KR102254341B1 - methods for diagnosing the high risk group of Diabetes based on Genetic Risk Score - Google Patents

Abstract

The present invention relates to a kit for diagnosing diabetes or predicting a disease occurrence risk and a diagnosis method using the same. The method calculates genetic risk scores for 175 genetic mutations, which are pre-reported in relation with second type diabetes, and 56 genetic mutations, which show an association with fasting blood sugar, so as to analyze the same in a complex manner, thereby selecting a genetic high risk group of the second type diabetes by using only genetic information of each person without additionally inspecting an age, a gender, an environmental effect, and the like and being used in customized diagnosis, treatment and prevention according to genetic high risk group selection of the second type diabetes.

Description

Diagnosing the high risk group of diabetes based on genetic risk assessment {methods for diagnosing the high risk group of Diabetes based on Genetic Risk Score}

The present invention relates to a method for diagnosing a high risk group of diabetes based on genetic risk assessment.

Diabetes, a metabolic disease with reduced carbohydrate utilization and enhanced lipid and protein utilization, is caused by an absolute or relative deficiency of insulin. In more severe cases, diabetes is characterized by chronic hyperlipidemia, diabetes, loss of water and electrolytes, ketoacidosis, and coma. Long-term complications include neuropathy, nephropathy, generalized degenerative changes in large and small vessels, and increased susceptibility to infection. The most common form of diabetes is type 2 diabetes, characterized by impaired insulin secretion in target tissues and hyperglycemia due to insulin resistance.

Type 2 diabetes is characterized by insulin resistance, and it is believed that the lack of response to insulin in human tissues is related to insulin receptors. An abnormal condition that appears in the early stages of type 2 diabetes is a decrease in insulin sensitivity, and at this stage, hyperglycemia can be reversed by medications and various measures that improve insulin sensitivity or reduce hepatic glucose production. Type 2 diabetes is primarily due to lifestyle factors or inheritance, and several lifestyle factors, including obesity (defined as a body mass index greater than 30), lack of exercise, poor diet, stress, and urbanization, are responsible for the development of type 2 diabetes. It is known to be important.

In addition, according to the Korean Diabetes Association, it is estimated that as of 2016, 1 out of 7 adults over the age of 30 is diabetic, and there are about 5,100,000 patients nationwide. Diabetes medical expenses increased 33.3% from 1.4 trillion won in 2010 to 1.8 trillion won in 2015, accelerating the socio-economic burden of diabetes in Korea.

Since the levels of clinical epidemiologic variables such as blood sugar and glycated hemoglobin and various markers, which have been used for diabetic diagnosis, mainly differ after the 40s when the incidence of diabetes increases rapidly, they can be used to predict diabetes against short-term changes. However, there is a problem that it is difficult to select high-risk groups early and use them for prevention before their 40s. In addition, a method for predicting type 2 diabetes using dozens of previously discovered genetic variation information has been proposed, but there is a disadvantage in that the disease prediction rate is low (about 70% level) and the statistical explanation for the disease is low. . In a recent study, about 300 genetic mutations related to type 2 diabetes have been reported using a genome-wide association study (GWAS). When analyzing the genetic high-risk group of type 2 diabetes, which is the top 5% of the population, using this genetic variation information, it was confirmed that the incidence of diabetes was about 2-3 times higher than that of the remaining 95% of the population. However, it was found that among the high-risk groups selected at this time, there were also a significant number of normal groups without diabetes. Therefore, it is necessary to develop a kit with excellent accuracy for screening a genetically high-risk group of type 2 diabetes.

An object of the present invention is to provide a method of providing information necessary for diagnosing or predicting the onset of type 2 diabetes.

In addition, another object of the present invention is to provide a polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes.

In addition, another object of the present invention is to provide a composition for diagnosing or predicting the onset of type 2 diabetes, including the marker composition.

In addition, another object of the present invention is to provide a kit for predicting the risk of developing type 2 diabetes, including the marker composition.

Calculating a genetic risk score (GRS) for a fasting blood sugar-related polymorphic marker including a polymorphic site located in the sixth nucleotide sequence of SEQ ID NOs: 1 to 26 from the biological sample;

Calculating a genetic risk score (GRS) for a type 2 diabetes-related polymorphic marker from the biological sample;

Selecting a high-risk group based on each of the fasting glucose genetic risk score and the type 2 diabetes genetic risk score;

A method of providing information necessary for diagnosing or predicting the onset of type 2 diabetes, including; determining as a high risk group for type 2 diabetes if it belongs to both the high risk group based on the genetic risk of fasting blood sugar and the high risk group based on the type 2 diabetes genetic risk. to provide.

Subsequently, in the polynucleotide represented by the nucleotide sequence of SEQ ID NO: 1 to 26, a polynucleotide composed of 10 or more consecutive nucleotide sequences including a polymorphic site located at the sixth position of each nucleotide sequence or a complementary polynucleotide thereof It provides a polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes, including primers or probes that specifically bind to nucleotides.

In addition, the present invention provides a composition for diagnosing or predicting the onset of type 2 diabetes including the marker composition.

Finally, the present invention provides a kit for diagnosing or predicting the onset of type 2 diabetes including the marker composition.

The method for diagnosing or predicting the onset of type 2 diabetes of the present invention scores the genetic risk of 175 genetic mutations previously reported in relation to type 2 diabetes and 56 genetic mutations that are associated with fasting blood sugar. By analyzing, it is possible to select the genetically high-risk group of type 2 diabetes using only the genetic information of each individual without additional tests such as age, sex, and environmental impact, and customized treatment and treatment according to the selection of the genetically high-risk group of type 2 diabetes. And can be used for prevention.

1 is a schematic diagram of a flow chart of quality control of Korean chip genome information.
2 is a table showing the main contents of the Korean chip.
3 is a table showing basic statistical data of an analysis subject.
4 is a graph showing the results of a Manhattan plot of 56 genetic variants related to fasting blood sugar.
5 is a graph showing the change in the prevalence of type 2 diabetes according to fasting blood sugar.

Hereinafter, terms used in the present invention will be described in detail.

The term “polymorphism” as used in the present invention refers to a case in which two or more alleles exist in one locus. Among the polymorphic sites, only a single base differs from one person to another. It is called single nucleotide polymorphism (SNP). Preferred polymorphic markers have two or more alleles that exhibit a frequency of occurrence of at least 1%, more preferably at least 5% or 10% in the selected population. Thus, a genetic association for a polymorphic marker means that there is an association for one or more specific alleles of a specific polymorphic marker. Markers can include any variant form of allele found in the genome, including single base polymorphism (SNP), microsatellite, insertions, deletions, duplications and translocations.

The term "allele" or "allele" used in the present invention refers to several types of one gene present at the same locus of a homologous chromosome. Alleles are also used to indicate polymorphism, for example, SNPs have two types of alleles.

The term “rs_id” used in the present invention refers to rs-ID, which is an independent marker assigned to all SNPs initially registered by NCBI, which has started accumulating SNP information since 1998. In the present invention, it is described in the same form as rs831571. The rs_id described in this table refers to the SNP marker, which is a polymorphic marker of the present invention. Those skilled in the art will be able to easily identify the location and sequence of the SNP using the rs_id. The specific sequence corresponding to the NCBI's dbSNP (The Single Nucleotide Polymorphism Database) number, rs_id, may change slightly over time. It will be apparent to those skilled in the art that the scope of the present invention also extends to the altered sequence.

The term “diagnosis or onset prediction” used in the present invention includes all types of analysis used to predict disease occurrence and determine or derive the risk of disease occurrence, and preferably determine whether or not type 2 diabetes is diagnosed. Or it could be predicting the risk of developing it.

Hereinafter, the present invention will be described in more detail.

The present inventors obtained about 8.3 million single nucleotide polymorphism (SNP) information through genomic information analysis of 125,872 people, and reported 26 new genetic mutations and 30 groups showing association with fasting blood sugar using this information. The genetic variation was discovered. In addition, the genetic risk of each of the 175 genetic mutations previously reported related to type 2 diabetes and 56 genetic mutations associated with fasting blood sugar, the higher the incidence of type 2 diabetes in the case of high-risk groups. It was confirmed that the prediction accuracy for the enemy high-risk group was remarkably increased, and the present invention was completed.

The present invention is a step of calculating a genetic risk score (GRS) for a fasting blood sugar-related polymorphic marker including a polymorphic site located at the sixth nucleotide sequence of SEQ ID NOs: 1 to 26 from a biological sample. ;

Selecting a high-risk group based on each of the fasting glucose genetic risk score and the type 2 diabetes genetic risk score;

A method of providing information necessary for diagnosing or predicting the onset of type 2 diabetes, including; determining as a high risk group for type 2 diabetes if it belongs to both the high risk group based on the genetic risk of fasting blood sugar and the high risk group based on the type 2 diabetes genetic risk. to provide.

In addition, the fasting blood glucose-related polymorphic marker may further include a polymorphic marker positioned at the 6th nucleotide sequence of SEQ ID NOs: 27 to 56, but is not limited as long as it is a polymorphic marker related to fasting blood glucose known in the art. .

In addition, the biological sample may be selected from the group consisting of tissue, cells, whole blood, serum, plasma, and saliva, but is not limited thereto.

In addition, the genetic risk (Genetic Risk Score, GRS) may be calculated using Equation 1.

[Equation 1]

In Equation 1,

n means the total number of polymorphic markers.

i means the polymorphic marker sequence number.

j stands for the sample number.

β is the beta coefficient, meaning the genetic effect (effect size) of the polymorphic marker.

x means the allele value of one of 0, 1, or 2 in the j sample depending on the presence or absence of an effective allele. The value of the allele is replaced with a real value between 0 and 2 when using genotype imputation.

In addition, the Genetic Risk Score (GRS) is preferably assigned the number of risk alleles present in each SNP locus as a detailed score and calculated as the sum of the detailed scores.

In one embodiment of the present invention, the step of calculating the genetic risk score is the effect of the locus of all polymorphic markers shown in Tables 1 and 2 below from a biological sample. Determining the presence or absence of; A step of assigning a value of one allele of 0, 1, or 2 to each locus according to the presence or absence of an effective allele of each locus, and in the absence of an effective allele Assigning a value of 0, 1 when the effect allele is present in only one haploid, and 2 when the effect allele is present in both haploids; And by comparing the frequency of the effect allele of each locus in type 2 diabetic patients with the frequency of the effect allele in the normal control group, and calculating the beta coefficient (β) at each locus to calculate the genetic risk score (GRS). It may include;

Here, as an example, the weighted genetic risk score (wGRS) method has been described for disease genetic risk, but the present invention is not limited thereto, and various methods such as genetic risk score, polygenic risk score, machine learning method, linear regression analysis method, etc. Risk calculation methods can be used.

In addition, the genetic risk may be converted into a relative value compared to the disease genetic risk of other users in the group to which the user belongs (eg, the same country, the same residential area, the same age group, etc.). At this time, the reason for converting the disease genetic risk to a relative value is that the absolute value of the genetic risk score can vary greatly depending on the number of genetic markers used, and the difference between the disease genetic risk and the phenotypic genetic risk. Since the calculation methods can be different from each other, this is for normalization in relative order.

In addition, the effective allele means an allele that is the criterion of the effect size, and in the present invention, in the polynucleotide represented by the nucleotide sequences of SEQ ID NOs: 1 to 26, the sixth of each nucleotide sequence If the polymorphic site is an effective allele, and the effect size is a positive mistake, it can be predicted that there is a high risk of fasting blood sugar and type 2 diabetes.

In addition, the above method can predict that the risk of developing type 2 diabetes is high if the polymorphic site of SEQ ID NOs: 1 to 26 has an isotype or heterogeneous allele.

Specifically, when the genotype of the polymorphic site of SEQ ID NO: 1 is A,

When the genotype of the polymorphic site of SEQ ID NO: 2 is A,

When the genotype of the polymorphic site of SEQ ID NO: 3 is ATCTCTC,

When the genotype of the polymorphic site of SEQ ID NO: 4 is G,

When the genotype of the polymorphic site of SEQ ID NO: 5 is A,

When the genotype of the polymorphic site of SEQ ID NO: 6 is C,

When the genotype of the polymorphic site of SEQ ID NO: 7 is T,

When the genotype of the polymorphic site of SEQ ID NO: 8 is A,

When the genotype of the polymorphic site of SEQ ID NO: 9 is T,

When the genotype of the polymorphic site of SEQ ID NO: 10 is A,

When the genotype of the polymorphic site of SEQ ID NO: 11 is C,

When the genotype of the polymorphic site of SEQ ID NO: 12 is A,

When the genotype of the polymorphic site of SEQ ID NO: 13 is T,

When the genotype of the polymorphic site of SEQ ID NO: 14 is C,

When the genotype of the polymorphic site of SEQ ID NO: 15 is A,

When the genotype of the polymorphic site of SEQ ID NO: 16 is C,

When the genotype of the polymorphic site of SEQ ID NO: 17 is C,

When the genotype of the polymorphic site of SEQ ID NO: 18 is G,

When the genotype of the polymorphic site of SEQ ID NO: 19 is C,

When the genotype of the polymorphic site of SEQ ID NO: 20 is AAAAC,

When the genotype of the polymorphic site of SEQ ID NO: 21 is C,

When the genotype of the polymorphic site of SEQ ID NO: 22 is A,

When the genotype of the polymorphic site of SEQ ID NO: 23 is G,

When the genotype of the polymorphic site of SEQ ID NO: 24 is T,

When the genotype of the polymorphic site of SEQ ID NO: 25 is G, or

When the genotype of the polymorphic site of SEQ ID NO: 26 is C, if the effect size is a positive real number, it can be predicted that the risk of developing fasting blood sugar and type 2 diabetes is high.

In addition, the diagnosis is preferably for Koreans, but is not limited thereto.

In addition, identification of the genotype for the polymorphic marker of the present invention includes sequencing analysis, sequencing analysis using an automatic sequencing analyzer, pyrosequencing, hybridization by microarray, PCR-RELP method (restriction fragment length polymorphism), PCR -SSCP method (single strand conformation polymorphism), PCR-SSO method (specific sequence oligonucleotide), ASO (allele specific oligonucleotide) hybridization method combining PCR-SSO method and dot hybridization method, TaqMan-PCR method, MALDI-TOF/MS method , RCA method (rolling circle amplification), HRM (high resolution melting) method, primer extension method, Southern blot hybridization method, dot hybridization method, and the like. The analysis results can be statistically processed using statistical analysis methods commonly used in the art, for example, Student's t-test, Chi-square test, Continuous variables, categorical variables, odds ratio, and 95% confidence intervals obtained through linear regression line analysis, multiple logistic regression analysis, etc. confidence interval).

Subsequently, in the polynucleotide represented by the nucleotide sequence of SEQ ID NO: 1 to 26, a polynucleotide composed of 10 or more consecutive nucleotide sequences including a polymorphic site located at the 6th nucleotide sequence or a complementary polynucleotide thereof It provides a polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes, including primers or probes that specifically bind to nucleotides.

In addition, the polymorphic site may be single nucleotide polymorphism, and if the polymorphic site has an effective allele, it can be predicted that the risk of developing type 2 diabetes is high. The effective alleles of the polymorphic sites of SEQ ID NOs: 1 to 26 are as described above.

In addition, the primer or probe that specifically binds to the polynucleotide or its complementary polynucleotide is allele-specific.

The "primer" refers to a short nucleic acid sequence that can form a base pair with a complementary template as a nucleic acid sequence having a short free hydroxyl group and serves as a starting point for copying the template strand. The primer of the present invention can be chemically synthesized using a method known in the art, such as, for example, a phosphoramidite solid support method.

The "probe" refers to a nucleic acid fragment such as RNA or DNA composed of several to hundreds of bases capable of specifically binding to mRNA, and is labeled so that the presence or absence of a specific mRNA can be confirmed. Probes may be prepared in the form of oligonucleotide probes, single-stranded DNA probes, double-stranded DNA probes, RNA probes, and the like, and may be labeled with biotin, FITC, rhodamine, DIG, or the like, or labeled with radioactive isotopes.

In addition, the probe can be labeled with a detectable substance such as a radioactive label that provides a suitable signal and has a sufficient half-life. Labeled probes can be hybridized to nucleic acids on solid supports as known from Sambook et al., Molecular Cloning, A LaboratoryMannual, 1989.

In addition, the probe is an allele-specific probe, and a polymorphic site exists in a nucleic acid fragment, and thus hybridizes to a nucleic acid fragment containing one allele, but may not hybridize to a nucleic acid fragment containing another allele. In this case, the hybridization conditions should be sufficiently stringent so that only one of the alleles is hybridized to show a significant difference in hybridization strength between alleles. Preferably, the probe may be single stranded for maximum efficiency in hybridization, but is not limited thereto.

In addition, the present invention provides a composition for diagnosing or predicting the onset of type 2 diabetes, including the polymorphic marker composition.

In addition, the composition is a second polymorphic marker including a primer or probe capable of detecting a polymorphic site corresponding to the sixth base in the polynucleotide represented by the nucleotide sequence of SEQ ID NO: 1 to 26, which is a characteristic of the present invention. Diabetes type diabetes can be diagnosed or predicted onset.

In addition, detection of a specific nucleic acid using a primer can be performed by amplifying the sequence of the target gene using an amplification method such as PCR, and then confirming whether the gene is amplified by a method known in the art. In addition, detection of a specific nucleic acid using a probe may be performed by contacting a sample nucleic acid with a probe under suitable conditions and then confirming the presence or absence of a hybridized nucleic acid.

In addition, methods for detecting a specific nucleic acid using the probe or primer include, but are not limited to, polymerase chain reaction (PCR), DNA sequencing, RT-PCR, primer extension method (Nikiforeov et al. ., Nucl Acids Res 22, 4167-4175, 1994), oligonucleotide extension analysis (Nickerson et al., Pro Nat Acad Sci USA, 87, 8923-8927, 1990), allele-specific PCR method (Rust et al. , Nucl Acids Res, 6, 3623-3629, 1993), RNase mismatch cleavage (Myers et al., Science, 230, 1242-1246, 1985), single strand conformation lymorphism, Orita et al., Pro Nat Acad Sci USA, 86, 2766-2770, 1989), SSCP and heteroduplex simultaneous analysis (Lee et al., Mol Cells, 5:668-672, 1995), denaturing gradient gel electrophoresis (DGGE , Cariello et al., Am J Hum Genet, 42, 726-734, 1988), denaturing high pressure liquid chromatography (Underhill et al., Genome Res, 7, 996-1005, 1997), hybridization reaction, DNA chip, etc. have. Examples of the hybridization reaction include Northern hybridization (Maniatis T. et al., Molecular Cloning, Cold Spring Habor Laboratory, NY, 1982), in situ hybridization (Jacquemier et al., Bull Cancer, 90:31-8). , 2003) and microarray (Macgregor, Expert, Rev Mol Diagn 3:185-200, 2003) methods.

The composition for diagnosing or predicting the onset of type 2 diabetes of the present invention may further include a reagent generally used in the method for detecting the above-described nucleic acid. For example, metal ion salts such as dNTP (deoxynulceotide triphosphate), heat-resistant polymerase, and magnesium chloride required for PCR reaction may be included, and dNTP and sequenase required for sequencing may be included. I can.

Finally, the present invention provides a kit for diagnosing or predicting the onset of type 2 diabetes, including the polymorphic marker composition.

In addition, the kit may include a polynucleotide, a primer, or a probe for identifying one or more of the polymorphic markers, as well as one or more other component compositions, solutions, or devices suitable for an analysis method.

For example, the kit of the present invention may be a kit including essential elements necessary for performing PCR. PCR kits, in addition to the polynucleotides, primers or probes specific for the polymorphic marker, test tubes or other suitable containers, reaction buffers (various in pH and magnesium concentration), deoxynucleotides (dNTPs), Taq-polymerase and reverse transcription. Enzymes such as enzymes, DNase, RNAse inhibitors, DEPC-water and sterile water may be included.

According to the method for providing information necessary for diagnosing or predicting the onset of type 2 diabetes of the present invention, 175 previously reported genetic mutations related to type 2 diabetes and 56 genetic mutations associated with fasting blood sugar are respectively associated with genetic risk. By scoring and complex analysis, type 2 diabetes can be diagnosed early and the risk of onset can be predicted.

Hereinafter, embodiments of the present invention will be described in more detail with reference to the accompanying drawings. However, the following examples are only for embodiing the contents of the present invention, and the present invention is not limited thereto.

<Example 1> Quality control of genome information

With reference to Fig. 1, genome information is produced using Korean chips for 134,721 participants in the genome epidemiological investigation project, and the produced genome information is genotype calling and low quality genetic mutations removed according to the quality control pipeline with reference to Fig. 2 , removal of low quality samples, and removal of outliers through MDS (Multi-dimensional scaling)/PCA (Principal Component Analysis), etc. were performed. After purification, genome information for 125,872 people was secured, and subsequent analysis was conducted using this.

<Example 2> Genomic information analysis

After purification, the genome information of 125,872 people was analyzed by phasing with Eagle software and imputation with Impute v4 software. Through genomic information analysis, information on about 8.3 million single nucleotide polymorphisms (SNPs) was obtained. Among the clinical epidemiological information of 125,872 people, fasting plasma glucose (FPG) and glycated hemoglobin (Hemoglobin A1c, HbA1c) were analyzed, and disease history and drug history that may affect each information were excluded. In particular, the type 2 diabetes patient group was excluded when analyzing each information (see FIG. 3).

Type 2 diabetes patients and normal groups were defined as follows according to the criteria presented by the American Diabetes Association (ADA). The number of type 2 diabetic patients selected according to the following criteria was 12,135.

Type 2 diabetes patients: FPG >= 126 mg/dL (7.0 mmol/L), blood glucose for 2 hours after oral glucose tolerance test (OGTT) >= 200 mg/dL (11.1 mmol/L), or HbA1c >= 6.5% ( 48 mmol/mol). Including cases diagnosed with type 2 diabetes based on other historical information.

Normal group: FPG <100 mg/dL (5.6 mmol/L), OGTT <140 mg/dL (7.8 mmol/L), or HbA1c <6% (48 mmol/mol). Not everyone should have been diagnosed with type 2 diabetes.

<Example 3> Method for analyzing full-length genetic association

In order to select a genetic variation that is associated with fasting blood sugar, a full-length genetic association analysis method was used. Using the data for which the missing value was predicted (Imputation) was completed and the clinical epidemiologic information, a correlation analysis was performed for fasting blood glucose in 125,872 patients and all genetic mutations. Previously reported genetic variation information related to type 2 diabetes was extracted. Among them, this study analyzed the association with type 2 diabetes using the previously reported 175 genetic variation information (Table 1). The association analysis was performed by linear regression and corrected for age, sex, and cohort area.

In order to secure the statistical power of the discovery of genetic mutations related to fasting blood glucose, inverse-variance weighted meta-analysis was performed by integrating with the results of the association analysis (about 6 million genetic mutations) between about 160,000 fasting blood glucose and genetic mutations published by Biobank Japan. Performed. After analysis, the results of high heterogeneity (P <0.001) were removed. As a result, 26 unreported new genetic mutations and 30 previously reported genetic mutations related to fasting blood glucose were identified. 56 genetic mutations related to fasting blood sugar are shown in Fig. 4 and Table 2. 4 is a graph showing the results of a Manhattan plot of 56 genetic mutations related to fasting blood sugar. The x-axis represents the chromosome, the y-axis represents -log10 (P-value), the blue represents the previously reported genetic mutation, and the red represents the new genetic variant.

Chromosome Position Effective allele Other allele Probe One 22068326 G A ACCAGACATGG One 40035928 T G ATGAAGAAACC One 51256091 T C ACAGGCGTGAG One 120526982 T C CACTACGGGTC One 177889025 C A CAAAAAGGAAT One 205114873 G C ACTCTCGGGCT One 206593900 G C GTGTCCTAGGA One 214159256 C T GTATATAGCCC One 219748818 G C TGCAACTCTTT One 229672955 A G TTTGGGAACTA One 235690800 A G GTTACGTTGGT 2 25643221 T G TGAAAGGTCAT 2 27730940 C T CTTGCTGGTGA 2 58921777 A G AATTTGTACTT 2 59307725 A G TCTGAGTGATT 2 60583665 A G CCAGAGTGTGG 2 65287896 G A TGTACATTGAG 2 120231070 G C ACAGGCCAGAT 2 147861633 T C CCCCACCTCAA 2 149455385 G A ATTCTAGTGCC 2 227101411 G A AAACAATCAGT 2 234303281 G C CCCACCCTTTT 3 12336507 A G GCAAGGTCATA 3 23455582 C T CCAAATGGTAG 3 46925539 C T GAATGTAGGTC 3 49980596 T C GGTTGCTTCTG 3 63962339 A G GGACAGTGATG 3 77671721 A C AAATACAAAAT 3 121956953 A G GAACAGTCACA 3 124926637 C T TGCACTGGCTG 3 152086533 G A AAGTTATATTC 3 170733076 A G AACATGGTGAA 3 183738460 C A ACAGGAGTGAG 3 185503456 A T AAAAATAATAA 3 186665645 T C CACACCCCTTG 4 744972 T G CGTTTGTGGGA 4 1784403 T C CCCAGCGCCTT 4 6306763 C G TCACCGTATGG 4 45186139 A G GATCTGCTAAG 4 71748245 A G ATGAGGGTCTA 4 83578271 G A TTGTCAATTTT 4 85301870 C T AGAAGTAGAAA 4 104140848 A C GCAGACATAGA 4 137083193 C A TTTACATAAGT 4 153513369 A T CACTTTCCTTT 5 44682589 G A TCTCAATTATT 5 52094244 AT A AGATAATGACA 5 52100489 G A TTCATATATTC 5 53271420 A G TGCTGGCTTTA 5 55808475 T C AGCATCCAGGG 5 75003678 C T CATAATGAAGC 5 76424949 A G CCAACGTTTTC 5 78430607 A C ATATGCCATGA 5 133864599 A G TAAAGGGCCAG 5 176513896 A C CGCGGCCACGC 6 7231843 A G GGGTGGACCTG 6 20679709 G A GTTTTAGATCT 6 40409243 C T GAGTCTGCCTG 6 43814190 T C ATATACGGTAG 6 50788778 C A GGACTAAGGTT 6 107431688 A G TTGGAGAAGGA 6 117996631 C T GTCCATGTACT 6 126792095 G A ATTTTACAGGC 6 127416930 G A AAAAAAAGTAA 6 137300960 G A CATGGAACATT 6 160770312 G A CGCTTATGGTC 6 164133001 T C ACAGGCGTGGG 7 13888699 C G AAACAGACACA 7 15063569 T C GAACTCCAACA 7 23512896 C G TTTAAGTTCCA 7 30728452 T C AGCCACGGCAA 7 44255643 A G GTGCCGTGGCA 7 69406661 T A CCATTATAGTC 7 103444978 T C ATCTACACACA 7 117495667 A C CCCCCCAAACA 7 130457914 G A TTACAACCTTA 7 156930550 C G ACCGTGCTCGG 8 19830921 T C AATCACTGTAC 8 30863938 C T AACCCTGTCTC 8 41508577 A C GCGCGCGAGAG 8 95961626 C T TCCGCTCGCCG 8 110123183 G C AGTTCCAGACC 8 118185025 A G TTCATGTCATG 8 129568078 T C CACAGCTAGTA 8 145507304 T C GAACTCCTGGG 9 4291928 C A CAGTCATTAAC 9 19067833 G A TTATAAGCATG 9 20241069 C T AGTAATGAAGA 9 22134068 A G TGTCAGCAGCT 9 28410683 C T CTGTATTTGAA 9 34074476 C T AGCCTTTAGAA 9 81359113 G C GCCGCCATGGT 9 81905590 G A ACAACATCTAT 9 84308948 A G TGTGAGTTGCT 9 97001682 C A TCCTCATAGCT 9 136149229 C T CCTTTTATGTG 9 139241030 G A TCCCAATTGGG 10 12307894 T C AGTGTCTACTG 10 77314617 A T AGAAATGAAGT 10 80952826 C G CTACCGAGGTT 10 94462427 C T TCTCTTCCATC 10 114758349 T C AGATACTATAT 10 122929493 C T TATAATCAGTC 10 124193181 G T CTTCCTGTGTG 11 2197286 G A GGAGGAGCCCA 11 2857194 C A GTCCCAGGGGT 11 34982148 A C TTATTCAGTCT 11 35437863 C G TTTCAGATGAT 11 65294799 T C AGCATCCCTAA 11 72460398 C A ACCTAATTTCT 11 92708710 G C CATCTCCTATC 11 128389391 T C AGCTACGCTGT 11 128398938 T C ATTTCCGAACT 12 12871099 G T GGATGTCAGCG 12 26453283 G A TGTCAATCTGT 12 27965150 T C CACATCCCAGA 12 66221060 T A AGAAAATATGT 12 71522953 C G GAGATGATGGG 12 97848775 G A TAACCATTGAA 12 108629780 A G TTGCCGCATTT 12 118406696 T C CCCCGCGCTGC 12 118412373 A G TCAACGGAGAG 12 133069698 A G CAAGAGCAAGG 13 26776999 G A GAGTCAGCAAC 13 31042452 T G CAGCGGTGCAA 13 33554302 A G CTAACGTTGAA 13 51088809 A G ATCAGGAAAGG 13 51096095 T A ATTACACAAGG 13 80717156 A G TTCATGGATAG 13 109947213 C T ATACATCCCTA 14 23288935 G C CCAAGCGTATC 14 38848419 T G AACATGGAGTT 14 91963722 G A CACACACGATA 15 41809205 A G CCACTGTGCCT 15 53091553 T C TCCTTCTCTAT 15 62394264 C G GGATGGCTTCC 15 63871292 T C ATTCACAGTAG 15 68080886 T A CTGTTACTGTT 15 75932129 T G GCCACGGCAGC 15 77818128 A C GTCCCCAGAAG 15 90423293 C T GTGCATCTTAG 16 295795 C T TTAGTTCATCA 16 3583173 T C TTCTGCCTCAT 16 20323168 A G ATGCTGACTAA 16 28915217 A G GGGCCGCTGGC 16 30045789 G C CCCAGCTATGG 16 53800954 C T CATGATATTGA 16 69651866 T C ACCACCCTGAC 16 75234872 A C CAGGACCAGCC 16 81534790 C T GGGCCTGATGG 16 89564055 A T TAAATTAATTA 17 36099952 A T TAATTTATTTA 17 40731411 C G GGGCTGGTGGG 17 47060322 A C TGGCACAATCT 17 65892507 C G AGAGGGTGGGA 18 7070642 C T AAGGCTCCTCT 18 57848369 T A TCATTAAAAAA 18 60845884 C T TTGGGTCTGAT 19 4948862 A G CAGGCGTGGTG 19 7970635 G A GAATCAGTCGT 19 13038415 A G GTGGCGGGTGC 19 19388500 T A CACAGACCCCA 19 22257558 C T TTTTATATTTT 19 33890838 G C TCCTTCGCCTG 19 45411941 C T ACGTGTGCGGC 19 46157019 G A GGGGAATTCAG 19 47569003 A G CATGCGCCAGG 20 21466795 C T ATTGCTTGAAC 20 32596704 A G AAGCAGTTCTG 20 48832135 T C GGGGCCGGTTA 20 50155386 C T AGAGATGAGAT 20 51223594 T A AATTTAAAAAA 20 57394628 G C TGTGTCTGATC 22 44324730 T C ATCCCCTTCTA 22 50356850 T C ACTTTCTCCTG

Chromosome, position information is written based on hg19.

Effective allele: allele, which is the criterion for effect size,

Other allele: Other allele.

Probe: A total of 11 base sequences with 5 bases on both sides based on the allele. The 6th nucleotide sequence corresponds to Other allele. If the allele is 2 or more bases, the length of the other allele allele from the 6th nucleotide sequence corresponds

Novelty rs_id CHR POS Effective
Allele Other
Allele Probe Sequence number new - One 43,457,376 T TA TTTTTTAAAAG 24 new rs2199501 One 226,442,395 A G ATCACGAGGTC One new rs348330 One 229,672,955 A G TTTGGGAACTA 2 new - 2 37,575,181 ATCTCTC A TTTCTATCTCT 3 new rs243018 2 60,586,707 G C TTCCACGTATA 4 new rs3849330 2 118,893,366 A T AGACATCCCTT 5 new rs832189 3 63,841,942 C T TGTGGTGGCAG 6 new rs7656416 4 1,254,535 T C CTACACGCCTC 7 new rs147834269 4 6,303,731 A G ACGGCGAGGCC 8 new rs1741676 6 117,277,631 T A TGAACAATCTT 9 new - 6 153,421,680 G GA AAAAAGAAAAA 25 new rs11145912 9 139,300,876 A G AGATCGTGCCA 10 new rs61848342 10 12,303,813 C T GGGCATGGTGG 11 new rs61839365 10 26,515,666 A G ATACTGTGTCA 12 new rs139027698 10 94,468,247 T C ACCTCCGTCTC 13 new - 12 97,853,954 C CA TTTCACAATTT 26 new rs4992759 12 133,140,409 C T GAGCTTGTGTG 14 new rs34238147 13 26,776,255 A G GAACCGAGGCC 15 new rs6495240 15 77,766,556 C T TGGCTTGAGCA 16 new rs7219123 17 17,337,839 C G GGCAGGTACAG 17 new rs9897302 17 63,163,415 G A CCCAGATTCAA 18 new rs2302783 17 66,447,073 C T TGGATTGATCA 19 new - 19 46,184,757 AAAAC A TCTTAAAAACA 20 new rs6021276 20 50,155,386 C T AGAGATGAGAT 21 new rs34885433 20 61,273,960 A G TGCTGGCGTAG 22 new rs1757697 20 62,137,971 G A CAGGTATGGTG 23 Flag report - 2 27,744,364 A ATT AGCTAATTTTT 27 Flag report rs12712928 2 45,192,080 C G TGTTTGCTGTC 28 Flag report rs781534836 2 169,775,546 A ATTTG ATCTTATTTGT 29 Flag report - 2 173,597,431 T TG AGAGATGGGGG 30 Flag report rs6767514 3 123,151,762 A C AAGCACCCAGA 31 Flag report rs9873618 3 170,733,076 A G AACATGGTGAA 32 Flag report rs11705729 3 185,507,299 T A CCACCAGCAGC 33 Flag report rs9379084 6 7,231,843 A G GGGTGGACCTG 34 Flag report rs10440833 6 20,688,121 A T TTTTTTAATTT 35 Flag report rs3765467 6 39,033,595 A G CAAGCGAGGGG 36 Flag report rs4721401 7 15,064,896 G T GAGTTTGGGGT 37 Flag report - 7 44,252,190 C CTG AGGCTCTGTGG 38 Flag report rs7791286 7 50,856,792 A G GTGAGGAGTTC 39 Flag report rs13266634 8 118,184,783 T C CCAGCCGGGAC 40 Flag report rs59757115 9 628,941 G A TGGGCATGGTG 41 Flag report - 9 4,284,961 A AT GTGCTATTTTT 42 Flag report rs10965248 9 22,132,878 C T GGGGTTTCACC 43 Flag report rs649129 9 136,154,304 T C CCTAACGCAGT 44 Flag report rs11595776 10 113,003,311 A G GTTGCGGGGCT 45 Flag report rs60808706 11 2,857,233 A G GAGGCGGGGCA 46 Flag report rs204926 11 8,255,106 A G GGGCTGGTCCA 47 Flag report rs174538 11 61,560,081 A G CGTCGGCAGGA 48 Flag report rs74333814 11 72,457,487 T C AAGCACGGCAC 49 Flag report - 11 92,695,708 TTGAGAAATCTCTATACTATTTTCCATAGTGACTG T GCTCTTTGAGA 50 Flag report rs671 12 112,241,766 A G ACACTGAAGTG 51 Flag report rs3812861 13 28,493,489 T A TTTTTAAAAAA 52 Flag report rs2858980 13 33,554,587 A G TAGACGAAATG 53 Flag report - 15 62,391,184 A AT CATATATTTTT 54 Flag report - 15 99,292,396 C CTT AAATCCTTTTT 55 Flag report - 20 22,562,326 AG A GGTGAAGAAGA 56

Chromosome, position information is written based on hg19.

Effective allele: allele, which is the criterion for effect size,

Other allele: Other allele.

Probe: A total of 11 base sequences with 5 bases on both sides based on the allele. The 6th nucleotide sequence corresponds to Other allele. If the allele is 2 or more bases, the length of the other allele allele from the 6th nucleotide sequence corresponds

<Example 4> Genetic risk analysis method

Using genome information, all polymorphic markers listed in Tables 1 and 2 were extracted from the Korean chip genome information used in the full-length genome association analysis method in Example 3, and the effect of each polymorphic marker was confronted. The presence or absence of a gene (Allele) was confirmed.

In the absence of an effective allele, a value of 0, a value of 1 when an effect allele exists in only one haploid, and 2 when an effect allele exists in both haploids. When the value of the allele is checked using genotype imputation, it has a real value between 0 and 2 depending on the posterior probability of the allele. The effect of each polymorphic marker locus in type 2 diabetic patients is compared with the allele frequency of the effect of the normal control group, and the beta coefficient (β) at each locus is calculated and substituted into the following equation (1), and the genetic risk score ( Genetic Risk Score, GRS) was calculated. The calculated genetic risk was standardized to have a standard normal distribution.

n: total number of polymorphic markers,

i: polymorphic marker sequence number,

j: Sample number.

β: beta coefficient (genetic effect of polymorphic markers (effect size))

x denotes an allelic value of one of 0, 1, or 2 in the j sample depending on the presence or absence of an effective allele. The value of the allele is replaced with a real value between 0 and 2 when using genotype imputation.

<Example 5> Genetic high risk group analysis

In order to analyze the genetic high-risk group, the calculated genetic risks were sorted in ascending order, and according to the sorted values, they were divided into percentiles or 30 groups.

The genetic high-risk group was compared with the prevalence of type 2 diabetes in the median group (15th out of 30 or 40-60% percentile group) and low-risk group (1st out of 30 (lower about 3.3%), etc.). Logistic regression was used as an analysis method, and sex, age, and cohort area were corrected.

As a result, when the genetic risk of fasting blood glucose was divided into 30 groups, the highest risk group (top 3.3%) had a prevalence of type 2 diabetes of 14.9%, which was 1.5 times higher than the middle group and the lowest group (bottom 3.3%). %), it was confirmed that the prevalence increased by 3 times (FIG. 5). In addition, it was confirmed that the risk of developing diabetes increased by 2.27 times in the top 1% and 2.04 times in the top 5% in the high risk group based on the genetic risk of fasting blood sugar. It was confirmed that the risk of diabetes was increased by 4.58 times for 1% and 3.43 times for the top 5% (Table 3).

Category GRS type Top GRS group Odds ratio 95% CI P-value Single FPG Top 20% 1.67 1.57-1.77 8.96E-66 Top 10% 1.9 1.77-2.04 4.45E-74 Top 5% 2.04 1.87-2.23 4.65E-59 Top 1% 2.27 1.92-2.69 3.38E-21 T2D Top 20% 2.27 2.14-2.40 1.39E-173 Top 10% 2.88 2.70-3.08 8.28E-221 Top 5% 3.43 3.17-3.71 2.71E-204 Top 1% 4.58 3.96-5.30 1.61E-93 Combination FPG and T2D Top 20% 3.26 2.91-3.66 7.29E-91 Top 10% 4.34 3.79-4.97 1.54E-99 Top 5% 5.28 4.40-6.34 2.75E-71 Top 1% 8.08 4.55-14.35 9.33E-13

<Example 6> Complex analysis of genetic high-risk group

The genetic risk of fasting blood glucose and the genetic risk of type 2 diabetes were divided into percentiles, and based on each genetic risk, all high-risk groups were compared with the rest of the groups. Logistic regression was used as an analysis method, and sex, age, and cohort area were corrected.

As a result, based on the genetic risk of fasting blood sugar and type 2 diabetes, it was confirmed that the risk of developing diabetes was increased by 8.08 times in the genetic high-risk group, and 5.28 times in the top 5% in the top 5%.

In order to verify the above results from the independent genome information, the same genetic mutation information was extracted from the genome information of Korean chips for 22,686 people, and the genetic risk was measured in the same manner as the above method, and the incidence of type 2 diabetes was analyzed. However, compared to the 126,000 people used in the initial analysis, the analysis of the high-risk group at the level of 1% was excluded from the analysis result due to the small number of samples. The results are shown in Table 4.

As shown in Table 4, the genetic high-risk group corresponding to the top 5% or higher in both the fasting blood sugar and the genetic risk of type 2 diabetes showed a 3.51 times the incidence of diabetes, and thus 1.94 to 2.65 times the fasting blood sugar or drug Based on the genetic risk of type 2 diabetes, the incidence of diabetes was more than that of the high-risk group was confirmed.

These results are based on a complex analysis of the genetic risk of each of the present invention, and type 2 diabetes in the case of both the high-risk group based on fasting blood sugar (FPG) and the high-risk group based on type 2 diabetes (T2D). The method of screening as a high-risk group for fasting blood sugar (FPG) is a high-risk group for type 2 diabetes by analyzing the genetic risk for the genetic mutation related to fasting blood sugar (FPG) and the genetic risk for the genetic mutation related to type 2 diabetes (T2D). The screening accuracy is remarkably superior compared to the existing method of screening for type 2 diabetes, and the genetic high-risk group with a higher incidence of type 2 diabetes can be precisely screened, tailored treatment, treatment and prevention according to the selection of the genetic high-risk group for type 2 diabetes. It means that you can use it.

Category GRS type Top GRS group Odds ratio 95% CI P-value Single FPG Top 20% 1.56 1.56-2.10 3.85E-15 Top 10% 1.85 1.85-2.61 3.38E-19 Top 5% 1.94 1.94-3.00 1.71E-15 T2D Top 20% 2.09 2.09-2.78 8.79E-33 Top 10% 2.48 2.48-3.47 5.13E-36 Top 5% 2.65 2.65-4.00 1.87E-29 Combination FPG and T2D Top 20% 2.84 2.84-5.04 9.16E-20 Top 10% 3.44 3.44-6.87 3.44E-19 Top 5% 3.51 3.51-9.13 1.16E-12

As described above, the specific embodiments of the present invention have been described in detail, but those skilled in the art who understand the spirit of the present invention can add, change, delete other elements within the scope of the same idea, and other regressive inventions. However, it will be possible to easily propose other embodiments included within the scope of the spirit of the present invention. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. The scope of the present invention is indicated by the scope of the claims to be described later rather than the detailed description described above, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts are included in the scope of the present invention. Should be interpreted as.

<110> Korea Disease Control and Prevention Agency, KDCA <120> methods for diagnosing the high risk group of Diabetes based on Genetic Risk Score <130> PN2007-308 <160> 56 <170> KoPatentIn 3.0 <210> 1 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 1 atcacgaggt c 11 <210> 2 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 2 tttgggaact a 11 <210> 3 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> ATCTCTC/A <400> 3 tttctatctc t 11 <210> 4 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> G/C <400> 4 ttccacgtat a 11 <210> 5 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/T <400> 5 agacatccct t 11 <210> 6 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 6 tgtggtggca g 11 <210> 7 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/C <400> 7 ctacacgcct c 11 <210> 8 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 8 acggcgaggc c 11 <210> 9 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/A <400> 9 tgaacaatct t 11 <210> 10 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 10 agatcgtgcc a 11 <210> 11 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 11 gggcatggtg g 11 <210> 12 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 12 atactgtgtc a 11 <210> 13 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/C <400> 13 acctccgtct c 11 <210> 14 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 14 gagcttgtgt g 11 <210> 15 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 15 gaaccgaggc c 11 <210> 16 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 16 tggcttgagc a 11 <210> 17 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/G <400> 17 ggcaggtaca g 11 <210> 18 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> G/A <400> 18 cccagattca a 11 <210> 19 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 19 tggattgatc a 11 <210> 20 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> AAAAC/A <400> 20 tcttaaaaac a 11 <210> 21 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 21 agagatgaga t 11 <210> 22 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 22 tgctggcgta g 11 <210> 23 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> G/A <400> 23 caggtatggt g 11 <210> 24 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> T/TA <400> 24 ttttttaaaa g 11 <210> 25 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> G/GA <400> 25 aaaaagaaaa a 11 <210> 26 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> C/CA <400> 26 tttcacaatt t 11 <210> 27 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(8) <223> A/ATT <400> 27 agctaatttt t 11 <210> 28 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/G <400> 28 tgtttgctgt c 11 <210> 29 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(10) <223> A/ATTTG <400> 29 atcttatttg t 11 <210> 30 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> T/TG <400> 30 agagatgggg g 11 <210> 31 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/C <400> 31 aagcacccag a 11 <210> 32 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 32 aacatggtga a 11 <210> 33 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/A <400> 33 ccaccagcag c 11 <210> 34 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 34 gggtggacct g 11 <210> 35 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/T <400> 35 ttttttaatt t 11 <210> 36 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 36 caagcgaggg g 11 <210> 37 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> G/T <400> 37 gagtttgggg t 11 <210> 38 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(8) <223> C/CTG <400> 38 aggctctgtg g 11 <210> 39 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 39 gtgaggagtt c 11 <210> 40 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/C <400> 40 ccagccggga c 11 <210> 41 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> G/A <400> 41 tgggcatggt g 11 <210> 42 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> A/AT <400> 42 gtgctatttt t 11 <210> 43 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> C/T <400> 43 ggggtttcac c 11 <210> 44 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/C <400> 44 cctaacgcag t 11 <210> 45 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 45 gttgcggggc t 11 <210> 46 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 46 gaggcggggc a 11 <210> 47 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 47 gggctggtcc a 11 <210> 48 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 48 cgtcggcagg a 11 <210> 49 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/C <400> 49 aagcacggca c 11 <210> 50 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> TTGAGAAATCTCTATACTATTTTCCATAGTGACTG/T <400> 50 gctctttgag a 11 <210> 51 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 51 acactgaagt g 11 <210> 52 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> T/A <400> 52 tttttaaaaa a 11 <210> 53 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> A/G <400> 53 tagacgaaat g 11 <210> 54 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(7) <223> A/AT <400> 54 catatatttt t 11 <210> 55 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6)..(8) <223> C/CTT <400> 55 aaatcctttt t 11 <210> 56 <211> 11 <212> DNA <213> Homo sapiens <220> <221> variation <222> (6) <223> AG/A <400> 56 ggtgaagaag a 11

Claims (11)

Calculating a genetic risk score (GRS) for a fasting blood sugar-related polymorphic marker including a polymorphic site located in the sixth nucleotide sequence of SEQ ID NOs: 1 to 26 from the biological sample;
Calculating a genetic risk score (GRS) for a type 2 diabetes-related polymorphic marker from the biological sample;
Selecting a high-risk group based on each of the fasting glucose genetic risk score and the type 2 diabetes genetic risk score;
A method of providing information necessary for diagnosing or predicting the onset of type 2 diabetes, including; determining as a high risk group for type 2 diabetes when belonging to both the high risk group based on the genetic risk of fasting blood sugar and the high risk group based on the type 2 diabetes .
The method of claim 1,
The fasting blood glucose-related polymorphic marker further comprises a polymorphic marker including a polymorphic site located at the 6th nucleotide sequence of SEQ ID NOs: 27 to 56, which is necessary for diagnosing or predicting the onset of type 2 diabetes. How to provide information.
The method of claim 1,
The biological sample is a method of providing information necessary for diagnosing type 2 diabetes, characterized in that it is selected from the group consisting of tissue, cells, whole blood, serum, plasma, and saliva.
The method of claim 1,
The genetic risk (Genetic Risk Score, GRS) is calculated using the following Equation 1, a method of providing information necessary for diagnosing type 2 diabetes or predicting the onset:

[Equation 1]

In Equation 1, n is the total number of polymorphic markers, i is the polymorphic marker sequence number, β is the genetic effect of the polymorphic marker, and x is the allele value of the sample j.
The method of claim 1,
When the genotype of the polymorphic site of SEQ ID NO: 1 is A,
When the genotype of the polymorphic site of SEQ ID NO: 2 is A,
When the genotype of the polymorphic site of SEQ ID NO: 3 is ATCTCTC,
When the genotype of the polymorphic site of SEQ ID NO: 4 is G,
When the genotype of the polymorphic site of SEQ ID NO: 5 is A,
When the genotype of the polymorphic site of SEQ ID NO: 6 is C,
When the genotype of the polymorphic site of SEQ ID NO: 7 is T,
When the genotype of the polymorphic site of SEQ ID NO: 8 is A,
When the genotype of the polymorphic site of SEQ ID NO: 9 is T,
When the genotype of the polymorphic site of SEQ ID NO: 10 is A,
When the genotype of the polymorphic site of SEQ ID NO: 11 is C,
When the genotype of the polymorphic site of SEQ ID NO: 12 is A,
When the genotype of the polymorphic site of SEQ ID NO: 13 is T,
When the genotype of the polymorphic site of SEQ ID NO: 14 is C,
When the genotype of the polymorphic site of SEQ ID NO: 15 is A,
When the genotype of the polymorphic site of SEQ ID NO: 16 is C,
When the genotype of the polymorphic site of SEQ ID NO: 17 is C,
When the genotype of the polymorphic site of SEQ ID NO: 18 is G,
When the genotype of the polymorphic site of SEQ ID NO: 19 is C,
When the genotype of the polymorphic site of SEQ ID NO: 20 is AAAAC,
When the genotype of the polymorphic site of SEQ ID NO: 21 is C,
When the genotype of the polymorphic site of SEQ ID NO: 22 is A,
When the genotype of the polymorphic site of SEQ ID NO: 23 is G,
When the genotype of the polymorphic site of SEQ ID NO: 24 is T,
When the genotype of the polymorphic site of SEQ ID NO: 25 is G, and
A method for providing information necessary for diagnosing or predicting the onset of type 2 diabetes, characterized in that it predicts that the risk of developing type 2 diabetes is high when the genotype of the polymorphic site of SEQ ID NO: 26 is C.
The method of claim 1,
The method for providing information necessary for diagnosing or predicting the onset of type 2 diabetes, characterized in that the diagnosis is for Koreans.
In the polynucleotide represented by the nucleotide sequence of SEQ ID NO: 1 to 26, a polynucleotide composed of 10 or more consecutive nucleotide sequences including a polymorphic site located at the 6th position of each nucleotide sequence or a complementary polynucleotide thereof is specifically A polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes, comprising a primer or probe that binds.
The method of claim 7,
The allele of the polymorphic site of SEQ ID NO: 1 is A,
The allele of the polymorphic site of SEQ ID NO: 2 is A,
The allele of the polymorphic site of SEQ ID NO: 3 is ATCTCTC,
The allele of the polymorphic site of SEQ ID NO: 4 is G,
The allele of the polymorphic site of SEQ ID NO: 5 is A,
The allele of the polymorphic site of SEQ ID NO: 6 is C,
The allele of the polymorphic site of SEQ ID NO: 7 is T,
The allele of the polymorphic site of SEQ ID NO: 8 is A,
The allele of the polymorphic site of SEQ ID NO: 9 is T,
The allele of the polymorphic site of SEQ ID NO: 10 is A,
The allele of the polymorphic site of SEQ ID NO: 11 is C,
The allele of the polymorphic site of SEQ ID NO: 12 is A,
The allele of the polymorphic site of SEQ ID NO: 13 is T,
The allele of the polymorphic site of SEQ ID NO: 14 is C,
The allele of the polymorphic site of SEQ ID NO: 15 is A,
The allele of the polymorphic site of SEQ ID NO: 16 is C,
The allele of the polymorphic site of SEQ ID NO: 17 is C,
The allele of the polymorphic site of SEQ ID NO: 18 is G,
The allele of the polymorphic site of SEQ ID NO: 19 is C,
The allele of the polymorphic site of SEQ ID NO: 20 is AAAAC,
The allele of the polymorphic site of SEQ ID NO: 21 is C,
The allele of the polymorphic site of SEQ ID NO: 22 is A,
The allele of the polymorphic site of SEQ ID NO: 23 is G,
The allele of the polymorphic site of SEQ ID NO: 24 is T,
The allele of the polymorphic site of SEQ ID NO: 25 is G and
The polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes, characterized in that the allele of the polymorphic site of SEQ ID NO: 26 is C.
The method of claim 7,
The diagnosis is a polymorphic marker composition for diagnosing or predicting the onset of type 2 diabetes, characterized in that for Koreans.
A composition for diagnosing or predicting the onset of type 2 diabetes, comprising the marker composition of any one of claims 7 to 9.
A kit for diagnosing or predicting the onset of type 2 diabetes, comprising the marker composition of any one of claims 7 to 9.

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