Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes

Aims/hypothesis

Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes.

Methods

The study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m2 and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case–control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans.

Results

Exome sequencing identified 70,182 polymorphisms with MAF >1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 × 10−14), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10−11) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10−10).

Conclusions/interpretation

We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.

Introduction

Over the last few years, genome-wide association studies (GWAS) have led to substantial progress in mapping common genetic variation with impact on common phenotypes including those of the metabolic syndrome [1–10]. This advance has revealed hundreds of genetic determinants of human complex phenotypes [1]. Despite this progress a major part of the heritable contribution to variation in most widespread metabolic traits remains unaccounted for [11]. Thus, for type 2 diabetes and related metabolic traits it has been estimated that 10–30% of the observed heritability can be attributed to the hitherto identified variants [2, 4, 8, 10].

DNA sequencing has emerged as a powerful technology enabling detection of low-frequency and rare variation not captured by initial GWAS design and in future studies the GWAS approach may be complemented by imputation of single nucleotide polymorphisms (SNPs) from whole-genome sequencing of a subset of individuals [12]. Sequencing of all genes in the genome (exome) [13, 14] is an alternative approach relying on the hypothesis that functional disease-associated variation resides in the coding regions. Exome sequencing has proven valuable in the search for mutations responsible for Mendelian diseases [15, 16] and emerging reports suggest the benefit of applying large-scale exome sequencing to uncover variation associated with complex human traits [17, 18].

Here we present the results of a first-generation medium-pass (8×) exome sequencing approach in 2,000 Danish individuals (stage 1) with follow-up of 16,192 SNPs in 15,989 Danes (stage 2) and replication of 45 SNPs, discovered in a joint analysis of stage 1 and 2, in up to 63,896 Europeans (stage 3) (Fig. 1). To achieve sufficient statistical power a large number of the SNPs selected from stage 1 were genotyped in the much larger sample size in stage 2 making the statistical power comparable to a study where all individuals from both stage 1 and 2 are genotyped for all SNPs [19]. Our objective was to find novel associations of coding variants at minor allele frequencies (MAFs) above 1% with metabolic phenotypes.

Fig. 1

Overview of the study. UTR, untranslated region

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