bmass {bmass} | R Documentation |
Bayesian multivariate analysis of summary statistics
(bmass
)
Description
Run bmass
on a set of phenotypes that each have
univariate GWAS statistics on the same set of SNPs
Usage
bmass(DataSources, GWASsnps = NULL,
SNPMarginalUnivariateThreshold = 1e-06,
SNPMarginalMultivariateThreshold = 1e-06, GWASThreshFlag = TRUE,
GWASThreshValue = 5e-08, NminThreshold = 0,
PrintMergedData = FALSE, PrintProgress = FALSE, ...)
Arguments
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 genome-wide significant in the phenotypes being used for
analysis; |
SNPMarginalUnivariateThreshold |
A numerical value indicating
the univariate p-value threshold to use when collecting marginally
significant SNPs for final |
SNPMarginalMultivariateThreshold |
A numerical value
indicating the basic multivariate p-value threshold to use when
collecting marginally significant SNPs for final |
GWASThreshFlag |
A logical |
GWASThreshValue |
A numerical value indicating the univariate
p-value 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. |
Value
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.
Other Examples
bmass(c("HDL","LDL","TG","TC"), GWASsnps, NminThreshold = 50000)
bmass(c("HDL","LDL","TG","TC"), GWASsnps, GWASThreshValue = 1e-8,
NminThreshold = 50000, PrintProgress = TRUE)
bmass(c("HDL", "LDL", "TG", "TC"), GWASsnps, GWASThreshFlag = FALSE,
SNPMarginalUnivariateThreshold = 1e-4,
SNPMarginalMultivariateThreshold = 1e-4,
PrintMergedData = TRUE)
bmassOutput <- bmass(c("HDL","LDL","TG","TC"),
GWASsnps, NminThreshold = 50000)
Examples
Phenotypes <- c("bmass_SimulatedData1", "bmass_SimulatedData2")
bmassOutput <- bmass(Phenotypes, bmass_SimulatedSigSNPs)
summary(bmassOutput)
bmassOutput$NewSNPs$SNPs