Simulating variance heterogeneity in quantitative genome wide association studies

Abstract

BackgroundAnalyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value.ResultsA handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity.ConclusionsWe demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

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“…It has been shown that vGWAS may complement conventional GWAS, by enabling the detection of genetic loci where significant change in variance heterogeneity may be introduced as a result of potential gene-gene or gene-environment interactions. In [3], Al Kawam et al present a novel simulation procedure that could be used for the quantitative performance assessment of vGWAS analysis methods. The utility of the proposed framework and algorithm is demonstrated based on several scenarios, where the evaluation results are used to highlight the limitations of current analysis techniques and the challenges that need to be addressed in the future.…”