geneSLOPE (version 0.38.2)

geneSLOPE: 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.

Author

Malgorzata Bogdan, Damian Brzyski, Emmanuel J. Candes, Christine Peterson, Chiara Sabatti, Piotr SobczykMaintainer: Piotr Sobczyk

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

References

SLOPE -- Adaptive Variable Selection via Convex Optimization, Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su and Emmanuel Candes

Examples

# \donttest{
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() }