LGRF (version 1.0)

LGRF.SSD.All: 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