From c8794f2165fc33cd9bf39972b9ed0a77553f620a Mon Sep 17 00:00:00 2001 From: Julian Stamp Date: Fri, 2 Dec 2022 11:57:16 -0500 Subject: [PATCH] MM-89: incorporate CRAN feedback (#95) --- DESCRIPTION | 38 ++++++++++++++++----------- vignettes/tutorial-docker-mvmapit.Rmd | 6 ++--- 2 files changed, 26 insertions(+), 18 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 56eceaa..1c41ef9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,21 +8,29 @@ Authors@R: c( role = c("cre", "aut"), comment = c(ORCID = "0000-0003-3014-6249")), person("Lorin", "Crawford", email = "lorin_crawford@brown.edu", role = "aut", comment = c(ORCID = "0000-0003-0178-8242"))) -Description: 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 study, 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 GWA studies. +Description: 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) . Stamp et al. (2022) . License: GPL (>= 3) diff --git a/vignettes/tutorial-docker-mvmapit.Rmd b/vignettes/tutorial-docker-mvmapit.Rmd index 5ae4807..46022ac 100644 --- a/vignettes/tutorial-docker-mvmapit.Rmd +++ b/vignettes/tutorial-docker-mvmapit.Rmd @@ -30,8 +30,6 @@ docker build -t mvmapit . This will produce an image named `mvmapit` that contains `Rstudio`, `mvMAPIT`, and all dependencies. -Alternatively, the image will be made available through Dockerhub. - ## Run the mvMAPIT Image With a local copy of the docker image `mvmapit` available, run the following code. @@ -49,5 +47,7 @@ container, `mvMAPIT` is already installed and can be imported and run in the R c ```{r run_mvmapit} library(mvMAPIT) -mvmapit(t(simulated_data$genotype[1:100,1:10]), t(simulated_data$trait[1:100,]), cores = 4, logLevel = "DEBUG") +mvmapit(t(simulated_data$genotype[1:100,1:10]), + t(simulated_data$trait[1:100,]), + cores = 2, logLevel = "DEBUG") ```