Method for determining risk of developing korean-specific metabolic syndrome by genetic polymorphism analysis

Abstract

The present invention relates to a method for determining the risk of developing Korean-specific metabolic syndrome by genetic polymorphism analysis, and specifically, to a method for determining single nucleotide polymorphism (SNP) from a biological sample, a biomarker composition for determining metabolic syndrome in Koreans including single nucleotide polymorphism (SNP), and a composition for predicting the risk of developing metabolic syndrome, comprising an agent capable of detecting the biomarker composition. According to the present invention, useful information can be provided for predicting the risk of developing metabolic syndrome in Koreans based on genetic polymorphism analysis.

Description

A method for determining the risk of developing metabolic syndrome specific to Koreans using genetic polymorphism analysis

[0001] The present invention relates to a method for determining the risk of developing metabolic syndrome specific to Koreans by genetic polymorphism analysis.

[0002] According to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) guidelines, metabolic syndrome (MetS) is defined as a cluster of metabolic abnormalities, including abdominal obesity, hyperglycemia, hypertension, and dyslipidemia. Accordingly, the prevalence of MetS in American adults was 34.2% and is reported to be increasing throughout the 21st century. A nationwide survey of Koreans diagnosed approximately 30% of the population with MetS.

[0003] Metabolic syndrome is clinically significant because it doubles the risk of cardiovascular diseases, including coronary artery disease and stroke, and fivefold increases the risk of type 2 diabetes. The prevalence of obesity and metabolic syndrome has increased over the past several years, leading to increased healthcare costs targeting these conditions. Furthermore, numerous treatments for metabolic syndrome have been proposed, but none have been successful, resulting in significant public health consequences.

[0004] Meanwhile, further investigation into metabolic syndrome is needed. Because its heritability exhibits considerable variability, as well as significant differences in clinical symptoms, single SNPs with significant influence on metabolic syndrome have not been adequately reported in large East Asian populations.

[0005] Accordingly, the inventors of the present invention performed a genome-wide association study (GWAS) on metabolic syndrome and identified numerous important genetic loci that influence metabolic factors, and confirmed that there was a significant correlation between SNPs located within the important genetic loci, thereby completing the present invention.

[0006] The purpose of the present invention is to provide a method for determining at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839 and rs429358 from a biological sample in order to provide information necessary for predicting the risk of developing metabolic syndrome.

[0007] Another object of the present invention is to provide a biomarker composition for determining metabolic syndrome in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839, and rs429358.

[0008] Another object of the present invention is to provide a composition for predicting the risk of developing metabolic syndrome, comprising a preparation capable of detecting the biomarker composition.

[0009] Other objects and advantages of the present invention will become more apparent from the detailed description, claims and drawings below.

[0010] In the present invention, we successfully calculated a polygenic risk score (PRS) for metabolic syndrome by utilizing genotypic and phenotypic information from the Korean Genome and Epidemiology Study (KoGES), which conducted genome-wide association analysis of metabolic syndrome on 58,600 Koreans aged 40 years or older, and verified that this could function appropriately in an independent Korean cohort.

[0011]

[0012] Hereinafter, the present invention will be described in detail.

[0013]

[0014] The present invention provides a method for determining at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839 and rs429358 from a biological sample in order to provide information necessary for predicting the risk of developing metabolic syndrome in Koreans.

[0015] In the present invention, polymorphism refers to the case where two or more alleles exist at one locus, and among the polymorphic sites, a polymorphism in which only a single base differs from person to person is called a single nucleotide polymorphism (SNP). A preferred polymorphic marker has two or more alleles that exhibit an occurrence frequency of 1% or more, more preferably 5% or 10% or more, in a selected population. Therefore, genetic association for a polymorphic marker means that there is an association for one or more specific alleles of a specific polymorphic marker. The marker may include any variant form of allele found in the genome, including single nucleotide polymorphisms (SNPs), microsatellites, insertions, deletions, duplications, and translocations.

[0016] In the present invention, an allele or allele type refers to multiple types of a gene existing at the same locus on homologous chromosomes. The term allele is also used to indicate polymorphism; for example, a single nucleotide polymorphism (SNP) has two types of alleles.

[0017] In the present invention, "rs_id" refers to an independent marker, rs-ID, assigned to all initially registered SNPs by NCBI, which began accumulating SNP information in 1998. In the present invention, it is described in the form rs2946370. 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.

[0018] In the present invention, rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839 and rs429358 used to predict the risk of metabolic syndrome in Koreans are each registered in the single nucleotide polymorphism database of the National Center for Biotechnology Information (NCBI dbSNP, http: / http: / www.ncbi.nlm.nih.gov / snp / ) of the United States.

[0019] In the present invention, the single nucleotide polymorphism (SNP) is located in the SNHG32 gene on chromosome 6 for rs112405902, in the LPL gene on chromosome 8 for rs59147390, in the LPL gene on chromosome 8 for rs6586892, in the MTNR1B gene on chromosome 11 for rs10830963, in the BUD13 gene on chromosome 11 for rs74368849, in the BUD13 gene on chromosome 11 for rs141740187, in the APOA5 gene on chromosome 11 for rs651821, in the CETP gene on chromosome 16 for rs17231506, in the CETP gene on chromosome 16 for rs2303790, For rs731839, it may be located in the PEPD gene on chromosome 19, and for rs429358, it may be located in the APOE gene on chromosome 19.

[0020] In one embodiment of the present invention, we performed a MetS GWAS on 58,600 Koreans from the KoGES database and identified 11 independent signals of genome-wide significance. Further analysis revealed a complex relationship between two SNPs (rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839, and rs429358) that were not apparent in the single GWAS, and elucidated the directionality of the effect of rs2266788 on metabolic syndrome (MetS), triglycerides (TG), and high-density lipoprotein (HDL).

[0021] According to one embodiment of the present invention, in order to predict the risk of developing the metabolic syndrome, a risk score can be calculated based on the following mathematical formula 1.

[0022] [Mathematical Formula 1]

[0023]

[0024] In the above mathematical formula 1,

[0025] PRSi is the genetic risk score of individual i,

[0026] i is an identification number that distinguishes an individual's genetic data,

[0027] j is an identification number to distinguish SNP data,

[0028] Is is the prior probability estimator,

[0029] is the log value of the odds ratio (OR) derived from the metabolic syndrome GWAS for the jth SNP,

[0030] dosage is a value of 0, 1, or 2 (0 is major homozygous, 1 is heterozygous, 2 is minor homozygous) depending on the jth SNP genotype of the ith individual.

[0031] In the present invention, the biological sample may be blood, tissue, cells, serum, plasma, saliva, urine, etc., but is not limited thereto. Furthermore, in the present invention, the subject of the biological sample may be a Korean.

[0032] In addition, the present invention provides a biomarker composition for determining metabolic syndrome in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839, and rs429358.

[0033] In addition, the present invention provides a composition for predicting the risk of developing metabolic syndrome, comprising a preparation capable of detecting the biomarker composition.

[0034] In the present invention, the agent capable of detecting the biomarker composition may be, but is not limited to, a primer or probe that specifically binds to a polynucleotide including a SNP marker or a complementary polynucleotide thereof.

[0035] In the present invention, the primer or probe that specifically binds to the polynucleotide or its complementary polynucleotide is allele-specific.

[0036] In the present invention, a primer refers to a short nucleic acid sequence with a short free hydroxyl group that can form base pairs with a complementary template 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.

[0037] In the present invention, the probe refers to a nucleic acid fragment such as RNA or DNA consisting of several to several hundred bases that can specifically bind to mRNA and is labeled so as to confirm the presence or absence of a specific mRNA. The probe can be produced in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, etc., and can be labeled with biotin, FITC, rhodamine, DIG, etc., or labeled with a radioisotope, etc.

[0038] In the present invention, the probe may be labeled with a detectable substance, for example, a radioactive label that provides a suitable signal and has a sufficient half-life. The labeled probe may be hybridized to a nucleic acid on a solid support, as described in the literature (Sambook et al., Molecular Cloning, A Laboratory Manual, 1989).

[0039] In the present invention, the probe is an allele-specific probe, and may hybridize to a nucleic acid fragment containing one allele due to the presence of a polymorphic site in the nucleic acid fragment, but may not hybridize to a nucleic acid fragment containing the other allele. In this case, the hybridization conditions must be sufficiently stringent to ensure a significant difference in hybridization intensity between the alleles and to hybridize only to one of the alleles. Preferably, the probe may be single-stranded to maximize hybridization efficiency, but is not limited thereto.

[0040] In the present invention, detection of a specific nucleic acid using the 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 has been amplified using a method known in the art. Furthermore, detection of a specific nucleic acid using a probe can be performed by contacting a sample nucleic acid with the probe under suitable conditions and then confirming the presence of a hybridized nucleic acid.

[0041] In the present invention, 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 (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 (Rust et al., Nucl Acids Res, 6, 3623-3629, 1993), RNase mismatch cleavage (RNase mismatch cleavage, Myers et al., Science, 230, 1242-1246, 1985), single strand conformational polymorphism (single strand conformational polymorphism, Orita et al., Pro Nat Acad Sci USA, 86, 2766-2770, 1989), simultaneous SSCP and heteroduplex 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 reactions, DNA chips, etc. Examples of the above hybridization reactions include Northern hybridization (Maniatis T. et al., Molecular Cloning, Cold Spring Harbor 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.

[0042] In addition, the present invention provides a kit for predicting the risk of developing metabolic syndrome, comprising the composition.

[0043] In the present invention, the kit may include a polynucleotide, primer or probe for identifying one or more markers among polymorphic markers, as well as one or more other component compositions, solutions or devices suitable for the analysis method.

[0044] In the present invention, the kit may be a kit containing essential elements necessary for performing PCR. In addition to a polynucleotide, primer, or probe specific for the polymorphic marker, the PCR kit may include a test tube or other appropriate container, a reaction buffer (with varying pH and magnesium concentrations), deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC water, and sterile water.

[0045] According to the present invention, there is an advantage in that it can be usefully used to predict the risk of developing metabolic syndrome in Koreans based on single nucleotide polymorphisms.

[0046] Figure 1 shows Manhattan and QQ plots for a genome-wide association study of metabolic syndrome in Koreans. The x-axis represents SNP markers on each chromosome. The y-axis represents a -log10 scale. The red horizontal line represents the genome-wide significance threshold of P = 5.0 χ 10 -8 , and the blue horizontal line represents the genome-wide suggestive threshold P = 1.0 Х 10 -5 . Five candidate loci for MetS are indicated.

[0047] Figure 2 shows a Manhattan plot of a genome-wide association study for metabolic syndrome components (BP: Blood pressure, FBS: Fasting blood sugar, HDL: High-density lipoprotein, TG: Triglycerides, WC: Waist circumference). (a) Results for WC. (b) Results for WC. (c) Results for BP. (d) Results for FBS. (e) Results for TG.

[0048] Figure 3 shows signals associated with MetS and GCTA-COJO-based components. These are SNPs associated with MetS and its components. Bold text indicates non-synonymous exonic SNPs.

[0049] Figure 4 shows a comparison of 11 MetS-related SNPs with each MetS component. Colors indicate the magnitude and direction of the beta effect. Asterisks indicate p-values ​​(*** < 5E-8, ** < 1E-05, * < 0.05).

[0050] Figure 5 shows a zoom plot of the APOA5 SNPs within metabolic syndrome, triglycerides, and high-density lipoprotein. (a) Results for MetS. (b) Results for TG. (c) Results for HDL.

[0051] Figure 6 shows the results of fine mapping for the KoGES and UKBiobank cohorts. The x-axis represents the genetic location of the APOA5 region on chromosome 11 (hg19), and the y-axis represents the posterior inclusion probability (PIP) scale. Red and blue circles represent SNPs from Korean and British populations. The dotted line indicates overlapping SNPs.

[0052] Figure 7 shows regional association plots of the APOA5 locus with triglycerides in Korean and UKBB data. (a) Koreans, unconditional results. (b) Koreans, rs651821-conditioned results. (c) UKBB, unconditional results. (d) UKBB, rs651821-conditioned results.

[0053] Figure 8 shows the multilevel effects of rs651821 and rs2266788. TG (ac) and HDL (df) levels (HDL: high-density lipoprotein, TG: triglyceride) according to the rs651821 and rs2266788 genotypes. (a) TG, results for rs651821. (b) TG, results for rs2266788. (c) TG, results for rs2266755 and rs651821. (d) HDL, results for rs65181. (e) HDL, results for rs2266788. (f) HDL, results for rs2266788 and rs651821.

[0054] Figure 9 shows the results of multilevel SNP and lifestyle factors. TG levels according to genotypes of rs651821 and rs2266788, in relation to stratified levels of smoking (a), drinking (b), and physical activity (c). The purple box indicates a small homozygous subgroup for rs651821 (CC genotype). All error bars represent standard errors (PA: physical activity, TG: triglyceride).

[0055] Figure 10 shows a PRS distribution histogram calculated based on the KoGES rural cohort.

[0056] Figure 11 shows the results of the PRS model verification task.

[0057] Figure 12 shows the patient ratio for each genetic risk score section.

[0058] Hereinafter, to aid understanding of the present invention, examples will be given in detail. However, the following examples are intended only to illustrate the scope of the present invention and are not intended to limit its scope. These examples are provided to more fully explain the present invention to those of average skill in the art.

[0059]

[0060] Example 1. Materials and Methods

[0061]

[0062] 1.1. Research Participants

[0063]

[0064] The Korean Genome and Epidemiology Study (KoGES) is a large-scale prospective cohort study designed to identify genetic and environmental factors and their interactions within common complex diseases such as metabolic syndrome, osteoporosis, and cancer. The KoGES cohort, which has followed health screening participants since 2007, primarily comprised population-based and gene-environment model studies. All ethical considerations and requirements were met, and participants were granted complete anonymity, waiving the need for informed consent. The dataset used for the analysis was the KoGES Health Examinee (HEXA) cohort, which primarily comprised patients recruited from hospitals in major cities in Korea. All participants were aged 40 years or older (with a starting population of 58,700). 100 participants with missing triglyceride (TG) blood test results or demographic information were excluded. Consequently, 58,600 study participants with no missing demographic variables and available blood test results were included. Institutional Review Board (IRB) approval was obtained from the Korea Centers for Disease Control and Prevention and the Seoul National University Hospital Clinical Research Institute (IRB No. C-2004-080-1117). Genotyping and phenotyping methods were performed in accordance with the Declaration of Helsinki.

[0065]

[0066] 1.2. Phenotypic Measurement

[0067]

[0068] Eligible participants completed a questionnaire that included questions about demographic factors, comorbidities, and medications for conditions such as hypertension, diabetes, and hypercholesterolemia. Blood tests, including fasting blood glucose (FBG), triglycerides, and high-density lipoprotein cholesterol (HDL-C), were measured using an automated analyzer after a 12-hour fast.

[0069]

[0070] 1.3. Definition of metabolic syndrome

[0071]

[0072] Metabolic syndrome (MetS) was defined according to the NCEP-ATP III and the American Heart Association / National Heart, Lung, and Blood Institute (AHA / NHLBI). The criteria for central obesity were modified according to Asian guidelines. Patients were selected if they met three or more of the following criteria: (1) waist circumference (WC) ≥ 90 cm in men and ≥ 80 cm in women; (2) TG levels ≥ 150 mg / dL or taking dyslipidemia medication; (3) HDL-C levels ≤ 40 mg / dL in men and ≤ 50 mg / dL in women; (4) blood pressure (BP) levels ≥ 130 / 85 mmHg or taking antihypertensive medication; and (5) fasting plasma glucose (FBS) levels ≥ 100 mg / dL or taking antidiabetic medication.

[0073]

[0074] 1.4. Check covariate variables

[0075]

[0076] Participants were classified into three groups based on alcohol consumption: nondrinkers, light drinkers, moderate drinkers, and heavy drinkers. Alcohol consumption was assessed by frequency, amount consumed per drink, and type of alcohol consumed. Participants who consumed more than 196 g of alcohol per week were defined as heavy drinkers. Those who consumed 98 to 196 g of alcohol per week were defined as moderate drinkers. Those who consumed less than 98 g of alcohol per week were defined as nondrinkers or light drinkers. Participants were classified as nonsmokers, former smokers, and current smokers based on smoking status. Physical activity was stratified into low, moderate, and high levels based on frequency, intensity, and duration.

[0077]

[0078] 1.5. Genotyping and attribution

[0079]

[0080] Samples were genotyped using KoreanChip version 1.1, a custom array optimized for the Korean population, and attributed to the Northeast Asian Reference Database Panel version 2. Finally, 7,857,085 variants with a MAF ≥ 0.01 and a Hardy-Weinberg equilibrium P value ≥ 1E were identified. -6 , missing rate < 0.1, Minimac4(R 2 ) ≥ 0.3 was used in the GWAS analysis. The locations of all variants included in the present invention were based on hg19.

[0081]

[0082] 1.6. Visualizing Results

[0083]

[0084] The R package "qqman" was used for GWAS Manhattan plots and qqplot visualization. The genome-wide significance level for P values ​​was set to 5E-8. ​​The LocusZoom plot was used to zoom in on the major APOA5 SNP on chromosome 11. LocusZoom visually displays regional information, such as the intensity of association signals for a genomic location.

[0085]

[0086] 1.7. Statistical Analysis

[0087]

[0088] Basic demographics were analyzed using STATA 17.0 (College Station, TX, StataCorp LLC). The GWAS assessed MetS and the five diagnostic criteria as binary variables. Logistic regression analysis implemented in PLINK (version 1.9; Free Software Foundation Inc., Boston, MA, USA) was used to analyze the genetic association between MetS and the five diagnostic criteria, adjusted for age and sex. All genotyping data were analyzed using the computing server of the Research Services Center of the Genomic Medicine Research Institute. Power analysis and sample size calculations were also performed, calculating TG values ​​for genotypes. With alpha set to 0.05 and power set to 0.8, the required sample size was 1,158. This was significantly lower than the population size of the present study or the study populations for all genotype subgroups (the minimum subgroup population was 2,857 for the rs2266788 GG genotype). We used the genome-wide composite trait analysis package with GWAS summary statistics (GCTA-COJO), conditional and joint association analysis, to extract additional statistically significant and independent variants beyond the most significant variants at each locus contributing to each specific phenotype of TG, HDL, FBS, HTN, WC, and MetS.

[0089] ANNOVAR was used to annotate information about the function, neighboring genes, and population frequency of each variant. Fine mapping was performed using the Sum of Single Effects statistical model, implemented using POLYgenic functionally informed fine mapping. From these methods, the reliable set for each analysis was limited to a total of 10 sets, and the posterior inclusion probability (PIP) value was calculated for each ±100 kb segment for each independent SNP derived from GCTA-COJO. This revealed the PIP value for each SNP. A PIP >0.9 was defined as a high probability of causality, and a PIP cutoff of 0.1 was applied for minimal causality. Fine mapping was performed on the UK Biobank (UKBB) and KoGES urban cohorts using TG binary variables as the phenotype. To compare groups with and without MetS, Student's t-tests were performed for continuous variables and chi-square tests were performed for categorical variables. For smoking, alcohol consumption, and exercise intensity, the Cochran-Armitage trend test was used to assess P values ​​corresponding to deviations from a linear trend.

[0090]

[0091] Example 2. Experimental Results

[0092]

[0093] 2.1. Analysis of basic characteristics of the study population

[0094]

[0095] A genome-wide association study of metabolic syndrome (GWAS) was conducted on 58,600 individuals assigned by the Korea Centers for Disease Control and Prevention (KoGES). Specifically, approximately 24% of the 58,600 KoGES study subjects met three or more of the five MetS diagnostic criteria. Lifestyle factors such as age, sex, body mass index, smoking, drinking, and physical activity varied significantly depending on the presence or absence of MetS (Table 1).

[0096]

[0097] CharacteristicsCase with MetSControlP-value Mean ± SDOr number (%)Mean ± SDOr number (%) Total number14323 (24.4%)44277 (75.6%) Age (years)56.4 ± 7.653.0 ± 8.0<0.001Male number (%)5538 (38.7%)14738 (33.3%)<0.001BMI (kg / m 2)25.9 ± 2.823.2 ± 2.6<0.001MetS Components WC (cm)87.2 ± 7.478.7 ± 7.9<0.001HDL Cholesterol (mg / dL)45.0 ± 10.056.6 ± 12.8<0.001TG (mg / dL)193.1 ± 115.2103.1 ± 58.5<0.001Fasting glucose (mg / dL)106.4 ± 27.691.4 ± 14.5<0.001Systolic BP (mmHg)130.9 ± 14.1119.7 ± 13.9<0.001Diastolic BP (mmHg)80.3 ± 9.474.3 ± 9.4<0.001 Health related behaviors Smoking 0.039Non-smoker3126 (69.6%)10127 (75.7%) Ex-smoker784 (17.5%)1917 (14.3%) Current smoker581 (12.9%)1331 (10.0%) Drinking <0.001Non to low drinker7593 (53.9%)22713 (52.1%) Moderate drinker4375 (31.0%)15945 (36.6%) Heavy drinker2124 (15.1%)4936 (11.3%) Physical activity category <0.001Low level6816 (48.6%)19708 (45.5%) Moderate level4347 (31.0%)14771 (34.1%) High level2856 (20.4%)8864 (20.4%)

[0098] Table 1. Baseline characteristics of the study population (BMI: Body Mass Index; BP: Blood Pressure; HDL: High Density Lipoprotein; MetS: Metabolic Syndrome; SD: Standard Deviation; TG: Triglycerides; WC: Waist Circumference). Student's t-test was performed for continuous variables, and chi-square test was performed for categorical variables. To compare groups with and without MetS, the Cochran-Armitage test for trend was performed for ordered categorical variables.

[0099]

[0100] GWAS and conditional and joint analysis (COJO) for MetS revealed 11 independent signals. APOA5 on chromosome 11 was the most significant signal (rs651821, odds ratio [OR] = 1.33, P-value = 1.21E-98), followed by BUD13 on chromosome 11 (rs74368849, OR = 1.25, P-value = 5.00E-62), CETP on chromosome 16 (rs17231506, OR = 0.84, P-value = 2.57E-23), and LPL on chromosome 8 (rs59147390, OR = 0.81, P-value = 1.00E-18) (Fig. 1 , Table 2 ).

[0101]

[0102] SNPCHR:POS(hg19)REFEffectalleleEAF(case / control)P-valueOR(95% CI)NearestGeneFunctionalConsequencers651821(SEQ ID NO: 1)11:116662579TC0.347 / 0.2821.25E-981.33 (1.29 - 1.38)APOA5(SEQ ID NO: 12)UTR5rs74368849(SEQ ID NO: 2)11:116622299GA0.107 / 0.0764.60E-621.25 (1.19 - 1.32)BUD13(SEQ ID NO: 13)intronicRs17231506(SEQ ID NO: 3)16:56994528CT0.154 / 0.1792.57E-230.84 (0.81 - 0.88)CETP(SEQ ID NO: 14)intergenicrs59147390(SEQ ID NO: 4)8:19843879TC0.110 / 0.1291.00E-180.81 (0.78 - 0.85)LPL(SEQ ID NO: 15)intergenicrs2303790(SEQ ID NO: 5)16:57017292AG0.038 / 0.0477.02E-130.82 (0.76 - 0.88)CETPExonic(NS D459G)rs112405902(SEQ ID NO: 6)6:31809504GA0.080 / 0.0694.49E-101.17 (1.12 - 1.24)SNHG32(SEQ ID NO: 16)Intergenicrs429358(SEQ ID NO: 7)19:45411941TC0.101 / 0.0908.62E-101.13 (1.1 - 1.21)APOE(SEQ ID NO: 17)Exonic(NS C130R)rs141740187(SEQ ID NO: 8)11:116637677GT0.207 / 0.2093.50E-090.98 (0.95 - 1.02)BUD13intronicRs10830963(SEQ ID NO: 9)11:92708710CG0.446 / 0.4269.47E-091.08 (1.05 - 1.11)MTNR1B(SEQ ID NO: 18)intronicrs6586892(SEQ ID NO: 10)8:19941145CA0.199 / 0.2101.58E-080.91 (0.88 - 0.94)LPLintergenicrs731839(SEQ ID NO: 11)19:33899065GA0.454 / 0.4712.50E-080.93 (0.90 - 0.96)PEPD(SEQ ID NO: 19)intronic.

[0103] Table 2. SNPs associated with MetS in Koreans based on conditional and joint analyses (CHR: Chromosome; CI: Confidence Interval; EAF: Effect Allele Frequency; NS: Non-synonymous; OR: Odds Ratio; POS: Position). SNPs are listed in order of increasing P value.

[0104]

[0105] 2.2. GWAS and COJO analyses performed for each of the five MetS diagnostic criteria (Figure 2, Tables 3 to 7)

[0106]

[0107] For each of the five MetS diagnostic criteria (Fig. 2, Tables 3 to 7), the number of independent signals that reached genome-wide significance was 18 for TG, 34 for HDL, 25 for FBS, 16 for BP, and 4 for WC, based on GWAS and COJO results. Of these, 10 signals were associated with two or more phenotypes (Fig. 3, Tables 3 to 7).

[0108]

[0109] MetS Component: BP rsIDChrPositionEffectAlleleOR95% CIP-valueEAF (case / control)Nearest GeneNearest Gene distanceFunctionalConsequenceExonic functionAA changers1275985_C_T(SEQ ID NO: 20)226911745T0.910.89-0.941.18E-090.207 / 0.22CIB4(SEQ ID NO: 36);KCNK3(SEQ ID NO: 37)dist=47511;dist=3845intergenic..rs117262811_G_C(SEQ ID NO: 21)254898273C1.221.15- 1.33.87E-100.042 / 0.035SPTBN1(SEQ ID NO: 38)UTR3..rs1470492_G_T(SEQ ID NO: 22)2165004184T0.900.88 - 0.922.61E-170.416 / 0.44FIGN(SEQ ID NO: 39);GRB14(SEQ ID NO: 40)dist=411666;dist=344743intergenic..rs3821843_G_A(SEQ ID NO: 23)353558012A1.081.05 - 1.118.36E-100.485 / 0.466CACNA1D(SEQ ID NO: 41).intronic..rs112478109_C_T(SEQ ID NO: 24)440604394T0.900.87 - 0.931.16E-080.129 / 0.14RBM47(SEQ ID NO: 42).intronic..rs12509595_T_C(SEQ ID NO: 25)481182554C1.181.15 - 1.211.58E-350.367 / 0.334PRDM8(SEQ ID NO: 43);FGF5(SEQ ID NO: 44)dist=57072;dist=5188intergenic..rs1051488_C_T(SEQ ID NO: 26)631322911C1.081.05 - 1.111.03E-090.452 / 0.435HLA-B(SEQ ID NO: 45).exonicnonsynonymousSNVA329Trs6463224_C_A(SEQ ID NO: 27)71140418A1.071.05 - 1.13.84E-080.364 / 0.35C7orf50(SEQ ID NO: 46).intronic..rs28394055_C_T(SEQ ID NO: 28)8143996923T0.920.9 - 0.944.35E-100.312 / 0.328CYP11B2(SEQ ID NO: 47).intronic..rs10883806_C_T(SEQ ID NO: 29)10104713076T0.890.86 - 0.915.29E-170.237 / 0.257CNNM2(SEQ ID NO: 48).intronic..rs1216743_G_A(SEQ ID NO: 30)11100573120A1.071.05 - 1.11.21E-080.473 / 0.457ARHGAP42(SEQ ID NO: 49).intronic..rs7136259_T_C(SEQ ID NO: 31)1290081188T0.890.87 - 0.919.28E-210.368 / 0.394ATP2B1(SEQ ID NO: 50).intronic..rs77768175_A_G(SEQ ID NO: 32)12112736118G0.830.8 - 0.856.40E-290.147 / 0.167HECTD4(SEQ ID NO: 51).intronic..rs112735431_G_A(SEQ ID NO: 33)1778358945A1.681.5 - 1.892.06E-180.014 / 0.009 RNF213 (SEQ ID NO: 52). exonic nonsynonymous SNVR4810Krs167479_G_T (SEQ ID NO: 34) 1911526765T 0.900.88 - 0.929.57E-180.482 / 0.505 RGL3 (SEQ ID NO: 53). exonic nonsynonymous SNVP162Hrs6108784_T_C (SEQ ID NO: 35) 2010964366C 1.071.05 - 1.12.07E-080.465 / 0.449 LOC101929413 (SEQ ID NO: 54); LINC02871 (SEQ ID NO: 55)dist=74446;dist=42826intergenic.

[0110]

[0111] MetS Component: WC rsIDChrPositionEffectAlleleOR95% CIP-valueEAF (case / control)Nearest GeneNearest Gene distanceFunctionalConsequenceExonic functionAA changers506589_T_C(SEQ ID NO: 56)1177894287C1.081.05 - 1.113.15E-080.294 / 0.28LINC01741(SEQ ID NO: 60);SEC16B(SEQ ID NO: 61)dist=214822;dist=3637intergenic..rs62048402_G_A(SEQ ID NO: 57)1653803223A1.131.09 - 1.173.01E-110.132 / 0.12FTO(SEQ ID NO: 62).intronic..rs9947403_C_T(SEQ ID NO: 58)1857869750T1.091.06 - 1.121.81E-090.242 / 0.228PMAIP1(SEQ ID NO: 63);MC4R(SEQ ID NO: 64)dist=298213;dist=168545intergenic..rs2302382_C_A(SEQ ID NO: 59)1946172569A1.131.09 - 1.181.12E-090.107 / 0.096GIPR(SEQ ID NO: 65).intronic..

[0112]

[0113] MetS Component: FBS rsIDChrPositionEffectAlleleOR95% CIP-valueEAF (case / control)Nearest GeneNearest Gene distanceFunctionalConsequenceExonic functionAA changers1260326_T_C(SEQ ID NO:66)227730940C1.151.12 - 1.185.03E-230.471 / 0.441GCKR.exonicnonsynonymous SNVL446Prs12712928_G_C(SEQ ID NO:67)245192080C1.131.1 - 1.162.05E-170.391 / 0.363SIX3;SIX2dist=18870;dist=40241intergenic..rs74870851_A_G(SEQ ID NO: 68)2169776223G1.151.11 - 1.186.47E-210.379 / 0.349G6PC2;ABCB11dist=9713;dist=1068intergenic..rs117809958_T_A(SEQ ID NO: 69)2234191103A1.261.16 - 1.362.21E-080.032 / 0.026ATG16L1.intronic..rs864975_T_C(SEQ ID NO: 70)363885615T0.920.89 - 0.957.60E-090.345 / 0.364ATXN7.intronic..rs1882099_A_G(SEQ ID NO: 71)41352685G0.920.9 - 0.952.91E-080.421 / 0.44UVSSA.intronic..rs9379084_G_A(SEQ ID NO: 72)67231843A0.890.86 - 0.935.64E-090.151 / 0.165RREB1.exonicnonsynonymous SNVD1171Nrs4710941_C_T(SEQ ID NO: 73)620694228T1.201.17 - 1.241.55E-400.497 / 0.454CDKAL1.intronic..rs9366994_C_T(SEQ ID NO: 74)639048060T0.910.88 - 0.941.07E-090.286 / 0.304GLP1R.intronic..rs10281892_A_G(SEQ ID NO: 75)714919852G1.111.08 - 1.143.25E-150.441 / 0.416DGKB.intronic..rs1558318_A_T(SEQ ID NO: 76)715065612A0.870.85 - 0.94.15E-200.302 / 0.33DGKB;AGMOdist=51129;dist=174330intergenic..rs1799831_C_T(SEQ ID NO: 77)744199142T1.101.07 - 1.149.42E-050.316 / 0.303GCK.intronic..rs6975024_T_C(SEQ ID NO: 78)744231886C1.201.16 - 1.243.66E-180.206 / 0.183GCK;YKT6dist=2848;dist=8691intergenic..rs56751449_C_T(SEQ ID NO: 79)7127422598T1.281.21 - 1.343.29E-210.083 / 0.066SND1.intronic..rs11558471_A_G(SEQ ID NO: 80)8118185733G0.880.85 - 0.91.14E-200.395 / 0.425SLC30A8UTR3..rs1574285_G_T(SEQ ID NO: 81)94283137G1.131.1 - 1.161.40E-170.446 / 0.418GLIS3.intronic..rs10965248_T_C(SEQ ID NO: 82)922132878C0.840.81 - 0.862.11E-360.408 / 0.449CDKN2B-AS1;DMRTA1dist=11785;dist=313945intergenic..rs11186976_A_G(SEQ ID NO: 83)1094163368G1.141.1 - 1.184.56E-140.22 / 0.2MARCHF5;MARK2P9dist=49647;dist=15050intergenic..rs2237897_C_T(SEQ ID NO: 84)112858546T0.820.8 - 0.843.36E-430.361 / 0.404KCNQ1.intronic..rs10830963_C_G(SEQ ID NO: 85)1192708710G1.201.16 - 1.233.54E-370.463 / 0.421MTNR1B.intronic..rs11066001_T_C(SEQ ID NO: 86)12112119171C0.840.81 - 0.872.12E-190.144 / 0.163BRAP.intronic..Rs12578282_T_C(SEQ ID NO: 87)12118399775C1.091.06 - 1.122.73E-080.275 / 0.258KSR2.intronic..rs7180689_T_A(SEQ ID NO: 88)1562397636A0.910.89 - 0.948.00E-100.318 / 0.337C2CD4A;C2CD4Bdist=34520;dist=58098intergenic..rs11657964_A_G(SEQ ID NO: 89)1736100767A1.121.08 - 1.153.90E-130.308 / 0.286HNF1B.intronic..rs4812810_T_C(SEQ ID NO: 90)2042899532T0.920.9 - 0.952.48E-080.431 / 0.45GDAP1L1.intronic.

[0114]

[0115] MetS Component: TG rsIDChrPositionEffectAlleleOR95% CIP-valueEAF (case / control)Nearest GeneNearest Gene distanceFunctionalConsequenceExonic functionAA changers12047226_T_C(SEQ ID NO: 91)163105538C0.870.84 - 0.91.19E-130.158 / 0.177DOCK7.intronic..rs13306194_G_A(SEQ ID NO: 92)221252534A0.860.82 - 0.93.87E-120.11 / 0.125APOB.exonicnonsynonymous SNVR532Wrs1260326_T_C(SEQ ID NO: 93)227730940C0.760.74 - 0.783.90E-860.399 / 0.465GCKR.exonicnonsynonymous SNVL446Prs35844368_C_T(SEQ ID NO: 94)488159371T1.081.06 - 1.114.98E-090.43 / 0.41KLHL8;MIR5705dist=17601;dist=62276intergenic..rs11751198_G_A(SEQ ID NO: 95)631753526A1.241.18 - 1.38.44E-170.081 / 0.067VARS1.intronic..rs13229619_G_A(SEQ ID NO: 96)773030175A0.790.76 - 0.831.70E-230.088 / 0.109MLXIPL.intronic..rs1059611_T_C(SEQ ID NO: 97)819824563C0.710.68 - 0.741.70E-490.101 / 0.133LPLUTR3..rs12542766_A_G(SEQ ID NO: 98)819939672G0.890.86 - 0.925.79E-060.201 / 0.213LPL;SLC18A1dist=114902;dist=62694intergenic..rs59206925_C_G(SEQ ID NO: 99)859350904G0.920.89 - 0.953.91E-080.256 / 0.272UBXN2B.intronic..rs174551_T_C(SEQ ID NO: 100)1161573684C1.111.08 - 1.146.02E-130.314 / 0.292 FADS1.intronic..rs74368849_G_A(SEQ ID NO: 101)11116622299A1.401.33 - 1.477.44E-1550.119 / 0.071 BUD13.intronic..rs112064363_G_A(SEQ ID NO: 102)11116622342A1.221.18 - 1.264.00E-020.201 / 0.205 BUD13.intronic..rs201247587_T_G(SEQ ID NO: 103)11116658553G1.601.42 - 1.791.31E-050.015 / 0.011ZPR1.exonicnonsynonymousSNVM52Lrs651821_C_T(SEQ ID NO: 104)11116662579C1.681.62 - 1.733.85E-2960.381 / 0.27APOA5UTR5..rs261334_G_C(SEQ ID NO: 105)1558726744G1.081.06 - 1.126.91E-090.387 / 0.368LIPC.intronic..rs58542926_C_T(SEQ ID NO: 106)1919379549T0.780.74 - 0.823.02E-190.062 / 0.078TM6SF2.exonicnonsynonymous SNVE167Krs438811_C_T(SEQ ID NO: 107)1945416741T1.241.2 - 1.291.16E-420.191 / 0.157APOC1dist=841upstream..rs5112_C_G(SEQ ID NO: 108)1945430280C0.900.88 - 0.933.20E-220.342 / 0.371APOC1P1.ncRNA_exonic.

[0116]

[0117] MetS Component: HDLrsIDChrPositionEffectAlleleOR95% CIP-valueEAF (case / control)Nearest GeneNearest Gene distanceFunctionalConsequenceExonic functionAA changers1180333_T_C (SEQ ID NO: 109)140020504T1.141.09 - 1.181.03E-090.115 / 0.102PPIEL.ncRNA_intronic..rs7517363_C_T (SEQ ID NO: 110)193652785C0.920.9 - 0.951.41E-090.357 / 0.375CCDC18.intronic..rs1344063_T_C (SEQ ID NO: 111)221184545T1.121.08 - 1.161.82E-100.162 / 0.147LDAH;APOBdist=161655;dist=39756intergenic..rs13198987_C_T(SEQ ID NO: 112)6161014107T1.081.05 - 1.117.00E-090.472 / 0.455LPA.intronic..rs75326924_C_T(SEQ ID NO: 113)780286003T0.820.78 - 0.873.99E-130.06 / 0.072CD36.exonicnonsynonymous SNVP56Srs12112428_C_T(SEQ ID NO: 114)799623442T1.131.09 - 1.181.07E-080.1 / 0.09ZKSCAN1.intronic..rs59147390_T_C(SEQ ID NO: 115)819843879C0.720.69 - 0.756.95E-500.102 / 0.133LPL;SLC18A1dist=19109;dist=158487intergenic..rs10086580_A_G(SEQ ID NO: 116)819919486G0.890.86 - 0.924.25E-060.179 / 0.19LPL;SLC18A1dist=94716;dist=82880intergenic..rs2737214_C_G(SEQ ID NO: 117)8116624987C1.111.07 - 1.141.66E-100.213 / 0.197TRPS1.intronic..rs2777805_G_A(SEQ ID NO: 118)9107571375G1.091.06 - 1.121.68E-080.351 / 0.335ABCA1.intronic..rs4149310_A_T(SEQ ID NO: 119)9107589134A1.091.06 - 1.132.74E-110.301 / 0.281ABCA1.intronic..rs2575876_G_A(SEQ ID NO: 120)9107665739A1.191.16 - 1.234.30E-350.273 / 0.238ABCA1.intronic..rs4917628_A_G(SEQ ID NO: 121)10113947678A0.920.9 - 0.954.44E-090.296 / 0.314GPAM;TECTBdist=4155;dist=95480intergenic..rs4575188_A_G(SEQ ID NO: 122)10113982162G1.091.06 - 1.121.68E-100.411 / 0.391GPAM;TECTBdist=38639;dist=60996intergenic..rs4938299_G_A(SEQ ID NO: 123)11116565732A1.111.08 - 1.151.81E-010.242 / 0.239LINC02702;BUD13dist=36763;dist=53154intergenic..rs547595106_T_G(SEQ ID NO: 124)11116586891G1.541.38 - 1.728.44E-070.015 / 0.012LINC02702;BUD13dist=57922;dist=31995intergenic..rs3741297_C_T(SEQ ID NO: 125)11116657667T1.521.45 - 1.65.70E-1600.113 / 0.065ZPR1.intronic..rs662799_G_A(SEQ ID NO: 126)11116663707G1.391.35 - 1.441.20E-1800.359 / 0.274APOA5dist=571upstream..rs12718465_C_T(SEQ ID NO: 127)11116707736T1.351.27 - 1.456.65E-150.045 / 0.036APOA1.exonicnonsynonymous SNVA61Trs11066001_T_C(SEQ ID NO: 128)12112119171C1.181.14 - 1.221.79E-200.173 / 0.152BRAP.intronic..rs10773112_C_T(SEQ ID NO: 129)12125338529C1.081.05 - 1.114.63E-090.416 / 0.398SCARB1.intronic..rs10162642_G_A(SEQ ID NO: 130)1558577163A1.181.13 - 1.229.41E-150.132 / 0.117AQP9;LIPCdist=99053;dist=147027intergenic..rs1532085_A_G(SEQ ID NO: 131)1558683366G1.211.18 - 1.251.91E-600.519 / 0.468AQP9;LIPCdist=205256;dist=40824intergenic..rs12593645_G_A(SEQ ID NO: 132)1558714417A1.111.08 - 1.147.25E-190.335 / 0.309AQP9;LIPCdist=236307;dist=9773intergenic..rs1077834_T_C(SEQ ID NO: 133)1558723479C0.790.77 - 0.818.28E-750.391 / 0.449LIPCdist=711upstream..rs17231506_C_T(SEQ ID NO: 134)1656994528T0.640.62 - 0.671.37E-1740.125 / 0.193HERPUD1;CETPdist=15752;dist=1334intergenic..rs7499892_C_T(SEQ ID NO: 135)1657006590T1.311.26 - 1.355.25E-950.204 / 0.155CETP.intronic..rs2303790_A_G(SEQ ID NO: 136)1657017292G0.520.48 - 0.573.90E-1010.025 / 0.053CETP.exonicnonsynonymous SNVD459Grs35137323_G_T(SEQ ID NO: 137)1681545764T0.920.9 - 0.958.05E-100.362 / 0.38CMIP.intronic..rs9953437_G_A(SEQ ID NO: 138)1847120600A0.860.84 - 0.881.86E-360.425 / 0.465LIPGUTR3..rs74633843_T_C(SEQ ID NO: 139)1847155167C1.181.12 - 1.246.81E-190.084 / 0.07LIPG;ACAA2dist=29613;dist=153367intergenic..rs111784051_T_G(SEQ ID NO: 140)1945402262G0.830.79 - 0.887.85E-150.06 / 0.073TOMM40.intronic..rs429358_T_C(SEQ ID NO: 141)1945411941C1.301.24 - 1.355.80E-360.109 / 0.086APOE.exonicnonsynonymous SNVC130Rrs5749600_A_G(SEQ ID NO: 142)2221999292G1.081.06 - 1.111.10E-090.475 / 0.456SDF2L1dist=704downstream.

[0118] Tables 3 to 7. Independent signals from GWAS for each metabolic syndrome component derived from conditional and joint multiple SNP analyses.

[0119]

[0120] The APOA5, APOE, BUD13, and LPL loci are all associated with MetS, TG, and HDL. These genes may be functionally elucidated or confirmed in GWAS related to lipid metabolism.

[0121]

[0122] 2.3. Identification and analysis of exonic and non-synonymous SNPs

[0123]

[0124] Additionally, 12 exonic and non-synonymous SNPs were identified (Table 8).

[0125]

[0126] rsIDChromosomePositionRefAltGeneCategoriesFunctionFunctional annotationAA changers13306194221252534GAAPOBTGexonicNonsynonymousR532Wrs1260326227730940TCGCKRTG, FBSexonicNonsynonymousL446Prs937908467231843GARREB1FBSexonicNonsynonymousD1171Nrs1051488631322911CTHLA-BBPexonicNonsynonymousA329Trs75326924780286003CTCD36HDL exonicNonsynonymousP90Srs230379011116707736TGAPOA1HDLexonicNonsynonymousM52Lrs20124758711116658553CTZPR1TGexonicNonsynonymousA61Trs23037901657017292AGCEETPMetS, HDLexonicNonsynonymousD399Grs1127354311778358945GARNF213BPexonicNonsynonymousR4810Krs1674791911526765GTRGL3B PexonicNonsynonymousP162Hrs585429261919379549CTTM6SF2TGexonicNonsynonymousE167Krs4293581945411941TCAPOEMetS, HDLexonicNonsynonymousC130R

[0127] Table 8. Exonic nonsynonymous SNPs of genome-wide significance

[0128]

[0129] Among the genes containing these SNPs, GCKR is associated with TG and FBS (Fig. 3).

[0130]

[0131] We investigated the genetic association of each variant with five MetS component phenotypes (Fig. 4 , Table 9) for 11 independent signals derived from the MetS GWAS (Table 2 ).

[0132]

[0133] MetSBPFBSHDLTGWCP value / ORAFP value / ORAFP value / ORAFP value / ORAFP value / ORAFP value / ORAFCHRrsIDEffect allele(case / control)(case / control)(case / control)(case / control)(case / control)(case / control)11rs651821C1.21E-98 / 1.330.347 / 0.2820.355 / 1.0120.299 / 0.2978.60E-01 / 1.0030.298 / 0.2985.64E-185 / 1.4870.359 / 0.2732.80E-287 / 1.6650.381 / 0.278.22E-01 / 10.298 / 0.29811rs74368849A5.00E-62 / 1.250.107 / 0.0760.838 / 0.9960.083 / 0.0833.48E-01 / 1.0230.085 / 0.0832.27E-153 / 1.7620.117 / 0.071.07E-150 / 1.7740.119 / 0.0712.35E-01 / 1.030.085 / 0.08316rs17231506T2.58E-23 / 0.830.154 / 0.1790.549 / 1.0090.174 / 0.1729.27E-01 / 1.0020.173 / 0.1737.26E-172 / 0.59660.125 / 0.1933.75E-06 / 0.920.164 / 0.1766.66E-01 / 1.010.174 / 0.1738rs59147390C1.00E-18 / 0.830.11 / 0.1290.549 / 1.0110.125 / 0.1241.57E-01 / 0.9710.122 / 0.1253.55E-50 / 0.7360.102 / 0.1333.94E-47 / 0.7320.1 / 0.1327.73E-01 / 0.990.124 / 0.12416rs2303790G7.00E-13 / 0.780.038 / 0.0470.245 / 1.0330.046 / 0.0448.68E-01 / 1.0050.045 / 0.0451.33E-103 / 0.44590.025 / 0.0534.35E-03 / 0.910.042 / 0.0462.88E-01 / 1.030.046 / 0.0456rs112405902A4.48E-10 / 1.180.080 / 0.0691E-04 / 1.0910.075 / 0.0693.16E-01 / 1.0270.073 / 0.0714.49E-04 / 1.090.076 / 0.071.22E-15 / 1.2210.082 / 0.0681.53E-02 / 1.060.074 / 0.0719rs429358C8.62E-10 / 1.150.101 / 0.090.114 / 0.9680.091 / 0.0947.28E-01 / 1.0080.093 / 0.0931.74E-35 / 1.3030.109 / 0.0863.26E-20 / 1.2270.106 / 0.0885.61E-01 / 0.990.092 / 0.09311rs141740187T3.50E-09 / 0.9840.207 / 0.2090.83 / 0.9970.208 / 0.2085.94E-01 / 1.0090.209 / 0.2083.98E-01 / 0.98670.207 / 0.2091.34E-01 / 0.9760.205 / 0.2092.58E-01 / 0.980.206 / 0.20911rs10830963G9.47E-09 / 1.080.446 / 0.4260.077 / 1.0210.434 / 0.4296.57E-36 / 1.1850.463 / 0.4216.67E-01 / 0.99440.43 / 0.4313.87E-02 / 1.0280.436 / 0.4293.20E-02 / 1.030.435 / 0.4298rs6586892A1.58E-08 / 0.930.199 / 0.210.135 / 1.0220.209 / 0.2061.75E-01 / 0.9780.204 / 0.2081.21E-05 / 0.93230.199 / 0.2115.28E-06 / 0.9270.198 / 0.217.26E-02 / 0.970.205 / 0.20919rs731839A2.50E-08 / 0.930.454 / 0.4710.019 / 0.9730.463 / 0.476.25E-03 / 0.9640.46 / 0.4691.71E-07 / 0.93470.455 / 0.4728.32E-04 / 0.9560.459 / 0.472.43E-02 / 1.030.471 / 0.465.

[0134] Table 9. Comparison of 11 independent signals related to each component and MetS (BP: Blood pressure, CHR: Chromosome, FBS: Fasting blood sugar, HDL: High density lipoprotein, MetS: Metabolic syndrome, OR: Odds ratio, TG: Triglyceride, WC: Waist circumference).

[0135]

[0136] The APOA5 locus, which was most significant in the MetS GWAS, was also most significant in the TG and HDL GWAS (Table 9, Fig. 5). HDL and TG levels overlapped most significantly with the MetS GWAS results, confirming that lipid profiles contribute significantly to the MetS phenotype.

[0137]

[0138] 2.4. Analysis of 11 MetS signals

[0139]

[0140] Additionally, among the 11 MetS signals, one signal overlapped with BP (SNHG32), three overlapped with WC (SNHG32, MTNR1B, PEPD), and two overlapped with FBS (MTNR1B, PEPD) all had the same OR valence except for rs731849 (PEPD) in WC (Fig. 4). Regarding the most significant APOA5 signal for MetS, TG, and HDL, rs651821 was the most significant variant for MetS and TG, whereas rs662799 was the most significant variant for HDL, with an LD (r2) >0.99 between the two variants. In addition to rs651821 and rs662799, there were other significant variants within the APOA5 locus, including rs2072560 and rs2266788 (Table 9).

[0141]

[0142] We further investigated the association between these variants and TG and HDL levels. Adjusting for the statistically significant rs651821, rs2072560, and rs2266788 reversed the OR direction for both TG and HDL. The LD (r2) between rs651821-rs2072560 and rs651821-rs2266788 was 0.65, respectively, whereas rs2072560 and rs2266788 showed a high correlation (LD relationship, r2 = 0.995). rs2072560 is located in the intron of the APOA5 gene, and rs2266788 is located in the 3' UTR of the APOA5 gene, of which rs2266788 is a risk variant for familial hypertriglyceridemia in ClinVar37. Within the 11 COJO-derived independent signals in the MetS GWAS, with the exception of rs651821 mentioned above, the signals remained significant, but there were no cases in which the ORs reversed after adjusting for the top SNPs. Conditional analyses of rs651821 and rs2266788 in the UKBB cohort showed different results from the KoGES results, where the OR direction was maintained even after conditional analyses. To investigate the underlying mechanism of these diverse results across populations, we performed fine mapping for each cohort. Fine mapping in the UKBB and KoGES revealed distinct characteristics. There were no overlapping SNPs among the SNPs with a PIP value greater than 0.9 (Fig. 6). In the KoGES cohort, rs651821, located on chromosome 11 of APOA5, was an independent SNP and causal variant in Koreans, with a PIP value of 0.998 in both the conditional analysis and fine mapping. However, when the same analysis was performed using UKBB data, rs964184, located downstream of ZPR1, was identified as the top SNP (Fig. 7) and was shown to have a high causal potential (Fig. 6, Table 10, Table 11).

[0143]

[0144]

[0145] Table 10. Fine mapping results (CHR: Chromosome, MAF: Minor allele frequency, PIP: Posterior inclusion probability, POS: Position, SE: Standard error) for KoGES participants based on binary TG values ​​(PIP>0.1). rs2266788 was added for comparison with rs651821 (PIP below the threshold).

[0146]

[0147]

[0148] Table 11. Fine mapping results (CHR: Chromosome, MAF: Minor allele frequency, PIP: Posterior inclusion probability, POS: Position, SE: Standard error) for UK Biobank participants based on binary TG values ​​(PIP>0.1). rs651821 and rs2266788 were added for comparison with rs964184 (PIP below the threshold).

[0149]

[0150] In the UKBB cohort, the PIP value of rs651821 was approximately 0 and was not included in the reliable UKBB fine-mapping analysis. However, rs4938311, located in BUD13, was the only SNP with a PIP ≥0.1 in both the KoGES and UKBB cohorts.

[0151]

[0152] 2.5. PRS Calculation Analysis Based on Genetic Risk Score

[0153]

[0154] The genetic risk score for metabolic syndrome was calculated as follows using the PRS calculation formula of the present invention.

[0155]

[0156] Using 11 SNPs identified through genome-wide association analysis in the Korean Genome and Epidemiology Study (KoGES) urban cohort of 58,600 individuals, we calculated a genetic risk score (PRS) in an independent rural cohort (approximately 8,000 individuals), divided the individuals into quintiles, and calculated the proportion of metabolic syndrome to verify the PRS.

[0157]

[0158] [Mathematical Formula 1]

[0159]

[0160] In the above mathematical formula 1,

[0161] PRSi is the genetic risk score of individual i,

[0162] i is an identification number that distinguishes an individual's genetic data,

[0163] j is an identification number to distinguish SNP data,

[0164] Is is the prior probability estimator,

[0165] is the log value of the odds ratio (OR) derived from the metabolic syndrome GWAS for the jth SNP,

[0166] dosage is a value of 0, 1, or 2 (0 is major homozygous, 1 is heterozygous, 2 is minor homozygous) depending on the jth SNP genotype of the ith individual.

[0167]

[0168] When the diagnostic criteria for metabolic syndrome are originally defined as a group with three or more risk factors out of five, approximately 44% correspond to metabolic syndrome as presented in the upper table of (Figure 6). In addition, when the group with four or more or all five risk factors of metabolic syndrome is defined as severe metabolic syndrome and the genetic risk score is divided into quintiles (named bin 1 to bin 5 in order from the group with the lowest genetic risk score to the group with the highest), and the proportion of metabolic syndrome in each group is calculated, it can be seen that the proportion of patient groups increases as it progresses from bin 1 to bin 5 (Figure 6).

[0169]

[0170] In addition, when comparing the criteria for increased risk factors in the form of three or more (MetS3, blue), four or more (MetS4, red), or all five (MetS5, gray) of the metabolic syndrome diagnostic criteria according to the genetic risk score (genetic risk score 1-bin standard ratio), it can be confirmed that the rate of increase according to the genetic risk score is the highest in the severe metabolic syndrome diagnostic criteria that satisfy all five criteria (Fig. 8). The above analysis process is illustrated in Fig. 9.

[0171]

[0172] While specific aspects of the present invention have been described in detail above, it will be apparent to those skilled in the art that these specific descriptions merely represent preferred embodiments and are not intended to limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents.

Claims

A method for determining at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839 and rs429358 from a biological sample to provide information necessary for predicting the risk of developing metabolic syndrome. In the first paragraph, The above single nucleotide polymorphisms (SNPs) are located in the SNHG32 gene on chromosome 6 for rs112405902, in the LPL gene on chromosome 8 for rs59147390, in the LPL gene on chromosome 8 for rs6586892, in the MTNR1B gene on chromosome 11 for rs10830963, in the BUD13 gene on chromosome 11 for rs74368849, in the BUD13 gene on chromosome 11 for rs141740187, in the APOA5 gene on chromosome 11 for rs651821, in the CETP gene on chromosome 16 for rs17231506, and in the CETP gene on chromosome 16 for rs2303790. A method characterized in that rs731839 is located in the PEPD gene on chromosome 19, and rs429358 is located in the APOE gene on chromosome 19. In the first paragraph, A method for predicting the risk of developing the metabolic syndrome, characterized in that a risk score is calculated based on the following mathematical formula 1. [Mathematical Formula 1] In the above mathematical formula 1, PRSi is the genetic risk score of individual i, i is an identification number that distinguishes an individual's genetic data, j is an identification number to distinguish SNP data, Is is the prior probability estimator, is the log value of the odds ratio (OR) derived from the metabolic syndrome GWAS for the jth SNP, dosage is a value of 0, 1, or 2 depending on the jth SNP genotype of the ith individual (0 is major homozygous, 1 is heterozygous, 2 is minor homozygous) am. In the first paragraph, A method, characterized in that the biological sample is any one selected from the group consisting of blood, tissue, cells, serum, plasma, saliva, and urine. In the first paragraph, A method characterized in that the subject of the above biological sample is Korean. A biomarker composition for determining metabolic syndrome in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs112405902, rs59147390, rs6586892, rs10830963, rs74368849, rs141740187, rs651821, rs17231506, rs2303790, rs731839, and rs429358. A composition for predicting the risk of developing metabolic syndrome, comprising a preparation capable of detecting the biomarker composition of Article 6.

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