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

Ioanna Tachmazidou,Jeremy Schwartzentruber,Jian’An Luan,Scott G Wilson,Celia M.T Greenwood,Marjolein N Kooijman,Paolo Gasparini,Anette Varbo,Marianne Benn,Dániel Süveges,Nicholas J Wareham,Claudia Langenberg,Børge G Nordestgaard,Giovanni Gambaro,Katerina Trajanoska,Kerrin S Small,Yali Xue,Markus Perola,Robert A Scott,Massimiliano Cocca,Lorraine Southam,Michela Traglia,Giovanni Malerba,John C Chambers,Narinder Bansal,Yasin Memari,Tom R Gaunt,William R Scott,Klaudia Walter,Inês Barroso,Alireza Moayyeri,Angela Matchan,Konstantinos Hatzikotoulas,Hashem A Shihab,Janine F Felix,André G Uitterlinden,Michael Boehnke,Abhishek Nag,Petr Danecek,Nicholas J Timpson,Eleftheria Zeggini,Emanuele Di Angelantonio,Josine L Min,Benjamin Lehne,Richard Durbin,Kalliope Panoutsopoulou,Brian R Walker,Audrey E Hendricks,Nicole Soranzo,Nigel W Rayner,George Davey Smith,Zhongsheng Chen,George Dedoussis,Cinzia Sala,Satu Männistö,Cristina Bombieri,Fernando Rivadeneira,Albert Hofman,Susan M Ring,Weihua Zhang,Jaspal S Kooner,Jie Huang,Francesco Cucca,Oliver Stegle,Adam S Butterworth,Carlo Sidore,Julia Steinberg,Pekka Jousilahti,John P Kemp,Martin D Tobin,Oscar H Franco,Vincent W.V Jaddoe,Veikko Salomaa,Lavinia Paternoster,Valentina Iotchkova,Andrew P Morris,Daniela Toniolo,Shane Mccarthy,Antonella Mulas,Alisa K Manning,Timothy D Spector,Chris Finan,Magdalena Żołędziewska ,María Soler Artigas ,Cecilia M Lindgren ,Carolina Medina‐Gomez ,Beaté St Pourcain ,Caroline L Relton ,Graham R S Ritchie ,Christopher J Hammond ,Adam E Locke ,Michał Szpak ,Marcus E Kleber ,Aliki Eleni Farmaki ,Paul R Burton ,Sarah Metrustry ,Wei Yu Lin ,Gianluigi Zaza ,Anne Tybjærg‐Hansen ,John R B Perry ,Andrew Crawford ,Ιωάννα Ντάλλα ,Mark Mccarthy

doi:10.1016/j.ajhg.2017.04.014

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.

Highlights

The escalating global epidemic of overweight and obesity can be ascribed to a complex interplay between environmental and genetic factors
Large-scale genome-wide association studies (GWASs) for body mass index (BMI), waist to hip ratio, and height have to date focused on the role of common-frequency variants and have unveiled numerous associations that explain a modest proportion of trait variance;[4,5,6] the role of low-frequency variants has not been systematically explored across the entire genome
Six signals reside in genomic regions that have not been implicated with related traits before, and 100 signals represent conditionally independent associated variants at previously reported loci (Tables 2 and S3, Figure S23)

Summary

Introduction

The escalating global epidemic of overweight and obesity can be ascribed to a complex interplay between environmental and genetic factors. Shape, and composition are anthropometric measures correlated with obesity and patterns of fat deposition and are associated with important metabolic health outcomes.[1,2,3] Large-scale genome-wide association studies (GWASs) for body mass index (BMI), waist to hip ratio, and height have to date focused on the role of common-frequency variants and have unveiled numerous associations that explain a modest proportion of trait variance;[4,5,6] the role of low-frequency variants has not been systematically explored across the entire genome. The application of whole-genome sequencing (WGS) at a population scale and generation of high performance imputation reference panels allows GWASs to systematically evaluate variation across the low- and commonfrequency minor allele frequency (MAF) spectra. We assessed the contribution of 15,844,966 sequence variants to 12 anthropometric traits of medical relevance using a hybrid approach of cohort-wide low-depth WGS7 and imputation based on a sequence-based reference panel comprising 9,746 haplotypes[8] in a discovery set of 57,129 individuals (stage 1, Table S1).

Methods

Results

Conclusion