Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

Abstract

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis.

Assessing Gene-Environment Interactions for Common and Rare Variants with Binary Traits Using Gene-Trait Similarity Regression

“…For the joint test (Figure 2, bottom), the relative power of SimReg vs. min-P is similar to what was observed for the G3E tests. Furthermore, the relative performance between SimReg and min-P for binary traits is similar to what was observed for quantitative traits (Tzeng et al 2011). …”

“…Recently, Lin et al (2013) proposed a generalized linear mixedeffect model (GLMM) for G3E interactions for binary and continuous traits and showed it has superior power and robustness over min-P methods. A similar method, similarity regression (SimReg), proposed by Tzeng et al (2011) to study marker-set G3E for continuous traits, was shown to be connected to linear mixed-effect models.In this article, we extend the SimReg G3E framework established in Tzeng et al (2011) to binary traits with common or rare variants. SimReg, which is inspired by HasemanElston regression for linkage analysis (Haseman and Elston 1972;Elston et al 2000) and haplotype similarity tests for regional association (Tzeng et al 2003;Beckmann et al 2005), uses a regression model to correlate trait similarity with genetic similarity across multiple loci and to account for covariates.…”

“…The false negative (underpower) issues can be addressed by evaluating G3E effects on a set of markers, e.g., on genes, linkage disequilibrium (LD) blocks, or pathways (Tzeng et al 2011;Lin et al 2013). Marker-set G3E analysis can improve power by aggregating effects across markers.…”

“…In this article, we extend the SimReg G3E framework established in Tzeng et al (2011) to binary traits with common or rare variants. SimReg, which is inspired by HasemanElston regression for linkage analysis (Haseman and Elston 1972;Elston et al 2000) and haplotype similarity tests for regional association (Tzeng et al 2003;Beckmann et al 2005), uses a regression model to correlate trait similarity with genetic similarity across multiple loci and to account for covariates.…”

“…For illustration, we consider the case where K E ¼ 1; but it is straightforward to extend the proposed work to K E . 1:We quantify the trait similarity for a pair of individuals i and j, T ij ; as the weighted sample covariance between their disease statuses; i.e., T ij ¼ fv i ðY i 2 m The proposed G3E gene-trait similarity regression model isBecause T ij incorporates baseline covariate information, model (1) The joint test is recommended if either the genetic heterogeneity or the G3E interaction mechanism is unknown (Kraft et al 2007;Tzeng et al 2011). …”