Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. To identify the genetic risk factors of METS, we carried out a genome-wide association study (GWAS) for 2,657 cases and 5,917 controls in Korean populations. As a result, we could identify 2 single nucleotide polymorphisms (SNPs) with genome-wide significance level p-values (<5 × 10-8), 8 SNPs with genome-wide suggestive p-values (5 × 10-8 ≤ p < 1 × 10-5), and 2 SNPs of more functional variants with borderline p-values (5 × 10-5 ≤ p < 1 × 10-4). On the other hand, the multiple correction criteria of conventional GWASs exclude false-positive loci, but simultaneously, they discard many true-positive loci. To reconsider the discarded true-positive loci, we attempted to include the functional variants (nonsynonymous SNPs [nsSNPs] and expression quantitative trait loci [eQTL]) among the top 5,000 SNPs based on the proportion of phenotypic variance explained by genotypic variance. In total, 159 eQTLs and 18 nsSNPs were presented in the top 5,000 SNPs. Although they should be replicated in other independent populations, 6 eQTLs and 2 nsSNP loci were located in the molecular pathways of
Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. METS includes multiple clinical traits, as follows: increased plasma glucose, abdominal obesity, dyslipidemia, and high blood pressure [
Recent advances in high-throughput genomics technologies have allowed massive testing of genetic variants in minimal time [
On the other hand, relatively little trait heritability can be explained by the conventional GWAS [
Recently, Fransen et al. [
Based on previous knowledge, we applied an alternative analysis strategy to understand the genetic components of METS. First of all, we conducted a conventional GWAS for METS cases and healthy controls to discover the top significant signals. Thereafter, we tried to uncover the functional variants, such as nonsynonymous SNPs (nsSNPs) and eQTLs, among the SNPs to be discarded using the stringent criteria of the conventional GWAS. Finally, we drew a pathway of the significantly associated GWAS SNPs and the remaining less significantly associated functional SNPs. The overall study design is schematically described in
The study subjects were originally derived from a part of the Korean Genome and Epidemiology Study (KoGES) project, which was the national project to establish genome epidemiology cohorts of Korean dwellers or immigrants/emigrants [
We used the general information on resident areas (Anseong or Ansan), sex, and age as the covariates and past disease history of diabetes, hypertension, and lipidemia as exclusion criteria for non-METS healthy controls. The height and body weight were used to calculate the body mass index (BMI) as another covariate, and waist circumference (WC), systolic and diastolic blood pressures (SBP and DBP), fasting plasma glucose levels (GLU0), high-density lipoprotein (HDL) cholesterol, and triglyceride (TG) were used to diagnose METS. METS was defined by the presence of three or more of the following five components according to the NCEP-ATPIII criteria using WC for Asians [
The genotyping of the cohort population was previously described for the KARE study [
The GWAS for METS cases and controls was conducted by logistic regression analysis, adjusting for residential area, sex, age, and BMI as covariates, implemented in PLINK version 1.07 [
The LD between the previously reported GWAS SNPs and the SNPs of the current GWAS was investigated with SNAP web-based software (
To maximize the candidate risk factors of METS, we selected additional functional SNPs in the eQTLs or nsSNP loci (5 × 10-5 ≤ p < 1 × 10-4). Among the Affymetrix 5.0 SNPs, we investigated the eQTL SNPs from regulomeDB (
The genetic variances of the top association SNPs were estimated by GCTA v1.24 [
The functional relevance of the associated SNP sites was analyzed by overlapping the gene-coding sequence or the Encyclopedia of DNA Element (ENCODE) regulatory element positions in the University of California Santa Cruz (UCSC) genome browser (
LD analysis using 10 SNPs was conducted with the previously reported GWAS SNPs. As a result, 5 SNPs had strong LD with the 15 highly linked GWAS SNPs (
Therefore, we discovered 10 significant or suggestive associated SNPs in the METS GWAS, but 6 of them were already reported or linked to the reported SNPs. The remaining 4 suggestive signals and 2 functional variants have been first reported in the current study, and a replication study should be performed in other independent populations.
The 10 associated SNPs and the LD SNPs were located in six functional gene regions, and one SNP was located in the intergenic region. The top signals were located downstream of a functional spliceosome-associated protein, named BUD13, a homolog of yeast (
The second significant SNP was rs6589566, which has 6 high-LD SNPs. Notably,
The third most significant signals were located in the lipoprotein lipase (
Although the remaining 6 SNPs and their nearest genes have not been functionally studied regarding METS-associated traits, further studies are required to elucidate for their role in METS.
The results of the V(G)/V(P) for 100 to 5,000 SNPs were plotted in
eQTLs and nsSNPs provide insights into the regulation of transcription and aid in the interpretation of GWASs [
Conclusively, our approach using the conventional GWAS, reconsidering functional variants and the pathway-based interpretation, suggests a useful method to understand the GWAS results of complex traits and can be expanded in other GWASs.
This work was supported by grants from the Korea Centers for Disease Control and Prevention (KCDC), Republic of Korea (4845-301, 4851-302, 4851-307). This study was also supported by an internal project, "Construction of databases and an analysis system for Korean reference genomes for disease researches" (2013-NG72001-00), of the Korea National Institute of Health, KCDC.
This is 2014 KNIH KARE best paper awarded.
Supplementary data including two tables and one figure can be found with this article online at
The list of functional SNPs among the top 5,000 SNPs associated with METS
Overall study design to understand the metabolic syndrome (METS) risk genetic factors in Korean. SNP, single nucleotide polymorphism.
Manhattan plot of metabolic syndrome genome-wide association study -log10(p-values). All black and grey circles indicate the individual single nucleotide polymorphisms (SNPs). The red horizontal line is the genome-wide significance level (p = 5 × 10-8), and the blue horizontal line is the genome-wide suggestive level (5 × 10-8 ≤ p < 1 × 10-5). The top significant SNPs are depicted on the right site of the SNP.
The proportion of phenotypic variance [V(P)] explained by the genotypic variance [V(G)]. The horizontal axis denotes the number of single nucleotide polymorphisms (SNPs). Approximately 50% of phenotypic variance could be explained by the top 5,000 SNPs.
Illustration of molecular pathway for significantly associated single nucleotide polymorphism (SNP) loci in the metabolic syndrome genome-wide association study (GWAS). The molecules depicted by the significant GWAS loci (reds), functional SNPs (expression quantitative trait loci or nonsynonymous SNPs) loci (green), and the other intermediate molecules (yellow) are illustrated on the cell organelles.
Clinical characteristics of metabolic syndrome-related traits
Total | Anseong | Ansan | p-value | |
---|---|---|---|---|
No. of individuals | 8,842 | 4,205 | 4,637 | |
Gender (men:women) | 4,183 (47.3):4,659 (52.7) | 1,809 (43.0):2,396 (57.0) | 2,374 (51.2):2,263 (48.8) | |
Age (y) | 52.2 ± 8.9 | 55.7 ± 8.7 | 49.1 ± 7.9 | <0.01 |
Height (cm) | 160.0 ± 8.7 | 158.3 ± 8.6 | 161.6 ± 8.4 | <0.01 |
Body mass index (kg/m2) | 24.6 ± 3.1 | 24.5 ± 3.3 | 24.7 ± 3.0 | <0.01 |
Fasting glucose (mg/dL) | 87.7 ± 21.9 | 85.9 ± 18.3 | 89.1 ± 24.4 | <0.01 |
DBP (mm Hg) | 80.3 ± 11.5 | 82.5 ± 10.9 | 78.2 ± 11.6 | <0.01 |
SBP (mm Hg) | 121.7 ± 18.6 | 126.6 ± 18.8 | 117.2 ± 17.3 | <0.01 |
Waist circumference (cm) | 82.7 ± 8.8 | 84.6 ± 8.7 | 81.0 ± 8.5 | <0.01 |
HDL cholesterol (mg/dL) | 44.7 ± 10.1 | 44.6 ± 10.3 | 44.7 ± 9.9 | 0.62 |
TG (mg/dL) | 162.9 ± 105.7 | 165.0 ± 107.2 | 161.0 ± 104.3 | 0.07 |
METS |
||||
METs case/control | 2,657/5,917 | 1,490/2,454 | 1,167/3,463 | <0.01 |
Values are presented as number (%) or mean ± SD.
DBP, diastolic blood pressure; SBP, systolic blood pressure; HDL, high-density lipoprotein; TG, triglyceride; MET, metabolic syndrome.
METs status: three or more of the component as follows: waist circumference (≥90 cm for men, ≥80 cm for women), HDL (<40 mg/dL for men, <50 mg/dL for women), TG (≥150 mg/dL), blood pressure (SBP ≥ 130 mm Hg or DBP ≥ 85 mm Hg), fasting glucose (≥100 mg/dL).
Genome-wide association results for METs case-control study in the Korean population
SNP ID | CHR | BP | Effect allele/other | EAF | Previous GWAS reports for the METS SNPs | OR | 95% Confidence interval |
p-value | |
---|---|---|---|---|---|---|---|---|---|
Lower | Upper | ||||||||
Genome-wide significant levels (p < 5 × 10–8) | |||||||||
rs11216126 | 11 | 116122450 | A/C | 0.798 | Decrease HDL [ |
1.33 | 1.21 | 1.46 | 7.15 × 10–9 |
rs180349 | 11 | 116117037 | A/T | 0.227 | - | 1.28 | 1.17 | 1.4 | 4.12 × 10–8 |
Genome-wide suggestive levels (5 × 10–8 ≤ p < 1 × 10–5) | |||||||||
rs6589566 | 11 | 116157633 | C/T | 0.218 | Increase triglycerides [ |
1.27 | 1.16 | 1.39 | 2.26 × 10–7 |
rs17410962 | 8 | 19892360 | G/A | 0.876 | - | 1.34 | 1.2 | 1.5 | 5.75 × 10–7 |
rs17482753 | 8 | 19876926 | G/T | 0.876 | Increase triglycerides [ |
1.34 | 1.2 | 1.51 | 6.34 × 10–7 |
rs10503669 | 8 | 19891970 | C/A | 0.879 | Increase triglycerides and decrease HDL cholesterol [ |
1.34 | 1.2 | 1.51 | 7.25 × 10–7 |
rs2350786 | 7 | 136327110 | A/G | 0.637 | - | 1.21 | 1.12 | 1.31 | 1.33 × 10–6 |
rs486394 | 11 | 116031532 | C/A | 0.122 | - | 1.32 | 1.18 | 1.47 | 1.64 × 10–6 |
rs1668775 | 10 | 36639540 | T/C | 0.211 | - | 1.24 | 1.14 | 1.36 | 2.07 × 10–6 |
rs605257 | 9 | 10300942 | T/A | 0.770 | - | 1.22 | 1.12 | 1.33 | 6.48 × 10–6 |
Expression quantitative trait loci (1 × 10–5 ≤ p < 1 × 10–4) | |||||||||
rs1996794 | 11 | 9779172 | C/A | 0.411 | eQTL of SWAP70 [ |
1.17 | 1.09 | 1.27 | 2.73 × 10–5 |
rs1032550 | 11 | 9769884 | C/T | 0.410 | eQTL of SWAP70 [ |
1.16 | 1.08 | 1.25 | 2.73 × 10–5 |
METS, metabolic syndrome; SNP, single nucleotide polymorphism; CHR, chromosome; BP, base pair based on the human reference genome (hg18); EAF, effect allele frequency; GWAS, genome-wide association study; OR, odds ratio; HDL, high-density lipoprotein; eQTL, expression quantitative trait loci.
Reported GWAS SNPs that show LD with our study SNPs
SNP |
LD states |
Reported traits | References | ||
---|---|---|---|---|---|
This study | Reported GWAS | r2 | D' | ||
Genome-wide significant levels (p < 5 × 10–8) | |||||
|
rs10790162 | 0.912 | 0.955 | HDL cholesterol and triglycerides | [ |
rs1558861 | 0.956 | 1 | Triglycerides | [ |
|
Genome-wide suggestive levels (5 × 10–8 ≤ p < 1 × 10–5) | |||||
rs6589566 | rs10790162 | 0.912 | 0.955 | HDL cholesterol and triglycerides | [ |
rs2075290 | 0.956 | 1 | HDL cholesterol and triglycerides | [ |
|
rs2160669 | 1 | 1 | Obesity-related traits | [ |
|
rs2266788 | 1 | 1 | HDL cholesterol and triglycerides | [ |
|
rs651821 | 1 | 1 | Triglycerides | [ |
|
rs964184 | 0.957 | 1 | HDL cholesterol | [ |
|
|
rs10096633 | 1 | 1 | Metabolic traits | [ |
rs17482753 | rs1059611 | 0.925 | 1 | Lipid metabolism phenotypes | [ |
rs10503669 | rs12678919 | 1 | 1 | HDL cholesterol | [ |
rs17091905 | 1 | 1 | Cardiovascular disease risk factors | [ |
|
rs328 | 1 | 1 | Triglycerides | [ |
|
rs7016880 | 0.925 | 1 | Hypertriglyceridemia | [ |
|
rs7841189 | 1 | 1 | Metabolic syndrome | [ |
The underlined SNPs indicate that the lead SNPs have not been reported, but there were highly linked GWAS SNPs.
GWAS, genome-wide association study; SNP, single nucleotide polymorphism; LD, linkage disequilibrium; HDL, high-density lipoprotein.
GWAS results of eQTLs and nonsynonymous SNPs consisting of the LPL and APOA5 pathways
CHR | SNP | BP | A1 | MAF | 95% Confidence interval |
p-value | Gene | Amino acid substitution | Description | ||
---|---|---|---|---|---|---|---|---|---|---|---|
OR | Lower | Upper | |||||||||
eQTLs among the METS GWAS | |||||||||||
21 | rs2236472 | 45727840 | A | 0.124 | 0.83 | 0.74 | 0.93 | 1.6E-03 | - | Collagen, type XVIII, alpha 1 | |
6 | rs4713671 | 33807877 | A | 0.191 | 1.13 | 1.04 | 1.24 | 7.2E-03 | - | Inositol 1,4,5-triphosphate receptor, type 3 | |
19 | rs344802 | 50496147 | T | 0.204 | 0.89 | 0.81 | 0.97 | 1.0E-02 | - | Creatine kinase, muscle | |
18 | rs4998986 | 55282713 | A | 0.369 | 0.88 | 0.82 | 0.96 | 2.0E-03 | - | Mucosa-associated lymphoid tissue lymphoma translocation gene 1 | |
18 | rs4998985 | 55282774 | A | 0.298 | 0.86 | 0.79 | 0.93 | 3.3E-04 | - | Mucosa-associated lymphoid tissue lymphoma translocation gene 1 | |
10 | rs871026 | 1.31E+08 | G | 0.384 | 0.88 | 0.81 | 0.95 | 9.8E-04 | - | O-6-Methylguanine-DNA methyltransferase | |
Nonsynonymous substitution SNPs among the METS GWAS | |||||||||||
9 | rs2296871 | 1.34E+08 | A | 0.263 | 0.89 | 0.82 | 0.97 | 8.6E-03 | E79G | Mitogen-activated protein kinase 7 | |
1 | rs11802875 | 1.62E+08 | A | 0.0333 | 1.35 | 1.10 | 1.65 | 4.0E-03 | S229L | NUF2, NDC80 kinetochore complex component, homolog ( |
GWAS, genome-wide association study; eQTL, expression quantitative trait loci; SNP, single-nucleotide polymorphism; LPL, lipoprotein lipase; APOA5, apolipoprotein A-V; CHR, chromosome; BP, base pair; A1, minor allele; MAF, minor allele frequency; OR, odds ratio; METS, metabolic syndrome.