mvMAPIT: Multivariate Genome Wide Marginal Epistasis Test

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 package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome 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 multi-trait 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. Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>. Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.

Version: 2.0.3
Depends: R (≥ 3.5)
Imports: checkmate, CompQuadForm, dplyr, foreach, harmonicmeanp, logging, mvtnorm, Rcpp, stats, tidyr, utils
LinkingTo: Rcpp, RcppArmadillo, RcppParallel, RcppProgress, RcppSpdlog, testthat
Suggests: GGally, ggplot2, ggrepel, kableExtra, knitr, markdown, RcppAlgos, rmarkdown, testthat
Published: 2023-09-26
Author: Julian Stamp ORCID iD [cre, aut], Lorin Crawford ORCID iD [aut]
Maintainer: Julian Stamp <julian_stamp at brown.edu>
License: GPL (≥ 3)
URL: https://github.com/lcrawlab/mvMAPIT, https://lcrawlab.github.io/mvMAPIT/
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: mvMAPIT results

Documentation:

Reference manual: mvMAPIT.pdf
Vignettes: Illustrating multivariate MAPIT with Simulated Data
Empirical comparison of P-value combination methods in mvMAPIT
Synergistic epistasis in binding affinity landscapes
Joint modeling of hematology traits yields epistatic signal in stock of mice
Dockerized mvMAPIT
Simulate Traits

Downloads:

Package source: mvMAPIT_2.0.3.tar.gz
Windows binaries: r-devel: mvMAPIT_2.0.3.zip, r-release: mvMAPIT_2.0.3.zip, r-oldrel: mvMAPIT_2.0.3.zip
macOS binaries: r-release (arm64): mvMAPIT_2.0.3.tgz, r-oldrel (arm64): mvMAPIT_2.0.3.tgz, r-release (x86_64): mvMAPIT_2.0.3.tgz
Old sources: mvMAPIT archive

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