On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

Abstract

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

“…Recent theoretical work showed that the within-genotype variance of a quantitative trait varies when a nonadditive genetic interaction or epistasis is present (Pare et al 2010;Struchalin et al 2010). Alternatively, the variance of a quantitative trait may be from the result of the interaction between genetic variants additively associated with the mean of the quantitative trait.…”

“…Through either epistatic or nonepistatic (e.g., additive and dominance) effects within or between loci, genetic interactions contribute to genotypic variance. Epistasis may increase the variance of a quantitative trait (Pare et al 2010;Ronnegard and Valdar 2012). However, it is extremely difficult to distinguish the contributions of epistatic or nonepistatic effects to variable expression of genes.…”

Meta-analysis of SNPs involved in variance heterogeneity using Levene’s test for equal variances

“…N 0 , N 1, and N 2 represent the pooled genotype counts in the major allele homozygote, heterozygote, and minor allele homozygote group, respectively. framework of variance prioritization 4,11 , according to which the sample size, number of SNPs, MAF, and the proportion of variance explained by the interactions and the covariate influenced the statistical power of variance prioritization. Beyond allowing the implementation of variance prioritization to select SNPs for a meta-analysis of genetic interactions or environmental sensitivity, there are many other potential applications of the meta-analysis of Levene's test.…”

“…For instance, an analysis of 21 799 individuals from the Women's Genome Health Study first identified SNPs with a genome-wide significant Levene's test P-value for C-reactive protein (rs12753193, P ¼ 8.0E-11) and soluble ICAM-1 (rs738409, P ¼ 1.9E-10; rs1799969, P ¼ 2.1E-09). 4 Although it is feasible to analyze the heterogeneity of variance in individually large studies, sufficient sample sizes for the detection of variants with small effects can only be practically reached through meta-analysis. Indeed, a recent report has associated an FTO variant (rs7202116) with the phenotypic variability of body mass index (BMI) (P ¼ 2.4E-10; N ¼ 131 233) in a meta-analysis using the squared residual as the response variable.…”

“…7,8 The discovery of novel genetic interactions through meta-analysis presents a promising strategy, as large international consortia provide the adequate sample sizes and methodologies for meta-analyzing interactions are quite well developed. 9,10 We have previously proposed a prioritization scheme-variance prioritization-in the context of quantitative traits based on the observation that the trait variance conditional on genotypes will vary when an interaction is present, 4 an active area of methodological research. [11][12][13] Prioritization is achieved by comparing the variances of a quantitative trait conditional on the genotypes using Levene's test.…”