Multivariate Genome Wide Marginal Epistasis Test
Overview
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. Stamp et al. (2025) doi:10.1016/j.ajhg.2025.07.004.
- Fix Bonferroni correction in the
mvMAPIT.Rmdvignette. - New function
binary_to_liabilityfor LT-MAPIT support and corresponding vignette - Fix CRAN Notes - dependency compatibility.
- Performance improvements in C++ implementation:
- Eliminated redundant matrix inverse computation in projection operations
- Optimized triple matrix products in Q matrix computation
- Added benchmark suite for tracking performance improvements
- Typical speedup: 3-8% depending on problem size and test method
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-09
- ERROR12 OK, 1 ERROR2026-09-04
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
Show 7 earlier changesHide 7 earlier changes
- ERROR12 OK, 1 ERROR2026-06-08
- OK12 OK2026-04-25
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
- NOTE12 OK, 2 NOTE2026-04-10
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-09
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 9
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 83%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- Illustrating multivariate MAPIT with Simulated Data HTML
- Dockerized mvMAPIT HTML
- Empirical comparison of P-value combination methods in mvMAPIT HTML
- Joint modeling of hematology traits yields epistatic signal in stock of mice HTML
- Liability threshold MAPIT HTML
- Simulate Traits HTML
- Synergistic epistasis in binding affinity landscapes HTML
Downloads
Repository
- PRs merged (all time)
- 92%
- Median days to close, 50 most recently updated closed issues
- 154
- Median days to close, 50 most recently updated closed PRs
- 0
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8 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/lcrawlab/mvmapit on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows
GitHub Pages site last deployed 2026-02-20 · The repository has a site deploy workflow
pkgdown site last built 2026-02-20, before release 2.0.4 (2026-02-22)
Dependencies
Installing mvMAPIT adds 52 packages (29 with compiled code, 29 MB of source tarballs). Heaviest: stringi 11 MB, Rcpp 1.8 MB, fs 1.4 MB.
Code & Tests
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- archivedRemoved from CRAN2023-01-10issues were not corrected in time
- 2.0.02022-12-05
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-12-05
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 205 KB / 3 files
- Download size
- 903 KB
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