ESHG 2014: Insights into the genetic architecture of anthropometric traits using whole genome sequence data

Title: C14.1 - Insights into the genetic architecture of anthropometric traits using whole genome sequence data
Keywords: body mass index; whole genome sequencing; association
Authors: E. Zeggini, UK10K consortium; Wellcome Trust Sanger Institute, Hinxton, United Kingdom.

Abstract: Body weight and fat distribution measures are associated with increased risk of cardiometabolic disease. As part of the UK10K study, we have investigated the genetic architecture of anthropometric traits in 3,538 individuals with 6.5x whole genome sequence (WGS) data from the ALSPAC and TwinsUK cohorts. Variants discovered through WGS, along with those from the 1000 Genomes Project (1KGP), were imputed into additional individuals from the ALSPAC and TwinsUK cohorts with GWAS data (total sample size 9,979). We investigated association between anthropometric traits and 8.6 million low frequency and common variants (MAF>0.01). We are in the process of obtaining in silico replication of prioritised signals. In interim replication analysis across ~15,000 samples, 43 out of 66 novel signals for BMI have the same direction of effect in the replication cohorts (p-value=0.0093). We examined the concordance of the direction of effect at established loci for each trait. Out of the 31 established independent loci for BMI that were present in our data, 28 have the same direction of effect (p-value=2.3e-06). For weight, 10 out of 11 known loci (p-value=0.006), and for height 151 out of 172 loci (p-value < 2.2e-16) have the same direction of effect, respectively. We estimated the improvement in genome-wide signal captured relative to those present in HapMap 2, HapMap 3 or 1KGP. We find no appreciable increase in variance explained as density increases, suggesting that the contribution of variants with MAF>0.01 are likely to be well-captured by existing GWAS implementation. Larger sample sizes will be required to refine these estimates.