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Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS
1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Genetic Epidemiology
|February 27, 2016
Summary
This study introduces a new mixture model test for genome-wide association studies (GWAS) to address genetic heterogeneity. The novel method identified significantly more genetic variants and risk loci for common diseases compared to traditional tests.
Area of Science:
- Genetics
- Statistical genetics
- Computational biology
Background:
- Genome-wide association studies (GWAS) have identified numerous genetic variants linked to common diseases.
- Existing GWAS methods, primarily single nucleotide polymorphism (SNP) trend tests, overlook genetic heterogeneity, potentially limiting discovery.
- Genetic heterogeneity, the phenomenon where different genetic factors influence a disease, is widely acknowledged but underutilized in GWAS analysis.
Purpose of the Study:
- To develop and apply a novel statistical method for GWAS that explicitly accounts for unknown genetic heterogeneity.
- To evaluate the performance of a mixture model-based single-SNP test in identifying genetic associations for complex diseases.
Main Methods:
- Applied a mixture model-based single-nucleotide polymorphism (SNP) test to analyze GWAS data.
- Utilized the Wellcome Trust Case Control Consortium (WTCCC) dataset, including traits for Crohn's disease, bipolar disorder, coronary artery disease, and type 2 diabetes.
- Compared the results with the traditional SNP trend test commonly used in GWAS.
Main Results:
- The mixture model-based test identified a substantially larger number of significant SNPs for each trait compared to the popular trend test.
- The method revealed a greater number of risk loci associated with Crohn's disease, bipolar disorder, coronary artery disease, and type 2 diabetes.
- Demonstrated a significant power gain in detecting genetic associations by incorporating genetic heterogeneity.
Conclusions:
- The mixture model-based single-SNP test offers a powerful approach to uncovering genetic associations in GWAS by addressing genetic heterogeneity.
- This method has the potential to enhance the discovery of genetic variants and loci contributing to common diseases.
- Future GWAS analyses should consider incorporating methods that account for genetic heterogeneity to maximize discovery potential.
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