# LGRF tests for a single region/gene using SSD format files Test the association between an outcome variable and one region/gene using SSD format files. ```r LGRF.SSD.OneSet_SetIndex(SSD.INFO, SetIndex, 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. - `SetIndex`: Set index. From 1 to the total number of sets. - `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". ## Returns - **p.value**: p-value of the LGRF test. - **n.marker**: number of tested SNPs in the SNP set. ## Examples ```r # * 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 a single region # out_single<-LGRF.SSD.OneSet_SetIndex(SSD.INFO=SSD.INFO, SetIndex=1, # result.null=result.null, MinP.compare=F) # Example result # $p.value # [1] 0.6284 # $n.marker # [1] 94 ## Test a single region, and compare with GEE based MinP test # out_single<-LGRF.SSD.OneSet_SetIndex(SSD.INFO=SSD.INFO, SetIndex=1, # result.null=result.null,MinP.compare=T) # $p.value # LGRF MinP # [1,] 0.6284 1 # $n.marker # [1] 94 ```

LGRF.SSD.OneSet_SetIndex function