How to obtain information on the risk of severe progression in broad-based primary open-angle glaucoma.

Abstract

To provide methods for obtaining information on the risk of the exacerbation of broad-defined primary open-angle glaucoma.SOLUTION: The present invention provides a method for obtaining information on the risk of exacerbation of broad-defined primary open-angle glaucoma, comprising: an allele measurement step of measuring alleles for at least 20 SNPs selected from SNPs identified by SNP IDs listed in Table 1 using a biological sample collected from a subject; and an information obtaining step of obtaining information on the risk of exacerbation of the broad-defined primary open-angle glaucoma in the subject based on the allele measurement results.SELECTED DRAWING: Figure 2A

Description

[Technical Field]

[0001] This invention relates to a method for obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma. [Background technology]

[0002] Broadly defined primary open-angle glaucoma is a disease in which retinal nerve cells are damaged, and it progresses slowly and irreversibly over a long period of time. It is empirically known that some patients' symptoms stabilize, while others worsen.

[0003] Patent Document 1 describes evaluating single nucleotide polymorphisms (SNPs) as a genetic factor in glaucoma patients. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] WO2008 / 130009 publication [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] However, Patent Document 1 evaluates the risk of developing the disease and the risk of progression, but does not evaluate the risk of severe disease. The object of the present invention is to provide a method for obtaining information on the risk of severe disease progression in broad-sense primary open-angle glaucoma. [Means for solving the problem]

[0006] The present invention relates to the following [1] to [2]. [1] An allele measurement step in which alleles are measured for at least 20 SNPs selected from the SNPs identified by the SNP IDs listed in Table 1 using a biological sample taken from the subject, and An information acquisition step to obtain information regarding the risk of severe progression of broad-sense primary open-angle glaucoma in the subject, based on the measurement results of the allele. Methods for obtaining information on the risk of severe progression of broad-based primary open-angle glaucoma, including [specific example]. [2] A method for obtaining information on the risk of progression of broad primary open-angle glaucoma, comprising an allele measurement step of measuring alleles for at least 20 SNPs selected from SNPs identified by the SNP IDs listed in Table 1 using a biological sample taken from a subject, wherein the allele measurement results serve as an indicator of the risk of progression of broad primary open-angle glaucoma in the subject. [Effects of the Invention]

[0007] This invention makes it possible to accurately determine the risk of severe progression of broad-sense primary open-angle glaucoma in a subject. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a schematic diagram of a DNA microarray used for allele measurement. [Figure 2A] Figure 2A shows an example of a graph used to determine the risk of severe illness when using sample group A, measuring SNPs selected from Table 1 to learn the cutoff value, with 60 SNPs and one analysis. In the bar graph below, open bars represent the mild case group, and filled bars represent the severe case group. [Figure 2B] Figure 2B shows an example of validation for sample group B using the cutoff value obtained in the learning process described above. [Figure 3] Figure 3 shows an example of a graph used to individually determine the risk of severe illness when using sample group A, measuring SNPs selected from Table 1, with 60 SNPs and 3 analyses performed. [Figure 4] Figure 4 shows an example of a graph used to comprehensively assess the risk of severe illness when using sample group A, measuring SNPs selected from Table 1, with 60 SNPs and 3 analyses. [Figure 5A]FIG. 5A is a diagram showing an example of the result of using Bayes' theorem for determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1, with the number of SNPs being 60 and the number of analyses being 3 times. [Figure 5B] FIG. 5B is a diagram showing a verification example using Specimen Group B. [Figure 6A] FIG. 6A is a diagram showing an example of a graph used for determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1 to learn the cut-off value, with the number of SNPs being 90 and the number of analyses being 1 time. [Figure 6B] FIG. 6B is a diagram showing a verification example in Specimen Group B based on the cut-off value obtained in the above learning. [Figure 7] FIG. 7 is a diagram showing an example of a graph used for individually determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1, with the number of SNPs being 90 and the number of analyses being 3 times. [Figure 8] FIG. 8 is a diagram showing an example of a graph used for comprehensively determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1, with the number of SNPs being 90 and the number of analyses being 3 times. [Figure 9A] FIG. 9A is a diagram showing an example of the result of using Bayes' theorem for determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1, with the number of SNPs being 90 and the number of analyses being 3 times. [Figure 9B] FIG. 9B is a diagram showing a verification example using Specimen Group B. [Figure 10A] FIG. 10A is a diagram showing an example of a graph used for determining the risk of deterioration when using Specimen Group A, measuring SNPs selected from Table 1 to learn the cut-off value, with the number of SNPs being 120 and the number of analyses being 1 time. [Figure 10B] FIG. 10B is a diagram showing a verification example in Specimen Group B based on the cut-off value obtained in the above learning. [Figure 11]FIG. 11 is a diagram showing an example of a graph used to individually determine the risk of exacerbation when using Specimen Group A, measuring SNPs selected from Table 1, setting the number of SNPs to 120, and performing the analysis three times. [Figure 12] FIG. 12 is a diagram showing an example of a graph used to comprehensively determine the risk of exacerbation when using Specimen Group A, measuring SNPs selected from Table 1, setting the number of SNPs to 120, and performing the analysis three times. [Figure 13A] FIG. 13A is a diagram showing an example of the result of using Bayes' theorem to determine the risk of exacerbation when using Specimen Group A, measuring SNPs selected from Table 1, setting the number of SNPs to 120, and performing the analysis three times. [Figure 13B] FIG. 13B is a diagram showing a verification example using Specimen Group B.

Mode for Carrying Out the Invention

[0009] As used herein, "broad POAG" refers to primary open-angle glaucoma in a broad sense (primary open-angle glaucoma in a broad sense, broad POAG; primary open-angle glaucoma in a narrow sense and normal tension glaucoma, Japanese Ophthalmological Society Journal, Vol. 116, No. 1, pp. 15-18).

[0010] As used herein, "risk allele" refers to an allele that is more frequent in the severe visual field defect group than in the mild visual field defect group among alleles associated with broad POAG. "Non-risk allele" refers to an allele that is not a risk allele.

[0011] In this specification, the broad-sense POAG severe visual field defect group (sometimes abbreviated as "severe group") refers to specimens that meet the criteria for grade 5 or 6 on the Aulhorn classification Greve modification or grade 5a or 5b on the Kozaki classification for two consecutive tests. On the other hand, the broad-sense POAG mild visual field defect group (sometimes abbreviated as "mild group") refers to specimens that meet the criteria for grade 0, 0-1 or 1 on the Aulhorn classification Greve modification or grade 1a or 1b on the Kozaki classification for two consecutive tests. In both cases, if both eyes are examined, the eye with the worse condition is used.

[0012] In this specification, "severe broad-sense POAG group" refers to the group of patients with broad-sense POAG classified as having severe visual field defects. Hereafter, this may simply be referred to as the "severe group." "mild broad-sense POAG group" refers to the group of patients with broad-sense POAG classified as having mild visual field defects. Hereafter, this may simply be referred to as the "mild group."

[0013] In this specification, "severity of broad-sense POAG" means that the visual field defects in POAG patients become more severe. "Risk of severe broad-sense POAG" means the risk that the subject's broad-sense POAG will become more severe.

[0014] [Methods for identifying marker SNPs and selected SNPs] SNPs identified by the SNP IDs listed in Table 1 (hereinafter abbreviated as "marker SNPs") used in the allele measurement process, and specific SNPs selected from the marker SNPs (hereinafter abbreviated as "selected SNPs"), can be found as follows.

[0015] (1) Method for identifying candidate SNPs for markers Genomic DNA will be extracted from the blood of both the high-severity and mild-severity groups. Genomic DNA in blood can be extracted by any known method. For example, DNA can be extracted by lysing cells to release the DNA, attaching it to the surface of silica-coated magnetic beads, and then separating and recovering it using magnetism.

[0016] The means for identifying alleles in SNPs within the extracted DNA sample are not particularly limited and can be appropriately selected from known SNP detection methods in this technology.

[0017] This section describes a method using genome-wide association studies (GWAS). Specifically, this can be performed using a DNA microarray containing SNPs distributed throughout the entire genome (for example, Affymetrix's Genome-Wide Human SNP Array 6.0). In this case, quality control may be used to select the SNPs to be extracted.

[0018] For quality control, it is desirable to select SNPs with call rates of 85% or higher, 90% or higher, or 95% or higher as criteria for SNP inclusion or exclusion. In addition, it is desirable to exclude SNPs with a minor allele frequency (MAF) of less than 0.01, and SNPs whose genotype distribution deviates significantly from the Hardy-Weinberg equilibrium (HWE) (false discovery rate of less than 0.001) from the candidates.

[0019] The SNPs selected in quality control can be further refined. For example, by performing a chi-squared test using statistical software, the p-value can be preferably 1 × 10⁻⁶. -3 The following is more preferably 3 × 10 -4 More preferably 1 × 10 -4 Select an SNP that satisfies the following conditions:

[0020] Next, for the extracted SNPs, a two-dimensional cluster plot analysis can be performed to exclude genotyping-poor SNPs. For example, by visually observing the cluster plot images obtained from genotyping software (e.g., Affymetrix's Genotyping Console), genotyping-poor SNPs can be excluded, and candidate marker SNPs can be determined.

[0021] The SNPs selected in this way can be referenced to databases of known sequences and SNPs, such as GenBank and dbSNP, to obtain information about their genomic location, sequence information, the gene in which the SNP resides or nearby genes, whether it is an intron or exon and its function if it resides on a gene, and homologous genes in other species.

[0022] (2) Method for identifying marker SNPs Next, marker SNPs can be extracted from the candidate marker SNPs obtained above. Specifically, marker SNPs can be extracted by quantifying the genotype data of the candidate marker SNPs.

[0023] In the quantification process, for example, genotype data and risk allele databases are referenced. For a given allele included in the genotype data, a numerical value of 2 is assigned if the risk allele is homozygous, a numerical value of 1 if the risk allele is heterozygous, and a numerical value of 0 if the non-risk allele is homozygous. The obtained numerical values ​​are then normalized using the mean frequency and observed frequency of each allele with the following formula to create a quantified genotype data matrix for the selected SNP.

[0024]

number

[0025] A risk allele refers to an allele that appears frequently in the high-risk group. In this specification, risk alleles are defined based on the odds ratio. The odds ratio is generally the ratio of the proportion of people with the risk factor to the proportion of people without it in the high-risk group, i.e., the odds, divided by the odds similarly calculated in the low-risk group, and is often used in case-control studies like the present invention. In this specification, the odds ratio is determined based on allele frequency and can be calculated by dividing the ratio of the occurrence frequency of a certain allele to the occurrence frequency of other alleles in the high-risk group by the ratio of occurrence frequencies similarly obtained in the low-risk group. The occurrence frequency of each allele can be recalculated as needed, for example, by adding and updating data as subject data is acquired.

[0026] Next, cluster analysis can be performed using the digitized genotype data matrix. For example, considering linkage disequilibrium (LD) between SNPs, SNPs that appear to have high independence can be determined by principal component analysis (PCA). Known methods can be used for principal component analysis. For example, for the SNPs selected above, information reduction is performed in the whole genome or chromosome, respectively, to calculate factor loadings (corresponding to the correlation coefficient between the principal component and the original variable). Then, candidate regions are determined based on these loadings, and the SNP with the lowest P value within that region is selected as the candidate SNP. In this way, the marker SNP in the present invention can be determined. However, SNPs that overlap between calculations from the whole genome and calculations from each chromosome are excluded. The extracted marker SNPs and their related information are shown in Table 1 below (in this specification, Tables 1-1 to 1-33 are collectively referred to as "Table 1"). Marker SNPs are identified by the number listed in the "SNP ID" column of Table 1. The genomic locations of these SNPs are listed in the "Chromosome" and "Location" columns of the table. The probe sequences used to detect alleles of each SNP are listed in the "Probe Sequence" column of the table. Risk alleles and non-risk alleles of the probe sequences are indicated in square brackets. Sequence numbers for sequences containing risk alleles are listed in the "Sequence Number A" column, and sequence numbers for sequences containing non-risk alleles are listed in the "Sequence Number B" column.

[0027] [Table 1-1]

[0028] [Table 1-2]

[0029] [Table 1-3]

[0030] Table 1-4

[0031] Table 1-5

[0032] Table 1-6

[0033] Table 1-7

[0034] Table 1-8

[0035] Table 1-9

[0036] Table 1-10

[0037] Table 1-11

[0038] Table 1-12

[0039] Table 1-13

[0040] Table 1-14

[0041] Table 1-15

[0042] Table 1-16

[0043] Table 1-17

[0044] Table 1-18

[0045] Table 1-19

[0046] Table 1-20

[0047] Table 1-21

[0048] Table 1-22

[0049] Table 1-23

[0050] Table 1-24

[0051] [Table 1-25]

[0052] [Table 1-26]

[0053] [Table 1-27]

[0054] [Table 1-28]

[0055] [Table 1-29]

[0056] [Table 1-30]

[0057] [Table 1-31]

[0058] [Table 1-32]

[0059] [Table 1-33]

[0060] Next, a method for further selecting specific SNPs from the marker SNPs in this specification will be described.

[0061] (3) Method for finding selected SNPs The selected SNPs can be determined by appropriately selecting at least 20 SNPs from the marker SNPs obtained above. When performing the analysis multiple times, for example, the χ² of chromosome number and allele can be used. 2 The results of the test are used as the basis. For example, if three analyses are performed, the first analysis will use SNPs located at chromosome numbers 1, 4, 7, 10, 13, 16, 19, and 22; the second analysis will use SNPs located at chromosome numbers 2, 5, 8, 11, 14, 17, and 20; and the third analysis will use SNPs located at chromosome numbers 3, 6, 9, 12, 15, 18, and 21 to determine the χ². 2 By performing the tests sequentially on those with low p-values, the selected SNP can be determined. Specific examples of selected SNPs will be shown in the examples described later.

[0062] [Allergen measurement process] In the allele measurement step, alleles are measured for at least 20 SNPs selected from the SNP IDs listed in Table 1 (i.e., marker SNPs) using a biological sample taken from the subject. The number of SNPs used in the allele measurement step may be at least 20, and may be 30 or more, 40 or more, 50 or more, or 60 or more.

[0063] Any biological sample from which genome-derived DNA can be extracted can be used in this invention. For example, one or more samples selected from the group consisting of whole blood, leukocytes, lymphocytes, plasma, serum, lymph, tears, saliva, nasal secretions, cerebrospinal fluid, bone marrow fluid, semen, sweat, mucosal tissue, skin tissue, and hair follicles can be used. Any known method can be used to extract DNA from such biological samples.

[0064] Regarding the SNP to be measured, the allele can be determined and the measurement result obtained according to known methods. Specifically, for example, each allele can be detected by hybridizing a probe specific to each allele (Table 2, Table 3, or Table 4 (in this specification, Tables 4-1 and 4-2 are collectively referred to as "Table 4")), designed based on the sequence information of the selected SNP, with DNA from the biological sample and detecting the resulting signal. Examples of methods for hybridizing using probes include the Tuckman method, Invader® method, Light cycler method, cyclin probe method, MPSS method, bead array method, DNA chip method, and microarray method. It is also possible to detect alleles without performing hybridization with probes. For example, PCR-RFLP method, SSCP method, mass spectrometry, next-generation sequencing method, and direct sequencing method can be used. These methods can be carried out according to known conditions.

[0065] Figure 1 is a schematic diagram of an example of a DNA microarray used for allele measurement. DNA microarray 1 has the probes listed in Table 1 immobilized on it and is designed to perform allele measurement of each SNP listed in Table 1. One embodiment of the present invention is the use of this microarray to generate information on the risk of severe broad POAG in a subject.

[0066] The measurement results thus obtained are then used in the next information acquisition process. Furthermore, these measurement results can serve as an indicator of the risk of severe POAG in the subject, according to the criteria shown in the information acquisition process section below.

[0067] [Information acquisition process] In the information acquisition step, information regarding the risk of severe POAG in the subject is obtained based on the allele measurement results. In this specification, information regarding the risk of severe POAG is, for example, information regarding the risk of severe illness. Here, the risk of severe illness may be expressed as the probability of developing severe illness. Furthermore, the risk of severe illness may be expressed in multiple stages such as high risk and low risk. The information acquisition step may include a step of determining the risk of severe illness.

[0068] From the viewpoint of improving judgment accuracy, the information acquisition process preferably includes a step of determining whether the measured allele is a risk allele, a step of calculating the total number of risk alleles, and a step of acquiring information on the risk of severe progression of broad-sense primary open-angle glaucoma based on the total number of risk alleles. Based on the "risk allele" information in Table 1, it is possible to determine whether the measured allele is a risk allele and calculate the total number of risk alleles (also called the number of risk alleles).

[0069] When obtaining information on the risk of severe development of broad-sense primary open-angle glaucoma based on the total number of risk alleles, it is preferable to use a predetermined cutoff value corresponding to the total number of risk alleles. For example, the total number of risk alleles may be compared with a predetermined cutoff value. If the total number of risk alleles is greater than or equal to the predetermined cutoff value, information may be obtained indicating that the subject has a high risk of severe development of broad-sense primary open-angle glaucoma. If the total number of risk alleles is less than the predetermined cutoff value, information may be obtained indicating that the subject has a low risk of severe development of broad-sense primary open-angle glaucoma.

[0070] For example, one method involves providing information that if the results regarding the subject obtained in the information acquisition process (number of risk alleles) are above the cutoff value determined in advance by ROC (Receiver Operating Characteristic) analysis based on the selected SNPs used in the allele measurement process, the subject has a high risk of developing severe broad-sense POAG, and if the results are below the cutoff value, the risk is low. More specifically, for example, the number of risk alleles in the subject is first compared to a predetermined cutoff value. In this specification, the cutoff value is an appropriate value that distinguishes between patients in the high-severity group and patients in the mild-severity group. By comparing this cutoff value with the subject's quantitative value, the subject's risk of developing severe broad-sense POAG can be determined.

[0071] (1) Setting the cutoff value The cutoff value can be set as follows. For example, when obtaining quantitative values ​​from a subject, the number of risk alleles can be measured using a biological sample taken from a subject who has been previously diagnosed as having a high risk of severe broad-sense POAG with respect to the same SNP selected as the measurement target. The correlation between the two data can then be analyzed by statistically processing the "risk of severe broad-sense POAG" and the "number of risk alleles." From the results of the analysis, the cutoff value can be set according to the purpose, such as whether to prioritize a high true positive rate (high sensitivity), a high true negative rate (high specificity), or the degree to which the true positive rate and true negative rate should be balanced. For example, if the selected SNP to be measured is different, the risk alleles present will naturally be different, so the cutoff value will vary depending on the selected SNP to be measured. Here, the true positive rate is the probability of correctly identifying individuals with a high risk of developing severe POAG (Provisional Orthopedic Aggregation) as having a high risk of developing severe POAG, while the true negative rate is the probability of correctly identifying individuals with a low risk of developing severe POAG as having a low risk of developing severe POAG.

[0072] As for specific methods for setting cutoff values, for example, first, for the same SNP selected as the measurement target when obtaining quantitative values ​​of the subject, an ROC curve is created with the true positive rate (sensitivity) on the vertical axis and the true negative rate (1-specificity) on the horizontal axis (ROC analysis is performed). Next, the point that is the minimum distance from the upper left corner of the graph may be set as the cutoff value, or the point that is furthest from the shaded line where the Area Under the Curve (AUC) is 0.5 may be set as the cutoff value, or any point that gives an arbitrary specificity or sensitivity may be set as the cutoff value. In this invention, it is preferable to set a cutoff value that gives a result that is closest to a sensitivity of 1 and (1-specificity) of 0, for example, [(1-sensitivity) 2 +(1-specificity) 2 The value that minimizes [the specified value] can be set as the cutoff value.

[0073] The cutoff value may be obtained separately at the same time as the quantitative values ​​of the subject are obtained, or it may be obtained in advance. Furthermore, when comparing it with the quantitative values ​​of the subject, the analysis results obtained so far may be added and updated as needed to obtain the cutoff value.

[0074] Furthermore, when setting the cutoff value, normalization and weighting may be performed from the perspective of improving the accuracy of the judgment. For example, as a method of normalization, a method of comparison with a normal distribution curve can be used. As a method of weighting, weighting can be performed by considering the odds ratio of each SNP.

[0075] By comparing the predetermined cutoff value with the subject's quantitative value, it is possible to determine whether the subject is at high risk of developing severe POAG (Provisional Oral Aggregation).

[0076] (2) Multiple analyses In the allele measurement step, if alleles are measured for at least 60 SNPs selected from the marker SNPs listed in Table 1, the information acquisition step can, for example, divide the at least 60 SNPs into multiple groups and analyze the allele measurement results. Analyzing the allele measurement results in multiple steps is preferable because it allows for the updating of prior probabilities by combining multiple discrimination results, which is expected to improve the positive predictive value and negative predictive value.

[0077] For example, the process includes obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma in the subject based on the measurement results of an allele of a first SNP group containing X SNPs out of 60 SNPs, an allele of a second SNP group containing Y SNPs different from those of the first SNP group out of 60 SNPs, and an allele of a third SNP group containing Z SNPs different from those of the first and second SNP groups out of 60 SNPs. X, Y, and Z can each be independently at least 20 integers.

[0078] When analyzing SNP groups by dividing them into a first SNP group, a second SNP group, and a third SNP group as described above, the information acquisition process, for example, includes the following steps. A process to determine whether the measured allele is a risk allele or not. The first calculation step involves calculating the total number of risk alleles in the first SNP group. The second calculation step involves calculating the total number of risk alleles in the second SNP group. The third calculation step involves calculating the total number of risk alleles in the third SNP group. The first comparison step involves comparing the total number of risk alleles obtained in the first calculation step with the first cutoff value. The second comparison step involves comparing the total number of risk alleles obtained in the second calculation step with the second cutoff value. A third comparison step involves comparing the total number of risk alleles obtained in the third calculation step with the third cutoff value, and A step of obtaining information on the risk of progression of broad-sense primary open-angle glaucoma in the subject, based on the comparison results in the first comparison step, the second comparison step, and the third comparison step.

[0079] When cutoff values ​​are used in each calculation process, for example, if the total number obtained in the first calculation process is greater than or equal to the first cutoff value, the total number obtained in the second calculation process is greater than or equal to the second cutoff value, and the total number obtained in the third calculation process is greater than or equal to the third cutoff value, then information is obtained that the subject has a high risk of developing severe primary open-angle glaucoma in the broad sense. If the total number obtained in the first calculation process is less than the first cutoff value, the total number obtained in the second calculation process is less than the second cutoff value, and the total number obtained in the third calculation process is less than the third cutoff value, then information is obtained that the subject has a low risk of developing severe primary open-angle glaucoma in the broad sense.

[0080] When analyzing SNPs by dividing them into multiple groups, the number of SNPs constituting each group may be the same or different. Furthermore, the selected SNPs in each group may or may not overlap. From the viewpoint of improving accuracy, it is preferable to have fewer overlapping SNPs. Specifically, the number of overlapping SNPs is preferably 10 or less, more preferably 5 or less, even more preferably 4 or less, even more preferably 3 or less, even more preferably 2 or less, even more preferably 1, and most preferably 0.

[0081] When analyzing SNPs by dividing them into multiple groups, it is possible to determine whether each group is positive or negative based on whether the total number of risk alleles in each group exceeds a predetermined cutoff value, and then calculate the number of such groups. When analyzing SNPs by dividing them into multiple groups, you may divide them into two groups, three groups, or even four or more groups.

[0082] When multiple groups composed of selected SNPs are used, one possible method is to provide information on the subject's risk of severe broad-sense POAG, using the analysis results (number of risk alleles held) for each group of subjects in the information acquisition process as an indicator, with a cutoff value predetermined by ROC analysis based on the selected SNPs used in the analysis for each group.

[0083] More specifically, for example, first, the number of risk alleles held by each subject is compared with a predetermined cutoff value for each group analysis to determine the risk of severe illness. The cutoff value can be set in the same manner as described above. Next, the results of the severe illness risk for each group analysis can be integrated.

[0084] For example, if the analysis is performed three times and a high risk of severe illness is displayed as "+" and a low risk as "-" for each analysis, the combined result of the first analysis being "+", the second analysis being "+", and the third analysis being "+" is "+++", the combined result of the first analysis being "+", the second analysis being "+", and the third analysis being "-" is "++-", and the combined result of the first analysis being "+", the second analysis being "-", and the third analysis being "+" is "+-+". Specifically, if the result is "+++", the risk of severe illness is determined to be high; if the result is "--", the risk of severe illness is determined to be low; and for all other results, the risk of severe illness can be determined to be moderate. In another embodiment, if there are two or more "+" results, the risk of severe illness is determined to be high; and if there are two or more "-" results, the risk of severe illness can be determined to be low.

[0085] Graphs showing the high-risk and low-risk groups for severe illness can be created as follows. For example, by obtaining the combined results for subjects who have been diagnosed in advance, the graphs can be created by aggregating the number of people with a high or low risk of severe illness according to the broad definition of POAG for each pattern such as "+++", "++-", and "+-+".

[0086] By obtaining information for the relevant category from the combined results of the subjects in the pre-created graphs of high-risk and low-risk groups for severe illness, it is possible to determine whether or not a subject is at high risk of developing severe illness in the broad sense of POAG.

[0087] (3) Process for calculating the probability that broad-sense POAG will become severe. Furthermore, the probability of broad-sense POAG becoming severe may be calculated by applying Bayes' theorem, described later, to the measurement results of the allele. This specification may further include a step for calculating such a probability.

[0088] A specific method for calculating the probability of broad-sense POAG becoming severe is, for example, to determine the risk of broad-sense POAG becoming severe by comparing the number of risk alleles with the cutoff value in the same manner as described above. Then, the probability of becoming severe is calculated by applying Bayes' theorem to the obtained allele measurement results. In Bayes' theorem method, the posterior probability (positive predictive value, negative predictive value) can be obtained by combining the prior probability (prevalence) with the sensitivity and specificity described above. Therefore, in this specification, the probability of broad-sense POAG becoming severe is expressed as a posterior probability. In other words, if Bayes' theorem method is not used, the probability of becoming severe is the same as the prevalence, but by using Bayes' theorem method, if a positive result is obtained by the test using the method of the present invention, the probability that the tested person will become severe can be expressed by the positive predictive value.

[0089] Specifically, the positive predictive value can be calculated as follows: Positive predictive value = Prevalence × Sensitivity / [Prevalence × Sensitivity + (1 - Prevalence) × (1 - Specificity)], and Negative predictive value = Specificity × (1 - Prevalence) / [Specificity × (1 - Prevalence) + Prevalence × (1 - Sensitivity)]. For example, if the prevalence is 10%, and the sensitivity and specificity of the test using the method of the present invention are 70%, the positive predictive value is calculated as 21% and the negative predictive value as 95%. Therefore, the risk of severe illness for a test subject who tests positive is 21%, which is higher than the prevalence, and further consultation can be advised. Also, for example, if the above test is performed in combination three times, the positive predictive value is calculated as 62% and the negative predictive value as 99%. Therefore, a test subject whose number of risk alleles exceeds the cutoff value in the third test can be determined to have a higher risk of severe illness from broad-sense POAG. By making a determination that includes the influence of prevalence as part of the overall background of the tested individuals, it becomes possible to present the probability of a broad POAG (Pregnancy-Oriented Aggregation) in the tested individuals becoming severe.

[0090] Alternatively, the Bayesian theorem can be applied to the allele measurement results to calculate the probability of severe illness (mean severe illness risk, 95% confidence interval) based on the number of risk alleles present, and further calculate the probability that broad POAG will become severe with a probability of being greater than or equal to a predetermined percentage (%). Here, the percentage can be set to any value, such as 70%, 80%, or 90%, and the higher the value, the more accurate the determination can be made.

[0091] More specifically, for each pattern of integrated results obtained from pre-diagnosed subjects, such as "+++", "++-", and "+-+", Bayes' theorem is used to calculate the probability density function of broadly defined POAG becoming severe. For example, to calculate a probability of 70% or higher, the area covered by the probability density function where the probability of becoming severe (a continuous random variable) is 70% or higher represents the probability of becoming severe.

[0092] Thus, in this specification, not only can it be determined whether the risk of progression of generalized POAG in a subject is high by comparing the total number of risk alleles with a cut-off value, but information regarding the risk of progression of generalized POAG in the subject can be provided in more detail by calculating the probability thereof.

[0093] By a follow-up study of the determination results obtained using the method of the present invention, it is possible to obtain a result as to whether generalized POAG has progressed. Based on such a follow-up study, accumulation and updating (additional learning) of data can be performed, and replacement of marker SNPs can be carried out. Additional learning updates the Bayesian prior distribution π(θ) using the determination results newly obtained by a follow-up study. A correlation analysis (χ test using allele data) in which the genotype data newly obtained by a follow-up study is added to the conventional results can be carried out, and replacement of marker SNPs can be carried out. Further, when replacement is carried out, the cut-off value may be reset by recalculating the ROC analysis of each analysis in the "information acquisition step", and the Bayesian prior distribution in the "information acquisition step" may be forgotten and recalculated as a uniform distribution (non-informative prior distribution). 2 When the result of the χ test using allele data changes due to accumulation and updating of data using genotype data, replacement of marker SNPs can be carried out. 2

[0094]

[0095] The steps according to a preferred embodiment in this information acquisition step may include an allele measurement step of measuring alleles for at least 20 SNPs selected from the SNPs specified by the SNP IDs shown in Table 1 using a biological sample collected from a subject, and the measurement result of the alleles may be included in a method for obtaining information regarding the risk of progression of generalized primary open-angle glaucoma, which serves as an index for the risk of progression of generalized primary open-angle glaucoma in the subject. [[ID=​​The method of this embodiment may further include a step of providing the information obtained in the information acquisition step described above. In the information provision step, the information obtained in the aforementioned information acquisition step is provided, for example, to the subject, a physician, or a paramedical staff member as information to assist in determining the subject's risk of severe POAG (Provisional Orthopedic Aggregation) in the subject. Examples of how the information may be provided include outputting to a computer screen, including a mobile device, or printing. [Examples]

[0096] The present invention will be specifically described below with reference to examples. These examples are merely illustrative of the present invention and do not imply any limitation. In the following examples, for commonly used molecular biological techniques that are not described in particular detail, refer to methods and conditions described in textbooks such as Molecular Cloning (Joseph Sambrook et al., Molecular Cloning - A Laboratory Manual, 3rd Edition, Cold Spring Harbor Laboratory Press, 2001).

[0097] Example 1: Selection of Marker SNPs Patients diagnosed with broad-sense POAG were classified into two groups: a mild visual field defect group and a severe visual field defect group (including surgical cases). All surgical cases were classified as severe. Blood samples were collected from the patient group and designated as sample group A and sample group B. Sample group A included 81 patients judged to be in the mild group and 372 patients judged to be in the severe group (247 of whom underwent surgery). Sample group B included 157 patients judged to be in the mild group and 388 patients judged to be in the severe group (210 of whom underwent surgery). Genomic DNA was extracted from these blood samples using a commercially available automated nucleic acid extractor. Genomic DNA extraction was performed according to the instruction manuals for the instrument and kit. Using this method, approximately 5 μg of genomic DNA was obtained from 350 μL of blood sample.

[0098] For SNP analysis, genotype data was acquired using a commercially available microarray-type SNP analysis kit (DNA microarray, Genome-Wide Human SNP Array 6.0 (Affymetrix) or Asian Screening Array-24 v1.0 BeadChip (Illumina)) capable of analyzing approximately 1 million known SNPs on the human genome. High-precision SNP data was then selected using a QC filter (Call Rate ≥ 0.95; MAF ≥ 0.01; HWE ≥ 0.001).

[0099] Furthermore, 420 marker SNPs (Table 1) were extracted based on the following conditions. (1) Genome-wide association study (χ² using allele data) 2 When using the statistical test, P < 0.05 was obtained for both sample group A and sample group B, and MAF ≥ 0.05 (157 SNPs were extracted as a result of performing (4) to (6) below). (2) Genome-wide association study (χ² using allele data) 2 When using the statistical test, in sample group A, P < 0.001 (except for (1)), the SNP was found in sample group B, the risk allele of the SNP is consistent with the SNP in question, and the MAF ≥ 0.1 (as a result of performing (4) to (6) below, 75 SNPs were extracted). (3) Genome-wide association study (χ² using allele data) 2 When using the statistical test, in sample group B, P < 0.001 (except for (1)) and MAF ≥ 0.1 were obtained (as a result of performing (4) to (6) below, 188 SNPs were extracted).

[0100] (4) For all extracted SNPs, cluster-poor SNPs were excluded by visual inspection by three examiners based on 2D cluster plot images obtained from Affymetrix's genotyping software (Genotyping Console). In addition, cluster-poor SNPs were manually corrected by visual inspection by three examiners based on 2D cluster plot images obtained from Illumina's genotyping software (GenomeStudio). (5) The genotyping data was coded (converted to numerical values) and normalized using the following procedure. (a) Risk Allele Homo: 2, Risk Allele Hetero: 1, Other Allele Homo: 0. (b) For numerical transformation, the mean and observed allele frequencies were used in each of the high-severity and low-severity groups, and the numerical values ​​were normalized according to the formula described above.

[0101] (6) Combinations of candidate marker SNPs considering linkage disequilibrium (LD) were calculated using cluster analysis with principal component analysis (PCA). (a) Using the marker SNP candidates obtained in (5), all samples from the high-grade and mild-grade groups were subjected to PCA, and factor loadings (corresponding to the correlation coefficient between the principal component and the original variable) obtained by information reduction (Cluster SNP) on the samples were calculated. Next, candidate regions were determined by clustering based on the principal component showing the highest absolute value of the factor loading for each SNP, and the SNP that obtained the smallest P value within each candidate region was selected as the candidate marker SNP. (b) Using the candidates obtained in (5) for each chromosome, candidate regions were determined in each chromosome in the same manner as in (a), and these were designated as candidate marker SNPs. (c) Candidate marker SNPs from (a) and (b) were combined and duplicates were removed.

[0102] Example 1 For 80 mild cases and 80 severe cases (including surgical cases; hereinafter simply referred to as the "severe case") of sample group A, blood was collected in the same manner as in Test Example 1, and genomic DNA was extracted. Allele data was obtained by hybridization using the probes shown in Example 1 (Table 2) as described below, and the total number of risk alleles was calculated (risk allele homozygous = 2, risk allele heterozygous = 1).

[0103] [Table 2]

[0104] (Aspect 1) Measurements were performed using all 60 probes shown in Table 2 above, and the total number of risk alleles was calculated. A frequency distribution plot was created with the total number of risk alleles on the x-axis, and the results of ROC analysis are shown in Figure 2A. From Figure 2A, it can be seen that the AUC is 0.948 when the cutoff value is 68, which has a sensitivity of 85.0% and a specificity of 93.8%, and the frequency distribution plot also shows that the group with a high risk of severe illness and the group with a low risk of severe illness can be distinguished.

[0105] Next, to verify the performance of the above distinction, 140 mild cases and 140 severe cases from sample group B were tested in the same manner as sample group A.

[0106] The results are shown in Figure 2B. As shown in Figure 2B, when the cutoff value was 68, the sensitivity was 85.0%, the specificity was 73.6%, and the discrimination rate was 79.3%. Therefore, it can be seen that the method of Embodiment 1 had a high discrimination rate in the verification using sample group B. Note that the discrimination rate is

[0107]

number

[0108] (Aspect 2) Next, the probes used in the above embodiment 1 were divided into groups of 20, and the analysis process of allele measurement results for sample group A was carried out in three groups: first analysis, second analysis, and third analysis, to calculate the total number of risk alleles.

[0109] The frequency distribution plots and ROC curves of the risk alleles were obtained from the results in the same manner as in the first embodiment described above. The results are shown in Figure 3. From Figure 3, it was found that the results of the first analysis yielded 24 first cutoff values ​​with a sensitivity of 76.2% and a specificity of 82.5%, the results of the second analysis yielded 19 second cutoff values ​​with a sensitivity of 80.0% and a specificity of 68.8%, and the results of the third analysis yielded 24 third cutoff values ​​with a sensitivity of 93.8% and a specificity of 62.5%, and that judgment results could be obtained for each analysis.

[0110] Furthermore, Figure 4 shows the results of integrating the judgment results of the three analyses. Specifically, it shows the results of classifying the judgment results of the three analyses into groups with a high risk of severe illness and groups with a low risk of severe illness. For example, the leftmost graph in Figure 4 corresponds to the integrated result of "---", that is, the integrated result where the judgment result in the first analysis is "-", the judgment result in the second analysis is "-", and the judgment result in the third analysis is "-". Figure 4 shows that it is possible to distinguish between groups with a high risk of severe illness and groups with a low risk of severe illness. When a "+" result indicates two or more occurrences of the high risk of severe illness, this is defined as "high risk," and when a "-" result indicates two or more occurrences of the low risk of severe illness, this method allows for accurate classification between mild and severe cases.

[0111] (Aspects 3 and 4) In the same manner as in Embodiment 2, the judgment results for each group of sample group A were obtained, and the probability of developing severe illness (mean risk of severe illness, 95% confidence interval) was calculated based on the number of risk alleles held using Bayes' theorem (Embodiment 3). Furthermore, the area of ​​the probability density function using Bayes' theorem where the probability of developing severe illness is 70% or higher was calculated, and the probability of developing severe illness with a probability of 70% or higher was calculated (Embodiment 4). The results are shown in Figure 5A.

[0112] The analysis using Bayes' theorem was performed according to the following procedure. (a) The prior distribution π(θ) was assumed to be a uniform distribution (non-informative prior distribution). The beta distribution was adopted as the prior distribution. (b) Likelihoods were calculated by randomly selecting 80 cases from the mild group and 80 cases from the severe group from all samples in the mild and severe groups, and using the number of data points (observations) and the number of severe cases (positive cases) for each risk allele count. A binomial distribution was used. (c) The posterior distribution was calculated using Bayes' theorem (posterior distribution π(θ|D)∝ prior distribution × likelihood). (d) From the posterior distribution, the mean risk of severe illness (%), the 95% confidence interval (%), and the probability of severe illness occurring with a probability of 70% or greater (%) were calculated.

[0113] (Appendix 5) Next, to verify the above performance, three analyses were performed on 140 mild cases and 140 severe cases of sample group B, similar to the analysis performed on sample group A.

[0114]

number

[0115] The discrimination rate for the risk of severe illness was calculated using the formula shown. The results are shown in Figure 5B. The results show that the discrimination rate for high risk of severe illness was 80.2% when the judgment result was "+" three times, and 58.8%, 57.9%, and 65.3% when the judgment result was "+" twice. The discrimination rate for low risk of severe illness was 100.0% when the judgment result was "-" three times, and 81.0%, 85.7%, and 89.5% when the judgment result was "-" twice. From this, it can be seen that, in addition to the methods described in 1 and 2, the results contribute to providing information that will assist in the practical diagnosis of the risk of severe illness.

[0116] Example 2 Data acquisition was performed in the same manner as in Example 1, except that a different probe was used. Specifically, the probes shown in Table 3 below were used.

[0117] [Table 3]

[0118] (Aspect 1) The total number of risk alleles was calculated by using all 90 probes shown in Table 3 at once. The frequency distribution of risk alleles and the results of ROC analysis, performed in the same manner as in Embodiment 1 of Example 1, are shown in Figure 6A. From Figure 6A, it can be seen that the AUC is 0.971 when the cutoff value is 98, which has a sensitivity of 91.2% and a specificity of 90.0%, and the frequency distribution also shows that the group with a high risk of severe illness and the group with a low risk of severe illness can be distinguished.

[0119] Next, to verify the performance of the above distinction, 140 mild cases and 140 severe cases from sample group B were tested in the same manner as sample group A.

[0120] The results are shown in Figure 6B. As shown in Figure 6B, when the cutoff value was 98, the sensitivity was 98.6%, the specificity was 64.3%, and the discrimination rate was 81.4%. Therefore, the method of Embodiment 1 showed a high discrimination rate in the verification using sample group B, and it can be seen that it has higher sensitivity and discrimination rate compared to the method with 60 SNPs. The discrimination rate was calculated in the same manner as in Embodiment 1 of Example 1.

[0121] (Aspect 2) Next, the probes used in the above embodiment 1 were divided into groups of 30, and the analysis process of allele measurement results for sample group A was carried out in three groups: first analysis, second analysis, and third analysis, to calculate the total number of risk alleles.

[0122] The frequency distribution plots and ROC curves of the risk alleles were obtained from the results in the same manner as described above. The results are shown in Figure 7. From Figure 7, it can be seen that the results of the first analysis yielded 33 first cutoff values ​​with a sensitivity of 87.5% and a specificity of 80.0%, the results of the second analysis yielded 32 second cutoff values ​​with a sensitivity of 85.0% and a specificity of 73.8%, and the results of the third analysis yielded 34 third cutoff values ​​with a sensitivity of 77.5% and a specificity of 77.5%, indicating that judgment results can be obtained for each analysis.

[0123] Furthermore, Figure 8 shows the results of integrating the judgments from the three analyses described above. Specifically, it shows the results of classifying the judgments from the three analyses into groups with a high risk of severe illness and groups with a low risk of severe illness. From this, it can be seen that a distinction can be made between the groups with a high risk of severe illness and the groups with a low risk of severe illness.

[0124] (Aspects 3 and 4) In the same manner as in Embodiment 2, the judgment results for each group of sample group A were obtained, and then, in the same manner as in Embodiments 3 and 4 of Example 1, the mean risk of severe illness (%), the 95% confidence interval (%), and the probability of severe illness occurring with a probability of 70% or more (%) were calculated. The results are shown in Figure 9A. From this, it can be seen that 3 judgment results of "+" accounted for 100%, 2 judgment results of "+" accounted for 85.1%, 99.0%, and 64.5%, 3 judgment results of "-" accounted for 0.0%, and 2 judgment results of 0.7%, 0.2%, and 0.0%.

[0125] (Appendix 5) Next, to verify the performance described above, the discrimination rate regarding the risk of severe illness was calculated for 140 mild cases and 140 severe cases of sample group B, in the same manner as in Embodiment 5 of Example 1. The results are shown in Figure 9B. The results show that the discrimination rate for high risk of severe illness was 81.7% for 3 judgment results of "+", and 68.6%, 62.5%, and 50.0% for 2 judgment results of "+", while the discrimination rate for low risk of severe illness was 100.0% for 3 judgment results of "-", and 89.7%, 90.9%, and 87.0% for 2 judgment results of "-". From this, it was found that, in addition to Embodiments 1 and 2, the results will lead to the provision of information that will assist in the practical diagnosis of the risk of severe illness.

[0126] Example 3 Data acquisition was performed in the same manner as in Example 1, except that the probe used was different from that used in Examples 1 and 2. Specifically, the probes shown in Table 4 below were used.

[0127] [Table 4-1]

[0128] [Table 4-2]

[0129] (Aspect 1) The total number of risk alleles was calculated by using all 120 probes shown in Table 4 at once. The frequency distribution of risk alleles and the results of ROC analysis, performed in the same manner as in Embodiment 1 of Example 1, are shown in Figure 10A. From Figure 10A, it can be seen that the AUC is 0.980 when the cutoff value is 128, which has a sensitivity of 92.5% and a specificity of 93.8%, and that the frequency distribution also shows that the group with a high risk of severe illness and the group with a low risk of severe illness can be distinguished.

[0130] Next, to verify the performance of the above distinction, 140 mild cases and 140 severe cases from sample group B were tested in the same manner as sample group A.

[0131] The results are shown in Figure 10B. As shown in Figure 10B, when the cutoff value was 128, the sensitivity was 92.9%, the specificity was 70.0%, and the discrimination rate was 81.4%. Therefore, it can be seen that the method of Embodiment 1 showed a high discrimination rate in the verification using sample group B, and demonstrated higher sensitivity and discrimination rate compared to 60 SNPs, and higher specificity compared to 90 SNPs. The discrimination rate was calculated in the same manner as in Embodiment 1 of Example 1.

[0132] (Aspect 2) Next, the probes used in the above embodiment 1 were divided into groups of 40, and the analysis process of allele measurement results for sample group A was carried out in three groups: first analysis, second analysis, and third analysis, to calculate the total number of risk alleles.

[0133] The frequency distribution plots and ROC curves of the risk alleles were obtained from the results in the same manner as described above. The results are shown in Figure 11. From Figure 11, it was found that the results of the first analysis yielded 41 first cutoff values ​​with a sensitivity of 87.5% and a specificity of 83.8%, the results of the second analysis yielded 42 second cutoff values ​​with a sensitivity of 76.2% and a specificity of 83.8%, and the results of the third analysis yielded 45 third cutoff values ​​with a sensitivity of 90.0% and a specificity of 78.8%, indicating that judgment results could be obtained for each analysis.

[0134] Furthermore, Figure 12 shows the results of integrating the judgments of the three analyses described above. Specifically, it shows the results of classifying the judgments of the three analyses into groups with a high risk of severe illness and groups with a low risk of severe illness. From this, it can be seen that a distinction can be made between the groups with a high risk of severe illness and the groups with a low risk of severe illness.

[0135] (Aspects 3 and 4) In the same manner as in Embodiment 2, the judgment results for each group of sample group A were obtained, and then, in the same manner as in Embodiments 3 and 4 of Example 1, the mean risk of severe illness (%), the 95% confidence interval (%), and the probability of severe illness occurring with a probability of 70% or more (%) were calculated. The results are shown in Figure 13A. From this, it can be seen that 3 judgment results of "+" account for 100%, 2 judgment results of "+" account for 70.3%, 97.2%, and 98.0%, 3 judgment results of "-" account for 0.0%, and 2 judgment results of 1.1%, 0.0%, and 0.0%.

[0136] (Appendix 5) Next, to verify the performance described above, the discrimination rate for the risk of severe illness was calculated for 140 mild cases and 140 severe cases in sample group B, in the same manner as in embodiment 5 of Example 1. The results are shown in Figure 13B. The results show that the discrimination rate for high risk of severe illness was 87.4% for 3 judgment results of "+", and 73.3%, 58.1%, and 68.4% for 2 judgment results of "+", while the discrimination rate for low risk of severe illness was 100.0% for 3 judgment results of "-", and 88.2%, 82.6%, and 91.2% for 2 judgment results of "-". From this, it can be seen that, in addition to embodiments 1 and 2, the results contribute to providing information that will assist in the practical diagnosis of the risk of severe illness. [Explanation of symbols]

[0137] 1 DNA microarray

Claims

1. Using biological samples collected from subjects, the following SNP IDs were used: rs10918342; rs16845638; rs1124070; rs4581328; rs11122331; rs757588; rs9524181; rs2341354; rs12761627; rs12268054; rs4951338; rs1535565; rs478410; rs4487396; rs1149332; rs650169; rs4721867; rs822625; rs11252457; rs4699365; rs2412970; rs12247999; rs4699667; rs75479 97; rs10242598; rs11672223; rs2421169; rs3003542; rs2888830; rs12029109; rs826474; rs4861656; rs3006268; 4; rs12640858; rs1201122; rs7204239; rs2480054; rs4490256; rs10137988; rs1459139; rs1113405; rs8014067; 22; rs2725338; rs2052074; rs1679870; rs17167799; rs1386234; rs1253113; rs16900519; rs10166301; rs2555143; 6841; rs2288183; rs7123826; rs11601571; rs7142344; rs11867479; rs6106895; rs7152384; rs2147866; rs41435651; rs6884797; 017940; rs1051920; rs6543289; rs28039; rs2679180; rs4495397; rs878076; rs12515514; rs606442; rs1114707; rs986612; ;rs8023279;rs10513446;rs6489190;rs10845720;rs6770415;rs4882785;rs412065;rs713177;rs17073507;rs7138951;rs9296249;rs7397555;An allele assay step, which involves measuring alleles for at least 60 SNPs selected from 120 SNPs identified by rs998636;rs778472;rs9853221;rs11179545;rs6488204;rs6926229;rs7973036;rs4683981;rs7630805;rs215939;rs9953270;rs12489992;rs10502675;rs1796361;rs922805;rs4767382;rs1700936;rs1796390;rs2071556;rs9870827;rs4767404;rs1877219;rs2645986 and rs10848501, and; An information acquisition step to obtain information regarding the risk of severe progression of broad-sense primary open-angle glaucoma in the subject, based on the measurement results of the allele. A method for obtaining information on the risk of progression of broad-sense primary open-angle glaucoma, including, The aforementioned information acquisition process, The process includes obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma in the subject, based on the measurement results of alleles of a first SNP group containing at least 20 SNPs from the at least 60 SNPs, the measurement results of alleles of a second SNP group containing at least 20 SNPs different from the first SNP group from the at least 60 SNPs, and the measurement results of alleles of a third SNP group containing at least 20 SNPs different from the first SNP group and the second SNP group from the at least 60 SNPs. The aforementioned information acquisition process further, A process to determine whether the measured allele is a risk allele or not. A first calculation step for calculating the total number of risk alleles in the first SNP group, A second calculation step to calculate the total number of risk alleles in the second SNP group, A third calculation step to calculate the total number of risk alleles in the third SNP group, A first comparison step involves comparing the total number obtained in the first calculation step with a first cutoff value. A second comparison step involves comparing the total number obtained in the second calculation step with a second cutoff value. A third comparison step involves comparing the total number obtained in the third calculation step with the third cutoff value, and A step of obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma in the subject, based on the comparison results in the first comparison step, the second comparison step, and the third comparison step. Methods that include...

2. In the aforementioned information acquisition process, If the total number obtained in the first calculation step is greater than or equal to the first cutoff value, the total number obtained in the second calculation step is greater than or equal to the second cutoff value, and the total number obtained in the third calculation step is greater than or equal to the third cutoff value, then information is obtained indicating that the subject has a high risk of developing severe broad-sense primary open-angle glaucoma. If the total number obtained in the first calculation step is less than the first cutoff value, the total number obtained in the second calculation step is less than the second cutoff value, and the total number obtained in the third calculation step is less than the third cutoff value, then information is obtained indicating that the subject has a low risk of developing severe primary open-angle glaucoma. The method according to claim 1.

3. The method according to claim 1 or 2, further comprising the step of applying Bayes' theorem to the measurement results of the allele to calculate the probability that the subject's broad primary open-angle glaucoma will worsen.

4. The method according to claim 1 or 2, further comprising the step of applying Bayes' theorem to the results obtained in the information acquisition step to calculate the probability that the subject's broad primary open-angle glaucoma will worsen to a predetermined percentage (%) or more.

5. The method according to any one of claims 1 to 4, wherein the biological sample is one or more selected from the group consisting of whole blood, white blood cells, plasma, serum, lymph, tears, saliva, nasal secretions, cerebrospinal fluid, bone marrow fluid, semen, sweat, mucous membrane tissue, skin tissue, and hair follicles.

6. Using biological samples collected from subjects, the following SNP IDs were used: rs10918342; rs16845638; rs1124070; rs4581328; rs11122331; rs757588; rs9524181; rs2341354; rs12761627; rs12268054; rs4951338; rs1535565; rs478410; rs4487396; rs1149332; rs650169; rs4721867; rs822625; rs11252457; rs4699365; rs2412970; rs12247999; rs4699667; rs75479 97; rs10242598; rs11672223; rs2421169; rs3003542; rs2888830; rs12029109; rs826474; rs4861656; rs3006268; 4; rs12640858; rs1201122; rs7204239; rs2480054; rs4490256; rs10137988; rs1459139; rs1113405; rs8014067; 22; rs2725338; rs2052074; rs1679870; rs17167799; rs1386234; rs1253113; rs16900519; rs10166301; rs2555143; 6841; rs2288183; rs7123826; rs11601571; rs7142344; rs11867479; rs6106895; rs7152384; rs2147866; rs41435651; rs6884797; 017940; rs1051920; rs6543289; rs28039; rs2679180; rs4495397; rs878076; rs12515514; rs606442; rs1114707; rs986612; ;rs8023279;rs10513446;rs6489190;rs10845720;rs6770415;rs4882785;rs412065;rs713177;rs17073507;rs7138951;rs9296249;rs7397555;rs998636; rs778472; rs9853221; rs11179545; rs6488204; rs6926229; rs7973036; rs4683981; rs7630805; rs215 939; rs9953270; rs12489992; rs10502675; rs1796361; rs922805; rs4767382; rs1700936; rs1796390; rs2071556; A method for obtaining information on the risk of progression of broad primary open-angle glaucoma, comprising an allele measurement step of measuring alleles for at least 60 SNPs selected from 120 SNPs identified in rs9870827;rs4767404;rs1877219;rs2645986 and rs10848501, wherein the allele measurement results serve as an indicator of the risk of progression of broad primary open-angle glaucoma in the subject; A step of obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma in the subject, based on the measurement results of alleles of a first SNP group including at least 20 SNPs from the at least 60 SNPs, the measurement results of alleles of a second SNP group including at least 20 SNPs different from the first SNP group from the at least 60 SNPs, and the measurement results of alleles of a third SNP group including at least 20 SNPs different from the first SNP group and the second SNP group from the at least 60 SNPs. A process to determine whether the measured allele is a risk allele or not. A first calculation step for calculating the total number of risk alleles in the first SNP group, A second calculation step to calculate the total number of risk alleles in the second SNP group, A third calculation step to calculate the total number of risk alleles in the third SNP group, A first comparison step involves comparing the total number obtained in the first calculation step with a first cutoff value. A second comparison step involves comparing the total number obtained in the second calculation step with a second cutoff value. A third comparison step involves comparing the total number obtained in the third calculation step with a third cutoff value, and A step of obtaining information regarding the risk of progression of broad-sense primary open-angle glaucoma in the subject, based on the comparison results in the first comparison step, the second comparison step, and the third comparison step. Methods that further include this.

7. If the total number obtained in the first calculation step is greater than or equal to the first cutoff value, the total number obtained in the second calculation step is greater than or equal to the second cutoff value, and the total number obtained in the third calculation step is greater than or equal to the third cutoff value, then information is obtained indicating that the subject has a high risk of developing severe broad-sense primary open-angle glaucoma. If the total number obtained in the first calculation step is less than the first cutoff value, the total number obtained in the second calculation step is less than the second cutoff value, and the total number obtained in the third calculation step is less than the third cutoff value, then information is obtained indicating that the subject has a low risk of developing severe primary open-angle glaucoma. The method according to claim 6.

8. The method according to claim 6 or 7, further comprising the step of applying Bayes' theorem to the measurement results of the allele to calculate the probability that the subject's broad primary open-angle glaucoma will worsen.

9. The method according to claim 6 or 7, further comprising the step of applying Bayes' theorem to the measurement results of the allele to calculate the probability that the subject's broad primary open-angle glaucoma will worsen to a predetermined percentage (%) or more.

10. The method according to any one of claims 6 to 9, wherein the biological sample is one or more selected from the group consisting of whole blood, white blood cells, plasma, serum, lymph, tears, saliva, nasal secretions, cerebrospinal fluid, bone marrow fluid, semen, sweat, mucous membrane tissue, skin tissue, and hair follicles.

11. An allele measurement step of measuring alleles for SNPs identified by rs1114707;rs986612;rs2676622;rs2199831;rs8023279;rs10513446;rs6489190;rs10845720;rs6770415;rs4882785;rs412065;rs713177;rs17073507;rs7138951;rs9296249;rs7397555;rs998636;rs778472;rs9853221 and rs11179545 using a biological sample taken from a subject, and An information acquisition step to obtain information regarding the risk of severe progression of broad-sense primary open-angle glaucoma in the subject, based on the measurement results of the allele. Methods for obtaining information on the risk of severe progression of broad-based primary open-angle glaucoma, including [specific example].

12. The method according to claim 11, further comprising measuring alleles for SNPs identified by rs6488204; rs6926229; rs7973036; rs4683981; rs7630805; rs215939; rs9953270; rs12489992; rs10502675 and rs1796361 in the measurement step.

13. The method according to claim 12, further comprising measuring alleles for SNPs identified by rs922805; rs4767382; rs1700936; rs1796390; rs2071556; rs9870827; rs4767404; rs1877219; rs2645986 and rs10848501 in the measurement step.

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