Genome-wide association studies

GRAB package gives a generic framework to analyze a wide variaty of phenotypes.

Quick start-up examples

The below gives an example to use POLMM and POLMM-GENE to analyze ordinal categorical trait.

library(GRAB)
PhenoFile = system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData = data.table::fread(PhenoFile, header = T)
PhenoData = PhenoData %>% mutate(OrdinalPheno = factor(OrdinalPheno, 
                                                       levels = c(0, 1, 2)))

# Step 1: fit a null model
SparseGRMFile =  system.file("SparseGRM", "SparseGRM.txt", package = "GRAB")
GenoFile = system.file("extdata", "simuPLINK.bed", package = "GRAB")
obj.POLMM = GRAB.NullModel(formula = OrdinalPheno ~ AGE + GENDER,
                           data = PhenoData, 
                           subjData = PhenoData$IID, 
                           method = "POLMM", 
                           traitType = "ordinal",
                           GenoFile = GenoFile,
                           SparseGRMFile =  SparseGRMFile,
                           control = list(showInfo = FALSE, 
                                          LOCO = FALSE, 
                                          tolTau = 0.2, 
                                          tolBeta = 0.1))                                                       

# Step 2(a): conduct a marker-level association study
GenoFile = system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputDir = system.file("results", package = "GRAB")
OutputFile = paste0(OutputDir, "/simuMarkerOutput.txt")
GRAB.Marker(obj.POLMM, GenoFile = GenoFile,
            OutputFile = OutputFile)

results = data.table::fread(OutputFile)
hist(results$Pvalue)

# Step 2(b): conduct a set-based association study
GenoFile = system.file("extdata", "simuPLINK_RV.bed", package = "GRAB")
OutputDir = system.file("results", package = "GRAB")
OutputFile = paste0(OutputDir, "/simuRegionOutput.txt")
GroupFile = system.file("extdata", "simuPLINK_RV.group", package = "GRAB")
SparseGRMFile = system.file("SparseGRM", "SparseGRM.txt", package = "GRAB")

GRAB.Region(objNull = obj.POLMM,
            GenoFile = GenoFile,
            GenoFileIndex = NULL,
            OutputFile = OutputFile,
            OutputFileIndex = NULL,
            GroupFile = GroupFile,
            SparseGRMFile = SparseGRMFile,
            MaxMAFVec = "0.01,0.005")

data.table::fread(OutputFile)

Step 1: choose traitType and method

Arguments traitType and method are to specify the type of phenotype data and the analysis approach. Currently, GRAB package supports the below combinations

phenotype traitType method Related subjects
binary trait “binary” “SAIGE” YES
quantitative trait “quantitative” “SAIGE” YES
ordinal categorical trait “ordinal” “POLMM” YES
time-to-event trait “time-to-event” “SPACox” NO

Step 2: choose Dense GRM or Sparse GRM

Both dense GRM and sparse GRM are supported in GRAB package to adjust for family relatedness, which can avoid inflated type I error rates.

Which GRM Pros. Cons Required arguments
Dense GRM More powerful Slow SparseGRMFile
Sparse GRM Fast Less powerful GenoFile

NOTE: Extensive simulation results suggests that, for binary and ordinal categorical data analysis, using dense and sparse GRM perform similarly in terms of both type I error rates and powers.

Note: about argument control

Argument control is to specify a list of parameters for controlling the fitting and association testing process.

Table of contents