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

Tachmazidou, Ioanna, Suveges, Daniel, Min, Josine L, Ritchie, Graham RS, Steinberg, Julia, Walter, Klaudia, Iotchkova, Valentina, Schwartzentruber, Jeremy, Huang, Jie, Memari, Yasin
et al (show 93 more authors) (2017) Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. AMERICAN JOURNAL OF HUMAN GENETICS, 100 (6). pp. 865-884.

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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.

Item Type: Article
Uncontrolled 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
Depositing User: Symplectic Admin
Date Deposited: 02 Jun 2017 09:59
Last Modified: 19 Jan 2023 07:03
DOI: 10.1016/j.ajhg.2017.04.014
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3007794