| LGRF.SSD.All {LGRF} | R Documentation |
LGRF tests for multiple regions/genes using SSD format files
Description
Test the association between an outcome variable and multiple regions/genes using SSD format files.
Usage
LGRF.SSD.All(SSD.INFO, result.null, Gsub.id=NULL, interGXT=FALSE, similarity='GR',
impute.method='fixed', MinP.compare=FALSE, ...)
Arguments
SSD.INFO |
SSD format information file, output of function “Open_SSD". The sets are defined by this file. |
result.null |
Output of function “null.LGRF". |
Gsub.id |
The subject id corresponding to the genotype matrix, an m dimensional vector. This is in order to match the phenotype and genotype matrix. The default is NULL, where the order is assumed to be matched with Y, X and time. |
interGXT |
Whether to incorperate the gene-time interaction effect. Incorperating this effect can improve power if there is any gene-time interaction, but has slight power loss otherwise. The default is FALSE. *Please note that the second column of time should be included as a covairate when interGXT is TRUE. |
similarity |
Choose the similarity measurement for the genetic variants. Can be either "GR" for genetic relationship or "IBS" for identity by state. The default is "GR" for better computational efficiency. |
impute.method |
Choose the imputation method when there is missing genotype. Can be "random", "fixed" or "bestguess". Given the estimated allele frequency, "random" simulates the genotype from binomial distribution; "fixed" uses the genotype expectation; "Best guess" uses the genotype with highest probability. |
MinP.compare |
Whether to compare with the GEE based minimum p-value (MinP) test. The default is FALSE. Please note that implementing the GEE based MinP test is time consuming. |
... |
Other options of the GEE based MinP test. Defined same as in function “test.MinP". |
Value
results |
First column contains the set ID; Second column contains the p-values; Third column contains the number of tested SNPs. |
Examples
# * Since the Plink data files used here are hard to be included in a R package,
# The usage is marked by "#" to pass the package check.
#library(LGRF)
##############################################
# Plink data files: File.Bed, File.Bim, File.Fam
# Files defining the sets: File.SetID, File.SSD, File.Info
# For longitudinal data, outcome and covariates are saved in a separate file: Y, time, X.
# Null model was fitted using function null.LGRF.
# Create the MW File
# File.Bed<-"./example.bed"
# File.Bim<-"./example.bim"
# File.Fam<-"./example.fam"
# File.SetID<-"./example.SetID"
# File.SSD<-"./example.SSD"
# File.Info<-"./example.SSD.info"
# Generate SSD file
# To use binary ped files, you have to generate SSD file first.
# If you already have a SSD file, you do not need to call this function.
# Generate_SSD_SetID(File.Bed, File.Bim, File.Fam, File.SetID, File.SSD, File.Info)
# SSD.INFO<-Open_SSD(File.SSD, File.Info)
# Number of samples
# SSD.INFO$nSample
# Number of Sets
# SSD.INFO$nSets
## Fit the null model
# Y: outcomes, n by 1 matrix where n is the total number of observations
# X: covariates, n by p matrix
# time: describe longitudinal structure, n by 2 matrix
# result.null<-null.LGRF(Y,time,X=cbind(X,time[,2]))
# *Please note that the second column of time should be included as a covairate if
# the gene by time interaction effect will be incorperated.
## Test all regions
# out_all<-LGRF.SSD.All(SSD.INFO, result.null)
# Example result
# out.all$results
# SetID P.value N.Marker
# 1 GENE_01 0.6568851 94
# 2 GENE_02 0.1822183 84
# 3 GENE_03 0.3836986 108
# 4 GENE_04 0.1265337 101
# 5 GENE_05 0.3236089 103
# 6 GENE_06 0.9401741 94
# 7 GENE_07 0.1043820 104
# 8 GENE_08 0.6093275 96
# 9 GENE_09 0.6351147 100
# 10 GENE_10 0.5631549 100
## Test all regions, and compare with GEE based MinP test
# out_all<-LGRF.SSD.All(SSD.INFO, result.null,MinP.compare=T)
# Example result
# out.all$results
# SetID P.value P.value.MinP N.Marker
# 1 GENE_01 0.62842 1.0000 94
# 2 GENE_02 0.06558 0.2718 84
# 3 GENE_03 0.61795 1.0000 108
# 4 GENE_04 0.39667 0.7052 101
# 5 GENE_05 0.17371 0.5214 103
# 6 GENE_06 0.90104 1.0000 94
# 7 GENE_07 0.10143 0.1188 104
# 8 GENE_08 0.78082 0.3835 96
# 9 GENE_09 0.21966 0.5364 100
# 10 GENE_10 0.25468 0.3527 100