Abstract
Most genome-wide association studies consider genes that are located closest to single nucleotide polymorphisms (SNPs) that are highly significant for those studies. However, the significance of the associations between SNPs and candidate genes has not been fully determined. An alternative approach that used SNPs in expression quantitative trait loci (eQTL) was reported previously for Crohn’s disease; it was shown that eQTL-based preselection for follow-up studies was a useful approach for identifying risk loci from the results of moderately sized GWAS. In this study, we propose an approach that uses eQTL SNPs to support the functional relationships between an SNP and a candidate gene in a genome-wide association study. The genome-wide SNP genotypes and 10 biochemical measures (fasting glucose levels, BUN, serum albumin levels, AST, ALT, gamma GTP, total cholesterol, HDL cholesterol, triglycerides, and LDL cholesterol) were obtained from the Korean Association Resource (KARE) consortium. The eQTL SNPs were isolated from the SNP dataset based on the RegulomeDB eQTL-SNP data from the ENCODE projects and two recent eQTL reports. A total of 25,658 eQTL SNPs were tested for their association with the 10 metabolic traits in 2 Korean populations (Ansung and Ansan). The proportion of phenotypic variance explained by eQTL and non-eQTL SNPs showed that eQTL SNPs were more likely to be associated with the metabolic traits genetically compared with non-eQTL SNPs. Finally, via a meta-analysis of the two Korean populations, we identified 14 eQTL SNPs that were significantly associated with metabolic traits. These results suggest that our approach can be expanded to other genome-wide association studies.
Highlights
Large-scale genome-wide association studies (GWAS) that comprised several thousands of samples have reported many novel findings in various diseases and disease-related phenotypes [1]
The Korean Association Resource (KARE) subjects consisted of two Korean populations (Ansung and Ansan), which we used as the replication set of genetic associations based on their differing clinical characteristics
The non-expression quantitative trait loci (eQTL) sites examined were approximately 66 times more examined single nucleotide polymorphisms (SNPs) than the eQTL sites; compared with those explained by non-eQTL SNPs, the proportions of phenotypic variance that were explained by eQTL SNPs were larger for GLU0 (1.9 in eQTL vs 0.2 in non-eQTL SNPs) and TG (1.3 in eQTL vs 0.1 in non-eQTL SNPs), and similar for blood urea nitrogen (BUN) (3.4 in eQTL vs 4.0 in non-eQTL SNPs)
Summary
Large-scale genome-wide association studies (GWAS) that comprised several thousands of samples have reported many novel findings in various diseases and disease-related phenotypes [1]. These findings have been enlightening the path to the identification of disease mechanisms and biomarkers. It was suggested that the missing heritability might come from the stringent multiple testing correction of GWAS analyses [5]. This multiple testing correction is necessary to exclude false-positive loci, but simultaneously may discard many true-positive loci [6]. The categorization of the genome-wide SNPs into functional categories provided the opportunity to reduce multiple testing and to identify functional variants [7, 8]
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