JP7064215B2 - How to determine the risk of developing desquamation syndrome or desquamation glaucoma - Google Patents

Description

The present invention relates to a method for determining the risk of developing exfoliation syndrome or exfoliation glaucoma, a device for determining the risk of developing desquamation syndrome or desquamation glaucoma, and a computer program for causing the device to execute.

Glaucoma is a neurodegenerative disease in which retinal ganglion cells are damaged and irreversibly progress to lead to blindness. Desquamation glaucoma, which is one of the types of glaucoma, is glaucoma that develops from desquamation syndrome in which desquamation substances are observed in the anterior chamber.

Non-Patent Document 1 discloses that specific SNPs are found in the LOXL1 gene in desquamation glaucoma or desquamation syndrome. In addition, Non-Patent Document 2 discloses that the TBC1D21 gene and the PML gene, and Non-Patent Document 3 discloses that the CACNA1A gene also contains SNPs related to desquamation glaucoma or desquamation syndrome.

http://science.sciencemag.org/content/sci/317/5843/1397.full.pdf http://www.readcube.com/articles/10.1038/srep05340 http://www.nature.com/ng/journal/v47/n4/pdf/ng.3226.pdf

An object of the present invention is to provide a method for determining the risk of developing desquamation syndrome or desquamation glaucoma with high accuracy, a device for determining the risk of developing desquamation syndrome or desquamation glaucoma that executes the method, and a computer program for causing the device to execute the method. It is to be.

As a result of diligent studies to solve the above-mentioned problems, the present inventors have conducted a high-density chip using a large number of desquamation syndrome or desquamation glaucoma patients and samples of non-desquamation syndrome and healthy non-desquamation subjects possessed by the present inventors. By conducting a genome-wide association study (GWAS) based on the acquired genotype data information, we identified the SNP group of risk markers for the onset of desquamation syndrome or desquamation glaucoma, and among them, for highly accurate determination. Onset of desquamation syndrome or desquamation glaucoma by finding a specific SNP group that contributes and measuring the total number of risk alleles in the sample for an SNP population that combines that SNP group with an SNP group selected from the remaining SNPs. We have found that the risk can be determined with high accuracy, and have completed the present invention.

That is, the present invention relates to the following [1] to [3].
[1] Based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects, the core SNP group consisting of 12 SNPs shown in Table 1 and the 450 core SNPs shown in Table 2 An allele measurement process that measures alleles for at least 30 SNPs including SNPs selected from the pool SNP group consisting of SNPs (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. A method of assisting a subject in diagnosing desquamation syndrome or the risk of developing desquamation glaucoma, which comprises an information providing step that provides information for determining the risk of developing desquamation glaucoma.
[2] A computer including a processor and a memory under the control of the processor is provided, and the memory includes a processor.
Based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects, it consists of a core SNP group consisting of 12 SNPs shown in Table 1 and 450 SNPs shown in Table 2. An allele measurement process that measures alleles for at least 30 SNPs including SNPs selected from the pool SNP group (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. A detection device for a subject at risk of developing desquamation syndrome or desquamation glaucoma, in which a computer program for causing the computer to execute an information providing process that provides information for determining the risk of developing desquamation glaucoma is recorded.
[3] A computer including a processor and a memory under the control of the processor.
Based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects, it consists of a core SNP group consisting of 12 SNPs shown in Table 1 and 450 SNPs shown in Table 2. An allele measurement process that measures alleles for at least 30 SNPs including SNPs selected from the pool SNP group (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. A computer program that runs an information-providing process that provides information to determine the risk of developing desquamation glaucoma.

By analyzing the SNP group of the present invention present in the sample derived from the subject by the method of the present invention, the presence or absence of the risk of developing desquamation syndrome or desquamation glaucoma in the subject can be determined, and further, the risk can be determined. High and low can be predicted. Based on this risk, the subject can take preventive measures for desquamation syndrome or desquamation glaucoma or receive appropriate treatment.

FIG. 1 is a schematic view showing an example of a device for determining the risk of developing desquamation syndrome or desquamation glaucoma in a subject. FIG. 2 is a block diagram showing a hardware configuration of the determination device shown in FIG. FIG. 3 is a flowchart for determining the risk of developing desquamation syndrome or desquamation glaucoma using the determination device shown in FIG. FIG. 4 is a diagram showing the results of determining the presence or absence of onset risk using a total of 30 SNPs shown in Table 1 and SNPs selected from Table 2. FIG. 5 shows an example of a graph in which the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, and the presence or absence of the onset risk is determined when the number of SNPs is 90 and the measurement step is one. It is a figure. FIG. 6 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 90 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 7 is a graph used for integrated determination of the presence or absence of onset risk when the number of SNPs is 90 and the measurement step is 3 times, with the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 8 shows the results of using Bayes' theorem to determine the presence or absence of the onset risk when the number of SNPs is 90 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 9 shows an example of a graph used for determining the presence or absence of the onset risk when the number of SNPs is 120 and the measurement step is one, targeting the SNPs shown in Table 1 and the SNPs selected from Table 2. It is a figure. FIG. 10 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 120 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 11 is a graph used when the presence or absence of the onset risk when the number of SNPs is 120 and the measurement step is 3 times is determined by integrating the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 12 shows the results of using the Bayes theorem to determine the presence or absence of the onset risk when the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, the number of SNPs is 120, and the measurement step is 3 times. It is a figure which showed an example. FIG. 13 shows an example of a graph in which the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, and the presence or absence of the onset risk is determined when the number of SNPs is 150 and the measurement step is one. It is a figure. FIG. 14 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 150 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 15 is a graph used when the presence or absence of the onset risk when the number of SNPs is 150 and the measurement step is 3 times is determined by integrating the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 16 shows the results of using the Bayes theorem to determine the presence or absence of the onset risk when the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, the number of SNPs is 150, and the measurement step is 3 times. It is a figure which showed an example.

The present invention is a method for determining the risk of developing desquamation syndrome or desquamation glaucoma, which comprises a step of detecting an allele in vitro for a specific single nucleotide polymorphism (hereinafter, also referred to as SNP). Desquamation syndrome or desquamation that measures the above-mentioned allele in a sample derived from an examiner and, if the allele is a risk allele, provides information that can be used to determine the risk of developing desquamation syndrome or desquamation glaucoma. It is a method to assist in diagnosing the risk of developing glaucoma. That is, in the present invention, a measurement target including a specific SNP group that contributes to highly accurate risk determination is set as a member, the total number of risk alleles detected there is counted, and the total number of the risk alleles is a preset threshold value. It is a major feature that it can provide information that there is a risk of developing desquamation syndrome or desquamation glaucoma when the amount exceeds. This makes it possible to assist in diagnosing the risk of developing desquamation syndrome or desquamation glaucoma. Therefore, the method of assisting the diagnosis of the onset risk of desquamation syndrome or desquamation glaucoma of the present invention is also a method of providing information on the onset risk of desquamation syndrome or desquamation glaucoma, and also the method of providing information on the onset risk of desquamation syndrome or desquamation glaucoma. It is also a method of judgment, evaluation, or inspection. In the present specification, the term "polymorphism" or "variant" means that there is diversity in the base sequence and structure (insertion / deletion, inversion, number of copies) at a specific position of the genome in a certain organism species. The site where a polymorphism is present (hereinafter, also referred to as a polymorphism site) is a site on the genome in which a variant such as a single nucleotide polymorphism (SNP) is recognized.

In the present invention, the "allele" refers to each type having different bases that can be taken at a certain polymorphic site, and the "risk allele" is one of the SNP alleles associated with desquamation syndrome or desquamation glaucoma. , An allele that is more frequent in the patient group with desquamation syndrome or desquamation glaucoma than in the healthy population without desquamation syndrome or desquamation glaucoma, and "non-risk allele" refers to an allele that is not a risk allele.

In the present invention, the "risk of developing desquamation syndrome or desquamation" is the risk of the subject regarding desquamation syndrome or desquamation, and refers to the possibility of developing desquamation syndrome or desquamation in the future, which is determined by the disease susceptibility. .. In the present invention, the risk prediction means determining the presence or absence of future risk of the subject at the present time, or determining the magnitude of the future risk of the subject at the present time.

The method of the present invention to assist in diagnosing the risk of developing desquamation syndrome or desquamation glaucoma is
Allele measurement process, which measures alleles for a specific SNP,
It includes an information acquisition step of acquiring information on a subject based on the measurement result of the allele, and an information providing step of providing information for determining the onset risk of the subject based on the information. The following will be described step by step, but first, the method for identifying the SNP group associated with the onset of desquamation syndrome or desquamation glaucoma, which is the measurement target in the present invention, will be described. The SNP group associated with the onset of desquamation syndrome or desquamation glaucoma may be referred to as the SNP group of the present invention.

(Identification of SNP group associated with desquamation syndrome or desquamation glaucoma)
In the present invention, the SNP group associated with the onset of desquamation syndrome or desquamation glaucoma is specifically, specifically, a patient diagnosed with desquamation syndrome or desquamation glaucoma, and not a desquamation syndrome or desquamation glaucoma, and , Genomic DNA is extracted from non-desquamation syndrome and healthy non-desquamation subjects (sometimes simply referred to as non-patients or controls) who are judged to have no family history of glaucoma by interview. Then, by comparing and variously analyzing the allele frequency in each SNP between the patient group and the non-patient group using about 200,000 to 5 million known SNPs on the human genome as an index, the difference in frequency is statistically significant. Find an SNP group of potential marker candidates. Then, by further analyzing from this candidate group, the SNP group (marker SNP group) used in the present invention is determined, and from among them, a specific SNP group (core SNP group) that gives a more accurate determination result. By setting the remaining SNP group (pool SNP group) and using these in combination, it is possible to determine the presence or absence of the risk of developing desquamation syndrome or desquamation glaucoma and predict the magnitude of the risk of developing desquamation syndrome or desquamation glaucoma. Become. Although details will be described in the section of Examples, the SNP group associated with desquamation syndrome or desquamation glaucoma disclosed in the present invention can be identified by the following method.

(1) Method for finding SNP groups of marker candidates Extract genomic DNA from the blood of each patient group and non-patient group. Genomic DNA in blood can be extracted by any known method. For example, DNA obtained by dissolving and eluting cells is bound to the surface of magnetic beads coated with silica and separated by magnetism. DNA can be extracted by recovery.

The means for identifying the allele in the SNP in the extracted DNA sample is not particularly limited, and an appropriate selection may be made from the SNP detection method and the SNP typing method known in the art.

Here, a method using genome-wide association study (GWAS) will be described. Specifically, for example, a DNA microarray containing SNPs distributed in the entire genome region (Affymetrix, Genome-Wide Human SNP Array 6.0) can be used. At that time, the SNP to be extracted may be selected by performing Quality control (hereinafter, also referred to as “QC”).

As the call rate as a criterion for acceptance or rejection of SNP in quality control, for example, it is desirable to adopt an SNP having a call rate of preferably 85% or more, more preferably 90% or more, still more preferably 95% or more. In addition, SNPs with a minor allele frequency (MAF) of less than 0.01 and SNPs whose genotype distribution deviates significantly from Hardy-Weinberg equilibrium (HWE) (false discovery rate is less than 0.001) are among the candidates. It is desirable to exclude it.

The SNPs selected in Quality control will be further narrowed down. Specifically, for example, by performing a chi-square test using statistical software, the P value is preferably 1 × 10 ^-3 or less, more preferably 3 × 10 ^-4 or less, still more preferably 1 × 10 ^-4 . Select an SNP that meets the following:

Next, for the extracted SNPs, a two-dimensional cluster plot analysis for excluding genotyping defective SNPs, for example, a cluster plot image obtained from genotyping software (Genotyping Console) is visually observed. Excludes SNPs with poor genotyping and determines the SNP group of marker candidates.

The SNPs selected in this way can be found in known sequences such as GenBank and dbSNP, or by referring to a database of known SNPs, to the position on the genome where the SNP is present, sequence information, the gene in which the SNP is present, or the vicinity thereof. Information such as existing genes, distinction between introns or exones when they are present on genes, their functions, and homologous genes in other biological species can be obtained.

(2) Method for finding marker SNP group Next, for the marker candidate SNP group obtained above, genotype data is quantified and extracted.

In quantification, refer to the genotype data and risk allele database, for example, in a predetermined allele contained in the genotype data, if the risk allele is homozygous, the numerical value is 2, and if the risk allele is heterozygous, the numerical value is 2. 1 is given, and when the non-risk allele is homozygous, a numerical value of 0 is given. Then, the obtained numerical values are normalized by the following formulas using the average value of the appearance frequency of each allele and the observed frequency to create a quantified genotype data matrix in the selected SNP group. ..

The risk allele is an allele that frequently appears in a patient group. In the present invention, risk alleles are defined based on odds ratios. The odds ratio is generally the ratio of the proportion of people with and without risk factors in the patient group, that is, the odds divided by the similarly determined odds in the non-patient group, as in the present invention. Often used in case-control studies. In the present invention, the odds ratio is determined based on the allele frequency, and can be calculated by dividing the ratio of one allele to another in the patient group by the ratio of the frequencies similarly obtained in the non-patient group. can. The frequency of appearance of each allele can be recalculated at any time by additionally updating the data as the data of the subject is acquired, for example.

Next, a cluster analysis is performed using the digitized genotype data matrix. Specifically, for example, considering the linkage disequilibrium (LD) between SNPs, the SNPs that are considered to have high independence are determined by principal component analysis (PCA). As a method of principal component analysis, a known method can be used. For example, for the SNP selected above, information reduction is performed in each of the whole genome or chromosome to perform factor loading (principal component and original). The marker SNP group of the present invention is selected by calculating (corresponding to the correlation coefficient between variables), determining a candidate region based on the calculation, and selecting the SNP having the lowest P value in the region as a candidate SNP. decide. However, duplicate SNPs are excluded from the calculation from the whole genome and the calculation from each of the chromosomes.

Next, a method of further selecting a specific SNP group that contributes to highly accurate determination from the marker SNP group of the present invention will be described. Hereinafter, such an SNP group will be referred to as a core SNP group.

(3) Method for finding core SNP group and pool SNP group For the core SNP group, about 50 SNP groups are selected from the marker SNP group obtained above in ascending order of P value, and new SNP groups are selected for the selected SNP group. It can be determined by performing a reproduction experiment by a group. The pool SNP group is a group obtained by excluding the core SNP group from the marker SNP group.

More specifically, for the SNP group selected as described above, identification of alleles using DNA collected from a group different from the patient group and the non-patient group used for identifying the marker candidate SNP group. I do. Here, the different group is a group in which completely non-identical is preferable, although some overlap may occur. As the analysis method, for example, a mass spectrometry method is preferable, and a known Mass ARRAY can be used. Quality control may be performed, and as the call rate at that time, for example, an SNP having a call rate of preferably 85% or more, more preferably 90% or more, still more preferably 95% or more may be adopted. desirable. In addition, SNPs with a minor allele frequency (MAF) of less than 0.01 and SNPs whose genotype distribution deviates significantly from Hardy-Weinberg equilibrium (HWE) (false discovery rate is less than 0.001) are among the candidates. It is desirable to exclude it.

Then, for the identified SNPs, a two-dimensional cluster plot analysis is performed in the same manner as described above to exclude genotyping defective SNPs.

For the SNPs selected in this way, for example, by performing a chi-square test using genotyping software, the P value is preferably 1 × 10 ^-3 or less, more preferably 3 × 10 ^-4 or less, still more preferable. Selects SNPs that satisfy 1 × 10 ^-4 or less.

Since it is preferable to integrate and judge the results of two analyzes for the selected SNP group, the analysis results should be integrated and evaluated by a known meta-analysis method, for example, the Cochran-Mantel-Henzel method. Can be done. Then, the SNPs having a P value of 3 × 10 ^-3 or less according to the analysis method are defined as the core SNP group (12 in total) in the present invention, and the remaining SNP groups (450 in total) that do not correspond to the core SNP group are pooled SNPs. Make a group. Table 1 shows the probe sequences containing SNPs in the core SNP group, and Table 2 (Tables 2-1 to 2-23) shows the probe sequences containing SNPs in the pooled SNP group. In the table, alleles of each SNP are described in parentheses in the probe sequence, and the sequence containing the risk allele is designated as SEQ ID NO: A and the sequence containing the non-risk allele is designated as SEQ ID NO: B.

Using the SNP group of the present invention thus selected, the following steps are performed on the sample derived from the subject.

[Allele measurement process]
In the allele measurement step, the core SNP group (Table 1) is always measured, and the SNP (pool selection SNP group) selected from the pool SNP group (Table 2) is also measured. The number of SNPs to be measured is not particularly limited as long as the total number including the core SNP group is at least 30, and for example, when the measurement target is 30, it is composed of 12 core SNPs and 18 pool selection SNPs. To. If the measurement target is 40, it is composed of 12 core SNPs and 28 pool selection SNPs. If the measurement target is 50, it is composed of 12 core SNPs and 38 pool selection SNPs. If the measurement target is 90, it is composed of 12 core SNPs and 78 pool selection SNPs. When the measurement target is 120, it is composed of 12 core SNPs and 108 pool selection SNPs. If the measurement target is 150, it consists of 12 core SNPs and 138 pool selection SNPs.

Further, in the present invention, one measurement result may be used to proceed to the next information acquisition step, but from the viewpoint of improving the determination accuracy, the present invention has a plurality of results obtained by performing a plurality of measurement steps. You can proceed to the information acquisition process. In this case, for the core SNP group consisting of 12 SNPs , all 12 can be used as measurement targets without duplication in any of the measurement steps. The number of core SNP groups per step may be the same or different. Further, the number of SNPs to be measured for each step may be the same or different, and is not particularly limited as long as the total number including the core SNP group is at least 30 in each step.

Specifically, for example, when the allele measurement step includes two measurement steps,
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. A step including a second measurement step for measuring the second SNP group is exemplified, and here, the first pool selection SNP group and the second pool selection SNP group are not the same. For example, the number of SNPs to be measured in each measurement step is 6 in the 1st core SNP group, 24 or more in the 1st pool selection SNP group, and the 2nd core SNP group in the 2nd SNP group. There are 6 cases, and there are 24 or more examples of the second pool selection SNP group. In addition, in this specification, "non-identical" means that they are not completely the same. For example, only one may be different, or the constituent SNPs of the pool selection SNP group may be all different. ..

Also, if the allele measurement process involves three measurement steps,
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. The second measurement step for measuring the second SNP group, and
At least one or more SNPs (third core SNP group) different from the first core SNP group and the second core SNP group and a plurality of SNPs selected from the pool SNP group (third pool selection SNP group) are combined. An example is a step including a third measurement step of measuring a third SNP group containing 30 SNPs. Here, the first pool selection SNP group, the second pool selection SNP group, and the third pool selection SNP group are defined. Both are non-identical. To exemplify the number of SNPs to be measured in each measurement step, for example, the first core SNP group in the first SNP group has four, the first pool selection SNP group has 26 or more, and the second core in the second SNP group. There are 4 SNP groups, 26 or more selected SNP groups in the second pool, 4 in the 3rd core SNP group in the 3rd SNP group, and 26 or more in the selected SNP group in the 3rd pool.

In the present invention, if necessary, the fourth and subsequent measurements can be performed.

The biological sample used in the present invention may be any sample as long as it can extract DNA derived from the genome. For example, whole blood, whole blood cells, leukocytes, lymphocytes, plasma, serum, lymph, tears, saliva, nasal juice, cerebrospinal fluid, bone marrow fluid, semen, sweat, mucosal tissue, skin tissue, hair roots and the like are used. As the method for extracting DNA from such a biological sample, any known method can be adopted.

For the SNP group to be measured, the allele is determined according to a known method and the measurement result is obtained. Specifically, for example, each allele-specific probe (Tables 1 to 2) designed based on the sequence information of the SNP group of the present invention is hybridized and the signal is detected. Alleles can be detected. Examples of methods for hybridization using a probe include the Tuckman method, the Invader (registered trademark) method, the light cycler method, the cyclin probe method, the MPSS method, the bead array method, the DNA chip method, and the microarray method. It is also possible to detect alleles without hybridization with a probe, and for example, PCR-RFLP method, SSCP method, mass spectrometry method, next-generation sequencing method, direct sequencing method and the like can be used. These methods can be performed according to known conditions.

The measurement result thus obtained is used for the next information acquisition step.

[Information acquisition process]
In the information acquisition process, information on the risk of developing desquamation syndrome or desquamation glaucoma is acquired based on the allele to be measured. The allele information may be information on whether or not a variant exists, information on the measured allele type, or information obtained from the number of alleles. Further, it may be calculated as a value (statistical value or the like) that correlates with SNP. Above all, in the present invention, from the viewpoint of improving the determination accuracy, it is preferable to determine whether or not the measured allele is a risk allele and count the total number of risk alleles. That is, based on the risk allele data acquired in advance, it is determined whether or not the measured allele is a risk allele, and the total number of alleles determined to be risk alleles in the entire SNP group to be measured (risk allele possession). Count). In addition, when performing a plurality of measurement steps, whether the total number of risk alleles exceeds a preset threshold value set in advance in each measurement is determined to be positive or negative, and the number thereof is counted. The total number of risk alleles thus obtained and, when performing multiple measurement steps, the number of positives and negatives of each measurement result are further recognized as quantitative values of the subject, and the following information is obtained. It is preferable to proceed to the providing process.

[Information provision process]
In the information providing process, information for determining the onset risk of the subject based on the obtained information is provided to the subject.

As a method for determining the onset risk, for example, when the information obtained by the above step is the number of risk alleles possessed, four specific embodiments can be mentioned.
Aspect 1: The presence or absence of the risk of onset is determined by the numerical value of the number of possessed risk alleles Aspect 2: The presence or absence of the onset risk is determined by integrating the presence or absence of the onset risk when a plurality of possessed risk alleles are obtained Aspect 3: The number of possessed risk alleles Aspect for calculating the probability of having an onset risk based on

As the method of aspect 1, for example, the result (number of risk alleles possessed) obtained by the information acquisition step is determined in advance by ROC (Receiver Operating Characteristic) analysis based on the SNP used in the allele measurement step. If it exceeds the cut-off value, the subject has a high risk of developing desquamation syndrome or desquamation glaucoma, and if it falls below the cut-off value, the risk is low.

More specifically, first, the number of risk alleles possessed by the subject is compared with a preset threshold value. The threshold value is an appropriate cut-off value that distinguishes between a patient and a non-patient, and by comparing the threshold value with a quantitative value, it can be determined whether or not there is a risk of developing desquamation syndrome or desquamation glaucoma.

The threshold can be set as follows. For the same SNP group as the SNP group selected as the measurement target when acquiring the quantitative value of the subject, using a biological sample collected from the subject who was previously diagnosed with desquamation syndrome or desquamation glaucoma, By measuring the number of risk alleles as described above and statistically processing "presence or absence of risk of developing desquamation syndrome or desquamation glaucoma" and "number of risk alleles", the correlation between the two data is analyzed. From the analyzed results, for example, whether to emphasize the high true positive rate (high sensitivity), the high true negative rate (high specificity), or the true positive rate and the true negative. The threshold value can be set according to the purpose such as how much the rate is balanced. That is, if the SNP group to be measured is different, the risk allele existing there is naturally different, so that the threshold value varies depending on the SNP group to be measured. Here, the true positive rate is the probability of correctly determining a person having a risk of developing desquamation syndrome or desquamation glaucoma as a person having a risk of developing desquamation syndrome or desquamation glaucoma, and the true negative rate is the desquamation syndrome. Or, it is the probability that a person who does not have the risk of developing desquamation glaucoma is correctly judged as a person who does not have the risk of developing desquamation syndrome or desquamation glaucoma.

As a specific method for setting the threshold value, which is an appropriate cutoff value for distinguishing between a patient and a non-patient, first, the same SNP group as the SNP group selected as the measurement target when acquiring the quantitative value of the subject is obtained. With respect to, a ROC curve created with a true positive rate (sensitivity) on the vertical axis and a true negative rate (1-specificity) on the horizontal axis is created (ROC analysis is performed). Next, the point where the distance from the upper left corner of the graph is the minimum may be the threshold value, and the point farthest from the diagonal line where the area under the curve (AUC) is 0.5 may be the threshold value, which is arbitrary. A point that has the specificity and sensitivity of may be set as a threshold value. In the present invention, it is preferable to set a threshold value at which the sensitivity is 1 and (1-specificity) is the closest to 0. For example, [(1-sensitivity) ² + (1-specificity) ² ] is the minimum. Can be set as a threshold value.

The threshold value may be acquired separately at the same time when the quantitative value of the subject is acquired, or may be acquired in advance. Further, when comparing with the quantitative value of the subject, the analysis result obtained so far may be additionally updated as needed and obtained.

Further, in setting the threshold value, normalization or weighting may be performed from the viewpoint of improving the determination accuracy. Specifically, for example, as a normalization method, a method of comparing with a normal distribution curve can be used. Further, as a weighting method, weighting can be performed in consideration of the odds ratio of each SNP.

By comparing the preset threshold value with the quantitative value of the subject, it is possible to determine whether or not the subject has a risk of developing desquamation syndrome or desquamation glaucoma.

As a method of the second aspect, for example, when the allele measurement step includes a plurality of measurement steps, the result (number of each risk allele possessed) in each measurement step regarding the subject obtained by the information acquisition step is obtained in each measurement step. Provides information on the high or low risk of the subject developing desquamation syndrome or desquamation glaucoma, using whether or not the cutoff value previously determined by ROC analysis is exceeded based on the SNP group used in the above as an index. The method can be mentioned.

More specifically, first, the presence or absence of risk of onset is determined by comparing the number of risk alleles possessed by the subject with a preset threshold value for each measurement step. The threshold value can be set in the same manner as in the first aspect. Next, the results of the presence or absence of onset risk for each measurement step are integrated. Specifically, for example, when the measurement step is performed three times and the case where there is a risk of onset is displayed as "+" and the case where there is no risk of onset is displayed as "-", the first judgment result is "+" and the second judgment. The result is "+", the third judgment result is "+", the integrated result is "+++", the first judgment result is "+", the second judgment result is "+", and the third judgment result. However, the integration result of "-" is "++-", the first judgment result is "+", the second judgment result is "-", and the third judgment result is "+". It is classified as "-+". Therefore, even if the probability of being negative once in three judgments is the same, "++-" and "++-" correspond to different classifications. Then, using the obtained integrated results, the risk of developing desquamation syndrome or desquamation glaucoma based on the tendency corresponding to the same classification on the graph of the group having the risk of developing the disease and the group having no risk of developing the disease, which is known in advance. Determines whether is high or low.

The graph of the group with the onset risk and the group without the onset risk can be created as follows. For example, whether or not there is a risk of developing desquamation syndrome or desquamation glaucoma for each pattern such as "++++", "++-", "++-+" by obtaining the integrated result for the pre-diagnosed subject. It can be created by accumulating the number of people.

In the graph of the group with the onset risk and the group without the onset risk prepared in advance in this way, by obtaining the information of the corresponding category from the integration result of the subject, the subject develops desquamation syndrome or desquamation glaucoma. It is possible to determine whether or not there is a risk.

In addition, in aspect 2, the Bayes' theorem described later may be applied to calculate the probability of having a risk of developing desquamation syndrome or desquamation glaucoma.

As the method of the third aspect, for example, a method of applying Bayes' theorem to the result obtained in the information acquisition step to calculate the probability of having a risk of developing desquamation syndrome or desquamation glaucoma can be mentioned. Generally, in clinical practice, it is unclear whether or not the test subject has a risk of developing the disease, and the presence or absence of the risk of developing the disease is estimated from the test results. Therefore, the positive predictive value of the test method is used. High (PPV) and negative predictive value (NPV) are also desired. Here, the positive predictive value is the percentage of those who have a disease when the test result is positive, and the negative predictive value is the percentage of those who do not have the risk of developing the disease when the test result is negative. Is. By presenting it as a probability of developing desquamation syndrome or desquamation glaucoma, it is easier to understand that the higher the probability, the higher the risk of developing desquamation syndrome or desquamation glaucoma.

As a method of calculating the probability of having a specific risk of onset, the onset of desquamation syndrome or desquamation glaucoma is performed by comparing the result (number of risk alleles possessed) obtained in the information acquisition process with the threshold in the same manner as in embodiment 1. The presence or absence of risk is determined, and the method of Bayes' theorem is applied to the result to calculate the probability of having the risk of developing the disease. In the method of the Bayes theorem, 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. The probability of developing scrap syndrome or scrap glaucoma is expressed as a posterior probability. In other words, if the Bayes'theorem method is not used, the probability of having a risk of developing the disease is the same as the prevalence, but if the test gives a positive result by using the Bayes' theorem method, the test is taken. It means that the probability of a person's risk of developing the disease can be expressed by a positive predictive value. Specifically, positive predictive value = prevalence x sensitivity / [prevalence x sensitivity + (1-prevalence) x (1-specificity)], negative predictive value = specificity x (1-). Prevalence) / [Specificity x (1-prevalence) + Prevalence x (1-sensitivity)], for example, the prevalence is 0.8% and the test sensitivity is 70. When the percentage and specificity are 70%, the positive predictive value is calculated to be 1.8% and the negative predictive value is calculated to be 99.7%. Therefore, since the risk of developing a positive result is 1.8%, it is higher than the prevalence rate, and it is possible to advise to receive further medical examination. In addition, when the above tests are performed in combination three times, the positive predictive value is calculated to be 9.3% and the negative predictive value is calculated to be 99.9%. Therefore, the number of risk alleles has a cutoff value in the third test. Exceeding test subjects can be determined to have a higher risk of developing desquamation syndrome or desquamation glaucoma.

In this way, by making a determination including the influence of the prevalence rate as the overall background of the subject, it is possible to present it as the probability of developing desquamation syndrome or desquamation glaucoma of the subject.

As the method of aspect 4, for example, the Bayes' theorem is applied to the result obtained in the information acquisition process to calculate the probability of having an onset risk based on the number of risk alleles possessed (average onset risk, 95% credible interval), and further. There is a method of calculating the probability of having a risk of developing scrap syndrome or scrap glaucoma with a probability of a preset percentage (% or more). Here, as the percentage to be set, for example, an arbitrary numerical value such as 70%, 80%, 90% or the like can be set, and the larger the value, the higher the accuracy of the determination.

More specifically, for example, desquamation syndrome or desquamation syndrome according to Bayes' theorem for each pattern such as "+++", "++-", "++-", etc. Calculate the probability density function at risk of developing desquamation glaucoma. For example, when calculating the probability of 70% or more, the area within the range of the density in which the probability of having the onset risk, which is a continuous random variable, is 70 or more from the probability density function is the probability of having the onset risk.

Thus, in the present invention, it is determined whether or not the subject has the risk of developing desquamation syndrome or desquamation glaucoma by comparing the total number of risk alleles with the threshold value for the risk of developing desquamation syndrome or desquamation glaucoma. Not only that, but by calculating the probability, it is possible to provide more detailed information on the risk of developing desquamation syndrome or desquamation glaucoma of the subject.

The present invention also provides an apparatus for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma.

The apparatus of the present invention includes a processor and a computer having a memory under the control of the processor, and the memory includes the following steps:
Based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects, it consists of a core SNP group consisting of 12 SNPs shown in Table 1 and 450 SNPs shown in Table 2. An allele measurement process that measures alleles for at least 30 SNPs including SNPs selected from the pool SNP group (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. A computer program is recorded for causing the computer to perform an information providing process that provides information for determining the risk of developing desquamation glaucoma.

The present invention also includes a computer program for causing a computer to determine the risk of developing desquamation syndrome or desquamation glaucoma in a subject. For example, such a computer program is as follows.

A computer program recorded on a computer-readable medium that involves the following steps:
Based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects, it consists of a core SNP group consisting of 12 SNPs shown in Table 1 and 450 SNPs shown in Table 2. An allele measurement process that measures alleles for at least 30 SNPs including SNPs selected from the pool SNP group (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. An information providing step for providing information for determining the risk of developing desquamation glaucoma is executed to determine the risk of developing desquamation syndrome or desquamation glaucoma in the subject.

The medium may be a medium on which the computer program is non-temporarily recorded and readable by a computer.

Hereinafter, an example of an apparatus suitable for carrying out the method of the present invention will be described with reference to the drawings. However, this embodiment is not limited to this example. FIG. 1 is a schematic view showing an example of a device for determining the risk of developing desquamation syndrome or desquamation glaucoma in a subject. The determination device 10 shown in FIG. 1 includes a measuring device 20 and a computer system 30 connected to the measuring device 20.

In this embodiment, the measuring device 20 is a scanner or a mass spectrometer that detects a signal based on DNA bound to a probe on a microarray. In this embodiment, the signal is optical information such as a fluorescent signal or the result of mass spectrometry. When the microarray in contact with the measurement sample is set in the measuring device 20, the measuring device 20 acquires optical information or mass spectrometry results based on the nucleic acid derived from the biological sample of the subject bound to the probe on the microarray. , The obtained optical information or mass spectrometry result is transmitted to the computer system 30.

The scanner is not particularly limited as long as it can detect a signal based on the DNA bound to the probe on the microarray. Since the signal differs depending on the labeling substance used for labeling the DNA derived from the biological sample of the subject, the scanner can be appropriately selected according to the type of the labeling substance. For example, when the labeling substance is a fluorescent substance, a microarray scanner capable of detecting the fluorescence generated from the fluorescent substance is used as the measuring device 20.

When the SNP is detected by the next-generation sequence method or the direct sequence method, the measuring device 20 may be a device including a DNA amplification device and a sequence analysis device. In this case, a reaction solution containing a measurement sample, an enzyme for DNA amplification, a primer and the like is set in the measuring device 20, and the DNA in the reaction solution is amplified by the DNA amplification method. Then, the measuring device 20 analyzes the base sequence of the amplification product, acquires sequence information, and transmits the obtained sequence information to the computer system 30.

The computer system 30 includes a computer main body 300, an input unit 301, and a display unit 302 for displaying sample information, determination results, and the like. The computer system 30 receives optical information, mass spectrometry results, or sequence information from the measuring device 20. Then, the processor of the computer system 30 executes a program for determining the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the optical information, the mass analysis result, or the sequence information. As shown in FIG. 1, the computer system 30 may be a device separate from the measuring device 20 or a device including the measuring device 20. In the latter case, the computer system 30 may itself be the determination device 10.

As shown in FIG. 2, the computer main body 300 includes a CPU (Central Processing Unit) 310, a ROM (Read Only Memory) 311, a RAM (Random Access Memory) 312, a hard disk 313, an input / output interface 314, and the like. It includes a reading device 315, a communication interface 316, and an image output interface 317. The CPU 310, ROM 311, RAM 312, hard disk 313, input / output interface 314, read device 315, communication interface 316, and image output interface 317 are connected by a bus 318 so that data can be communicated. Further, the measuring device 20 is communicably connected to the computer system 30 by the communication interface 316.

The CPU 310 can execute the program stored in the ROM 311 and the program loaded in the RAM 312. The CPU 310 calculates the effectiveness prediction value, reads the discriminant stored in the ROM 311 and determines the effectiveness. The CPU 310 outputs the determination result and displays it on the display unit 302.

The ROM 311 is composed of a mask ROM, a PROM, an EPROM, an EEPROM, and the like. As described above, the program executed by the CPU 310 and the data used for the program are recorded in the ROM 311. A preset threshold value or the like may be recorded in the ROM 311.

The RAM 312 is composed of SRAM, DRAM, and the like. The RAM 312 is used to read the program recorded in the ROM 311 and the hard disk 313. The RAM 312 is also used as a work area for the CPU 310 when executing these programs.

The hard disk 313 is installed with a computer program such as an operating system for the CPU 310 to execute, an application program (a computer program for determining the risk of developing scrap syndrome or scrap glaucoma), and data used for executing the computer program. .. A preset threshold value or the like may be recorded on the hard disk 313.

The reading device 315 is composed of a flash memory, a flexible disc drive, a CD-ROM drive, a DVD ROM drive, and the like. The reading device 315 can read the program or data recorded on the portable recording medium 40. It may also be described as a reading device.

The input / output interface 314 is composed of, for example, a serial interface such as USB, IEEE1394, RS-232C, a parallel interface such as SCSI, IDE, IEEE1284, and an analog interface including a D / A converter and an A / D converter. It is configured. An input unit 301 such as a keyboard and a mouse is connected to the input / output interface 314. The operator can input various commands to the computer main body 300 by the input unit 301.

The communication interface 316 is, for example, an Ethernet (registered trademark) interface or the like. The computer body 300 can also transmit print data to a printer or the like by the communication interface 316.

The image output interface 317 is connected to a display unit 302 composed of an LCD, a CRT, or the like. As a result, the display unit 302 can output a video signal corresponding to the image data given by the CPU 310. The display unit 302 displays an image (screen) according to the input video signal.

Next, a processing procedure for determining the high or low risk of developing desquamation syndrome or desquamation glaucoma by the determination device 10 will be described. Here, a case where risk allergen information is acquired from fluorescence information based on DNA derived from a biological sample of a subject bound to a probe on a microarray and a judgment is performed using the obtained measured values will be described as an example. However, this embodiment is not limited to this example.

With reference to FIG. 3, in step S101, the CPU 310 of the determination device 10 acquires fluorescence information from the measuring device 20. Next, in step S102, the CPU 310 calculates the fluorescence intensity from the acquired fluorescence information and stores it in the RAM 312. Then, in step S103, the CPU 310 determines the presence or absence of each variant and its type from the fluorescence intensity stored in the RAM 312, and calculates the total number of risk alleles according to the allele data stored in the ROM 311 or the hard disk 313.

Then, in step S104, the CPU 310 uses the calculated effectiveness prediction value and the preset threshold value stored in the ROM 311 or the hard disk 313 to increase or decrease the risk of developing desquamation syndrome or desquamation glaucoma in the subject. Is determined. Here, when the total number of risk alleles is smaller than the preset threshold value, the process proceeds to step S105, and the CPU 310 determines that the subject has a low risk of developing desquamation syndrome or desquamation glaucoma. Remember in. On the other hand, when the total number of risk alleles is not lower than the preset threshold value (that is, when the total number of risk alleles is equal to or greater than the cutoff value), the process proceeds to step S106, and the CPU 310 determines the desquamation syndrome in the subject. Alternatively, the determination result indicating that the risk of developing desquamation glaucoma is high is stored in the RAM 312.

Then, in step S107, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide a doctor or the like with information that assists in determining whether or not the subject has a high risk of developing desquamation syndrome or desquamation glaucoma.

Hereinafter, the present invention will be specifically described with reference to examples. This example is merely an example of the present invention and does not mean any limitation. In the following examples, molecular cloning (Joseph Sambrook et al., Molecular Cloning --A Laboratory Manual, 3rd Edition, Cold Spring Harbor Laboratory) will be described for commonly used molecular biology techniques that are not described in detail. The methods and conditions described in the textbooks such as Press, 2001) are used.

Test Example 1 Selection of marker SNP (core SNP + pool SNP) 201 patients diagnosed with shavings syndrome or shavings glaucoma (patients with shavings syndrome or shavings glaucoma), and 201 patients diagnosed as not having shavings syndrome or shavings glaucoma, and , 697 non-patients who were judged not to have a family history of glaucoma by interview, and genomic DNA was extracted from each blood using a commercially available automatic nucleic acid extractor. Genomic DNA was extracted according to the instruction manual of the device and kit. By this method, about 5 μg of genomic DNA was obtained from 350 μL of blood sample.

For SNP analysis, 906,600 SNP genotype data using a commercially available microarray type SNP analysis kit DNA microarray (Genome-Wide Human SNP Array 6.0) capable of analyzing about 1 million known SNPs on the human genome. Was obtained, and 652,792 high-precision SNP data were selected using a QC filter (Call Rate, ≧ 0.95; MAF, ≧ 0.01; HWE, ≧ 0.001). Furthermore, 697 SNP marker candidate groups were extracted by the following process.
(1) P <0.001 was used as the extraction condition in genome-wide association analysis (χ ² test using allelic data).
(2) For all the extracted SNPs, cluster defective SNPs were excluded by visual judgment by three examiners based on 2D cluster plot images obtained from Affymetrix's Genotyping Console.

462 marker SNPs were selected from the SNP marker candidate group by the following procedure. The marker SNP consists of 12 core SNPs and 450 pool SNPs.
(1) The genotype data was coded (numerical conversion) and normalized according to the following procedure.
(a) Risk Allele Homo: 2, Risk Allele Hetero: 1, Other Allele Homo: 0.
(b) Numerical conversion was performed by normalizing the numerical values according to the above formula using the mean value and the observed allele frequency in each group of patients with desquamation syndrome or desquamation glaucoma and non-patients.
(2) The combination of SNP marker candidate groups considering linkage disequilibrium (LD) was calculated by cluster analysis using principal component analysis (PCA).
(a) Using the SNP marker candidate group, all the samples of the patient group and the non-patient group are submitted to the PCA, and the factor loading (principal component and original variables) obtained by information reduction (Cluster SNP) with the samples. Corresponds to the correlation coefficient between) was calculated. Next, candidate regions were determined by clusters based on the principal component showing the absolute value of the highest factor loading of each SNP, and the SNP that obtained the smallest P value in each candidate region was used as a marker SNP candidate. ..
(b) Using the SNP marker candidate group for each chromosome, candidate regions were determined for each chromosome in the same manner as in (a), and were used as marker SNP candidates.
(c) The marker SNP candidates of (a) and (b) were combined to eliminate duplication.

Acquisition of core SNPs Using the top 67 marker SNPs, mass array reproduction experiments were performed using 254 patients and 1,052 non-patients in different populations. A related analysis (χ ² test with allelic data) was performed using the SNP group that passed through the QC filter (Call Rate, ≧ 0.9; MAF, ≧ 0.01; HWE, ≧ 0.001). Regarding the reproducibility of genome-wide association studies and reproduction experiments using mass arrays, 12 SNPs based on the Cochran-Mantel-Henzel test results (P <0.0008) were used as core SNPs.

Acquisition of pooled SNPs The group obtained by removing core SNPs from marker SNPs was designated as pooled SNPs.

Example 1
For 200 patients in the patient group and 200 in the non-patient group, blood was collected and genomic DNA was extracted in the same manner as in Test Example 1, and allelic data was hybridized using the probes shown in Example 1 (Table 3). And counted the total number of risk alleles. The probes used in Example 1 are the 12 probes shown in Table 1 and the 18 probes selected from Table 2.

A frequency distribution map with the total number of risk alleles on the horizontal axis is created, and the results of ROC analysis are shown in FIG. From FIG. 4, in Example 1, the sensitivity is 72.5%, the specificity is 80.0%, the AUC is 0.846 when the cutoff value is 31, and the frequency distribution map shows that there is no risk of onset and the group has no risk of onset. It can be seen that the group can be distinguished.

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

(Aspect 1)
Measurements were performed using all 90 probes (12 in the core SNP group and 78 in the pool SNP group) shown in Table 4 above, and the total number of risk alleles was counted. A frequency distribution map with the total number of risk alleles on the horizontal axis is created, and the results of ROC analysis are shown in FIG. From FIG. 5, the AUC is 0.961 when the cutoff value is 67 with a sensitivity of 90.5% and a specificity of 85.5%, and the frequency distribution chart also distinguishes between a group having an onset risk and a group having no onset risk. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the first aspect were divided into 30 probes, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 6, the result of the first measurement is the sensitivity of 79.0% and the cutoff value at the specificity of 77.0% is 20, and the result of the second measurement is the cutoff value at the sensitivity of 81.5% and the specificity of 73.0%. The number was 22, and the result in the third measurement was a sensitivity of 77.5% and the cutoff value at a specificity of 72.5% was 25, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 7 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in aspect 2, after obtaining the judgment results for each group, the number probability (mean risk of onset, 95% credible interval) having the onset risk based on the number of risk alleles possessed was calculated using Bayes' theorem. .. In addition, using Bayes' theorem, we calculated the area within the range of the density at which the probability of having the probability of developing the probability density function is 70 or more, and showed the probability of the risk of developing it with a probability of 70% or more. The results are shown in FIG.

The analysis using Bayes' theorem was performed according to the following procedure.
(a) Prior distribution π (θ) is a uniform distribution (non-information prior distribution). The beta distribution was adopted as the prior distribution.
(b) For the likelihood, 200 patients in the patient group and 200 patients in the non-patient group were randomly selected from all the samples in the patient group and the non-patient group, and the number of data (observations) and the number of patients (positive) for each risk allele possession number. Calculated from the number). The distribution adopted the binomial distribution.
(c) The posterior distribution was calculated from Bayes' theorem (posterior distribution π (θ | D) ∝ prior distribution × likelihood).
(d) From the posterior distribution, the mean onset risk (%), 95% credible interval (%), and probability of having onset risk (%) were calculated.

Example 3
Data acquisition was performed in the same manner as in Example 1 except that the probes used in Examples 1 and 2 were different. Specifically, the probes shown in Table 5 below were used.

(Aspect 1)
Measurements were performed using all 120 probes (12 in the core SNP group and 108 in the pool SNP group) shown in Table 5 above, and the total number of risk alleles was counted. FIG. 9 shows the frequency distribution map of the risk allele and the result of ROC analysis in the same manner as in Example 1. From FIG. 9, the AUC is 0.959 when the cutoff value is 93 with a sensitivity of 92.5% and a specificity of 84.5%, and the frequency distribution chart also distinguishes between a group having an onset risk and a group having no onset risk. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the above aspect 1 were divided into 40 pieces each, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 10, the result of the first measurement is the sensitivity of 76.5% and the cutoff value at the specificity of 79.5% is 27, and the result of the second measurement is the cutoff value at the sensitivity of 70.0% and the specificity of 86.5%. The number was 34, the result in the third measurement was sensitivity 73.0%, and the cutoff value at specificity 80.0% was 36, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 11 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in the second aspect, after obtaining the judgment result in each group, the average onset risk (%), the 95% credible interval (%), and the onset with a probability of 70% or more are the same as in the second embodiment. The probability of risk (%) was calculated. The results are shown in FIG. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

Example 4
Data acquisition was performed in the same manner as in Example 1 except that the probes used in Examples 1, 2 and 3 were different. Specifically, the probes shown in Table 6 below were used.

(Aspect 1)
Measurements were performed using all 150 probes (12 in the core SNP group and 138 in the pool SNP group) shown in Table 6 above, and the total number of risk alleles was counted. FIG. 13 shows the frequency distribution map of the risk allele and the result of ROC analysis in the same manner as in Example 1. From FIG. 13, the AUC is 0.971 when the cutoff value is 120 with a sensitivity of 90.0% and a specificity of 91.5%, and the frequency distribution chart also distinguishes between a group having an onset risk and a group having no onset risk. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the above aspect 1 were divided into 40 pieces each, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 14, the result of the first measurement is the sensitivity of 80.0% and the cutoff value at the specificity of 80.5% is 38, and the result of the second measurement is the cutoff value at the sensitivity of 78.5% and the specificity of 81.5%. The number was 40, the result in the third measurement was sensitivity 76.5%, and the cutoff value at specificity 75.5% was 43, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 15 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in the second aspect, after obtaining the judgment result in each group, the average onset risk (%), the 95% credible interval (%), and the onset with a probability of 70% or more are the same as in the second embodiment. The probability of risk (%) was calculated. The results are shown in FIG. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

Example 5 Marker SNP Feedback Improvement Loop by Bayesian Approach Accumulation of data by obtaining onset and negative results of desquamation syndrome or desquamation glaucoma by post-judgment follow-up study of Example 2 or Example 3 or Example 4. And update (additional learning). Additional learning updates the Bayesian prior distribution π (θ) with the classification results newly obtained by the follow-up study.
-Perform a related analysis (χ ² test using allelic data) by adding the genotype data newly obtained by the follow-up study to the conventional results, and replace the pool SNP. In addition, when replacing, the cutoff value is reset by recalculating the ROC analysis of each measurement in the "information acquisition process", the prior distribution of Bayes in the "information provision process" is forgotten, and the uniform distribution is performed. Recalculate as (no information prior distribution).
-If the result of the χ ² test using allelic data changes due to the accumulation and update of data, the marker candidate SNPs are replaced.

By analyzing the SNP allele of the present invention on the DNA derived from the subject by the method of the present invention, it is possible to determine the high or low risk of developing desquamation syndrome or desquamation glaucoma of the subject. Based on this risk, the subject can take preventive measures for desquamation syndrome or desquamation glaucoma or receive appropriate treatment, including preemptive treatment. Further, by using the SNP of the present invention to select a person having a high risk of developing desquamation syndrome or desquamation glaucoma and conducting a clinical trial of a glaucoma therapeutic agent, the period of the clinical trial of the glaucoma therapeutic agent can be shortened, which is useful. Is.

10 Judgment device 20 Measuring device 30 Computer system 40 Recording medium 300 Computer body 301 Input unit 302 Display unit 310 CPU
311 ROM
312 RAM
313 Hard disk 314 Input / output interface 315 Read device (reader)
316 communication interface 317 image output interface 318 bus

Claims (14)

Based on single nucleotide polymorphism (SNP) allele information in biological samples containing genomic DNA collected from subjects, a core SNP group consisting of 12 SNPs shown in Table 1 and 450 pieces shown in Table 2 Aller measurement process for measuring alleles for at least 30 SNPs including SNPs selected from the pool SNP group consisting of SNPs (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. Including an information providing step that provides information for determining the risk of developing desquamation glaucoma.
The at least 30 SNPs include a core SNP group consisting of the 12 SNPs listed in Table 1.
The information acquisition step includes a step of determining whether or not the measured allele is a risk allele and counting the total number of risk alleles.
In the information acquisition step, if the total number exceeds a preset threshold, the subject has a high risk of developing desquamation syndrome or desquamation glaucoma, and if it falls below the total number, the subject develops desquamation syndrome or desquamation glaucoma. Information is obtained that the risk of glaucoma is low,
A method to assist the subject in diagnosing the risk of developing desquamation syndrome or desquamation glaucoma. The allele measurement process
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. This is a step including a second measurement step of measuring the second SNP group.
Here, the method according to claim 1, wherein the first pool selection SNP group and the second pool selection SNP group are not the same. The allele measurement process
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. The second measurement step for measuring the second SNP group, and
At least one or more SNPs (third core SNP group) different from the first core SNP group and the second core SNP group and a plurality of SNPs selected from the pool SNP group (third pool selection SNP group) are combined. It is a step including a third measurement step of measuring the third SNP group including 30 SNPs.
Here, the method according to claim 1, wherein the first pool selection SNP group, the second pool selection SNP group, and the third pool selection SNP group are all non-identical. The method according to any one of claims 1 to 3, wherein the biological sample containing genomic DNA is a biological sample containing blood cells. The method according to any one of claims 1 to 3, wherein the biological sample containing genomic DNA is a biological sample containing leukocytes. When the information providing step includes a plurality of measurement steps, the result of each measurement step regarding the subject obtained by the information acquisition step is based on the SNP group used in each measurement step. Any of claims 2 to 5, which comprises a step of providing information on the high or low risk of the subject developing desquamation syndrome or desquamation glaucoma, using whether or not the preset threshold is exceeded as an index. The method described. The information providing step is a step including applying the Bayes' theorem to the result obtained in the information acquisition step to calculate the probability that the subject has a risk of developing desquamation syndrome or desquamation glaucoma, claim 1. The method described in any of 5 to 5. The information providing process applies the Bayes' theorem to the results obtained in the information acquisition process to determine the probability that the subject has a risk of developing desquamation syndrome or desquamation glaucoma with a preset percentage (%) or more. The method according to any one of claims 1 to 5, which is a step including a calculation step. The memory comprises a processor and a computer including a memory under the control of the processor.
Based on single nucleotide polymorphism (SNP) allele information in biological samples containing genomic DNA collected from subjects, a core SNP group consisting of 12 SNPs shown in Table 1 and 450 pieces shown in Table 2 Aller measurement process for measuring alleles for at least 30 SNPs including SNPs selected from the pool SNP group consisting of SNPs (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. A computer program for causing the computer to execute an information providing process that provides information for determining the risk of developing desquamation glaucoma is recorded.
The at least 30 SNPs include a core SNP group consisting of the 12 SNPs listed in Table 1.
The information acquisition step includes a step of determining whether or not the measured allele is a risk allele and counting the total number of risk alleles.
In the information acquisition step, if the total number exceeds a preset threshold, the subject has a high risk of developing desquamation syndrome or desquamation glaucoma, and if it falls below the total number, the subject develops desquamation syndrome or desquamation glaucoma. Information is obtained that the risk of glaucoma is low,
A detector for a subject at risk of developing desquamation syndrome or desquamation glaucoma. The detection device according to claim 9, wherein the biological sample containing genomic DNA is a biological sample containing blood cells. The detection device according to claim 9, wherein the biological sample containing genomic DNA is a biological sample containing white blood cells. A computer that includes a processor and memory under the control of the processor.
Based on single nucleotide polymorphism (SNP) allele information in biological samples containing genomic DNA collected from subjects, a core SNP group consisting of 12 SNPs shown in Table 1 and 450 pieces shown in Table 2 Aller measurement process for measuring alleles for at least 30 SNPs including SNPs selected from the pool SNP group consisting of SNPs (pool selection SNP group).
An information acquisition step for acquiring information on the risk of developing desquamation syndrome or desquamation glaucoma in the subject based on the measurement results of the allele, and the desquamation syndrome or the desquamation syndrome of the subject based on the information obtained above. Execute an information providing process that provides information for determining the risk of developing desquamation glaucoma .
The at least 30 SNPs include a core SNP group consisting of the 12 SNPs listed in Table 1.
The information acquisition step includes a step of determining whether or not the measured allele is a risk allele and counting the total number of risk alleles.
In the information acquisition step, if the total number exceeds a preset threshold, the subject has a high risk of developing desquamation syndrome or desquamation glaucoma, and if it falls below the total number, the subject develops desquamation syndrome or desquamation glaucoma. Information is obtained that the risk of glaucoma is low,
Computer program. The computer program according to claim 12, wherein the biological sample containing genomic DNA is a biological sample containing blood cells. The computer program according to claim 12, wherein the biological sample containing genomic DNA is a biological sample containing white blood cells.

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