Method for calculating disease risk score and matching score, and matching system

Abstract

A calculation method and matching system for calculating disease risk based on genotype data for each of n polymorphic loci are provided. [Solution] In the matching system 1, the disease risk score calculation means 113 of the information processing unit 11 calculates the disease risk score as the sum of the values ​​obtained by multiplying the number of disease-associated alleles (effect alleles) at each polymorphic locus by the phenotype severity of the disease-associated alleles at each polymorphic locus, based on the genotype data of each of n polymorphic loci in an individual organism.

Description

[Technical Field]

[0001] The present invention relates to a technique for calculating disease risk and genetic compatibility based on genetic information. [Background technology]

[0002] Livestock such as cows and pigs, and companion animals such as dogs and cats, have been selected for specific desirable traits to improve and establish breeds. As a result of such trait selection, the frequency of specific gene mutations in animal populations exhibiting specific traits is higher than in wild animal populations. Therefore, mating of males and females with the same mutant allele is likely to produce individuals that are homozygous for the mutant allele. Such individuals are more likely to develop or become severely ill with diseases caused by the mutant allele.

[0003] Due to these problems, there has been an increasing demand for genetic testing of animals in recent years. However, commonly conducted genetic testing for animals is conducted by narrowing down the target to a single gene (see, for example, Non-Patent Document 1), and no attempt has been made to simultaneously test multiple alleles and combine the results to quantify disease risk. Furthermore, no attempt has been made to conduct genetic testing of both males and females to consider optimal matching to reduce the risk of genetic diseases in offspring. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Nippon Veterinary and Life Science University Research Report No. 58, 17-22, (2009) Summary of the Invention [Problem to be solved by the invention]

[0005] The first objective of the present invention is to provide a technique for calculating disease risk based on genotype data for each of n polymorphic loci, and the second objective is to provide a technique for scoring male-female combinations whose offspring are less likely to develop genetic diseases based on their genetic backgrounds. [Means for solving the problem]

[0006] The present invention, which solves the first or second problem, is as follows.

[0007] [1] A calculation method comprising: calculating a disease risk score based on genotype data for each of n polymorphic loci in an individual organism, the calculation essentially including calculation according to the following formula (1):

number

[0008] [2] The calculation method according to [1], wherein the calculation essentially including the calculation according to the formula (1) is the calculation expressed by the following formula (2).

number

[0009] [3] The calculation method according to [2], wherein c is a predetermined value (DR value) depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive.

[0010] [4] The above a. The CAV value is a predetermined value determined by the presence or absence of one or more chromosomal abnormalities selected from the group consisting of chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities, or a numerical value obtained by adding or multiplying a predetermined numerical value (CAV value) based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities, and a numerical value indicating the severity of the phenotype of the chromosomal abnormality; The calculation method according to [2] or [3].

[0011] [5] The calculation method according to any one of [1] to [4], which comprises ranking the disease risk scores of specific individual organisms in the population of organisms.

[0012] [6] A calculation method comprising: performing a calculation essentially including calculation using a formula selected from the following formulas (7), (18), and (19) based on genotype data for each of n polymorphic loci in each of two different biological individuals; and calculating the genetic compatibility between the biological individuals as a matching score.

number

number

[0013] [7] The calculation method according to [6], wherein the calculation essentially including the calculation according to the formula (7) is the calculation expressed by the following formula (8).

number

[0014] [8] The calculation method according to [7], wherein c is a predetermined value (DR value) depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive.

[0015] [9] The above a. A predetermined value (CAV value) determined based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number variations in specific DNA regions, and chromosomal structural abnormalities in either or both of the male and the female, or a numerical value obtained by adding or multiplying a numerical value (CAV value) predetermined based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities in either or both of the male and the female, and a numerical value indicating the severity of the phenotype of the chromosomal abnormality; The calculation method described in [8].

[0016]

[10] The calculation method described in any one of [6] to [9], which includes ranking matching scores between specific individual organisms in a set of matching scores between any individual organisms in the population of organisms.

[0017]

[11] A matching system for evaluating genetic compatibility, comprising a memory unit and an information processing unit, the storage unit stores a database that is a collection of genetic data relating to a plurality of individual organisms; The information processing unit Calculating the genetic compatibility of two different organisms as a matching score based on their genetic data; or calculating a genetic compatibility between the specific biological individual and each of the plurality of biological individuals as a matching score based on the genetic data of the specific biological individual and the genetic data of the plurality of biological individuals stored in the storage unit; A matching score calculation means is provided, The genetic data includes: Genotype data for each of n polymorphic loci in an individual organism, and / or Data on disease risk scores calculated from the genotype data for each of n polymorphic loci in an individual organism That is the matching system.

[0018]

[12] The matching system described in

[11] , wherein the information processing unit is provided with an extraction means for extracting biological individuals that exhibit a matching score that meets a certain standard when combined with the specific biological individual from the plurality of biological individuals registered in the database.

[0019]

[13] The disease risk score is calculated by the calculation method according to any one of [1] to [4], The matching system according to

[11] or

[12] , wherein the matching score is calculated by the calculation method according to any one of [6] to [9]. [Effects of the Invention]

[0020] According to the present invention, the disease risk of an individual organism or the genetic compatibility between males and females can be presented as a simple score. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a schematic diagram illustrating an embodiment of a hardware configuration of a matching system. [Figure 2] FIG. 1 is a schematic diagram illustrating an embodiment of calculation of a matching score by a matching system. [Figure 3] FIG. 1 is a schematic diagram illustrating an embodiment of calculation of a matching score by a matching system. [Figure 4] FIG. 10 illustrates an embodiment in which an operation is performed at the request of user 1 or user 2. [Figure 5] FIG. 1 is a schematic diagram illustrating an embodiment of calculation of a matching score by a matching system. DETAILED DESCRIPTION OF THE INVENTION

[0022] The present invention will be described in detail below by way of example. In the embodiments of the present invention, A (numerical value) to B (numerical value) means A or more and B or less. Furthermore, the preferred and more preferred embodiments exemplified below can be used in appropriate combinations with each other, regardless of expressions such as "for example," "one," "preferably," and "more preferred." Furthermore, the descriptions of numerical ranges are merely examples, and ranges obtained by appropriately combining the upper and lower limits of each range and the numerical values ​​of the examples can also be preferably used (for example, when A to B or C to D is described, the combinations A to D or C to B can be used). Furthermore, terms such as "contain" or "comprise" may be interpreted as "essentially consisting of" or "consisting only of."

[0023] [Disease risk score calculation] The present invention relates to a calculation method for calculating a disease risk score. Here, the "disease risk score" as used herein does not refer to evaluation of disease risk based on the presence or absence of a single disease-causing gene mutation. In the present invention, the "disease risk score" refers to a numerical value calculated by combining the presence or absence of one or more chromosomal abnormalities selected from mutations in genes responsible for single-gene diseases, chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities, and genotype data for multiple polymorphic loci.

[0024] The present invention involves performing a calculation that essentially includes a calculation according to the formula (1) described below, based on genotype data for each of n polymorphic loci in an individual organism. The genotype data may be obtained by any analytical method. Specific examples include base sequence analysis, mass spectrometry, digital PCR, SNV microarray, and real-time PCR. Specific means for base sequence analysis include next-generation sequencers (NGS). Next-generation sequencers are sequencing methods that enable massive parallel sequencing of clonally amplified molecules and single nucleic acid molecules. Any NGS system may be employed in the present invention. For example, Pyrosequencing (GS Junior (Roche) etc.), Sequencing by synthesis using reversible dye terminators (e.g., MiSeq (Illumina)), Sequencing by ligation (SeqStudio Genetic Analyzer (Thermo Fisher Scientific) etc.), Ion semiconductor sequencing (Ion Proton System (Thermo Fisher Scientific) etc.), These include sequencing using CMOS (complementary metal oxide semiconductor) chips (such as the iSeq 100 System (Illumina)), sequencing using probe-anchor synthesis (cPAS) and improved DNA nanoball technology (DNBSEQ (BGI)), and nanopore sequencing (PromethION (Oxford Nanopore Technologies)).

[0025] The organism to be evaluated is not particularly limited and may be a plant or an animal. Examples of plants include rice, wheat, barley, soybean, and corn, and examples of animals include, without limitation, humans, dogs, cats, cows, horses, chickens, mice, rats, and sheep.

[0026] The disease risk score is a numerical value obtained by performing a calculation that essentially includes calculation according to the following formula (1) based on the genotype data of each of n polymorphic loci in an individual organism.

[0027]

number

[0028] In formula (1), n ​​represents an arbitrary integer of 1 or 2 or more. In formula (1), E k is the number of disease-associated alleles at each polymorphic locus or a value proportional to that number. However, the n polymorphic loci that are the targets of formula (1) are polymorphic loci in which at least one of the alleles that appears is a disease-associated allele.

[0029] Here, disease-associated alleles broadly include alleles that have a negative or positive effect on the onset of a disease. Alleles that have a negative effect include alleles that have mutations directly responsible for a disease, as well as mutant alleles that increase the risk of developing a disease, mutant alleles that lead to aggravation of the disease, and mutant alleles that cause the onset of a disease, increase the risk of developing a disease, or aggravate the disease in combination with other factors. Alleles that have a positive effect include alleles that confer resistance to a disease, such as alleles that result in a constitution that is less susceptible to or less likely to progress with a disease.

[0030] Polymorphic loci discovered to date are recorded in various databases, linked to associated diseases, allele frequency, mapping information, etc. By searching these databases, polymorphic loci known to be associated with diseases can be easily identified. For example, the following databases can be listed as examples of databases relating to single nucleotide polymorphism loci: dbSNP: https: / / www.ncbi.nlm.nih.gov / snp / SNPedia: https: / / www.snpedia.com / GWAS catalog: https: / / www.ebi.ac.uk / gwas /

[0031] In equation (1), S k is a numerical value indicating the phenotype severity. The phenotype severity is a numerical value assigned to each polymorphic locus and indicates the magnitude of the effect caused when a disease-associated allele appears at that polymorphic locus. In particular, when the disease-associated allele is an allele that has a negative effect, the phenotype severity indicates the severity of the disease caused by the disease-associated allele.

[0032] The severity of the phenotype may be evaluated using a scale of two or more levels. For example, a seven-level scale based on the following criteria may be used in an embodiment, depending on the magnitude of the impact caused by the appearance of a disease-associated allele. Note that the seven-level scale described below is merely an example. The number of levels and the evaluation criteria can be set as appropriate. -5 Causes embryonic lethality or death before reaching adulthood. -4 Causes severe illness or disability, making daily life difficult. -3 Causes a disease or disorder requiring ongoing treatment. -2 Causes a mild disease or disability that can be prevented by preventive medicine. -1 It only affects the constitution to a certain extent. +1 Disease Resistance

[0033] As described above, the severity of the phenotype can be set as appropriate, but it is preferable to set it so that the absolute value of the severity of the phenotype increases as the impact of the appearance of a disease-associated allele increases.

[0034] The severity of the phenotype may be determined by assigning a negative value to a negative impact and a positive value to a positive impact, as in the example criteria described above. In this case, the larger the disease risk score calculated by Equation (1), the better the score can be evaluated.

[0035] Furthermore, the severity of the phenotype may be determined by assigning a positive value to a negative impact and a negative value to a positive impact, as opposed to the above-mentioned standard example. In this case, the smaller the disease risk score calculated by Equation (1), the better the score can be evaluated.

[0036] In some embodiments, the severity of a phenotype at each of n specific polymorphic loci is defined in advance. The severity of a phenotype assigned to each polymorphic locus can be appropriately determined based on existing research reports and clinical records regarding specific disease-associated alleles that appear at each polymorphic locus.

[0037] A preferred embodiment is one in which a table is prepared in advance in which each of the n specific polymorphic loci is associated with the severity of the phenotype. The table may be stored in a database as electronic data and referenced during calculation using formula (1).

[0038] Alternatively, the severity of a phenotype may be determined by a machine learning model's estimate of the effect of a genetic mutation. Such a machine learning model is trained using a machine learning algorithm. By using existing data and research results on disease-associated alleles and their effects, the frequency of disease-associated alleles, the evolutionary conservation of the bases involved in the mutations, and other data, the model learns patterns and correlations and acquires the ability to predict the clinical impact of disease-associated alleles. Machine learning models that estimate the functional effects of genetic mutations are widely used in the fields of genetics and medicine, so these existing models may be used.

[0039] Existing tools for predicting the impact of gene mutations on the function of gene products include PolyPhen-2 (Polymorphism Phenotyping v2), SIFT (Sorting Intolerant From Tolerant), CADD (Combined Annotation Dependent Depletion), GERP (Genomic Evolutionary Rate Profiling), DeepVariant, etc. These tools can be used to set the severity of the phenotype for each polymorphic locus.

[0040] The summation operation in equation (1) is E1×S1+E2×S2++E k ×S k +···+E n-1 ×S n-1 +E n ×S n Each term constituting this summation is an index corresponding to each polymorphic locus. In this specification, each term constituting this summation is referred to as a disease risk index. In other words, the disease risk score can be expressed as the sum of the disease risk indexes at each polymorphic locus.

[0041] "Operations that essentially include a calculation using formula (1)" include not only the summation operation represented by formula (1) itself, but also any operation that includes formula (1) as part of the expression, such as an operation that adds an arbitrary constant or variable to the sum of formula (1), an operation that multiplies the sum of formula (1) by an arbitrary constant or variable, or an operation that multiplies each term of formula (1) by an arbitrary constant or variable.

[0042] The "operation essentially including a calculation according to formula (1)" may be an operation expressed by the following formula (2).

number

[0043] In formula (2), a may be any constant (including 0) or any variable, b may be any constant (excluding 0) or any variable, and c may be any constant (including 0) or any variable.

[0044] When c is a variable depending on the polymorphic locus, equation (2) can be expressed as the following equation (3).

number

[0045] The variable c in formula (3) can be a predetermined value (DR value) depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive. In this case, formula (3) can be expressed as the following formula (4):

number

[0046] There are no particular limitations on how the DR value is assigned, but it is preferable that the DR value assigned when the disease-associated allele is dominant be set to a value greater than the DR value assigned when the disease-associated allele is recessive. More specifically, the DR value assigned when the disease-associated allele is dominant is preferably set to twice the DR value assigned when the disease-associated allele is recessive. For example, the DR value assigned when the disease-associated allele is dominant can be set to 4, and the DR value assigned when the disease-associated allele is recessive can be set to 2.

[0047] Furthermore, a calculation may be performed in which the presence or absence of one or more chromosomal abnormalities selected from the chromosome number, copy number variation in a specific DNA region, and chromosomal structural abnormalities is reflected in the disease risk score (hereinafter, chromosomal number abnormalities, copy number variation in a specific DNA region, and chromosomal structural abnormalities may be collectively referred to as "chromosomal abnormalities"). Specifically, a variable reflecting the presence or absence of chromosomal abnormalities may be used as "a" in any of formulas (2) to (4). Any of the following values ​​can be selected as "a". (i) A predetermined value depending on the presence or absence of chromosomal abnormalities (ii) A numerical value obtained by adding or multiplying a predetermined numerical value depending on the presence or absence of a chromosomal abnormality and a numerical value indicating the severity of the phenotype of the chromosomal abnormality.

[0048] In this specification, the "predetermined numerical value depending on the presence or absence of chromosomal abnormalities" may be referred to as the "chromosome abnormality value (CAV)."

[0049] Generally, individuals with chromosomal abnormalities often develop serious disorders or diseases. Therefore, the second term (E k ×S k It is preferable to set the CAV so that the presence of a chromosomal abnormality can be immediately evaluated as a high disease risk, regardless of the content of the second term (the sum of Ek × Sk × c). In other words, it is preferable to set the CAV in the case of a chromosomal abnormality to a value whose absolute value is significantly larger than the value expected to be indicated by the second term (the sum of Ek × Sk × c).

[0050] On the other hand, if there are no chromosomal abnormalities, the second term (E k ×S kIt is preferable to set the CAV so that the disease risk can be evaluated only by the second term (E × c). k ×S k It is preferable to set it to a value (for example, 0) that is significantly smaller than the value that is expected to be indicated by the sum of (x, y, y, y) of (x, y, y) and (x, y, y).

[0051] The CAV may be set according to the type of chromosome. If the organism to be evaluated has m types of chromosomes, a CAV can be set for each of chromosomes 1 to m, and "a" can be set as a variable expressed by the following formula (5).

number

[0052] Furthermore, when the above-mentioned (ii) is adopted, "a" can be set to a value expressed by the following formula (6).

number

[0053] In equation (6), CAV k is a predetermined value depending on the presence or absence of chromosomal abnormalities in chromosome k, and CS k is a number indicating the severity of the phenotype of chromosomal abnormalities on chromosome k.

[0054] If the effect of chromosomal abnormalities on chromosome k is small, CS k The value of CS should be set small, and if the impact of chromosomal abnormality on chromosome k is serious (e.g., embryonic lethality), CS k The value of can be set large.

[0055] "a" in any of formulas (2) to (4) may reflect an index that reflects the presence or absence of chromosomal abnormalities. Examples of such an index include known indexes such as Z-score or NCV (Normalized Chromosome Value). For calculation methods of these indexes, see, for example, Japanese Patent Publication No. 7331325.

[0056] In addition, an embodiment may be adopted in which genotyping information and information regarding the presence or absence of chromosomal abnormalities obtained during the course of implementing the analytical techniques disclosed in Japanese Patent Publication No. 7331325, which perform two or more types of genetic analysis from among phenotype analysis, chromosomal abnormality analysis, and parent-child testing, are diverted to calculate a disease risk score.

[0057] In an embodiment, after calculating the disease risk score of the specific individual organism, the disease risk score may be ranked. Specifically, the disease risk scores of the specific individual organisms in the population of organisms are ranked.

[0058] The specific embodiment of the ranking is not particularly limited. For example, one or more reference values ​​for the disease risk score may be set in advance, and the disease risk score of the individual organism to be evaluated may be compared with the reference value to perform ranking. For example, an embodiment may be such that if the disease risk score is equal to or greater than a certain reference value, the disease risk score is ranked A, and if the disease risk score is less than a certain reference value and equal to or greater than a certain reference value, the disease risk score is ranked B.

[0059] Furthermore, a disease risk score is calculated in advance for a population consisting of multiple individual organisms (preferably a statistically significant number of samples), and a score threshold for belonging to any top percentage is set to 1 or 2 or more. Depending on which threshold or higher and / or which specific threshold the disease risk score calculated for the specific organism to be evaluated is below, the disease risk score of the specific organism to be evaluated in the population of individual organisms can be ranked.

[0060] [Matching score calculation] The present invention also relates to a calculation method that includes calculating the genetic compatibility between two individual organisms as a matching score.

[0061] As used herein, the term "matching score" refers to a score that reflects the magnitude of disease risk in offspring resulting from mating between a male and a female individual, a score that reflects the genetic similarity between individuals of the opposite sex or the same sex, and a combined score of these. These will be explained in order below.

[0062] <Calculation of a score that reflects the degree of disease risk of offspring resulting from mating between male and female organisms> First, an embodiment will be described in which a score reflecting the magnitude of disease risk of offspring resulting from mating between a male individual organism and a female individual organism is calculated. Specifically, this embodiment is a calculation method that includes performing a calculation that essentially includes a calculation according to the following formula (7) based on genotype data for each of n polymorphic loci in a male biological individual and a female biological individual, and calculating the genetic compatibility between the male and the female as a matching score.

[0063]

number

[0064] In addition, the organisms, genotype data, polymorphic loci, disease-associated alleles, and phenotype severity to be evaluated in the method for calculating the matching score are the same as those explained in the section on calculating the disease risk score above.

[0065] In equation (7), ME k is the number of disease-associated alleles at each polymorphic locus in males or a value proportional to that number. FE k is the number of disease-associated alleles at each polymorphic locus in females or a value proportional to that number. S kis a numerical value indicating the severity of the phenotype of the disease-associated allele at each polymorphic locus, and the specific aspects thereof are as described above. The n polymorphic loci that are the targets of formula (7) are polymorphic loci in which at least one of the alleles that appears is a disease-associated allele.

[0066] "Operations that essentially include a calculation using formula (7)" include not only the summation operation represented by formula (7) itself, but also any operation that includes formula (7) as part of its expression, such as an operation of adding an arbitrary constant or variable to the sum of formula (7), an operation of multiplying the sum of formula (7) by an arbitrary constant or variable, or an operation of multiplying each term of formula (7) by an arbitrary constant or variable.

[0067] The "operation essentially including the calculation according to equation (7)" may be an operation expressed by the following equation (8).

number

[0068] The contents of a to c in the formula (8) explained in the section on calculating the disease risk score above are applicable as is.

[0069] When c is a variable depending on the polymorphic locus, equation (8) can be expressed as the following equation (9).

number

[0070] The variable c in Eq. (9) k A predetermined value (DR value) can be used as , depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive. In this case, equation (9) can be expressed as the following equation (10).

number

[0071] DR in Equation (10) k The same applies as explained above in the section on calculating disease risk scores.

[0072] Furthermore, when disease risk scores have already been calculated for both males and females, the following formula (11) is also included in the "computation that essentially includes the calculation according to formula (7)."

number

[0073] MDI in Eq. (11) k is the disease risk index for each polymorphic locus in males, ME k ×S k It is a number calculated using the formula ×c. FDI in Eq. (11) k is the disease risk index for each polymorphic locus in females, FE k ×S k It is a number calculated using the formula ×c. MDI k and FDI k Multiplication of (S k ×c) 2 Since it contains k × c. When equation (11) is expanded, it becomes the same as equation (8).

[0074] Furthermore, a calculation may be performed to reflect the presence or absence of chromosomal abnormalities in the matching score. Specifically, a variable reflecting the presence or absence of chromosomal abnormalities may be used as "a" in any of the formulas (8) to (11). Any of the following values ​​can be selected as "a". (iii) A predetermined value (CAV value) based on the presence or absence of chromosomal abnormalities in either or both of the male and the female. (iv) A numerical value obtained by adding or multiplying a predetermined numerical value (CAV value) based on the presence or absence of chromosomal abnormalities in either or both of the male and the female and a numerical value indicating the severity of the phenotype of the chromosomal abnormality.

[0075] Generally, if one or both of the individuals have a chromosomal abnormality, the offspring born from mating of these individuals often suffer from serious disorders or diseases. Therefore, the second term (ME) in Equations (8) to (11) k ×FE k ×S k It is preferable to set the CAV so that if there is a chromosomal abnormality in either or both sexes, the matching score can be immediately evaluated as small, regardless of the content of the second term (ME × c). k ×FE k ×S k It is preferable to set the absolute value of the value to be significantly larger than the expected value of the sum of the values ​​...

[0076] On the other hand, if there are no chromosomal abnormalities in either the male or female, then the second term (ME k ×FE k ×S k It is preferable to set the CAV so that the matching score can be evaluated only by the sum of the two terms (ME × c). In other words, the value of CAV when there are no chromosomal abnormalities in either the male or female is calculated by the second term (ME k ×FE k ×S k It is preferable to set it to a value (for example, 0) that is significantly smaller than the value that is expected to be indicated by the sum of (x, y, y, y) of (x, y, y) and (x, y, y).

[0077] In the case of (iii) above, "a" in formulas (8) to (11) may be in a form in which the CAV of males and females are evaluated separately. In other words, the value of "a" MCAV+FCAV or MCAV×FCAV It may be a variable expressed by the following formula. Here, "MCAV" is a CAV that is preset depending on the presence or absence of chromosomal abnormalities in males, and "FCAV" is a CAV that is preset depending on the presence or absence of chromosomal abnormalities in females.

[0078] The CAV may be set according to the type of chromosome. If the organism to be evaluated has m types of chromosomes, a CAV may be set for each of chromosomes 1 to m, and "a" may be a variable expressed by the following formula (12), (13), or (14).

number

[0079] In equation (12), CAV k is a predetermined value depending on the presence or absence of chromosomal abnormalities in chromosome k in either or both sexes. CAV is graded for each of the following cases: when there are no chromosomal abnormalities in either sex, when there are chromosomal abnormalities in one sex, and when there are chromosomal abnormalities in both sexes. k may be pre-assigned a value of

[0080]

number

[0081]

number

[0082] In equation (13) or (14), MCAV k is a predetermined value depending on the presence or absence of chromosomal abnormalities in chromosome k in males, and FCAV k is a predetermined value depending on the presence or absence of chromosomal abnormalities in chromosome k in females.

[0083] In addition, in equation (14), if there is no chromosomal abnormality in chromosome k, MCAV k and FCAV k It is preferable not to use "0" as the value assigned in advance to . This is because if one of the sexes has no chromosomal abnormality but the other does, the value of equation (14) will be "0".

[0084] Furthermore, when the above-mentioned (iv) is adopted, "a" can be a numerical value expressed by the following formula (15), formula (16), or formula (17).

number

number

number

[0085] In equation (15), CAV k is a value that is preset depending on whether or not there is a chromosomal abnormality in chromosome k. In equations (16) and (17), MCAVk is a predetermined value depending on whether or not there is a chromosomal abnormality in chromosome k in males, and FCAVk is a predetermined value depending on whether or not there is a chromosomal abnormality in chromosome k in females. In equations (15) to (17), CS k is a number indicating the severity of the phenotype of chromosomal abnormalities on chromosome k.

[0086] If the effect of chromosomal abnormalities on chromosome k is small, CS k The value of CS should be set small, and if the impact of chromosomal abnormality on chromosome k is serious (e.g., embryonic lethality), CS k The value of can be set large.

[0087] In addition, in equation (17), if there is no chromosomal abnormality in chromosome k, MCAV k and FCAV k It is preferable not to use "0" as the value assigned in advance to . This is because if one of the sexes has no chromosomal abnormality but the other does, the value of equation (17) will be "0".

[0088] "a" in any of formulas (8) to (11) may reflect an index that reflects the presence or absence of chromosomal abnormalities. Examples of such an index include known indexes such as Z-score or NCV (Normalized Chromosome Value). For methods of calculating these indexes, see, for example, Japanese Patent Publication No. 7331325.

[0089] In addition, an embodiment may be adopted in which genotyping information and information regarding the presence or absence of chromosomal abnormalities obtained during the course of implementing the analytical techniques disclosed in Patent Publication No. 7331325, which perform two or more types of genetic analysis from among phenotype analysis, chromosomal abnormality analysis, and parent-child testing, are used to calculate the matching score.

[0090] <Score reflecting genetic similarity between opposite-sex or same-sex individuals> This embodiment involves performing a calculation that essentially includes calculation according to the following equation (18) based on genotype data for each of n polymorphic loci in each of two different biological individuals, and calculating the genetic compatibility between the biological individuals as a matching score.

[0091]

number

[0092] In equation (18), R l is a numerical value determined according to the degree of similarity between the genotypes at each polymorphic locus of the two individual organisms. However, the m polymorphic loci that are the subject of equation (18) are polymorphic loci in which all the alleles that appear are constitution / talent-related alleles and no disease-related alleles appear.

[0093] Here, "constitution / talent-related alleles" broadly include alleles related to constitutions or talents that are not related to disease in an individual organism. For example, "constitution / talent-related alleles" include alleles that affect appearance (skin color, hair color, eye color, body length, body shape, etc.), athletic ability (muscle strength, endurance, explosive power, etc.), learning ability, memory, resistance to mental stress, and musical or artistic talent.

[0094] As can be seen from the fact that ability tests, talent tests, etc. are offered in DTC (Direct to Consumer) genetic testing services, many predisposition / talent-related alleles have been reported in academic papers, etc. Furthermore, many predisposition / talent-related alleles as referred to in the present invention are registered in databases such as the GWAS catalog (https: / / www.ebi.ac.uk / gwas / ). Specific m polymorphic loci known to have these known predisposition / talent-related alleles can be preset as targets for formula (18).

[0095] "Operations that essentially include a calculation using equation (18)" include not only the summation operation represented by equation (18) itself, but also any operation that includes equation (18) as part of its expression, such as an operation of adding an arbitrary constant or variable to the sum of equation (18), an operation of multiplying the sum of equation (18) by an arbitrary constant or variable, or an operation of multiplying each term of equation (18) by an arbitrary constant or variable.

[0096] R in equation (18) l can be assigned according to predetermined criteria depending on the degree of similarity between the genotypes of the same polymorphic locus in two individual organisms. For example, suppose there are two types of constitution / talent-related alleles, A and B, that appear at a certain polymorphic locus. In this case, a score can be assigned based on the combination of the genotype in one individual organism and the genotype in the other individual organism, as follows: [Table 1]

[0097] The ratings in Table 1 are merely examples. A higher score may be set for higher genotype similarity, as shown in Table 1. In this case, the higher the matching score calculated by equation (18), the better the genetic compatibility between the two individuals can be evaluated. Alternatively, a lower score may be set for higher genotype similarity. In this case, the lower the matching score calculated by equation (18), the better the genetic compatibility between the two individuals.

[0098] <Integrated score> This embodiment involves performing a calculation that essentially includes calculation using a formula selected from the following formula (19) based on genotype data for each of n polymorphic loci in each of two different biological individuals, and calculating the genetic compatibility between the biological individuals as a matching score. Formula (7) + Formula (18)... Formula (19)

[0099] The above formula (19) is expressed as the sum of formulas (7) and (18). The specific contents of formulas (7) and (18) are as described above.

[0100] In one embodiment, the method includes performing a calculation that essentially includes calculation using a formula selected from the following formula (20) based on genotype data for each of n polymorphic loci in each of two different biological individuals, and calculating the genetic compatibility between the biological individuals as a matching score. Formula (8) + Formula (18)... Formula (20)

[0101] The above formula (20) is expressed as the sum of formulas (8) and (18). The specific contents of formulas (8) and (18) are as described above.

[0102] <Matching score ranking> An embodiment may be adopted in which the matching scores between two biological individuals are calculated and then ranked. Specifically, the matching scores of specific combinations of biological individuals in a set of matching scores between any two biological individuals are ranked.

[0103] The specific embodiment of ranking is not particularly limited. For example, one or more reference values ​​for the matching score may be set in advance, and the matching scores of two organisms to be evaluated may be compared with the reference values ​​to perform ranking. For example, an embodiment may be such that if the matching score is equal to or greater than a certain reference value, the matching score is evaluated as rank A, and if the matching score is less than a certain reference value and equal to or greater than a certain reference value, the matching score is evaluated as rank B.

[0104] Two other embodiments of ranking for calculating a score that reflects the magnitude of disease risk in offspring resulting from mating between a male individual and a female individual will be described below.

[0105] In a first embodiment relating to ranking of matching scores, matching scores are calculated in advance in a round-robin manner between a population of multiple males and a population of multiple females. The number of individual organisms constituting the population is preferably a statistically significant sample number. In the set of matching scores calculated in this way, one or more score thresholds are set for belonging to any top percentage. Matching scores of combinations of males and females to be evaluated in the set can be ranked based on whether the matching score calculated for a specific male and female to be evaluated is above a threshold and / or below a specific threshold.

[0106] In a second embodiment relating to ranking of matching scores, biological individuals of one sex are selected from a specific male-female combination to be evaluated. Then, a matching score is calculated between the selected specific biological individual of that sex and each of multiple biological individuals of a different sex. In the set of matching scores calculated in this way, one or more score thresholds are set to belong to any top percentage. The matching scores of the male-female combinations to be evaluated in the set can be ranked based on whether the matching scores calculated for the specific male and female to be evaluated are above a certain threshold and / or below a certain threshold.

[0107] [Matching System] The present invention also relates to a matching system for evaluating genetic compatibility. The matching system of the present invention includes a memory unit and an information processing unit. FIG. 1 is a diagram showing an example of the hardware configuration of the matching system 1. As shown in FIG. 1, the matching system 1 includes an information processing unit 11, a memory unit 12, an input unit 13, an output unit 14, a peripheral interface 15, and a network interface 16.

[0108] The information processing unit 11 includes one or more processors such as a CPU or an MPU, and controls the overall operation and processing of the matching system 1 by executing an OS and other applications.

[0109] The memory unit 12 is a storage device such as an HDD or optical disk, or a semiconductor memory element such as an SSD, ROM, or RAM, and stores a program for causing a computer to execute the calculation method of the present invention, as well as various data such as data and software used when the information processing unit 11 executes processing based on the program.

[0110] The input unit 13 is an input device such as a keyboard, a mouse, a touch panel, an OCR (Optical Character Reader), or a microphone, and inputs an operation request from a user to the information processing unit 11.

[0111] The output unit 14 is an output device such as a display, a projector, or a speaker, and displays the results of processing by the information processing unit 11, etc.

[0112] Peripheral interface 15 comprises the hardware, firmware, and / or software required to communicate with peripherals, including media devices such as magnetic or optical disk drives, other processing devices, or other input sources, used in connection with the present invention.

[0113] Network interface 16 comprises known hardware, firmware, and / or software that enables information processing unit 11 to communicate with other devices over wired or wireless networks, local or wide area networks, private or public networks, including, for example, well-known networks such as the World Wide Web, the Internet, or corporate networks.

[0114] 2, the matching system 1 has, as its functional configuration, a genetic analysis means 112, a disease risk score calculation means 113, a matching score calculation means 114, an extraction means 115, and a ranking means 116. These are information processing performed by software stored in the storage unit 12, which is specifically realized by hardware such as the information processing unit 11 and the storage unit 12.

[0115] The genetic analysis means 112 is a means for analyzing raw data output by various analytical devices and analyzing the genotype of the subject to be evaluated. Its specific form is not particularly limited, and can be realized by known analytical software. The matching system 1 is configured to be able to receive the raw data output by various analytical devices via the peripheral interface 15 or the network interface 16.

[0116] In this embodiment, the genotype data analyzed by the genetic analysis means 112 is stored in a database stored in the storage unit 12.

[0117] The disease risk score calculation means 113 is a means for calculating a disease risk score based on the genotype data of the subject to be evaluated. The specific manner of calculating the disease risk score is as described above.

[0118] The genotype data to be calculated by the disease risk score calculation means 113 may be generated by the genetic analysis means 112, or may be imported from outside via the peripheral interface 15 or the network interface 16.

[0119] In this embodiment, the disease risk score calculated by the disease risk score calculation means 113 is stored in a database stored in the storage unit 12.

[0120] The storage unit 12 stores a database that is a collection of genetic data relating to a plurality of individual organisms. The genetic data here may be either or both of the following (a) and (b): (a) Genotype data for each of n polymorphic loci in an individual organism (b) Data on disease risk scores calculated from the genotype data of each of n polymorphic loci in an individual organism.

[0121] The specific aspects of the genotype data referred to herein may be directly applied to the explanation of the invention relating to the calculation method above. The specific aspects of the disease risk score may be directly applied to the explanation of the invention relating to the calculation method above, but any evaluation value that qualitatively or quantitatively represents the disease risk calculated or estimated based on genetic data may also be used as the disease risk score.

[0122] The matching score calculation means 114 is a means for calculating genetic compatibility as a matching score. The matching score calculation means 114 can execute an arithmetic process to calculate genetic compatibility between a specific biological individual and each of a plurality of biological individuals as a matching score, based on the genetic data of the specific biological individual and the genetic data of the plurality of biological individuals stored in the storage unit 12.

[0123] The contents described in the above section [Calculation of Matching Score] can be applied to specific aspects of calculation of the matching score by the matching score calculation means 114. As described in the same section, the matching score calculated by the matching score calculation means 114 includes a score reflecting the magnitude of disease risk of offspring resulting from mating of a male biological individual with a female biological individual, a score reflecting genetic similarity between opposite-sex or same-sex individuals, and a combined score of these.

[0124] Below, we will specifically explain an embodiment in which the matching score calculation means 114 performs a calculation process to calculate the genetic compatibility of these male and female biological individuals as a matching score based on the genetic data of a specific male biological individual and the genetic data of a specific female biological individual.

[0125] An example is shown in Figure 2 for further details. In the example of Figure 2, the specific biological individual is a male, and the database stores genetic data for x female biological individuals. In this embodiment, a matching score is calculated for each combination of the specific biological individual (male) and x biological individuals (female) registered in the database. The method for calculating the matching score is not particularly limited, but it is preferably calculated by performing an operation that essentially includes the above-mentioned formula (7).

[0126] Further details will be provided with reference to another example shown in Figure 3. In the example of Figure 3, the specific biological individual is a male, and the database stores data relating to the disease risk scores of x female biological individuals (specifically, disease risk indices). As described above, the disease risk index is a numerical value corresponding to each term of a summation operation essentially including equation (1). In this embodiment, a matching score is calculated for each combination of the specific biological individual (male) and x biological individuals (female) registered in the database. The method for calculating the matching score is not particularly limited, but it is preferably calculated by executing the operation of equation (11) described above.

[0127] The extraction means 115 is a means for extracting biological individuals (however, of the opposite sex to the specific biological individual) that exhibit a matching score that meets a certain standard when combined with the specific biological individual from the plurality of biological individuals registered in the database.

[0128] 2 and 3, the extraction means 115 extracts combinations showing matching scores that satisfy a certain standard from among the matching scores calculated between a specific biological individual (male) and each of x biological individuals (female) registered in the database. In other words, it extracts biological individuals (female) whose genetic compatibility with the specific biological individual (male) satisfies a certain standard.

[0129] The specific embodiment of the "certain criterion" here is not particularly limited, and any criterion can be set in advance. For example, it may be set to extract those whose matching score is equal to or greater than a certain threshold, or it may be set to extract those that fall within a certain ranking when sorted in order of best matching score.

[0130] In this embodiment, the information processing unit 11 includes a ranking means 116. The ranking means 116 is a means for executing ranking of the disease risk scores calculated by the disease risk score calculation means 113 or the matching scores calculated by the matching score calculation means 114. As for specific aspects of ranking, the matters described in the section [Calculation of disease risk scores] or [Calculation of matching scores] can be applied.

[0131] Another embodiment of the system configuration shown in Fig. 1 will be described with reference to Fig. 4. In this embodiment, data on the genotype of each of n polymorphic loci in an individual organism is transmitted from an information terminal 21 held by user 1 via a network NW, and the matching system 1 receives this data via a network interface 16. Then, a disease risk score is calculated by a disease risk score calculation means 113 provided in the information processing unit 11, and the information is transmitted from the network interface 16 to user 1's information terminal 21 via the network NW.

[0132] In parallel with this processing, the information processing unit 11 adds the genotype data and / or information on the disease risk score received from the information terminal 21 of the user 1 to the database stored in the storage unit 12.

[0133] In other words, in this embodiment, while responding to a request from user 1 to know the disease risk score of a specific individual organism, the genotype data and / or disease risk score received from user 1 can be added to and updated in the database.

[0134] In this embodiment, genotype data for each of n polymorphic loci in a biological individual is transmitted from the information terminal 22 possessed by the user 2 via the network NW, and the matching system 1 receives this data via the network interface 16. The information processing unit 11 calculates, via the matching score calculation means 114, the genetic compatibility between the specific biological individual and each of multiple biological individuals (provided that the specific biological individual is of the opposite sex) as a matching score based on the genotype data of the specific biological individual received from the user 2 and the genetic data of the multiple biological individuals stored in the memory unit 12. Then, the extraction means 115 extracts, from the multiple biological individuals registered in the database, biological individuals (provided that the specific biological individual is of the opposite sex) that exhibit a matching score that satisfies a certain criterion in combination with the specific biological individual of the user 2. Thereafter, the information processing unit 11 transmits information about the extracted biological individuals to the user 2 information terminal 22 via the network interface 16 and the network NW.

[0135] Alternatively, the genetic data of multiple biological individuals may be linked to specific information for identifying them and stored in the storage unit 12. Examples of the specific information include an individual number for identifying the biological individuals and a test ID for the test performed to obtain the genetic data. In this embodiment, a signal specifying the specific information is transmitted from the information terminal 22 held by the user 2 via the network NW, and the matching system 1 receives this signal via the network interface 16. The information processing unit 11 identifies the genetic data of the specific biological individual specified by the user 2 using the specific information from the data stored in the storage unit 12. Then, the matching score calculation means 114 calculates the genetic compatibility between the specific biological individual and each of the multiple biological individuals (however, the specific biological individual is of the opposite sex) as a matching score based on the genotype data of the specific biological individual specified by the user 2 using the specific information and the genetic data of the other multiple biological individuals stored in the storage unit 12. The subsequent information processing flow is as described above.

[0136] This embodiment can meet the needs of user 2 who wants to efficiently search for organisms of the opposite sex that have excellent genetic compatibility with an organism that he or she owns, manages, or designates.

[0137] Next, an embodiment will be described in which the matching score calculation means 114 calculates a score that reflects the genetic similarity between opposite-sex or same-sex individuals.

[0138] An example is shown in Figure 5, which will be explained in more detail. In the example of Figure 5, the specific individual organism is A1, and its sex does not matter. The database contains individuals A2 to A x In this embodiment, a matching score is calculated for each combination of a specific individual organism and x-1 individual organisms registered in the database. The method for calculating the matching score is not particularly limited, but it is preferably calculated by performing an operation that essentially includes the above-mentioned formula (18).

[0139] Biological individuals A2~A x The sex of the individual organisms A2 to A1 may be the same as that of A1, or may be of the opposite sex, or may include both the same sex and the opposite sex. x The embodiment in which the gender of the biological individual A1 is the same as the gender of the biological individual A2 can be applied to a service that scores the genetic compatibility (genetic similarity) of same-sex human couples, for example.

[0140] The extraction means 115 is means for extracting, from the plurality of individual organisms registered in the database, individual organisms that exhibit a matching score that satisfies a certain standard in combination with the specific individual organism.

[0141] 5, the extraction means 115 extracts combinations showing matching scores that satisfy a certain standard from among the matching scores calculated between a specific individual organism and each of the x-1 individual organisms registered in the database. In other words, it extracts individual organisms whose genetic compatibility (genetic similarity) with the specific individual organism satisfies a certain standard.

[0142] The specific aspects of the "certain criteria" mentioned here are as explained in the embodiment of FIGS. 2 and 3 also apply to the specific aspects of the information processing unit 11 in the embodiment of FIG. 5.

[0143] Even when the embodiment shown in FIG. 5 is adopted, the configuration shown in FIG. 4 can be applied. In this case, genotype data for each of n polymorphic loci in a biological individual is transmitted from the information terminal 22 possessed by the user 2 via the network NW, and the matching system 1 receives this data via the network interface 16. The information processing unit 11 calculates, using the matching score calculation means 114, the genetic compatibility between the specific biological individual and each of the multiple biological individuals as a matching score based on the genotype data of the specific biological individual received from the user 2 and the genetic data of the multiple biological individuals stored in the memory unit 12. Then, the extraction means 115 extracts, from the multiple biological individuals registered in the database, biological individuals that exhibit a matching score that meets a certain criterion in combination with the specific biological individual of the user 2. Thereafter, the information processing unit 11 transmits information about the extracted biological individuals to the user 2's information terminal 22 via the network interface 16 and the network NW.

[0144] 5, it is also possible to apply an embodiment in which genetic data of multiple individual organisms is linked to specific information for identifying the data and stored in the storage unit 12. The specific information has been described above. This embodiment can meet the needs of user 2 who wants to efficiently search for biological individuals that have excellent genetic compatibility (genetic similarity) with a biological individual that he or she owns or manages, or that he or she specifies. [Industrial Applicability]

[0145] The present invention can be applied to the assessment of disease risk and genetic compatibility. The present invention can be applied to assessing disease risks in livestock and companion animals, and to examining breeding combinations. Furthermore, while conventional matchmaking sites have evaluated items such as appearance and annual income, the application of the present invention makes it possible to realize matching based on genetic compatibility. More specifically, the present invention can be applied to services that score genetic compatibility from the perspective of disease risk in offspring, or services that score the genetic similarity of same-sex couples.

Claims

1. A calculation method comprising: calculating a disease risk score based on genotype data for each of n polymorphic loci in an individual organism, the calculation essentially including calculation according to the following formula (1): [Equation 1] Formula (1) E ｋ is the number of disease-associated alleles at each polymorphic locus or a value proportional to that number. S ｋ is a numerical value indicating the severity of the phenotype of the disease-associated allele at each polymorphic locus. However, the n polymorphic loci that are the targets of formula (1) are polymorphic loci in which at least one of the alleles that appears is a disease-associated allele.

2. 2. The method according to claim 1, wherein the operation essentially including a calculation according to the formula (1) is an operation expressed by the following formula (2). [Equation 2] Formula (2) a is an arbitrary constant or an arbitrary variable. b is an arbitrary constant (except 0) or an arbitrary variable. c is an arbitrary constant or an arbitrary variable.

3. 3. The calculation method according to claim 2, wherein the c is a predetermined value (DR value) depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive.

4. The a is A predetermined value (CAV value) determined based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities, or a numerical value obtained by adding or multiplying a numerical value (CAV value) predetermined based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities, and a numerical value indicating the severity of the phenotype of the chromosomal abnormality; The calculation method according to claim 2.

5. The method of claim 1 , further comprising: performing a ranking of disease risk scores for particular individual organisms in the population of organisms.

6. A calculation method comprising: performing a calculation essentially including calculation using a formula selected from the following formulas (7), (18), and (19) based on genotype data for each of n polymorphic loci in each of two different biological individuals; and calculating the genetic compatibility between the biological individuals as a matching score. [Equation 7] Formula (7) [Equation 18] Formula (18) Formula (7) + Formula (18)... Formula (19) In formula (7), ME ｋ is the number of disease-associated alleles at each polymorphic locus in the male when the two biological individuals are male and female, or a numerical value proportional to said number. FE ｋ is the number of disease-associated alleles at each polymorphic locus in the female when the two biological individuals are male and female, or a numerical value proportional to said number. S ｋ is a numerical value indicating the severity of the phenotype of the disease-associated allele at each polymorphic locus. However, the n polymorphic loci that are the targets of formula (7) are polymorphic loci in which at least one of the alleles that appears is a disease-associated allele (effect allele). In formula (18), R ｌ is a numerical value determined according to the degree of similarity between the genotypes at each polymorphic locus of the two individual organisms. However, the m polymorphic loci that are the targets of formula (18) are polymorphic loci in which all the alleles that appear are constitution / talent-related alleles and no disease-related alleles appear.

7. 7. The method according to claim 6, wherein the operation essentially including the calculation according to the formula (7) is an operation expressed by the following formula (8). [Equation 8] Formula (8) a is an arbitrary constant or an arbitrary variable. b is an arbitrary constant (except 0) or an arbitrary variable. c is an arbitrary constant or an arbitrary variable.

8. 8. The calculation method according to claim 7, wherein the c is a predetermined value (DR value) depending on whether the disease-associated allele at each polymorphic locus is dominant or recessive.

9. The a is a predetermined value determined based on the presence or absence of one or more chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities in either or both of the male and the female; or a numerical value obtained by adding or multiplying a predetermined numerical value determined based on the presence or absence of one or more types of chromosomal abnormalities selected from chromosomal numerical abnormalities, copy number mutations in specific DNA regions, and chromosomal structural abnormalities in either or both of the male and the female, and a numerical value indicating the severity of the phenotype of the chromosomal abnormality; The calculation method according to claim 7.

10. The calculation method according to claim 6 , further comprising: ranking matching scores between specific individual organisms in a set of matching scores between arbitrary individual organisms in the population of organisms.

11. A matching system for evaluating genetic compatibility, comprising a memory unit and an information processing unit, the storage unit stores a database that is a collection of genetic data relating to a plurality of individual organisms; The information processing unit Calculating the genetic compatibility of two different organisms as a matching score based on the genetic data of these two organisms; or calculating a genetic compatibility between the specific biological individual and each of the plurality of biological individuals as a matching score based on the genetic data of the specific biological individual and the genetic data of the plurality of biological individuals stored in the storage unit; A matching score calculation means is provided, The genetic data includes: Genotype data for each of n polymorphic loci in an individual organism, and / or Data on disease risk scores calculated from genotype data for each of n polymorphic loci in an individual organism That is the matching system.

12. 12. The matching system according to claim 11, wherein the information processing unit comprises an extraction means for extracting, from the plurality of biological individuals registered in the database, biological individuals that exhibit a matching score that satisfies a certain standard in combination with the specific biological individual.

13. The disease risk score is calculated by the calculation method according to any one of claims 1 to 4, The matching system according to claim 11, wherein the matching score is calculated by the calculation method according to any one of claims 6 to 9.

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