Simulating Variance Heterogeneity in Quantitative Genome Wide Association Studies

Abstract

Background: Analyzing 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. Results: A 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. Conclusions: We 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.

Genes, Gender Inequality, and Educational Attainment

“…Some scholars have speculated that this approach leads to polygenic scores disproportionately reflecting genetic variants that are least likely to be influenced by broader social conditions and environments (Conley 2016). To address this, researchers have begun to propose GWAS that, rather than predicting means, predict high levels of variance in the relationship between genetic variants and outcomes (Al Kawam et al 2018;. The implications of this for our study are that we may have produced a lower-bound estimate of the role of gender in modifying the relationship between the education polygenic score and educational attainment.…”

How interactions between ADHD and schools affect educational achievement: a family‐based genetically sensitive study

“…The interaction between genetic risk for ADHD and schools is likely to be larger than estimated here. We anticipate that future ADHD‐GWAS (ideally within‐family GWAS (Howe et al., 2021), capturing SNP effects on ADHD and variability in ADHD (Al Kawam, Alshawaqfeh, Cai, Serpedin, & Datta, 2018)) will facilitate more powerful PGS to be used in gene–environment interaction studies.…”