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

Am J Hum Genet
Authors
Ioanna Tachmazidou
Dániel Süveges
Josine Min
Graham Ritchie
Julia Steinberg
Klaudia Walter
Valentina Iotchkova
Jeremy Schwartzentruber
Jie Huang
Yasin Memari
Shane McCarthy
Andrew Crawford
Cristina Bombieri
Massimiliano Cocca
Aliki-Eleni Farmaki
Tom Gaunt
Pekka Jousilahti
Marjolein Kooijman
Benjamin Lehne
Giovanni Malerba
Satu Männistö
Angela Matchan
Carolina Medina-Gomez
Sarah Metrustry
Abhishek Nag
Ioanna Ntalla
Lavinia Paternoster
Nigel Rayner
Cinzia Sala
William Scott
Hashem Shihab
Lorraine Southam
Beate St Pourcain
Michela Traglia
Katerina Trajanoska
Gialuigi Zaza
Weihua Zhang
María Artigas
Narinder Bansal
Marianne Benn
Zhongsheng Chen
Petr Danecek
Wei-Yu Lin
Adam Locke
Jian'an Luan
Alisa Manning
Antonella Mulas
Carlo Sidore
Anne Tybjaerg-Hansen
Anette Varbo
Magdalena Zoledziewska
Chris Finan
Konstantinos Hatzikotoulas
Audrey Hendricks
John Kemp
Alireza Moayyeri
Kalliope Panoutsopoulou
Michal Szpak
Scott Wilson
Michael Boehnke
Francesco Cucca
Emanuele Di Angelantonio
Claudia Langenberg
Cecilia Lindgren
Mark McCarthy
Andrew Morris
Børge Nordestgaard
Robert Scott
Martin Tobin
Nicholas Wareham
SpiroMeta Consortium
GoT2D Consortium
Paul Burton
John Chambers
George Smith
George Dedoussis
Janine Felix
Oscar Franco
Giovanni Gambaro
Paolo Gasparini
Christopher Hammond
Albert Hofman
Vincent Jaddoe
Marcus Kleber
Jaspal Kooner
Markus Perola
Caroline Relton
Susan Ring
Fernando Rivadeneira
Veikko Salomaa
Timothy Spector
Oliver Stegle
Daniela Toniolo
André Uitterlinden
arcOGEN Consortium
Understanding Society Scientific Group
UK10K Consortium
Inês Barroso
Celia Greenwood
John Perry
Brian Walker
Adam Butterworth
Yali Xue
Richard Durbin
Kerrin Small
Nicole Soranzo
Nicholas Timpson
Eleftheria Zeggini
Keywords
Female
Humans
Male
Syndrome
Cohort Studies
DNA Methylation
Genome-Wide Association Study
Genetic Variation
Quantitative Trait Loci
Sequence Analysis, DNA
Genome, Human
Physical Chromosome Mapping
Obesity
Body Height
Databases, Genetic
Sex Characteristics
Meta-Analysis as Topic
United Kingdom
Anthropometry
Lipodystrophy
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.
Year of Publication
2017
Journal
Am J Hum Genet
Volume
100
Issue
6
Pages
865-884
Date Published
2017 Jun 01
ISSN
1537-6605
DOI
10.1016/j.ajhg.2017.04.014
PubMed ID
28552196
PubMed Central ID
PMC5473732
Links
Grant list
MR/K002414/1 / Medical Research Council / United Kingdom
G0900753 / Medical Research Council / United Kingdom
MC_UU_12013/3 / Medical Research Council / United Kingdom
RG/08/014/24067 / British Heart Foundation / United Kingdom
MR/J012165/1 / Medical Research Council / United Kingdom
MR/L003120/1 / Medical Research Council / United Kingdom
RG/11/4/28734 / British Heart Foundation / United Kingdom
MR/N01104X/1 / Medical Research Council / United Kingdom
CZB/4/733 / Chief Scientist Office / United Kingdom
MC_UU_12015/2 / Medical Research Council / United Kingdom
MC_PC_15018 / Medical Research Council / United Kingdom
648916 / European Research Council / International
MC_UU_12013/1 / Medical Research Council / United Kingdom
MR/K002279/1 / Medical Research Council / United Kingdom
G0902313 / Medical Research Council / United Kingdom
P30 DK020572 / DK / NIDDK NIH HHS / United States
G0600237 / Medical Research Council / United Kingdom
G0100594 / Medical Research Council / United Kingdom
G0901461 / Medical Research Council / United Kingdom
MC_UU_12013/8 / Medical Research Council / United Kingdom
MC_UU_12013/2 / Medical Research Council / United Kingdom

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