Method for determining risk of developing korean-specific lipid-related disease by analyzing apolipoprotein a5 gene polymorphism

Abstract

The present invention relates to a method for determining the risk of developing a Korean-specific lipid-related disease by analyzing apolipoprotein A5 gene polymorphism and, specifically, to a method for determining single nucleotide polymorphisms (SNPs) from a biological sample, a biomarker composition for determining lipid-related diseases in Koreans, comprising SNPs, and a composition for predicting the risk of developing a lipid-related disease, comprising a reagent capable of detecting the biomarker composition. According to the present invention, useful information for predicting the risk of developing a lipid-related disease in Koreans on the basis of the analysis of apolipoprotein A5 gene polymorphism can be provided.

Description

A method for determining the risk of developing lipid-related diseases specific to Koreans by analyzing apolipoprotein A5 gene polymorphisms.

[0001] The present invention relates to a method for determining the risk of developing a lipid-related disease specific to Koreans by analyzing apolipoprotein A5 gene polymorphism.

[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 lipid-related diseases is needed. Their heritability exhibits considerable variability, not to mention significant differences in clinical symptoms. Single SNPs with significant influence on lipid-related diseases 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 lipid-related diseases, 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 one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs651821 and rs2266788 from a biological sample in order to provide information necessary for predicting the risk of developing a lipid-related disease.

[0007] Another object of the present invention is to provide a biomarker composition for determining lipid-related diseases in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs651821 and rs2266788.

[0008] Another object of the present invention is to provide a composition for predicting the risk of developing a lipid-related disease, 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 this study, we aimed to identify the interaction between rs651821 and rs2266788, major variants of APOA5 (apolipoprotein A5), located at the most significant genetic locus identified through a genome-wide association study (GWAS) for MetS, and to determine how they collectively affect triglyceride and high-density lipoprotein (HDL) cholesterol levels. The study included 58,600 Korean individuals from the Korean Genome and Epidemiology Study (KoGES) cohort, for whom biochemical information and demographic variables regarding metabolic syndrome were available.

[0011] Furthermore, we found that the APOA5 SNP rs651821 was significantly associated with MetS and triglyceride levels, whereas rs662799 was associated with HDL levels. Furthermore, rs2266788 was significantly associated with both triglyceride and HDL levels. However, in a conditional analysis using rs651821, the odds ratios for rs2266788 were reversed for both triglyceride and HDL cholesterol levels. Therefore, rs651821 and rs2266788 showed independent and opposite signals in the expanded GWAS analysis. This phenomenon was replicated in another independent KoGES cohort but not in the UK Biobank cohort. While alleles may appear to be associated with disease risk, when the influence of the lead SNP is removed, a specific SNP may exhibit an independent protective signal, indicating a multi-layered effect between the two SNPs.

[0012]

[0013] Hereinafter, the present invention will be described in detail.

[0014]

[0015] The present invention provides a method for determining one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs651821 and rs2266788 from a biological sample in order to provide information necessary for predicting the risk of developing a lipid-related disease in Koreans.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] In the present invention, rs651821 and rs2266788, which are used to predict the risk of developing lipid-related diseases 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 / ).

[0020] In the present invention, the single nucleotide polymorphism (SNP) may be located on chromosome 11 of the apolipoprotein A5 (APOA5) gene.

[0021] In one embodiment of the present invention, we performed a genetically modified organism (GWAS) for MetS in 58,600 Korean individuals from the KoGES database, identifying 11 independent genome-wide signals of significant significance. Further analysis revealed a complex relationship between two SNPs (rs651821 and rs2266788) that was not apparent in the single GWAS, and elucidated the directionality of the rs2266788 effect on metabolic syndrome (MetS), triglycerides (TG), and high-density lipoprotein (HDL).

[0022] In one embodiment of the present invention, we performed a GWAS on MetS and its components, and extracted the most influential SNPs. The effects of APOA5 SNPs rs651821 and rs2266788 on serum TG levels were analyzed along with gene-environment interactions. Similar trends were observed for HDL and APOA5 SNPs rs662799 and rs2266788. As a result, rs651821 was the most significantly associated with triglycerides and metabolic syndrome, and rs662799 was the most significantly associated with HDL. In addition, rs2266788, which has a high LD with rs651821, was significantly associated with similar risks for the corresponding traits and diseases.

[0023] According to one embodiment of the present invention, the multi-layered effects of APOA5 gene polymorphism markers involved in triglyceride HDL and metabolic syndrome were confirmed. Through this multi-layered effect, it was confirmed that the risk of lipid-related diseases and traits differed depending on the combination of markers for each individual. Specifically, it was confirmed that the magnitude of risk varied depending on drinking, smoking, and exercise.

[0024] In the present invention, the risk may be considered based on factors such as drinking, smoking, and exercise. According to one embodiment of the present invention, environmental factors such as drinking, smoking status, and exercise frequency were considered to assess the risk of lipid-related diseases, and the relationships between these environmental factors and various genotypes were evaluated. Subgroup analysis of these lifestyle factors (smoking, drinking, and exercise intensity) revealed that the association between TG and rs2266788 genotypes was opposite to the association between TG and rs651821 CC genotype subgroups.

[0025] According to one embodiment of the present invention, when the genotype of the rs651821 single nucleotide polymorphism is CC and the genotype of the rs2266788 single nucleotide polymorphism is AA, it can be determined that the risk of metabolic syndrome, a lipid-related disease, is increased. In addition, the rs651821 is at position 116,662,579 of the hg19 reference standard of chromosome 11, and the rs2266788 is at position 116,660,686 of the hg19 reference standard of chromosome 11. Therefore, it was confirmed that the case of rs651821 CC and rs2266788 AA can be used for disease prevention tailored to individual characteristics by controlling risk factors such as smoking and excessive drinking.

[0026] According to one embodiment of the present invention, individuals with the rs651821 CC and rs2266788 AA genotypes were found to have significantly higher TG levels when they consumed excessive alcohol or smoked. Therefore, risk factors based on an individual's genotype should be considered. In addition to TG and HDL, a GWAS of systolic blood pressure, diastolic blood pressure, and fasting blood sugar (FBS), which are included in the MetS diagnostic criteria, revealed numerous significant associations, reaching a genome-wide significance level of 5E-8.

[0027] In the present invention, the lipid-related disease may be caused by neutral fat, HDL cholesterol, or metabolic syndrome.

[0028] 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.

[0029] In addition, the present invention provides a biomarker composition for determining lipid-related diseases in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs651821 and rs2266788.

[0030] In addition, the present invention provides a composition for predicting the risk of developing a lipid-related disease, comprising a preparation capable of detecting the biomarker composition.

[0031] 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.

[0032] In the present invention, the primer or probe that specifically binds to the polynucleotide or its complementary polynucleotide is allele-specific.

[0033] 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.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] In addition, the present invention provides a kit for predicting the risk of occurrence of a lipid-related disease, comprising the composition.

[0040] 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.

[0041] 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.

[0042] According to the present invention, there is an advantage in that it can be usefully used to predict the risk of developing lipid-related diseases in Koreans based on single nucleotide polymorphisms.

[0043] 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.

[0044] 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.

[0045] Figure 3 shows a zoom plot of the APOA5 SNPs within metabolic syndrome, triglycerides, and high-density lipoprotein (HDL). (a) Results for MetS. (b) Results for TG. (c) Results for HDL.

[0046] Figure 4 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.

[0047] Figure 5 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).

[0048] 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.

[0049]

[0050] Example 1. Materials and Methods

[0051]

[0052] 1.1. Research Participants

[0053]

[0054] 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.

[0055]

[0056] 1.2. Phenotypic Measurement

[0057]

[0058] 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.

[0059]

[0060] 1.3. Definition of metabolic syndrome

[0061]

[0062] 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 for men and ≥80 cm for women; (2) TG levels ≥150 mg / dL or taking dyslipidemia medication; (3) HDL-C levels ≤40 mg / dL for men and ≤50 mg / dL for 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.

[0063]

[0064] 1.4. Check covariate variables

[0065]

[0066] 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.

[0067]

[0068] 1.5. Genotyping and attribution

[0069]

[0070] 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 ) An estimated imputation accuracy of ≥0.3 was used in the GWAS analysis. The locations of all variants included in the present invention were based on hg19.

[0071]

[0072] 1.6. Visualizing Results

[0073]

[0074] 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.

[0075]

[0076] 1.7. Statistical Analysis

[0077]

[0078] 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 the rs651821 and rs2266788 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.

[0079] 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.

[0080]

[0081] Example 2. Experimental Results

[0082]

[0083] 2.1. Analysis of basic characteristics of the study population

[0084]

[0085] 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).

[0086]

[0087] 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%)

[0088] 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.

[0089]

[0090] 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 ).

[0091]

[0092] 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.

[0093] 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.

[0094]

[0095] 2.2. GWAS and COJO analyses performed for each of the five MetS diagnostic criteria (Figure 2, Tables 3 to 7)

[0096]

[0097] For each of the five MetS diagnostic criteria (Figure 2, Tables 3–7), 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. Of these, 10 signals were associated with two or more phenotypes (Tables 3–7).

[0098]

[0099] 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.

[0100]

[0101] 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..

[0102]

[0103] 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.

[0104]

[0105] 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.

[0106]

[0107] 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.

[0108] Tables 3 to 7. Independent signals from GWAS for each metabolic syndrome component derived from conditional and joint multiple SNP analyses.

[0109]

[0110] 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.

[0111]

[0112] 2.3. Identification and analysis of exonic and non-synonymous SNPs

[0113]

[0114] Additionally, 12 exonic and non-synonymous SNPs were identified (Table 8).

[0115]

[0116] rsIDChromosomePositionRefAltGeneCategoriesFunctionFunctional annotationAA changers13306194221252534GAAPOBTGexonicNonsynonymousR532Wrs1260326227730940TCGCKRTG, FBSexonicNonsynonymousL446Prs937908467231843GARREB1FBSexonicNonsynonymousD1171Nrs1051488631322911CTHLA-BBPexonicNonsynonymousA329Trs75326924780286003CTCD36HDL exonicNonsynonymousP90Srs230379011116707736TGAPOA1HDLexonicNonsynonymousM52Lrs20124758711116658553CTZPR1TGexonicNonsynonymousA61Trs23037901657017292AGCETPMetS, HDLexonicNonsynonymousD399Grs1127354311778358945GARNF213BPexonicNonsynonymousR4810Krs1674791911526765GTRGL3B PexonicNonsynonymousP162Hrs585429261919379549CTTM6SF2TGexonicNonsynonymousE167Krs4293581945411941TCAPOEMetS, HDLexonicNonsynonymousC130R

[0117] Table 8. Exonic nonsynonymous SNPs of genome-wide significance

[0118]

[0119] Among the genes containing these SNPs, GCKR is associated with TG and FBS.

[0120]

[0121] We investigated the genetic association of each variant with five MetS component phenotypes (Table 9) for 11 independent signals derived from the MetS GWAS (Table 8).

[0122]

[0123] 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.

[0124] 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).

[0125]

[0126] The APOA5 locus, which was most significant in the MetS GWAS, was also most significant in the TG and HDL GWAS (Table 9, Fig. 3). HDL and TG levels overlapped most significantly with the MetS GWAS results, confirming that lipid profiles contribute significantly to the MetS phenotype.

[0127]

[0128] Based on the above results, a genome-wide association study of Koreans with triglycerides, HDL, and metabolic syndrome identified single nucleotide polymorphism (SNP) markers around the APOA5 gene associated with these traits (Fig. 2). The SNP most significantly associated with triglycerides and metabolic syndrome was rs651821, while the most significant SNP for HDL was rs662799. Additionally, rs2266788, which has a high LD with rs651821, was found to be significantly associated with similar risks for these traits and diseases.

[0129]

[0130] Furthermore, the highest correlation was confirmed at the APOA5 gene locus (Figs. 1, 2, and 3). Conditional analysis of rs651821, the genetic variant most likely to be associated with the function of the APOA5 gene, confirmed the presence of genetic variants and influences independent of rs651821.

[0131]

[0132] 2.4. Confirmation of multi-level effects of the APOA5 genetic marker

[0133]

[0134] In 58,600 Koreans, the values ​​according to the genotypes of APOA5 gene loci rs651821 and rs2266788 were compared according to triglyceride and HDL, and according to the combination of each genotype.

[0135] As triglyceride levels increase, the risk of lipid-related diseases increases, and it was confirmed that triglyceride levels increase as the minor allele of the genotype increases due to the existence of strong LD between rs651821 and rs2266788. However, when classified according to the combination of genotypes, it was confirmed that in the hetero or minor homo group (CT or CC) of rs651821, triglyceride levels decrease as the minor allele (G) of rs2266788 increases.

[0136] Therefore, rs2266788 is a protective SNP that reduces triglyceride concentrations, but its multi-level effect, which was previously obscured by LD with rs651821, was confirmed. Furthermore, using the same method, we confirmed that as HDL levels decrease, the risk of lipid-related diseases increases, and that the risk effect and multi-level effect on lipid-related diseases, similar to triglycerides, are confirmed.

[0137]

[0138] In addition, we performed conditional analysis on rs651821, the most significant genetic variant of the APOA5 gene, to identify genetic variants and their influence that act independently of rs651821. rs2266788 had ORs of 1.45 and 1.24 for triglycerides and HDL, respectively, but when rs651821 was conditional analyzed, ORs of 0.76 and 0.69, respectively, confirming the opposite direction.

[0139]

[0140] SNPEffectalleleCHR:POS (hg19)EAFFunctionalconsequence* LD (r 2 )TG GWASTG rs651821ConditionedHDL GWASHDL rs651821ConditionedP-valueORP-valueORP-valueORP-valueORrs651821(SEQ ID NO: 143)C11:1166625790.2595'-UTR12.80E-2871.67--1.71E-1801.50--rs662799(SEQ ID NO: 144)G11:1166637070.298Intergenic0.9953.34E-2841.660.230.781.09E-1801.500.141.32rs2072560(SEQ ID NO: 145)T11:1166618260.235Intronic0.6542.30E-1141.458.51E-260.772.53E-461.256.39E-490.69rs2266788(SEQ ID NO: 146)G11:1166606860.2183'-UTR0.6536.87E-1131.451.09E-260.761.41E-451.246.68E-500.69

[0141] Table 10. GWAS and conditional results of APOA5 major SNPs on TG and HDL (CHR: Chromosome; EAF: Effect allele frequency; HDL: High density lipoprotein; LD: Linkage Disequilibrium; OR: Odds ratio; POS: Position; SNP: Single Nucleotide Polymorphism; TG: Triglycerides; UTR: Untranslated region. * LD with rs651821)

[0142]

[0143] This was classified into rs651821 and rs2266788 and rs651821 CC groups in 58,600 Koreans according to rs2266788 genotypes, and classified according to smoking, drinking, and exercise status to confirm the influence. As a result, smoking acted as a risk factor for increased triglycerides up to 1.6 times depending on rs651821 and rs2266788 genotypes, but in the rs651821 CC and rs2266788 AA group, smoking acted as a risk factor for increased triglycerides 2.6 times, and it was confirmed that the average triglyceride level of smokers was 551. Similarly, depending on the rs651821 and rs2266788 genotypes, drinking was a risk factor for increased neutral fat levels up to 1.4 times, but in the rs651821 CC and rs2266788 AA group, heavy drinking was a risk factor for increased neutral fat levels up to 1.8 times, and the average neutral fat level of heavy drinkers was confirmed to be 372. Exercise decreased neutral fat levels depending on exercise intensity, but was confirmed to not be greatly affected by genotype.

[0144]

[0145] 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

1. A method for determining one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs651821 and rs2266788 from a biological sample to provide information necessary for predicting the risk of developing a lipid-related disease.

2. In paragraph 1, A method characterized in that the above single nucleotide polymorphism (SNP) is located in the apolipoprotein A5 (APOA5) gene of chromosome 11.

3. In paragraph 1, A method characterized in that the genotype of the above rs651821 single nucleotide polymorphism is CC and the genotype of the rs2266788 single nucleotide polymorphism is AA.

4. In paragraph 1, A method characterized in that the above rs651821 is position 116,662,579 based on the hg19 reference of chromosome 11, and the above rs2266788 is position 116,660,686 based on the hg19 reference of chromosome 11.

5. In paragraph 1, A method characterized in that the above risk is considered by factors such as drinking, smoking, and exercise.

6. In paragraph 1, A method wherein the lipid-related disease is characterized by neutral fat, HDL cholesterol or metabolic syndrome.

7. In paragraph 1, 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.

8. In paragraph 1, A method characterized in that the subject of the above biological sample is Korean. A biomarker composition for determining lipid-related diseases in Koreans, comprising at least one single nucleotide polymorphism (SNP) selected from the group consisting of 9.rs651821 and rs2266788.

10. A composition for predicting the risk of developing a lipid-related disease, comprising a preparation capable of detecting the biomarker composition of Article 9.

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