Genome-wide Association Studies

The GRAB package provides a generic framework to analyze a wide variety of phenotypes.

Quick Start-up Examples

Here is a quick tutorial for GWAS of a time-to-event trait using SPAmix.

Step 1: fit a null model

library(GRAB)
PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- data.table::fread(PhenoFile, header = TRUE)

obj.SPAmix <- GRAB.NullModel(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER + PC1 + PC2,
  data = PhenoData,
  subjData = IID,
  method = "SPAmix",
  traitType = "time-to-event",
  control = list(PC_columns = "PC1,PC2")
)

Step 2: conduct score test

GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputFile <- file.path(tempdir(), "Results_SPAmix.txt")

GRAB.Marker(
  objNull = obj.SPAmix,
  GenoFile = GenoFile,
  OutputFile = OutputFile,
  control = list(outputColumns = "zScore")
)
data.table::fread(OutputFile)

Step 1: Choose `traitType` and `method`

Arguments method and traitType specify the type of phenotype data and the analysis approach. Currently, GRAB.NullModel() supports the following combinations:

method	traitType	Related subjects	Other features
`POLMM`, `POLMM-GENE`	`ordinal`	Yes	POLMM-GENE is a variant-set-based test
`SPACox`, `SPAmix`	`time-to-event`	No	SPAmix is designed for admixed population using individual-specific AF
`WtCoxG`	`time-to-event`	Yes	WtCoxG boosts power using reference population AF

Step 2: Choose Dense GRM or Sparse GRM

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

GRM Type	Advantages	Disadvantages	Required arguments
Dense GRM	More powerful	Slow	`GenoFile`
Sparse GRM	Fast	Less powerful	`SparseGRMFile`

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

Note About the `control` Argument

The control argument specifies a list of parameters for controlling the fitting and association testing process.