Introduction | Installation | Run mv_G_GE | Run WHT and SST
Package: MPGE 1.0.1
MPGE: A Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, T Haldar, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1093/bioinformatics/btaa1083>.
Authors:
MPGE_1.0.1.tar.gz
MPGE_1.0.1.zip(r-4.7)MPGE_1.0.1.zip(r-4.6)MPGE_1.0.1.zip(r-4.5)
MPGE_1.0.1.tgz(r-4.6-any)MPGE_1.0.1.tgz(r-4.5-any)
MPGE_1.0.1.tar.gz(r-4.7-any)MPGE_1.0.1.tar.gz(r-4.6-any)
MPGE_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MPGE/json (API)
| # Install 'MPGE' in R: |
| install.packages('MPGE', repos = c('https://arunabhacodes.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/arunabhacodes/mpge/issues
Datasets:
- environment_data - An example of data of the environmental variable (e.g., smoking status). Here, environment_data is a data frame with single column for the environmental variable. The order of the 500 individuals in the row must be the same as provided in the phenotype and genotype data. Here, the environmental variable has two categories which were coded as 1 and 0 (e.g., smokers and non-smokers). Instead of numeric values, these can also be considered to be factors in the absence of a defined order in the categories.
- genotype_data - An example of genotype data for two genetic variants (SNPs). Here, genotype_data is a data.frame with the columns as SNPs (e.g., rs1 and rs2 here). The rows correspond to the 500 individuals in the same order as in the phenotype data.
- mv_G_GxE_pvalues - An example of step 1 (marginal genetic association) and step 2 (GxE interaction) p-values across genetic variants (SNPs). Here, mv_G_GxE_pvalues is a data.frame with three columns. First column lists the set of 1000 genetic variants. Second column provides the vector of p-values obtained from testing the marginal multivariate genetic association for these SNPs. And the third column provides the vector of p-values obtained from testing the overall GxE effect in presence of possible marginal genetic effect and marginal environmental effect.
- phenotype_data - An example of phenotype data.
Last updated from:090da3655d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 122 | ||
| source / vignettes | OK | 179 | ||
| linux-release-x86_64 | OK | 141 | ||
| macos-release-arm64 | OK | 112 | ||
| macos-oldrel-arm64 | OK | 153 | ||
| windows-devel | OK | 98 | ||
| windows-release | OK | 81 | ||
| windows-oldrel | OK | 91 | ||
| wasm-release | OK | 136 |
Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo
