bmass {bmass}  R Documentation 
bmass
)Run bmass
on a set of phenotypes that each have
univariate GWAS statistics on the same set of SNPs
bmass(DataSources, GWASsnps = NULL, SNPMarginalUnivariateThreshold = 1e06, SNPMarginalMultivariateThreshold = 1e06, GWASThreshFlag = TRUE, GWASThreshValue = 5e08, NminThreshold = 0, PrintMergedData = FALSE, PrintProgress = FALSE, ...)
DataSources 
A string indicating the variable names of the input datafiles and phenotypes. No default value. 
GWASsnps 
A data.table containing rows of SNPs that were
univariate genomewide significant in the phenotypes being used for
analysis; 
SNPMarginalUnivariateThreshold 
A numerical value indicating
the univariate pvalue threshold to use when collecting marginally
significant SNPs for final 
SNPMarginalMultivariateThreshold 
A numerical value
indicating the basic multivariate pvalue threshold to use when
collecting marginally significant SNPs for final 
GWASThreshFlag 
A logical 
GWASThreshValue 
A numerical value indicating the univariate
pvalue threshold to use in conjunction with the 
NminThreshold 
A numerical value that indicates a sample size
threshold to use where SNPs below which are removed. Default is

PrintMergedData 
A logical 
PrintProgress 
A logical 
... 
Additional optional arguments. 
A list containing model, SNP, and posterior information for
both the previously significant univariate SNPs (PreviousSNPs
)
and the newly significant multivariate SNPs (NewSNPs
). For a
full breakdown of the bmass
output list structure, please see
the associated vignettes.
bmass(c("HDL","LDL","TG","TC"), GWASsnps, NminThreshold = 50000)
bmass(c("HDL","LDL","TG","TC"), GWASsnps, GWASThreshValue = 1e8,
NminThreshold = 50000, PrintProgress = TRUE)
bmass(c("HDL", "LDL", "TG", "TC"), GWASsnps, GWASThreshFlag = FALSE,
SNPMarginalUnivariateThreshold = 1e4,
SNPMarginalMultivariateThreshold = 1e4,
PrintMergedData = TRUE)
bmassOutput < bmass(c("HDL","LDL","TG","TC"),
GWASsnps, NminThreshold = 50000)
Phenotypes < c("bmass_SimulatedData1", "bmass_SimulatedData2") bmassOutput < bmass(Phenotypes, bmass_SimulatedSigSNPs) summary(bmassOutput) bmassOutput$NewSNPs$SNPs