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

Lookup NU author(s): Professor Paul Burton, Professor Brian Walker

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2017 The AuthorsDeep 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.


Publication metadata

Author(s): Tachmazidou I, Suveges D, Min JL, Ritchie GRS, Steinberg J, Walter K, Iotchkova V, Schwartzentruber J, Huang J, Memari Y, McCarthy S, Crawford AA, Bombieri C, Cocca M, Farmaki A-E, Gaunt TR, Jousilahti P, Kooijman MN, Lehne B, Malerba G, Mannisto S, Matchan A, Medina-Gomez C, Metrustry SJ, Nag A, Ntalla I, Paternoster L, Rayner NW, Sala C, Scott WR, Shihab HA, Southam L, St Pourcain B, Traglia M, Trajanoska K, Zaza G, Zhang W, Artigas MS, Bansal N, Benn M, Chen Z, Danecek P, Lin W-Y, Locke A, Luan J, Manning AK, Mulas A, Sidore C, Tybjaerg-Hansen A, Varbo A, Zoledziewska M, Finan C, Hatzikotoulas K, Hendricks AE, Kemp JP, Moayyeri A, Panoutsopoulou K, Szpak M, Wilson SG, Boehnke M, Cucca F, Di Angelantonio E, Langenberg C, Lindgren C, McCarthy MI, Morris AP, Nordestgaard BG, Scott RA, Tobin MD, Wareham NJ, Burton P, Chambers JC, Smith GD, Dedoussis G, Felix JF, Franco OH, Gambaro G, Gasparini P, Hammond CJ, Hofman A, Jaddoe VWV, Kleber M, Kooner JS, Perola M, Relton C, Ring SM, Rivadeneira F, Salomaa V, Spector TD, Stegle O, Toniolo D, Uitterlinden AG, Barroso I, Greenwood CMT, Perry JRB, Walker BR, Butterworth AS, Xue Y, Durbin R, Small KS, Soranzo N, Timpson NJ, Zeggini E

Publication type: Article

Publication status: Published

Journal: American Journal of Human Genetics

Year: 2017

Volume: 100

Issue: 6

Pages: 865-884

Print publication date: 01/06/2017

Online publication date: 25/05/2017

Acceptance date: 21/04/2017

Date deposited: 22/12/2017

ISSN (print): 0002-9297

ISSN (electronic): 1537-6605

Publisher: Cell Press

URL: https://doi.org/10.1016/j.ajhg.2017.04.014

DOI: 10.1016/j.ajhg.2017.04.014

PubMed id: 28552196


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