TY - JOUR
T1 - Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution
AU - COGENTKidney Consortium, T2D-GENES Consortium
AU - Mägi, Reedik
AU - Horikoshi, Momoko
AU - Sofer, Tamar
AU - Mahajan, Anubha
AU - Kitajima, Hidetoshi
AU - Franceschini, Nora
AU - McCarthy, Mark I.
AU - Morris, Andrew P.
N1 - Funding Information:
Andrew P Morris is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant number WT098017). Reedik Ma€gi is funded by EU H2020 grants 692145, 692065, 676550, and 654248, Estonian Research Council Grant IUT20-60, and European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.15-0012 GENTRANSMED). Funding from the National Institutes of Health is acknowledged under awards U01-DK085526, U01-DK085501, U01-DK085524, U01-DK085545, U01-DK085584, U01-DK088389 and U01-DK105535. Funding for the genetic analysis of HCHS/SOL is acknowledged from the National Heart Lung and Blood Institute under contract HHSN268201300005C. Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust.
Publisher Copyright:
© The Author 2017.
PY - 2017/9
Y1 - 2017/9
N2 - 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 finemapping 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.
AB - 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 finemapping 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.
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U2 - 10.1093/hmg/ddx280
DO - 10.1093/hmg/ddx280
M3 - Article
C2 - 28911207
AN - SCOPUS:85040673553
VL - 26
SP - 3639
EP - 3650
JO - Human Molecular Genetics
JF - Human Molecular Genetics
SN - 0964-6906
IS - 18
ER -