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A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations.

Publication ,  Journal Article
Wang, X; Liu, X; Sim, X; Xu, H; Khor, C-C; Ong, RT-H; Tay, W-T; Suo, C; Poh, W-T; Ng, DP-K; Liu, J; Aung, T; Chia, K-S; Wong, T-Y; Tai, E-S; Teo, Y-Y
Published in: Eur J Hum Genet
April 2012

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.

Duke Scholars

Published In

Eur J Hum Genet

DOI

EISSN

1476-5438

Publication Date

April 2012

Volume

20

Issue

4

Start / End Page

469 / 475

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Linkage Disequilibrium
  • Humans
  • Genotype
  • Genome-Wide Association Study
  • Genetics, Population
  • Genetics & Heredity
  • Diabetes Mellitus, Type 2
  • Data Interpretation, Statistical
  • 3202 Clinical sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, X., Liu, X., Sim, X., Xu, H., Khor, C.-C., Ong, R.-H., … Teo, Y.-Y. (2012). A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations. Eur J Hum Genet, 20(4), 469–475. https://doi.org/10.1038/ejhg.2011.219
Wang, Xu, Xuanyao Liu, Xueling Sim, Haiyan Xu, Chiea-Chuen Khor, Rick Twee-Hee Ong, Wan-Ting Tay, et al. “A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations.Eur J Hum Genet 20, no. 4 (April 2012): 469–75. https://doi.org/10.1038/ejhg.2011.219.
Wang X, Liu X, Sim X, Xu H, Khor C-C, Ong RT-H, et al. A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations. Eur J Hum Genet. 2012 Apr;20(4):469–75.
Wang, Xu, et al. “A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations.Eur J Hum Genet, vol. 20, no. 4, Apr. 2012, pp. 469–75. Pubmed, doi:10.1038/ejhg.2011.219.
Wang X, Liu X, Sim X, Xu H, Khor C-C, Ong RT-H, Tay W-T, Suo C, Poh W-T, Ng DP-K, Liu J, Aung T, Chia K-S, Wong T-Y, Tai E-S, Teo Y-Y. A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations. Eur J Hum Genet. 2012 Apr;20(4):469–475.

Published In

Eur J Hum Genet

DOI

EISSN

1476-5438

Publication Date

April 2012

Volume

20

Issue

4

Start / End Page

469 / 475

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Linkage Disequilibrium
  • Humans
  • Genotype
  • Genome-Wide Association Study
  • Genetics, Population
  • Genetics & Heredity
  • Diabetes Mellitus, Type 2
  • Data Interpretation, Statistical
  • 3202 Clinical sciences