Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Abstract

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the “multivariate MArginal ePIstasis Test” (mvMAPIT)—a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact—thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections

“…By contrast, the amino acid change most strongly associated with a negative effect on viral copies was ORF1ab:L5086I (𝑝 = 9.20 × 10 "&% , 𝛽 = -1.20, 𝜎 = 0.13). We further conducted a marginal epistasis test [28][29][30] to detect the epistatic effects of SNPs on viral copies. We discovered multiple pairs of SNPs that exhibit positive epistatic effects on viral copies, with most interactions occurring in the S gene (Fig.…”

“…We applied a marginal epistasis test method to explore the interactions between SNPs on viral copies, using an R package mvMAPIT (v.2.0.3) [28][29][30]. This method maps SNPs with non-zero marginal epistatic effects-the combined pairwise interaction effects between a given SNP and all other SNPs-identifying candidate variants involved in epistasis without needing to identify the exact partners with which the variants interact.…”

‘Evolutionary poker’: an agent‐based model of interactome emergence and epistasis tested against Lenski's long‐term E. coli experiments

“…Recent improvements in the methodologies of genome-wide association studies have led increasingly to recognition that most traits are epistatic and involve networks of multiple SNPs involved in functional modules (e.g. Crawford et al, 2017;Kadarmideen & Carmelo, 2021;Stamp et al, 2023). In other words, most traits are not inherited in a Mendelian fashion as individual gene variants but are manifestations of highly conserved modular interactomes.…”