test.LGRF: Test the association between an outcome variable and a...
In LGRF: Set-Based Tests for Genetic Association in Longitudinal Studies

Description Usage Arguments Value Examples

Once the model under the null model is fitted using "null.LGRF()", this function tests a specifc region/gene.

1 2	test.LGRF(Z, result.null, Gsub.id=NULL, interGXT = FALSE, similarity = "GR", impute.method="fixed")

`Z`	Genetic variants in the target region/gene, an m*q matrix where m is the subject ID and q is the total number of genetic variables. Note that the number of rows in Z should be same as the number of subjects.
`result.null`	The 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.

`p.value`	p-value of the LGRF test.
`n.marker`	number of tested SNPs in the SNP set.

## null.LGRF fits the null model. 
# Input: Y, time, X (covariates)
## test.LGRF tests a region and give p-value. 
# Input: Z (genetic variants) and result of null.longGRF

library(LGRF)

# Load data example
# 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
# Z: genotype matrix, m by q matrix where m is the total number of subjects

data(LGRF.example)
Y<-LGRF.example$Y;time<-LGRF.example$time;X<-LGRF.example$X;Z<-LGRF.example$Z

# Fit the null model
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.  

# The LGRF-G test
pLGRF_G<-test.LGRF(Z,result.null)

# The LGRF-GT test
pLGRF_GT<-test.LGRF(Z,result.null,interGXT=TRUE)

# The LGRF-G test using the IBS similarity
pLGRF_G_IBS<-test.LGRF(Z,result.null,similarity="IBS")

# The LGRF-GT test, main effect is modeled using the IBS similarity
pLGRF_GT_IBS<-test.LGRF(Z,result.null,interGXT=TRUE,similarity="IBS")