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Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.

TitleTrans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.
Publication TypePublication
Year2017
AuthorsMägi R, Horikoshi M, Sofer T, Mahajan A, Kitajima H, Franceschini N, McCarthy MI, Morris AP
Corporate AuthorsCOGENT-Kidney Consortium, T2D-GENES Consortium
JournalHum Mol Genet
Volume26
Issue18
Pagination3639-3650
Date Published2017 09 15
ISSN1460-2083
KeywordsAlleles, Diabetes Mellitus, Type 2, ethnic groups, European Continental Ancestry Group, Gene Frequency, genome-wide association study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software
Abstract

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

DOI10.1093/hmg/ddx280
Alternate JournalHum Mol Genet
PubMed ID28911207
PubMed Central IDPMC5755684
Grant ListU01 DK085501 / DK / NIDDK NIH HHS / United States
HHSN268201300005C / HL / NHLBI NIH HHS / United States
U01 DK085524 / DK / NIDDK NIH HHS / United States
U01 DK085545 / DK / NIDDK NIH HHS / United States
U01 DK105535 / DK / NIDDK NIH HHS / United States
RC2 DK088389 / DK / NIDDK NIH HHS / United States
U01 DK085526 / DK / NIDDK NIH HHS / United States
U01 DK085584 / DK / NIDDK NIH HHS / United States
R21 HL123677 / HL / NHLBI NIH HHS / United States
/ / Wellcome Trust / United Kingdom
MS#: 
0613
Manuscript Lead/Corresponding Author Affiliation: 
Affiliated Investigator - Not at HCHS/SOL site
ECI: 
Manuscript Affiliation: 
Coordinating Center - Collaborative Studies Coordinating Center - UNC at Chapel Hill
Manuscript Status: 
Published and Public