Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1016/j.ajhg.2017.04.014

Type

Journal article

Journal

American journal of human genetics

Publication Date

06/2017

Volume

100

Pages

865 - 884

Addresses

The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.

Keywords

SpiroMeta Consortium, GoT2D Consortium, arcOGEN Consortium, Understanding Society Scientific Group, UK10K Consortium, Humans, Lipodystrophy, Obesity, Syndrome, Anthropometry, Body Height, Cohort Studies, Physical Chromosome Mapping, Sequence Analysis, DNA, DNA Methylation, Sex Characteristics, Quantitative Trait Loci, Genome, Human, Databases, Genetic, Female, Male, Meta-Analysis as Topic, Genetic Variation, Genome-Wide Association Study, United Kingdom