CRAN/E | mvMAPIT

mvMAPIT

Multivariate Genome Wide Marginal Epistasis Test

Installation

About

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.

github.com/lcrawlab/mvMAPIT
lcrawlab.github.io/mvMAPIT/

Key Metrics

Version 2.0.3
R ≥ 3.5
Published 2023-09-26 376 days ago
Needs compilation? yes
License GPL (≥ 3)
CRAN checks mvMAPIT results

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Maintainer

Maintainer

Julian Stamp

Authors

Julian Stamp

cre / aut

Lorin Crawford

aut

Material

README
NEWS
Reference manual
Package source

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

mvMAPIT archive

Depends

R ≥ 3.5

Imports

checkmate
CompQuadForm
dplyr
foreach
harmonicmeanp
logging
mvtnorm
Rcpp
stats
tidyr
utils

Suggests

GGally
ggplot2
ggrepel
kableExtra
knitr
markdown
RcppAlgos
rmarkdown
testthat

LinkingTo

Rcpp
RcppArmadillo
RcppParallel
RcppProgress
RcppSpdlog
testthat