{ "_id": "6a0ff527acfb0bcc41c778de", "Type": "Package", "Package": "mvMAPIT", "Title": "Multivariate Genome Wide Marginal Epistasis Test", "Version": "2.0.4", "URL": "https://github.com/lcrawlab/mvMAPIT,\nhttps://lcrawlab.github.io/mvMAPIT/", "Authors@R": "c(\nperson(\"Julian\", \"Stamp\", email = \"julian.d.stamp@gmail.com\",\nrole = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0003-3014-6249\")),\nperson(\"Lorin\", \"Crawford\", email = \"lorin_crawford@brown.edu\",\nrole = \"aut\", comment = c(ORCID = \"0000-0003-0178-8242\")))", "Description": "Epistasis, commonly defined as the interaction between\ngenetic loci, is known to play an important role in the\nphenotypic variation of complex traits. As a result, many\nstatistical methods have been developed to identify genetic\nvariants that are involved in epistasis, and nearly all of\nthese approaches carry out this task by focusing on analyzing\none trait at a time. Previous studies have shown that jointly\nmodeling multiple phenotypes can often dramatically increase\nstatistical power for association mapping. In this package, we\npresent the 'multivariate MArginal ePIstasis Test' ('mvMAPIT')\n– a multi-outcome generalization of a recently proposed\nepistatic detection method which seeks to detect marginal\nepistasis or the combined pairwise interaction effects between\na given variant and all other variants. By searching for\nmarginal epistatic effects, one can identify genetic variants\nthat are involved in epistasis without the need to identify the\nexact partners with which the variants interact – thus,\npotentially alleviating much of the statistical and\ncomputational burden associated with conventional explicit\nsearch based methods. Our proposed 'mvMAPIT' builds upon this\nstrategy by taking advantage of correlation structure between\ntraits to improve the identification of variants involved in\nepistasis. We formulate 'mvMAPIT' as a multivariate linear\nmixed model and develop a multi-trait variance component\nestimation algorithm for efficient parameter inference and\nP-value computation. Together with reasonable model\napproximations, our proposed approach is scalable to moderately\nsized genome-wide association studies. Crawford et al. (2017)\n. Stamp et al. (2023)\n. Stamp et al. (2025)\n.", "License": "GPL (>= 3)", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "RoxygenNote": "7.3.1", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://lcrawlab.r-universe.dev", "Date/Publication": "2026-02-20 23:31:01 UTC", "RemoteUrl": "https://github.com/lcrawlab/mvmapit", "RemoteRef": "HEAD", "RemoteSha": "848d756e9475e2cf9ecaf4d4b94ca73628ba0a48", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-22 06:07:38 UTC", "User": "root" }, "Author": "Julian Stamp [cre, aut] (ORCID:\n),\nLorin Crawford [aut] (ORCID: )", "Maintainer": "Julian Stamp ", "MD5sum": "126bf1d89770954ae4dddf0f26eaae38", "_user": "lcrawlab", "_type": "src", "_file": "mvMAPIT_2.0.4.tar.gz", "_fileid": "2aaa3277471f818c9dd1d681a2de78366ebe5c8f3846a895ffb9158c971a2ecb", "_filesize": 1332441, "_sha256": "2aaa3277471f818c9dd1d681a2de78366ebe5c8f3846a895ffb9158c971a2ecb", "_created": "2026-05-22T06:07:38.000Z", "_published": 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