JP7108571B2 - How to determine your risk of anorexia - Google Patents

Description

The present invention relates to a method for determining the risk of anorexia.

Identification of the relationship between single nucleotide polymorphisms (hereinafter also referred to as "SNPs") and diseases is in progress for use in determining disease risk. The NCBI SNP Database is a database that summarizes human SNPs, and manages the SNPs by assigning rs numbers. The rs number in this specification also means the accession number in this NCBI SNP Database.

The relationships between the SNPs identified by the rs numbers in this specification and the diseases, pathologies, conditions, etc. associated with the SNPs disclosed in non-patent literature and the like are as follows.
rs4820268: SNP related to iron concentration in blood (Non-Patent Document 1)
rs3809060: SNP related to inguinal hernia (Non-Patent Document 2)
rs4273077: SNP related to multiple myeloma (Non-Patent Document 3)
rs10937405: SNP related to lung cancer (lung adenocarcinoma) (Non-Patent Document 4)
rs7097: SNP related to large B-cell lymphoma (Non-Patent Document 5)
rs2853677: SNP related to lung cancer (lung adenocarcinoma) (Non-Patent Document 6)
rs6570507: SNP related to scoliosis (Non-Patent Document 7)
rs4680: SNP related to smoking amount (Non-Patent Document 8)
rs4680: SNP related to pain sensitivity (Non-Patent Document 9)
rs4680: SNP related to harmony (for men) (Non-Patent Document 10)
rs4680: SNP related to extroversion (for women) (Non-Patent Document 11)
rs4680: SNP for loss avoidance (for men) (Non-Patent Document 12)
rs4680: SNP on loss avoidance (for women) (Non-Patent Document 13)
rs4680: SNP related to control desire (Non-Patent Document 14)
rs4680: SNP related to tendency to avoid failure (Non-Patent Document 15)
rs4680: SNP related to tendency to seek short-term profits (Non-Patent Document 16)
rs4680: SNP related to recognition of sad facial expressions (Non-Patent Document 17)
rs4680: SNP related to recognition of angry facial expressions (Non-Patent Document 18)
rs6010620: SNP related to glioma (Non-Patent Document 19)

Pichler I, Minelli C, Sanna S, Tanaka T, Schwienbacher C, Naitza S, et al. Identification of a common variant in the TFR2 gene implicated in the physiological regulation of serum iron levels. Hum. Mol. Genet. 2011;20: 1232-40. Jorgenson E, Makki N, Shen L, Chen DC, Tian C, Eckalbar WL, et al. A genome-wide association study identifies four novel susceptibility loci underlying inguinal hernia. Nat Commun 2015;6: 10130. Chubb D, Weinhold N, Broderick P, Chen B, Johnson DC, Forsti A, et al. Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat. Genet. 2013; 45: 1221-1225. Shiraishi K, Kunitoh H, Daigo Y, Takahashi A, Goto K, Sakamoto H, et al. A genome-wide association study identifies two new susceptibility loci for lung adenocarcinoma in the Japanese population. Nat. Genet. 2012;44: 900- 3. Kumar V, Matsuo K, Takahashi A, Hosono N, Tsunoda T, Kamatani N, et al. Common variants on 14q32 and 13q12 are associated with DLBCL susceptibility. J. Hum. Genet. 2011;56: 436-9. Shiraishi K, Kunitoh H, Daigo Y, Takahashi A, Goto K, Sakamoto H, et al. A genome-wide association study identifies two new susceptibility loci for lung adenocarcinoma in the Japanese population. Nat. Genet. 2012;44: 900- 3. Kou I, Takahashi Y, Johnson TA, Takahashi A, Guo L, Dai J, et al. Genetic variants in GPR126 are associated with adolescent idiopathic scoliosis. Nat. Genet. 2013;45: 676-9. Tochigi M, Suzuki K, Kato C, Otowa T, Hibino H, Umekage T, et al. Association study of monoamine oxidase and catechol-O-methyltransferase genes with smoking behavior. Pharmacogenet. Genomics 2007;17: 867-72. Zubieta JK, Heitzeg MM, Smith YR, Bueller JA, Xu K, Xu Y, et al. COMT val158met genotype affects mu-opioid neurotransmitter responses to a pain stressor. Science 2003;299: 1240-3. Aoki J, Iwahashi K, Ishigooka J, Ikeda K. Association study on catechol-O-methyltransferase (COMT) Val158Met gene polymorphism and NEO-FFI. Psychiatry Res 2011;187: 312-3. Stein MB, Fallin MD, Schork NJ, Gelernter J. COMT polymorphisms and anxiety-related personality traits. Neuropsychopharmacology 2005;30: 2092-102. Hashimoto R, Noguchi H, Hori H, Ohi K, Yasuda Y, Takeda M, et al. A possible association between the Val158Met polymorphism of the catechol-O-methyl transferase gene and the personality trait of harm avoidance in Japanese healthy subjects. Lett. 2007;428: 17-20. Hashimoto R, Noguchi H, Hori H, Ohi K, Yasuda Y, Takeda M, et al. A possible association between the Val158Met polymorphism of the catechol-O-methyl transferase gene and the personality trait of harm avoidance in Japanese healthy subjects. Lett. 2007;428: 17-20. Gong P, Zheng A, Zhang K, Lei X, Li F, Chen D, et al. Association analysis between 12 genetic variants of ten genes and personality traits in a young chinese Han population. J. Mol. Neurosci. 2010;42: 120-6. Frank MJ, D'Lauro C, Curran T. Cross-task individual differences in error processing: neural, electrophysiological, and genetic components. Cogn Affect Behav Neurosci 2007;7: 297-308. Boettiger CA, Mitchell JM, Tavares VC, Robertson M, Joslyn G, D'Esposito M, et al. Immediate reward bias in humans: fronto-parietal networks and a role for the catechol-O-methyltransferase 158(Val/Val) genotype J. Neurosci. 2007;27: 14383-91. Weiss EM, Stadelmann E, Kohler CG, Brensinger CM, Nolan KA, Oberacher H, et al. Differential effect of catechol-O-methyltransferase Val158Met genotype on emotional recognition abilities in healthy men and women. J Int Neuropsychol Soc 2007;13: 881 -7. Weiss EM, Stadelmann E, Kohler CG, Brensinger CM, Nolan KA, Oberacher H, et al. Differential effect of catechol-O-methyltransferase Val158Met genotype on emotional recognition abilities in healthy men and women. J Int Neuropsychol Soc 2007;13: 881 -7. Shete S, Hosking FJ, Robertson LB, Dobbins SE, Sanson M, Malmer B, et al. Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 2009;41: 899-904.

An object of the present invention is to provide a method for determining the risk of anorexia (hereinafter also referred to as "this disease").

The present inventors have made intensive studies to solve the above problems. As a result, we found that individual single nucleotide polymorphisms, which at first glance seem to be unrelated to this disease, are related to this disease when viewed as a set. Then, by using the relationship, the present invention for determining the risk of this disease has been completed.

That is, in the method of the present invention, a single nucleotide polymorphism set (hereinafter referred to as "this SNP The risk of this disease is determined based on the genotype information of the set.

In the method of the present invention, the "single nucleotide polymorphism set" means a set of multiple single nucleotide polymorphisms, and this one set has been found to be associated with the disease. .

In addition, the "genotype information" in the method of the present invention refers to single nucleotide polymorphisms classified into two homozygous types (AA, BB) and heterozygous types (AB) for single nucleotide polymorphisms. It means genotype information, and "genotype information of this SNP set" means a set of genotype information of each single nucleotide polymorphism identified in this SNP set, in other words For example, it is a set of information on the polymorphic bases of each SNP in the base sequence indicated by each rs number. The genotype information of this SNP set is as shown in FIG.

According to the present invention, the risk of this disease can be determined.

The genotype information of this SNP set is shown. An example of a conversion table showing the relationship between the genotype information of this SNP set and the mating type for each SNP is shown. The ROC curve and AUC of the model using this SNP set are shown. A SNP set containing N−1 SNPs obtained by arbitrarily removing one SNP from the main SNP set containing N SNPs is also referred to as a “comparison SNP set”. The comparison SNP set 1 and the comparison SNP set 2 are described. The ROC curve and AUC of the model using comparative SNP set 1 are shown. The ROC curve and AUC of the model using comparative SNP set 2 are shown. The ROC curve and AUC of the model with comparative SNP set 3 are shown. The ROC curve and AUC of the model with comparative SNP set 4 are shown. The ROC curve and AUC of the model with comparative SNP set 5 are shown. The ROC curve and AUC of the model with comparative SNP set 6 are shown. The ROC curve and AUC of the model using comparative SNP set 7 are shown. The ROC curve and AUC of the model using the comparative SNP set 8 are shown. The ROC curve and AUC of the model with comparative SNP set 9 are shown. The ROC curve and AUC of the model with comparative SNP set 10 are shown.

An embodiment of the present invention will be described. The following embodiments are examples for explaining the present invention, and are not meant to limit the present invention only to these embodiments. The present invention can be embodied in various forms without departing from the gist thereof.

In the present embodiment, anorexia is one of eating disorders, and refers to a condition in which a person cannot eat without physical illness. Anorexia nervosa is also known as anorexia nervosa or anorexia nervosa.

In addition, in this embodiment, the disease is generally defined as a disease diagnosed according to the guidelines published by the medical society regarding this disease, a disease described in the column of efficacy and effect in the package insert of ethical drugs, Alternatively, it can be understood to mean at least one of diseases understood as a term commonly used in the pharmaceutical and medical industries.

In the method of this embodiment, the risk of this disease is determined using a predetermined number of single nucleotide polymorphism sets that seemingly have no association with this disease.

The risk of this disease means the possibility of contracting this disease, such as susceptibility or difficulty of contracting this disease. "Risk determination" includes, for example, dividing the present or future possibility of contracting the disease into several levels and outputting it numerically. Determining risk for the disease involves assessing genetic factors or genetic susceptibility to the disease, such as predisposition or predisposition to the disease.

In determining the risk of this disease, it is necessary to check whether the subject who is to be assessed for the risk of this disease actually has (has developed) this disease at the time of the risk assessment of this disease. does not matter.

In the method of the present embodiment, the present SNP is a set of genotypes in which genotypes of each SNP specified in the present SNP set are classified into two homozygous types (AA, BB) and a heterozygous type (AB). A set of genotype information is used. Then, based on the genotype information of this SNP set, the subject's risk of this disease is determined.

This SNP set used in the method of this embodiment is a set containing SNPs that have not been found to be related to this disease. That is, normally, even if the SNPs included in this SNP set are individually analyzed, the risk of this disease cannot be determined. However, in the method of this embodiment, the risk of this disease can be determined by analyzing the genotype information of each SNP included in this SNP set as a set. Further, when comparing the case of analyzing the present SNP set and the case of analyzing the comparative SNP set, the case of analyzing the present SNP set yields statistically significant results. That is, in the method of this embodiment, by analyzing this SNP set and determining the risk of this disease, it is possible to provide a highly accurate or highly predictive risk determination method.

Below, in relation to each SNP included in this SNP set, the rs number, the chromosome number where each SNP exists (indicated by X or Y in the case of sex chromosomes), and the position of each SNP on the chromosome , and the nucleotide sequence corresponding to the rs number. In addition, in the base sequence indicated by each rs number, SNPs are enclosed in brackets []. For example, [A/G] indicates that there is a single nucleotide polymorphism of A or G at the position of the base sequence. Further, information such as base sequences and diseases related to each SNP can be obtained, for example, by searching the NCBI SNP Database based on the rs number. Their information can be referenced by the Database and is incorporated herein by reference. The positions on the chromosome described below correspond to the assembly genome version GRCh37.

rs4820268
chromosome number 22
Chromosome location 37469591
Nucleotide sequence CCCTACCTTCCTGGCACTGCTCTTC[A/G]TCGCTGCCGTTGAGACAATCAGGCT (SEQ ID NO: 1)

rs3809060
chromosome number 11
Chromosome location 32458807
Nucleotide sequence GGCCACCTTTGCTGCTTGACTTTGC[T/G]TCGTCTCCACTCAGAAGCTTTTTAAA (SEQ ID NO: 2)

rs4273077
Chromosome number 17
Chromosome location 16849139
Nucleotide sequence CCCCCTCCCCCAGCTTGCTCCTTCC[A/G]CCAAGAGGTGGCGTCTGTTTCCCCCT (SEQ ID NO: 3)

rs10937405
Chromosome number 3
Chromosome location 189383183
Nucleotide sequence CAACCCTACTTCAACATTATAATCT [T/C] GTGTTCAAACCTGATAAGCTACTTT (SEQ ID NO: 4)

rs7097
chromosome number 13
Chromosome location 28197436
Nucleotide sequence TCTAATTCTGGGTTCTGCACCATCA [A/G] GAAGAGAATATCCTACAGGACAGTT (SEQ ID NO: 5)

rs2853677
Chromosome number 5
Chromosome location 1287194
Nucleotide sequence TCATTTTTTGTCACTAGAGACCCG [T/C] CTGGTGCACTCTGATTCTCCACTTG (SEQ ID NO: 6)

rs6570507
Chromosome number 6
Chromosome location 142679572
Nucleotide sequence AGTGAATTCAGTCCTTGTAGAATAA [A/G] TCTGCCTTGCCACTGTGACTGCTAA (SEQ ID NO: 7)

rs4680
chromosome number 22
Chromosome location 19951271
Nucleotide sequence CCAGCGGATGGTGGATTTCGCTGGC[A/G]TGAAGGACAAGGTGTGCATGCCTGA (SEQ ID NO: 8)

rs6010620
chromosome number 20
Chromosome location 62309839
Nucleotide sequence TCCCTTTTTGAGACCTGGGAGGAGC[A/G]CCTGCTTTGCATGATCTGGTTGCTG (SEQ ID NO: 9)

In the method of this embodiment, each SNP that constitutes this SNP set can be identified by referring to the base sequence identified by the rs number. and a new rs number is assigned, the applicable rs number in this specification also means the merged rs number and the merged other rs number. In addition, when the rs number described in this specification is a number assigned by merging multiple rs numbers, the applicable rs number in this specification also means other original rs numbers.

In addition, although the base sequences indicated by the respective rs numbers related to SNPs are shown as specific base sequences, the base sequences in portions other than the relevant SNPs in the base sequences may be changed due to differences in race, etc. good.

Although the method of the present embodiment can be used for subjects of any race, it is particularly suitable for Asian subjects. Among Asians, it can be preferably used for East Asian subjects such as Japanese. Moreover, the method of the present embodiment may be used for subjects of any gender.

One aspect of the method for determining the risk of this disease by analyzing the genotype information of this SNP set will be described below. However, the determination method is not limited to the following.

First, a subject's sample is used to identify the genotype of each SNP included in this SNP set in the sample. The sample used for SNP detection is not particularly limited as long as it contains chromosomal DNA. Such samples include, for example, body fluid samples such as saliva, blood and urine; cell samples such as oral mucosa; body hair such as hair. For detection of SNPs, chromosomal DNA isolated from these samples by a conventional method may be directly used, or the isolated chromosomal DNA may be amplified and the amplified chromosomal DNA may be used.

SNP detection can be performed by a normal genetic polymorphism analysis method. For example, DNA chip method (DNA microarray), sequence analysis using a conventional sequencer or next generation sequencer (NGS; Next Generation Sequencer) using the Sanger method, PCR (Polymerase Chain Reaction), hybridization, invader method, etc. include, but are not limited to:

In the DNA chip method, a DNA chip is used in which a large number of DNA fragments (probes) containing SNP sites are arranged on a substrate, chromosomal DNA is hybridized with the probes on the chip, and binding sites are detected by fluorescence or electric current. analyzes the sequence of chromosomal DNA. A DNA chip used for SNP analysis includes a chip on which oligonucleotide probes capable of detecting base sequences containing SNP sites are arranged.

Also, sequence analysis can be performed by the usual Sanger method. For example, a sequence reaction is performed using a primer set at a position several tens of bases on the 5' side of a base exhibiting a polymorphism, and the type of base at the corresponding position is determined from the analysis results. can be done. In addition, it is preferable to amplify the fragment containing the SNP site in advance by PCR or the like before the sequencing reaction. From an efficiency point of view, NGS technology may be used.

Moreover, detection of SNPs can be performed, for example, by examining the presence or absence of amplification by conventional PCR. For example, primers each having a sequence corresponding to a region containing a base exhibiting a polymorphism and having a 3′ end corresponding to each polymorphism are prepared. PCR can be performed using each primer and the type of polymorphism can be determined by the presence or absence of the amplified product.また、ＬＡＭＰ法（Ｌｏｏｐ－Ｍｅｄｉａｔｅｄ Ｉｓｏｔｈｅｒｍａｌ Ａｍｐｌｉｆｉｃａｔｉｏｎ；特許第３３１３３５８号明細書）、ＮＡＳＢＡ法（Ｎｕｃｌｅｉｃ Ａｃｉｄ Ｓｅｑｕｅｎｃｅ－Ｂａｓｅｄ Ａｍｐｌｉｆｉｃａｔｉｏｎ；特許２８４３５８６号明細書）、ＩＣＡＮ法（Ｉｓｏｔｈｅｒｍａｌ ａｎｄ Ｃｈｉｍｅｒｉｃ ｐｒｉｍｅｒ－ｉｎｉｔｉａｔｅｄ Ａｍｐｌｉｆｉｃａｔｉｏｎ ｏｆ Ｎｕｃｌｅｉｃ ａｃｉｄｓ； The presence or absence of amplification can also be checked by Japanese Patent No. 3433929). In addition, a single-strand amplification method or an analysis method using NGS may be used.

It is also possible to amplify a DNA fragment containing the SNP site and determine which type of polymorphism it is based on the difference in electrophoretic mobility of the amplified product. Examples of such methods include the PCR-SSCP (single-strand conformation polymorphism) method (Genomics. 1992 Jan 1; 12(1): 139-146.). Specifically, first, DNA containing the SNP of interest is amplified, and the amplified DNA is dissociated into single-stranded DNA. The dissociated single-stranded DNA is then separated on a non-denaturing gel and the type of polymorphism can be determined by the difference in mobility of the separated single-stranded DNA on the gel.

Furthermore, when a base exhibiting polymorphism is included in the restriction enzyme recognition sequence, it can be analyzed based on the presence or absence of cleavage by the restriction enzyme (RFLP (Restriction Fragment Length Polymorphism) method). In this case, first, the DNA sample is cleaved with a restriction enzyme. The DNA fragments can then be separated and the type of polymorphism determined by the size of the detected DNA fragment.

It is also possible to analyze the type of polymorphism by examining the presence or absence of hybridization. That is, it is also possible to examine which base the SNP is by preparing probes corresponding to each base and examining which probe it hybridizes to.

In this way, the subject's genotype data can be determined for each SNP in the SNP set. Here, "subject's genotype data" refers to genotype information possessed by the subject.

Then, the risk of this disease is determined based on the genotype information of this SNP set. Any model can be used to determine risk. The model is not particularly limited, but for example, using the genotype information of the present SNP set, a logistic regression model in which the feature value calculated from the genotype data of the subject is input and the risk of this disease is output. can be used. In the logistic regression model, the parameters are machine-learned in advance using the genotype data of humans affected by this disease and the genotype data of humans not affected by this disease as learning data.

In addition, as a model for determining the risk of disease, any kernel function such as a multilayer perceptron, a neural network such as a CNN (Convolutional Neural Network) and an RNN (Recurrent Neural Network), or a Gaussian kernel is used instead of the logistic regression model. Various other models such as support vector machines, random forests modeled as regression trees, multiple regression analysis, models utilizing hidden Markov models, statistical models and probabilistic models can also be employed. It is also possible to employ a model that combines various models to make a comprehensive determination.

Next, an example of risk determination for this disease using a model will be described. First, the genotype data of the subject whose risk of this disease is to be determined is converted into a feature quantity that can be input to the model. The feature amount in the method of the present embodiment is, for example, whether the genotype data of the subject is homozygous (AA), homozygous (BB), or heterozygous (AB) for each SNP in the SNP set. It is a parameter that indicates whether Genotypes are represented by nucleotides such as "GG" indicating that both SNPs on homologous chromosomes are G (guanine), or "AG" indicating that one is G (guanine) and the other is A (adenine). Therefore, the subject's genotype data is converted into parameters that can be input to a model that uses the genotype information of this SNP set. However, if the model is one that does not require such transformations into parameters, then such transformations are not required.

Conversion of the subject's genotype data into feature quantities can be performed, for example, by assigning a value to the subject's genotype data for each SNP included in this SNP set. For example, for each SNP, a value (for example, , 0 or 1). Thereby, the subject's genotype data can be converted into a feature amount. In the following, the case where the value associated with each SNP is 0 or 1 will be described as an example, but the value associated with the SNP is not limited to 0 or 1.

A value associated with a mating type can be determined for each SNP. For example, a SNP associates a value of 1 when the subject's genotype data is homozygous (AA), and a value of 0 when the subject is homozygous (BB) and heterozygous (AB). As such, other SNPs are associated with a value of 1 when the subject's genotype data is heterozygous (AB), and a value of 0 when they are homozygous (AA) and homozygous (BB). may be associated with each other. In addition, if the genotype data of the subject is heterozygous (AB) and homozygous (BB), the value 1 is associated, and if the subject is homozygous (AA), the value 0 is associated. good.

As described above, a subject's genotype data can be converted into feature quantities. Values used for correspondence in the conversion to this feature quantity can be arbitrarily determined. For example, based on the above non-patent literature, a value of 1 is associated with genotypes that are highly related to the disease to which each SNP is originally associated, and genotypes that are less related to the disease to which each SNP is related are associated with A value of 0 can be associated.

Such a relationship between the mating type for each SNP and the value associated with the mating type can be expressed as a conversion table such as that shown in FIG. 2, for example, based on the genotype information of this SNP set as shown in FIG. can. In the conversion table of FIG. 2, the value associated with the SNP is set to 1 if it matches the shaded genotype, and 0 if it does not match. 1 and 2, A represents adenine, G represents guanine, C represents cytosine, and T represents thymine. However, the format of the feature amount conversion table is not limited to that shown in FIG.

Finally, the subject's risk of the disease is determined based on the genotype information of the SNP set. More specifically, using a conversion table based on the genotype information of this SNP set, the genotype data of the subject is calculated as a feature amount converted so that it can be input to the model, and the feature amount is converted into a predetermined judgment model. to determine the subject's risk of this disease.

In the judgment model, for each SNP in the SNP set, a weighting indicating that there is a positive correlation with the risk of this disease and a weighting that indicates that there is a negative correlation with the risk of this disease. can do. For example, the values (feature values) associated with rs4820268, rs7097, rs3809060, and rs6010620 are weighted to indicate that there is a positive correlation with the risk of this disease, and are associated with rs6570507, rs4680, rs2853677, and rs4273077. The value (feature value) associated with rs10937405 is weighted to indicate that there is a negative correlation with the risk of this disease, and the value (feature value) associated with rs10937405 is positively or negatively correlated with the risk of this disease. can be weighted to indicate that there is

For example, when weighting features, the genotype of rs10937405 is TC, the genotype of rs4820268 is AG, the genotype of rs7097 is AA, the genotype of rs3809060 is TT, and the genotype of rs6010620 is AA. If , weighted to indicate that there is a positive correlation with the risk of this disease, the rs10937405 genotype is CC, the rs6570507 genotype is AG, the rs4680 genotype is GG, and the rs2853677 genotype is TT. , and rs4273077 genotype AG can be weighted to indicate that there is a negative correlation with the risk of this disease. In addition, in the case of each SNP genotype associated with a value of 0 as a feature quantity, it can be evaluated that there is no correlation with the risk of this disease or that it is negligibly low.

Such weighting, which represents the correlation with the risk of this disease, is performed by using the genotype data of humans affected by this disease and the genotype data of humans not affected by this disease as learning data. is identified by machine learning. At this time, if a certain SNP is weighted to indicate that there is a positive correlation with the risk of this disease in a certain model, the SNP is similarly positively correlated with the risk of this disease in other models. It is normal to have some weighting. In other words, it is difficult to imagine a situation in which the correlation with the risk of this disease is reversed for a certain SNP, depending on the type of model or the like. Note that specific weighting values differ depending on the model and are not particularly limited.

Here, in this SNP set, a group of SNPs that are weighted to indicate that there is a positive correlation with the risk of this disease is called a "positive correlation SNP set", and that there is a negative correlation with the risk of this disease. A group of SNPs weighted to represent is called a "negatively correlated SNP set". This SNP set includes a positive correlation SNP set and a negative correlation SNP set. and risk reduction factors can be comprehensively determined.

The determination result obtained as described above is also used as an aid in diagnosing this disease by a specialist of this disease. In addition, the determination result of the risk of the disease may be corrected based on the risk of the disease determined as described above and the results of questionnaires from the subjects. In addition, based on the risk of this disease and the results of a questionnaire from the subject, advice on life improvement may be output to the subject.

The present invention can also provide test reagents such as primers and probes. Examples of such probes include probes that contain the above SNP site and can determine the type of base at the SNP site based on the presence or absence of hybridization. Further, examples of primers include primers that can be used for PCR for amplifying the SNP site, and primers that can be used for sequence analysis of the SNP site. In addition to these primers and probes, the test reagents of the present embodiment may also contain polymerases and buffers for PCR, reagents for hybridization, and the like.

EXAMPLES Hereinafter, the present embodiment will be described in more detail with reference to examples. However, the present embodiment is not limited to these examples.

The association between this SNP set and this disease was verified as follows.

With the consent of the users, we collected saliva samples and information on the prevalence of various diseases from more than 73,000 users of the genetic analysis service. The disease information is, for example, a numerical value that is 1 when the subject is afflicted with the disease and 0 when the subject is not afflicted with the disease. We identified the genotype data of each user from the saliva samples, and built a database that correlates the user's genotype data with various disease information. From this database, a case-control set of 103 subjects with the disease and 103 controls without the disease was constructed.

α＝０．１

Next, the genotypes of each SNP in this SNP set of subjects and controls were classified into two homozygous types (AA, BB) and a heterozygous type (AB). Then, if the genotype matches the _genotype in the _{shaded conversion} table shown in FIG. It was used as an explanatory variable of the logistic regression model represented by (1). For example, in the case of rs4820268, the value of x1 is set to ₁ when the genotype is "AG", and the value of x1 is set to ₀ when the genotype is "AA" or "GG". Note that N=10 in this embodiment. The objective variable of the logistic regression model represented by the following formula was a value p (morbidity information) between 0 and 1 representing the probability of being afflicted with this disease.

α=0.1

1. Validation of model by AUC The accuracy of the determination method using this SNP set will be described. From the above database, a data set was created for testing, in which user's genotype information and disease information were associated with each other. The genotype of each SNP in this SNP set of each user in the data set is classified into homozygous type (AA, BB) and heterozygous type (AB), and each classified genotype is shaded in FIG. The value of x _i was evaluated as 1 when it matched the genotype obtained by the calculation, and evaluated as 0 when it did not match, and x ₁ to x _N were calculated as feature amounts.

Input the feature amount related to this SNP set for each user into the above logistic regression model (hereinafter also referred to as "determination model"), predict whether each user is suffering from this disease, and detect the false positive A rate and a true positive rate were calculated, and ROC (Receiver Operating Characteristic) curve and AUC (Area Under the Curve) were determined respectively. More specifically, 5-fold cross-validation is performed on the decision model, five ROC curves (ROC fold 1 to ROC fold 5) are obtained, and the mean (Mean ROC) and standard deviation (± 1 std. dev.) are calculated. asked. The dashed line (Luck) in FIG. 3 indicates the case of randomly outputting whether or not the subject has the disease, and corresponds to the ROC curve of a model with no predictive ability.

Similarly, a logistic regression model (hereinafter also referred to as a "comparative determination model") was created for each comparison SNP set, excluding one SNP from the main SNP set. Then, input the feature amount related to each comparative SNP into each comparative judgment model, predict whether each user is suffering from this disease, calculate the false positive rate and true positive rate, and calculate the ROC curve and AUC. sought each. The results are shown in FIG. 4 and subsequent figures.

When the disease was determined using this SNP set, the AUC was 0.80 ± 0.07, which was significantly higher than the case of random output (AUC = 0.5). It can be confirmed that the prediction ability of the model is high.

On the other hand, in the case of the comparative decision model using each comparative SNP set, the AUC is lower than in the case of using the present SNP set. Therefore, although the AUC of the comparative decision model using each comparative SNP set was higher than the case of random output (AUC=0.5), the AUC of the decision model using this SNP set (0.80±0.07) can be seen to be generally lower than

Therefore, by using all the SNPs included in this SNP set, it is possible to determine whether or not the patient is suffering from the disease with higher accuracy than when using each comparison SNP set excluding one SNP from this SNP set. was found to be predictable.

2. Verification by Wilcoxon Rank Sum Test In order to confirm that the judgment model using this SNP set is significantly superior to the comparison judgment model using each comparative SNP set, Wilcoxon rank sum test, which is a kind of nonparametric test, was performed. A sum test was performed. Specifically, a null hypothesis that there is no difference between the AUC of the judgment model using this SNP set and the AUC of the comparison judgment model using each comparison SNP set is set, and the significance level is set to 0.01, and the Wilcoxon rank A sum test was performed.

As a result, the p-value was 4.20×10 ⁻¹⁸ in the comparative SNP set excluding rs2853677 (TT) and 4.08×10 ⁻¹⁸ in the comparative SNP set excluding rs6570507 (AG). , 3.96×10 ⁻¹⁸ and 4.20×10 ⁻¹⁸ at maximum in other comparison SNP sets, confirming that the null hypothesis is rejected. That is, there is a statistically significant difference between the AUC of the judgment model using this SNP set and the AUC of the comparison judgment model using each comparison SNP set, and the judgment model using this SNP set has each comparison SNP set. It can be said that this model is superior to the comparative judgment model used.

As described above, the method of the present embodiment has the effect that the accuracy of predicting whether or not the person has the disease is significantly higher than the accuracy of random prediction. In addition, the method of the present embodiment has a significant difference between the determination result of the disease based on the genotype information of the present SNP set and the determination result of the disease based on the genotype information of the comparative SNP set. have the effect of being This effect is considered to be based on the potential correlation between the genotype information of this SNP set and this disease, which has not been found so far. The logistic regression model exemplified above and other models are based on the genotype information of this SNP set, and the genotype data and disease information of humans affected by this disease and those not affected by this disease are used as learning data. It is obtained by machine learning the parameters using as In other words, any model is just one phenotype that expresses the potential correlation, and the type of model used in implementing the method of the present embodiment is not particularly limited.

The method of the present invention contributes to determining the risk of this disease and its prevention and/or treatment in fields related to medicine and healthcare.

Claims (2)

rs4820268, which has a positive correlation with anorexia, using a machine-learned model using genotype data of a human suffering from anorexia and genotype data of a human not suffering from anorexia as learning data, rs6570507, rs4680, rs2853677, and rs4273077 negatively correlated with anorexia rs10937405 positively or negatively correlated with anorexia genotype information,
rs10937405 genotype is TC, rs4820268 genotype is AG, rs7097 genotype is AA, rs3809060 genotype is TT, rs6010620 genotype is AA, rs10937405 genotype is CC, rs6570507 genotype is AG, rs4680 genotype of GG, genotype of rs2853677 is TT, and genotype of rs4273077 is AG. 12. The method of claim 1, wherein the body fluid sample, cell sample or hair of the subject undergoing risk determination is used.

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