Genome-Wide Association Study of Metabolic Syndrome in Koreans.
- Author:
Seok Won JEONG
1
;
Myungguen CHUNG
;
Soo Jung PARK
;
Seong Beom CHO
;
Kyung Won HONG
Author Information
- Publication Type:Original Article
- Keywords: expression quantitative trait loci; genome-wide association study; metabolic networks and pathways; single nucleotide polymorphism
- MeSH: Cardiovascular Diseases; Genome-Wide Association Study*; Hand; Metabolic Networks and Pathways; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Risk Factors
- From:Genomics & Informatics 2014;12(4):187-194
- CountryRepublic of Korea
- Language:English
- Abstract: 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 x 10(-8)), 8 SNPs with genome-wide suggestive p-values (5 x 10(-8) < or = p < 1 x 10(-5)), and 2 SNPs of more functional variants with borderline p-values (5 x 10(-5) < or = p < 1 x 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 LPL, APOA5, and CHRM2, which were the significant or suggestive loci in the METS GWAS. Conclusively, our approach using the conventional GWAS, reconsidering functional variants and pathway-based interpretation, suggests a useful method to understand the GWAS results of complex traits and can be expanded in other genomewide association studies.
