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

Abstract

Aims/hypothesisHuman 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.MethodsThe 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.ResultsExome 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/interpretationWe 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-012-2756-1) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

“…One recent study of the MTNR1B locus encoding melatonin receptor 1B indicated that this locus may not only contain common variants with low effect sizes (ORs <1.4), but may also contain rare variants with considerably stronger associations with the risk of type 2 diabetes (OR 5.7, 95% CI 2.2, 14.8) for rare lossof-function variants of the receptor [63]. Sequencing of all genes in the genome (exome sequencing), as recently reported for a Danish case-control study [64], and whole-genome resequencing, as performed in the 1000 Genomes Project [65], will improve our understanding of the potential relevance of low-frequency (0.5-5%) and rare (<0.5%) variants in the development of type 2 diabetes [66]. It remains to be seen to what extent ongoing studies and analyses of other kinds of genetic variations such as copy number and structural variations will contribute to more precise risk assessment.…”

Diabetes in Population Isolates: Lessons from Greenland

“…In the past decade, GWAS in large, open populations such as Europeans and Asians have proven highly successful in identifying common variants associated with complex diseases and traits, among them T2D and related metabolic traits [15][16][17][18][19][20]. Developments in genotyping and sequencing technologies have further enabled the study of lowfrequency and rare genetic variation [18,21,22].…”

Genetic architecture of type 2 diabetes

“…For five loci (SLC30A8, GCKR, PPARG, KCNJ11-ABCC8, PAM), the coding variants identified had previously been nominated as causal for their respective GWAS signals 2,7,13 . For the other seven loci, GWAS metaanalyses had previously highlighted a lead variant in non-coding sequence 2,5,6 .…”

“…However, the contribution to T2D risk attributable to lower-frequency variants remains a matter of considerable debate, not least because of the relevance of disease architecture to clinical application 11 . Next-generation sequencing enables direct evaluation of the role of lower-frequency variants to disease risk 7,12,13 . This paper describes the efforts of the coordinated, complementary strategies pursued by the Genetics of Type 2 Diabetes (GoT2D) and T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) Consortia.…”