Skip to main content
Journal cover image

Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies.

Publication ,  Journal Article
Wang, X; Chua, H-X; Chen, P; Ong, RT-H; Sim, X; Zhang, W; Takeuchi, F; Liu, X; Khor, C-C; Tay, W-T; Cheng, C-Y; Suo, C; Liu, J; Aung, T ...
Published in: Hum Mol Genet
June 1, 2013

Genome-wide association studies (GWASs) have discovered thousands of variants that are associated with human health and disease. Whilst early GWASs have primarily focused on genetically homogeneous populations of European, East Asian and South Asian ancestries, the next-generation genome-wide surveys are starting to pool studies from ethnically diverse populations within a single meta-analysis. However, classical epidemiological strategies for meta-analyses that assume fixed- or random-effects may not be the most suitable approaches to combine GWAS findings as these either confer low statistical power or identify mostly loci where the variants carry homogeneous effect sizes that are present in most of the studies. In a trans-ethnic meta-analysis, it is likely that some genetic loci will exhibit heterogeneous effect sizes across the populations. This may be due to differences in study designs, differences arising from the interactions with other genetic variants, or genuine biological differences attributed to environmental, dietary or lifestyle factors that modulate the influence of the genes. Here we compare different strategies for meta-analyzing GWAS across genetically diverse populations, where we intentionally vary the effect sizes present across the different populations. We subsequently applied the methods that yielded the highest statistical power to a trans-ethnic meta-analysis of seven GWAS in type 2 diabetes, and showed that these methods identified bona fide associations that would otherwise have been missed by the classical strategies.

Duke Scholars

Published In

Hum Mol Genet

DOI

EISSN

1460-2083

Publication Date

June 1, 2013

Volume

22

Issue

11

Start / End Page

2303 / 2311

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Models, Statistical
  • Meta-Analysis as Topic
  • Humans
  • Genome-Wide Association Study
  • Genetics & Heredity
  • Ethnicity
  • Diabetes Mellitus, Type 2
  • 3105 Genetics
  • 11 Medical and Health Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, X., Chua, H.-X., Chen, P., Ong, R.-H., Sim, X., Zhang, W., … Teo, Y.-Y. (2013). Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum Mol Genet, 22(11), 2303–2311. https://doi.org/10.1093/hmg/ddt064
Wang, Xu, Hui-Xiang Chua, Peng Chen, Rick Twee-Hee Ong, Xueling Sim, Weihua Zhang, Fumihiko Takeuchi, et al. “Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies.Hum Mol Genet 22, no. 11 (June 1, 2013): 2303–11. https://doi.org/10.1093/hmg/ddt064.
Wang X, Chua H-X, Chen P, Ong RT-H, Sim X, Zhang W, et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum Mol Genet. 2013 Jun 1;22(11):2303–11.
Wang, Xu, et al. “Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies.Hum Mol Genet, vol. 22, no. 11, June 2013, pp. 2303–11. Pubmed, doi:10.1093/hmg/ddt064.
Wang X, Chua H-X, Chen P, Ong RT-H, Sim X, Zhang W, Takeuchi F, Liu X, Khor C-C, Tay W-T, Cheng C-Y, Suo C, Liu J, Aung T, Chia K-S, Kooner JS, Chambers JC, Wong T-Y, Tai E-S, Kato N, Teo Y-Y. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum Mol Genet. 2013 Jun 1;22(11):2303–2311.
Journal cover image

Published In

Hum Mol Genet

DOI

EISSN

1460-2083

Publication Date

June 1, 2013

Volume

22

Issue

11

Start / End Page

2303 / 2311

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Models, Statistical
  • Meta-Analysis as Topic
  • Humans
  • Genome-Wide Association Study
  • Genetics & Heredity
  • Ethnicity
  • Diabetes Mellitus, Type 2
  • 3105 Genetics
  • 11 Medical and Health Sciences