Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS

Zhiyuan Xu, Wei Pan

Research output: Contribution to journalArticlepeer-review


Genome-wide association studies (GWAS) have confirmed the ubiquitous existence of genetic heterogeneity for common disease: multiple common genetic variants have been identified to be associated, while many more are yet expected to be uncovered. However, the single SNP (single-nucleotide polymorphism) based trend test (or its variants) that has been dominantly used in GWAS is based on contrasting the allele frequency difference between the case and control groups, completely ignoring possible genetic heterogeneity. In spite of the widely accepted notion of genetic heterogeneity, we are not aware of any previous attempt to apply genetic heterogeneity motivated methods in GWAS. Here, to explicitly account for unknown genetic heterogeneity, we applied a mixture model based single-SNP test to the Wellcome Trust Case Control Consortium (WTCCC) GWAS data with traits of Crohn's disease, bipolar disease, coronary artery disease, and type 2 diabetes, identifying much larger numbers of significant SNPs and risk loci for each trait than those of the popular trend test, demonstrating potential power gain of the mixture model based test.

Original languageEnglish (US)
Pages (from-to)202-209
Number of pages8
JournalGenetic epidemiology
Issue number3
StatePublished - Apr 1 2016

Bibliographical note

Funding Information:
The authors thank the reviewers for constructive comments. This research was supported by NIH grants R01GM113250, R01HL105397, and R01HL116720, and by the Minnesota Supercomputing Institute at the Univeristy of Minnesota. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available Funding for the project was provided by the Wellcome Trust under award 076113.

Publisher Copyright:
© 2016 Wiley Periodicals, Inc.


  • Hardy-Weinberg equilibrium test
  • Likelihood ratio test
  • Logistic regression
  • Power
  • Trend test


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