Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Abstract

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common-and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

“…The discovery that low-frequency variation, especially near TP53, is associated with HC demonstrates the scientific value of testing for variation in the lower allele frequency spectrum and the utility of comprehensive imputation templates. Low-frequency variants identified in this study had larger effects than common variants ( Supplementary Figures S7 and S8), in keeping with findings from a range of complex phenotypes including anthropometric traits [17,51,52]. Nevertheless, despite having sufficient power to detect low-frequency variation explaining as little as 0.11% of the variance in HC, this study was underpowered for rare variant analysis (Supplementary Note 4), underlining the need for even larger research efforts.…”

“…Exploiting whole-genome sequence data together with high-density imputation panels such as the joint UK10K and 1000 genomes (UK10K/1KGP) [14] and the haplotype reference consortium (HRC) [15], that have previously facilitated the discovery of low-frequency genetic variants for a range of traits [16,17], we carried out GWAS for final HC. Specifically, we aim to a) study low-frequency and common variants for final HC, allowing for age-specific effects through meta-analyses of mid-childhood and/or adulthood datasets, b) investigate genetic variants influencing a combined phenotype of (near) final HC and ICV, termed final cranial dimension, and c) explore developmental changes in the genetic architecture of HC through longitudinal modelling of genetic variances in unrelated individuals as well as growth curve modelling of HC trajectories for carriers and non-carriers of high risk variants.…”

A big-data approach to understanding metabolic rate and response to obesity in laboratory mice

“…6E and 6F). We found 4 strains with lower metabolic rate that might predict obesity susceptibility, Pax5 +/- (Melka et al, 2012), Chst8 -/- (Tachmazidou et al, 2017), Tfap2b +/- (Lindgren et al, 2009), and Pald1 -/- (Cotsapas et al, 2009). We also found 3 KO strains which might protect from obesity, Pepd -/- (Shungin et al, 2015), Klf12 +/- (Jiang et al, 2018), Figure 6.…”

Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank (n=408,317)

“…Previous reports have associated DLEU7 with heel bone mineral density, 24,35 BMI, 36,37 height, 38,39 cardiovascular diseases, 40 systolic blood pressure 40 and pulmonary function decline (forced expiratory volume). 41 The association between snoring genes and heel bone mineral density could be mediated by BMI due to the association between BMI and bone density documented previously.…”