| geneSLOPE {geneSLOPE} | R Documentation |
Genome-Wide Association Study with SLOPE
Description
Package geneSLOPE performes genome-wide association study (GWAS) with SLOPE, short for Sorted L-One Penalized Estimation. SLOPE is a method for estimating the vector of coefficients in linear model. For details about it see references.
Details
GWAS is split into three steps:
In the first step data is read using bigmemory package and immediatly screened using marginal tests for each Single Nucleotide Polymorphism (SNP)
SNPs are clumped based on their correlations
SLOPE is performed on data where each clump has one representative (therefore we ensure that variables in linear model are not strongly correlated)
Version: 0.38.2
Author(s)
Malgorzata Bogdan, Damian Brzyski, Emmanuel J. Candes, Christine Peterson, Chiara Sabatti, Piotr Sobczyk
Maintainer: Piotr Sobczyk pj.sobczyk@gmail.com
References
SLOPE – Adaptive Variable Selection via Convex Optimization, Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su and Emmanuel Candes
Examples
famFile <- system.file("extdata", "plinkPhenotypeExample.fam", package = "geneSLOPE")
mapFile <- system.file("extdata", "plinkMapExample.map", package = "geneSLOPE")
snpsFile <- system.file("extdata", "plinkDataExample.raw", package = "geneSLOPE")
phe <- read_phenotype(filename = famFile)
screening.result <- screen_snps(snpsFile, mapFile, phe, pValMax = 0.05, chunkSize = 1e2)
clumping.result <- clump_snps(screening.result, rho = 0.3, verbose = TRUE)
slope.result <- select_snps(clumping.result, fdr=0.1)
if(interactive()) { gui_geneSLOPE() }