoma, cerebral aneurysm, stroke, polycystic ovary syndrome, multiple sclerosis, gallstone, Lugrick's disease, lupus, rheumatoid arthritis, Rheumatic heart disease, chronic kidney disease, knee osteoarthritis, pathological myopia (high myopia), Becht's disease, cataract, vitiligo, obesity, nonalcoholic fatty liver, myocardial infarction, atrial fibrillation, atopic skin Inflammation, allergic food allergy, gestational diabetes, pregnancy addiction, asthma, disc herniation, dementia, Crohn's disease, gout, Parkinson's disease, sebaceous gland chronic disease, macular degeneration, liver cancer, thyroid cancer, testicular cancer, Oral cancer, acute myeloid leukemia, ovarian cancer, biliary tract cancer, colorectal cancer, head and neck cancer, diffuse gastric cancer, bladder cancer, childhood leukemia, esophageal cancer, kidney cancer, stomach cancer, Adenocarcinoma, cervical cancer, endometrial cancer, prostate cancer, pancreatic cancer, lung cancer, skin cancer, ADHD, panic disorder, alcohol dependence, bipolar disorder, depression, autism, and schizophrenia can be classified Easy to calculate prevalence of disease for known databases; aspirin allergic chronic urticaria, triglyceride levels, methamphetamine-induced psychosis, angiotensin-converting enzyme inhibitor sensitivity, warfarin drug sensitivity Sex, propofol anesthesia sensitivity, obstructive pulmonary disease, and nicotine addiction can be classified as diseases that are not easily calculated from known databases, but the scope of the present invention is not Limited to this.

In the present invention, for a disease group that is easy to calculate the prevalence rate, an algorithm is set by determining a weighted average score of all genes in the population based on the prevalence of the corresponding race and the genotype frequency, and predicting the population in the population. The relative risks and incidence of the subjects. In order to judge, the average risk score and the genotype frequency (%) were used as criteria for judgment. Specifically, when the subject’s average risk score At 1 o'clock, it is judged as "standard stage", and when the average risk score of the subject is > 1, it is judged as "warning care stage" and "attention care stage". In addition, when the frequency sum of the genotype combination whose average risk score is higher than the average risk score of the subject is 5% or less, it is judged as "attention care stage", and when the frequency is over 5%, It is judged as "warning care stage". In addition to the algorithm, the reference value that distinguishes the frequency of the warning care phase from the attentional care phase can be adjusted. For a disease group that does not easily calculate the prevalence rate, the genotypes identified from the first database to the third database of the present invention can be judged at three levels (low/medium/high), thereby generating a relative response to a specific disease and drug response. Risk and incidence prediction model.

The present invention provides a system and apparatus for disease-related gene analysis using single nucleotide polymorphisms. More specifically, according to the present invention, in the gene prediction of diseases and drug reactions, the following information is stored: information on the cause, diagnosis, prevention, etc. of the overall disease and drug reaction; genetic information on genetic factors, such as genetic factors Search for related genes, single nucleotide polymorphism names and loci of the searched gene, etc.; and analyze to statistically prove or verify that the searched gene is a related gene. Based on the review of stored data, combined with the study of dual gene association The general characteristics of genetic and genetic association studies, reviewing the priority of genes and the priority of gene-related research, and selecting the single nucleotide polymorphisms and related values to be applied to statistical prediction algorithms. Therefore, a statistical prediction model for each disease and drug response is generated.

The generation of a statistical prediction model includes the following steps: searching for general information about related diseases and drug reactions; searching for related genes as genetic factors in causes; and searching for genetic information.

The steps to search for general information about the disease and drug response are steps to gather information about the disease and drug response, and to confirm the definition, cause, diagnosis, treatment, prevention, and care, and to re-examine whether the disease is genetically The process caused by the factor.

The step of searching for a related gene is a step of retrieving a dual gene association study (including all studies demonstrating or attempting to prove association with a gene), and is a process for confirming whether a related gene can be regarded as a genetic factor based on experimental results. The step of searching for genetic information is a process of confirming the distribution state of related genes in each race, and the LD relationship with other genes.

The present invention provides a system and apparatus for examining a gene for a disease and a drug response requested by a requester, and then performing a statistical prediction model based on the corresponding disease and drug reaction, thereby calculating an individual genotype and The resulting value of the correlation between the disease and the drug response is requested.

More specifically, according to the present invention, in order to accurately predict diseases and drug reactions, relevant information is collected, and information about related single nucleotide polymorphisms is stored. Go to the database to select candidate single nucleotide polymorphisms. Using this information, a prediction algorithm for accurately predicting the prognosis of the disease or the like is applied to generate each prediction model.

In addition, the disease and drug reaction related database (the first database in the present invention) stores information on disease-related genes and diseases after searching by corresponding queries, and also stores, for example, the prevalence rate depending on the subject/ Sources such as incidence rates and sources such as clinical information and health guidance information.

The research database (the second database in the present invention) stores, for example, a PubMed identifier (PMID), a research object, a research method, a research cycle, a research result, a literature information, and the like of a paper that proves or attempts to prove a related gene. .

After searching for a gene retrieved from the disease database by query, the gene database (the third database in the present invention) stores, for example, trait information of a particular single nucleotide polymorphism associated with a particular disease. , chromosome number, loci and dual gene information.

As shown in FIG. 1 to FIG. 4, the disease-single nucleotide polymorphism association result derived from the first database to the third database is compared with the genetic information of the requested sample, thereby deriving the request for providing The probability of occurrence (risk) of a particular disease in an individual of the sample, or the derivation of a drug candidate that is highly sensitive to a particular disease, or the derivation of a drug candidate with a high risk of side effects. In the comparison, information about the age and gender of the requested sample, family history information of his/her spouse, child, parent, cousin, etc., environmental factor information and/or habit information related to the disease-related location, nutritional status Information, lifestyle information and athletic performance information can be added separately Reflect to get results. If there are two or more single nucleotide polymorphisms in the genetic information of the requested sample that are highly correlated with the occurrence of a particular disease, a weighted score may be given to the risk of the disease, and may be based on a single nucleotide The correlation between states is given a weighted score differentially. In this case, the higher the correlation between single nucleotide polymorphisms, the higher the weighted score that can be administered.

In the process of deriving the disease-single nucleotide polymorphism association result from the first database to the third database, as described above for deriving the occurrence (risk) of a specific disease in the individual providing the requested sample The probability algorithm can give priority to the selection of information on research methods, research objects, research cycles, research results, and literature information. For example, if the database is the second database, the priority may be whether the study is repeated after the Genome-Wide Association Study (GWAS) analysis, or whether the paper is a recently published paper. Whether the document has a high impact factor, or what result (risk, confidence interval, p value, etc.), etc., but is not limited thereto.

In addition, the priority is determined using a combination of the first database to the third database, and the priority may be as follows, but is not limited thereto.

1. Exploring the heritability of the target disease: how the genetic factor affects the target disease. Depending on heritability, the research method is selected and the likelihood of success in the association study is predicted, so that the heritability of the target disease is explored based on the prevalence/incidence rate of the target disease.

2. Confirmation of the choice of phenotype of the study subject: Exploring whether a biochemical phenotype will be selected based on the biochemical phenotype, which is a patient grouping criterion and is important for the development of the disease. This is referred to as the intermediate phenotype associated with the disease. In association with a specific gene Under the expectation that sex becomes stronger rather than complicated, it is meaningful to explore whether this phenotype will be studied instead of the disease.

3. Check the sample size used in the analysis: The sample size affects the efficacy of the assay. Statistical significance means that genetic variants that are not actually associated with the disease are considered to be statistically significant (false positives), or genes associated with the disease are considered to be statistically insignificant (false negatives). The value obtained by subtracting this probability from probability 1 is called the power of the check. Since statistical significance is determined by genetic models, gene frequency, relative disease risk, and sample size, it makes sense to identify these factors.

4. Identify the ethnicity of the study: When multiple races with different genetic backgrounds are involved, the frequency of different genotypes based on race is most likely due to racial differences rather than disease. For this reason, racial identification makes sense for confirming whether the study is a study of heterogeneous ethnic groups or a study of homogeneous ethnic groups. If you use a heterogeneous ethnic group, you should check to see if you want to consider the differences between the groups.

5. Check the collection of genetic samples, clinical information, phenotypes and environmental information of the study subjects: due to the type of sample, the type of DNA extraction method, genetic complexity and phenotypic complexity, it is necessary to check whether accurate clinical information and diseases have been collected. The classification is detailed, and since most diseases are complex, it is necessary to check whether various environmental factors have been collected and applied to the analysis.

6. Confirmation of methods for selecting candidate genes for genotyping and candidate single nucleotide polymorphisms: confirmation of selection for single nucleotide polymorphisms associated with disease The method and whether the gene frequency will be explored to determine the efficacy of the statistical test.

7. Confirm if repeated experiments will be performed: If significant results are found, the experiment should be repeated to prove whether the results are statistical errors. In this study, it should be checked whether statistical significance is observed repeatedly by statistical iteration or functional iteration. First, it is necessary to confirm how to select the nucleotide sequence variation to be studied in past or current research, and to confirm significant results by repeated experiments.

8. Confirm the statistical analysis results of the above related research. In the statistical analysis of the genetic characteristics of single nucleotide polymorphisms, significant differences were determined based on the distribution of dual gene frequencies and genotype frequencies, thereby estimating the relative risk (winning ratio) of single nucleotide polymorphisms. Depending on the genotype, correlation analysis was performed based on genetic models to estimate possible genotypes. The genetic model includes three models: dominant model, recessive model, and additive model. Using these models, it was verified whether the single nucleotide polymorphisms were significantly classified into patient groups and control groups under specific conditions, and the genetic characteristics of each single nucleotide polymorphism were verified. Similarly, estimate the relative risk and consider the confidence interval for the estimated relative risk and the corresponding p-value. In order to check for errors in the statistical analysis results, it should be verified whether the errors caused by multiple comparisons related to the study design are checked.

9. Finally, confirm the impact factors and publication years of the published journals used in the association study, and whether the study will be repeated based on the same study. The higher the impact factor of the publication journal, and the closer the research publication is, and the more research, the higher the priority.

Consider all of the above to set priorities and set up algorithms for calculating information from the first to the third database. Applying these prediction algorithms To generate predictive models for specific diseases and drug responses.

These priority criteria are used as objective criteria, such as whether to validate related diseases, drugs, and characteristics based on the accumulation of information in order to follow objective judgment criteria that are not based on arbitrary judgments or knowledge accumulation. The subject's prognosis is predicted by considering information about candidate genes selected based on these criteria and considering prevalence/incidence. Based on the predicted results, disease information and health guidelines such as prevention methods can be selected from the first database to the third database and provided to the subject.

In one embodiment, the invention provides a method for calculating a weighted score for each disease occurrence risk, the method comprising the steps of: (a) estimating the relative risk of genotype of each gene; (b) generalizing The relative risk of the genotype of each gene in the population; (c) the score of the relative risk of each gene; (d) the average score of the relative risk of all genes; (e) the score of the genotype of the subject And (f) calculating the relative risk of the subject's genotype. In the method, the relative risk in step (a) is calculated by the odds ratio / ((1 prevailing rate) + (prevailing rate * odds ratio)). In the method, the relative risk in the population in step (b) is calculated by the genotype frequency corresponding to the relative risk*. In the method, the fraction of the relative risk in step (c) is calculated as the sum of the scores of the relative risks of the genotype of each gene. In the method, the average score of the relative risk in step (d) is calculated as the product of the fraction of the relative risk of each gene. In the method, the score in step (e) is calculated as the product of the relative risk of the genotype of each gene of the subject. In the method, the relative risk in step (f) is by the score in step (e) / the relative in step (d) The average score of the risk is calculated.

In another embodiment, the invention provides a method of predicting a disease, the method comprising the steps of: (a) extracting DNA from the requested sample; (b) extracting genetic information from the DNA; (c) by The genetic information is compared to a disease-single nucleotide polymorphism association result from a first database to a third database to measure a risk of a particular disease; (d) when two or more are associated with the particular When the disease-associated single nucleotide polymorphism is present in the genetic information of the requested sample, the relative risk of each genotype is calculated by applying the weighted score calculated by the above method; (e) calculating the The relative risk (%) and incidence (%) of the requested sample; and (f) the risk of disease in the sample being requested. In the method, the first database in step (c) is a disease and drug reaction related database, and the disease and drug reaction related database includes information related to a specific single nucleotide polymorphism. Information on the symptoms of a particular disease, the type of prescription drug, the concentration of a prescription drug, the frequency of a drug prescription, the cycle of a drug prescription, and side effects. In the method, the second database in step (c) is a research database containing research papers on specific diseases associated with a particular single nucleotide polymorphism, and Contains the PubMed identifier of the paper, research subjects, research methods, research cycles, research results, journal information, and reproducibility information for the study. In the method, the third database in step (c) is a gene database comprising a chromosome number, a locus and a specific single nucleotide polymorphism associated with a specific disease. Dual gene information. In the method, the relative risk in step (d) is 1 or greater than 1 ( 1), the relative risk (%) in step (e) is calculated by (the average risk score of the subject -1) * 100, and the relative risk in step (d) is less than 1 (<1), the relative risk (%) in step (e) is calculated by (1 - average risk score of the subject) * 100. In the method, the incidence rate (%) in step (e) is calculated by the relative risk* prevalence rate in step (d). In the method, the determining in the step (f) includes when the relative risk (%) in the step (e) is 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) is greater than 1 (>1), it is judged as a warning or attention.

In still another embodiment, the present invention provides an apparatus for predicting a disease, the apparatus comprising: (a) an extracting unit configured to extract DNA from a requested sample; (b) an input unit configured to self-use The DNA extracts genetic information; (c) a comparison unit configured to measure the genetic information by correlating the disease-single nucleotide polymorphisms from the first database to the third database a risk of a particular disease; (d) an arithmetic unit configured to when two or more single nucleotide polymorphisms associated with the particular disease are present in the genetic information of the requested sample, Applying a weighted score calculated according to the method as described above to calculate a relative risk for each genotype; (e) a calculation unit configured to calculate a relative risk (%) and an incidence rate (%) of the requested sample; and f) a judging unit configured to determine a risk of occurrence of the disease of the requested sample. In the device, the first database in step (c) is a disease and drug reaction related database, and the disease and drug reaction related database contains information about a specific single nucleotide polymorphism. Information on the symptoms of a particular disease, the type of prescription drug, the concentration of a prescription drug, the frequency of a drug prescription, the cycle of a drug prescription, and side effects. In the device, the second database in step (c) is a research database comprising research papers on specific diseases associated with a particular single nucleotide polymorphism, and Contains the PubMed identifier of the paper, research subjects, research methods, research cycles, research results, journal information, and reproducibility information for the study. In the device, the third database in step (c) is a gene database comprising a chromosome number, a locus and a specific single nucleotide polymorphism associated with a specific disease. Dual gene information. In the device, the relative risk in step (d) is 1 or greater than 1 ( 1), the relative risk (%) in step (e) is calculated by (the average risk score of the subject -1) * 100, and the relative risk in step (d) is less than 1 (<1), the relative risk (%) in step (e) is calculated by (1 - average risk score of the subject) * 100. In the device, the incidence rate (%) in step (e) is calculated by the relative risk* prevalence rate in step (d). In the device, the determining in the step (f) includes when the relative risk (%) in the step (e) is 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) is greater than 1 (>1), it is judged as a warning or attention.

Hereinafter, each step of the present invention will be explained in detail.

Hereinafter, the present invention will be further elaborated with reference to examples. It is obvious to those skilled in the art that the examples are for illustrative purposes only and are not intended to limit the scope of the invention as defined by the appended claims.

Throughout the specification, when any part "includes" any element, It is intended to include other elements rather than exclude other elements, unless otherwise stated.

Example 1: Data Collection

First, collect information on the prevalence, odds ratio, and genotype frequency of the target disease.

Specifically, the prevalence rate is collected based on, for example, the following public information: National Health and Nutrition Survey, Ministry of Health and Welfare-Mental Illness Epidemiology Survey, Central Dementia Center Annual Report, Public Health Assessment and Public Disclosure Agency Public Disclosure 3.0 Public Data, and/or Resident Nutrition Group (Resident) Nutrition Population), with the source indicated.

For odds ratios, papers related to related diseases such as patient (control) group studies, meta-analysis, family studies, generational studies, etc., and the papers are not limited, as long as academic papers (such as PubMed, Google Scholar, etc.) can Just find it.

The collected data are selected in the order of group data, good significance, and the like.

Genotype frequencies are collected based on public data such as Hapmap 3, 1000 Genomes project.

Example 2: Calculating the weighted score for the risk of each disease

The risk of each disease is calculated by steps 1 through 8 below. Steps 1 to 4 are steps for calculating the risk of each group; Step 5 is a step of calculating the risk of each subject; Step 6 is a step of calculating an average score of the subjects in the group; and Step 7 Step 8 is a step of calculating the relative risk (%) and incidence (%) of the subject in the population.

More specifically, step 1 is a step of estimating the relative risk of the genotype of each gene. That is, in this step, the relative risk is estimated using the odds ratio of the genotype of each gene as a risk factor for the relevant disease. The relative risk is calculated using the following equation: relative risk = odds ratio / ((1 prevalence rate) + (prevailing rate) * odds ratio))).

Step 2 is the step of generalizing the relative risk of the genotype of each gene. That is, this step is a process of generalizing the genotype risk of each gene based on the corresponding nationality and ethnicity. The risk is calculated using the following equation: relative risk score for the population = relative risk * corresponding genotype frequency.

Step 3 is the step of calculating the score of the relative risk of each gene. In this step, the sum of the scores of the relative risks of the genotype of each gene is calculated.

Step 4 is the step of calculating the average score of the relative risks of all genes. In this step, the average score is calculated as the product of the average score of the gene relative risk.

Step 5 is the step of calculating the score of the genotype of the subject. In this step, the score is calculated as the product of the relative risk of the subject's genotype for each gene.

Step 6 is to calculate the relative wind of the subject's genotype using the following equation Risk Steps: The average risk score of the subjects in the population = (the score of the subject's genotype (the score in step 5)) / (the average score of the relative risk of all genes (the score in step 4)).

Step 7 is a step of calculating the relative risk (%) of the subject. In this step, if the subject's average risk score is 1 or greater than 1 ( 1), the relative risk is calculated by "(subject's average risk score -1) * 100", and if the subject's average risk score is less than 1 (<1), the relative risk is by " (1 - average risk score of the subject) * 100" is calculated.

Step 8 is a step of calculating the incidence rate (%) of the subject by "average risk score of the subject in the population * prevalence rate".

Steps 1 through 8 are shown in FIG.

Example 3: Result judgment

The risk of developing a disease in the subject is judged based on the average risk score in the population and the genotype frequency (%). The judgment is preferably a two-stage (standard/warning) type judgment or a three-stage (standard/warning/attention) type judgment, but is not limited thereto.

In a two-stage judgment or a three-stage judgment, the criterion is based on the average risk score of the subject being 1 or less (1) 1) The case, and the warning or caution is based on the case where the subject's average risk score is greater than (>1). More specifically, the average risk score of each combination of the genotypes of the genes corresponding to the disease and the frequency (%) in the general population are calculated, and the frequency at which the average risk score is higher than the average risk score of the subject is increased. When the frequency is 5% or less, it is judged as a warning, and when the frequency is more than 5%, it is judged as attention. Exceptionally, for diseases that cannot calculate prevalence (eg, triglyceride levels, etc.), judged at three levels (low/medium/high) based on the genotype identified in the reference paper.

Although the present invention has been described in detail with reference to the specific features thereof, it is obvious to those skilled in the art that this description is only for the preferred embodiments thereof, and does not limit the scope of the invention. Therefore, the scope of the invention is defined by the scope of the appended claims and their equivalents.

Claims (15)

A method of calculating a weighted score for the risk of occurrence of each disease, comprising the steps of: (a) estimating the relative risk of the genotype of each gene; (b) generalizing the relative risk of the genotype of each gene in the population; Calculating the score of the relative risk of each gene; (d) calculating the average score of the relative risk of all genes; (e) calculating the score of the genotype of the subject; and (f) calculating the genotype of the subject The relative risk, wherein the relative risk in step (a) is calculated by the odds ratio / ((1 prevailing rate) + (prevailing rate * odds ratio)); the relative in the group in step (b) The risk is calculated by the genotype frequency corresponding to the relative risk*; the score of the relative risk in step (c) is calculated as the sum of the scores of the relative risks of the genotype of each gene; the relative risk in step (d) The average score is calculated as the product of the scores of the relative risks of each gene; the score in step (e) is calculated as the product of the relative risk of the genotype of each gene of the subject; and step (f) The relative risk in the step is the relative value in step (e) / step (d) The average score of the risk is calculated. A method of predicting disease comprising the following steps: (a) extracting DNA from the requested sample; (b) extracting genetic information from the DNA; (c) by locating the genetic information with disease-single nucleotides from the first to the third database State correlation results are compared to measure the risk of a particular disease; (d) when two or more single nucleotide polymorphisms associated with the particular disease are present in the requested sample of the genetic information Calculating the relative risk of each genotype by applying a weighted score calculated according to the method of calculating the weighted score of each disease occurrence risk as described in claim 1 of the patent application; (e) calculating the request The relative risk (%) and incidence (%) of the sample; and (f) the risk of developing the disease in the sample requested. The method for predicting disease according to claim 2, wherein the first database in the step (c) is a disease and drug reaction related database, and the disease and drug reaction related database includes Information on the symptoms of a particular disease associated with a single nucleotide polymorphism, the type of prescription drug, the concentration of the prescribed drug, the frequency of drug prescription, the cycle of the drug prescription, and side effects. The method for predicting disease according to claim 2, wherein the second database in the step (c) is a research database, and the research database comprises related to a specific single nucleotide polymorphism. Research papers on specific diseases, including the PubMed identifier, research objects, research methods, research cycles, research results, journal information, and research reproducibility information. A method for predicting a disease as described in claim 2, wherein The third database in step (c) is a gene database containing the number of chromosomes, loci and dual gene information of a particular single nucleotide polymorphism associated with a particular disease. A method for predicting disease as described in claim 2, wherein the relative risk in step (d) is 1 or greater (1) 1), the relative risk (%) in step (e) is calculated by (subject's average risk score -1) * 100, and the relative risk in step (d) is less than 1 (<1) The relative risk (%) in step (e) is calculated by (1 - average risk score of the subject) * 100. The method for predicting disease according to claim 2, wherein the occurrence rate (%) in the step (e) is calculated by the relative risk* prevalence rate in the step (d). The method for predicting disease according to claim 2, wherein the judgment in the step (f) comprises the relative risk (%) in the step (e) being 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) in step (e) is greater than 1 (>1), it is judged as warning or attention. A device for predicting a disease, comprising: (a) an extraction unit configured to extract DNA from a requested sample; (b) an input unit configured to extract genetic information from the DNA; (c) a comparison unit, Configuring to measure the risk of a particular disease by comparing the genetic information to a disease-single nucleotide polymorphism associated with the first to third databases; (d) an arithmetic unit configured Two or more related to the specific disease When the single nucleotide polymorphism is present in the genetic information of the requested sample, the calculation is performed by applying a method of calculating a weighted score for each disease occurrence risk as described in claim 1 of the scope of the patent application. a weighted score to calculate the relative risk of each genotype; (e) a calculation unit configured to calculate a relative risk (%) and an incidence rate (%) of the requested sample; and (f) a judgment unit configured The risk of developing a disease of the sample of the request is determined. The device for predicting disease according to claim 9, wherein the first database in the (c) comparison unit is a disease and drug reaction related database, and the disease and drug reaction related database includes Information about the symptoms of a particular disease associated with a particular single nucleotide polymorphism, the type of prescription drug, the concentration of the prescribed drug, the frequency of drug prescription, the duration of the drug, and the side effects. The device for predicting disease according to claim 9, wherein the second database in the (c) comparison unit is a research database, the research database comprising more about a specific single nucleotide. Research papers on specific diseases associated with the state, and also include the PubMed identifier, research objects, research methods, research cycles, research results, journal information, and research reproducibility information. The device for predicting disease according to claim 9, wherein the third database in the (c) comparison unit is a gene database containing a specific list associated with a specific disease. Nucleotide number, locus and dual gene information of nucleotide polymorphisms. A device for predicting disease according to claim 9 wherein the relative risk in the (d) arithmetic unit is 1 or greater (1) 1), (e) The relative risk (%) in the calculation unit is calculated by (subject's average risk score -1) * 100, and when (d) the relative risk in the arithmetic unit is less than 1 (< 1), (e) The relative risk (%) in the calculation unit is calculated by (1 - average risk score of the subject) * 100. The apparatus for predicting disease according to claim 9, wherein (e) the occurrence rate (%) in the calculation unit is calculated by (d) a relative risk* prevalence rate in the arithmetic unit. The apparatus for predicting a disease according to claim 9, wherein (f) the judgment in the judgment unit includes when the relative risk (%) in the (e) calculation unit is 1 or less than 1 ( 1), judged as a standard, and when the relative risk (%) in the (e) calculation unit is greater than 1 (>1), it is judged as warning or caution.