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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

Am J Hum Genet

Publication Date

01/06/2017

Volume

100

Pages

865 - 884

Keywords

DXA traits, UK Biobank, UK10K, anthropometry, genetic association study, imputation, next-generation whole-genome sequencing, Anthropometry, Body Height, Cohort Studies, DNA Methylation, Databases, Genetic, Female, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Lipodystrophy, Male, Meta-Analysis as Topic, Obesity, Physical Chromosome Mapping, Quantitative Trait Loci, Sequence Analysis, DNA, Sex Characteristics, Syndrome, United Kingdom