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

Abstract

Meta-analysis is a commonly used approach to increase the sample size for genome-wide association searches when individual studies are otherwise underpowered. Here, we present a meta-analysis procedure to estimate the heterogeneity of the quantitative trait variance attributable to genetic variants using Levene's test without needing to exchange individual-level data. The meta-analysis of Levene's test offers the opportunity to combine the considerable sample size of a genome-wide metaanalysis to identify the genetic basis of phenotypic variability and to prioritize single-nucleotide polymorphisms (SNPs) for gene-gene and gene-environment interactions. The use of Levene's test has several advantages, including robustness to departure from the normality assumption, freedom from the influence of the main effects of SNPs, and no assumption of an additive genetic model. We conducted a meta-analysis of the log-transformed body mass index of 5892 individuals and identified a variant with a highly suggestive Levene's test P-value of 4.28E-06 near the NEGR1 locus known to be associated with extreme obesity.

Analytical strategies to include the X‐chromosome in variance heterogeneity analyses: Evidence for trait‐specific polygenic variance structure

“…This work was motivated by the recent call to include X‐chromosome in “whole‐genome” scans (Wise et al, ), as well as the recent development of identifying autosomal SNPs associated with phenotypic variance (Deng et al, ; Shungin et al, ; Soave & Sun, ; Struchalin et al, ; Yang et al, ). To pave the way for future XCHR‐wide study of variance heterogeneity and subsequent joint location‐scale test (Aschard et al, ; Cao et al, ; Soave et al, ), we examined a catalogue of analytical strategies and recommended two robust and powerful approaches.…”

“…Since variance‐testing requires a larger sample size than mean testing, detecting individual variance signals that are significant at the XCHR‐wide or genome‐wide level requires studies of very large size that might only be viable through meta‐analysis. Meta‐analyses of variance heterogeneity (Deng et al, ) for XCHR variants can be conducted in parallel to that of a single study incorporating the analytical strategies proposed for autosomal variants (Deng et al, ). Note that Levene's test statistic is asymptotically χ 2 (with degrees of freedom equal to the number of groups subtracted by 1) distributed without an apparent “direction of effect,” so the traditional meta‐analysis that combines the weighted (directional) Z ‐values for testing mean effect is not immediately applicable here.…”

“…Note that Levene's test statistic is asymptotically χ 2 (with degrees of freedom equal to the number of groups subtracted by 1) distributed without an apparent “direction of effect,” so the traditional meta‐analysis that combines the weighted (directional) Z ‐values for testing mean effect is not immediately applicable here. A nondirectional meta‐analysis of Levene's test has been proposed in the context of autosomal SNPs (Deng et al, ), but the model‐based approach proposed here is more applicable for meta‐analysis of XCHR variants to combine the regression coefficients from Stage 2 jointly (Manning et al, ).…”

“…Several recent reports have examined autosomal genetic loci contributing to phenotypic variance (as opposed to mean) for a wide range of complex traits (Pare, Cook, Ridker, & Chasman, 2010;Shungin et al, 2017;Yang et al, 2012), and corresponding methodology development remains an active area of research (Aschard, Zaitlen, Tamimi, Lindstrom, & Kraft, 2013;Cao, Wei, Bailey, Kauwe, & Maxwell, 2014;Deng & Pare, 2011;Deng, Asma, & Pare, 2014;Hulse & Cai, 2013;Soave & Sun, 2017;Soave et al, 2015;Struchalin, Dehghan, Witteman, van Duijn, & Aulchenko, 2010;Sun, Elston, Morris, & Zhu, 2013). One possible reason for such phenotypic variance and single nucleotide polymorphism (SNP) genotype association, or variance heterogeneity, is that genotype-stratified variances of a trait differ in the presence of gene-gene (GxG) or gene-environment (GxE) interactions; both referred to as GxE hereinafter.…”

Genotypic Context and Epistasis in Individuals and Populations

“…In model and agricultural organisms, experimental populations can be contrived that have nearly equal allele frequencies, and this is one reason why high levels of statistical epistasis are much more often reported in these organisms than in humans. This is true even with statistical methods designed specifically for increased power to detect statistical epistasis in human populations (Deng et al, 2014). Furthermore, J-shaped or U-shaped distributions of allele frequencies imply that many combinations of alleles that might potentially be strongly epistatic in the physiological sense will be rare or nonexistent in samples typical of those studied in human populations.…”