Application of MOSS algorithm to genome-wide association study (GWAS)


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Documentation for package ‘genMOSSplus’ version 1.0

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genMOSSplus-package Application of MOSS algorithm to dense SNP array data
ex2plink Convert example dataset to Plink format
genMOSSplus Application of MOSS algorithm to dense SNP array data
genos.clean Removes badly predicted SNPs by MaCH
genos.clean.batch Removes badly predicted SNPs by MaCH for all files
get.data.dims Obtains matrix dimensions
get.file.copy Copies files from one directory to another
MOSS.GWAS A function implementing the MOSS algorithm for the analysis of GWAS data.
pre0.dir.create Generate working subdirectory structure
pre1.plink2mach Convert Plink to MaCH input format
pre1.plink2mach.batch Convert Plink to MaCH input format for all files
pre2.remove.genos Remove genos with many empty values
pre2.remove.genos.batch Remove genos with many empty values for all files
pre3.call.mach Call MaCH imputation with and without Hapmap
pre3.call.mach.batch Call MaCH imputation with and without Hapmap
pre4.combine.case.control Combine CASE and CONTROL files
pre4.combine.case.control.batch Combine CASE and CONTROL files for all files
pre5.genos2numeric Categorize genotype data into 3 levels
pre5.genos2numeric.batch Categorize genotype data into 3 levels for each file
pre6.merge.genos Combine geno files across all chromosomes
pre7.add.conf.var Append confounding variables
pre7.add.conf.var.unix Append confounding variables using Linux
pre8.split.train.test Split dataset into TRAIN and TEST files
pre8.split.train.test.batch Split dataset into TRAIN and TEST files for all files
run1.moss Runs MOSS regression algorithm
tune1.subsets Imputes dense map of SNPs on chromosome regions with MaCH