iECAT: Integrating External Controls to Association Tests
In iECAT: Integrating External Controls into Association Test

Description Usage Arguments Value Author(s) Examples

Test for association of a set of variants with integrating external study samples to improve power

iECAT(Z, obj, tbl.external.all, weights.beta=c(1,25), weights = NULL, 
r.corr=0, method="davies", missing_cutoff=0.15, MAC.lowlimit=3, 
MAF.limit=1)

iECAT.SSD.OneSet_SetIndex(EC.INFO, SetIndex,  obj, ..., obj.SNPWeight=NULL)

`Z`	a numeric genotype matrix with each row as a different individual and each column as a separate gene/snp. Each genotype should be coded as 0, 1, 2, and 9 (or NA) for AA, Aa, aa, and missing. A does not need to be a major allele.
`obj`	an output object of the SKAT_Null_Model function.
`tbl.external.all`	a p x 2 matrix of allele count of the matrix. Each row should be matched with the each column in Z.
`weights.beta`	a numeric vector of parameters for the beta weights for the weighted kernels. If you want to use your own weights, please use the “weights” parameter. It will be ignored if “weights” parameter is not null.
`weights`	a numeric vector of weights for the weighted kernels. It is √{w} in the SKAT paper. So if you want to use the Madsen and Browning (2009) weight, you should set each element of weights as 1/ √{p(1-p)}, not 1/ p(1-p). When it is NULL, the beta weight with the “weights.beta” parameter is used.
`r.corr`	the ρ parameter for the compound symmetric correlation structure kernels (default= 0). If you give a vector value, SKAT will conduct the optimal test. It will be ignored if method=“optimal” or method=“optimal.adj”. See details.
`method`	a method to compute the p-value (default= "davies"). "davies" represents an exact method that computes the p-value by inverting the characteristic function of the mixture chisq, "liu" represents an approximation method that matches the first 3 moments, "liu.mod" represents modified "liu" method that matches kurtosis instead of skewness to improve tail probability approximation, "optimal" represents a SKAT-O based on an unified approach.
`missing_cutoff`	a cutoff of the missing rates of SNPs (default=0.15). Any SNPs with missing rates higher than the cutoff will be excluded from the analysis.
`MAC.lowlimit`	a cutoff for MAC low limit (default=3). Variants with internal study MAC <= MAC.lowlimit will be excluded from the analysis.
`MAF.limit`	a cutoff for MAF upper limit (default=1). Variants with internal study MAF > MAF.limit will be excluded from the analysis.
`EC.INFO`	an EC_INFO object returned from Open_SSD_wEC.
`SetIndex`	a numeric value of Set index. A set index of each set can be found from SetInfo object in EC.INFO$SSD.INFO.
`...`	further arguments to be passed to “iECAT”
`obj.SNPWeight`	an output object of Read_SNP_WeightFile (default=NULL). If NULL, the beta weight with the “weights.beta” parameter will be used.

`p.value`	p-value of iECAT.
`p.value.noadj`	p-value without the adjustment of possible batch effects
`p.value.internal`	SKAT/SKAT-O p-values with only internal study samples
`param`	estimated parameters of each method.
`param$n.marker`	a number of variants in the genotype matrix (Z).
`param$n.marker.test`	a number of variants used for the test.

Seunggeun (Shawn) Lee

library(SKAT)

data(Example, package="iECAT")
attach(Example)

# iECAT-O
# test the first gene

obj<-SKAT_Null_Model(Y ~ 1, out_type="D")
Z = Z.list[[1]]
tbl.external.all = tbl.external.all.list[[1]]

iECAT(Z, obj, tbl.external.all, method="optimal")


# test for the first 3 genes in the Example dataset
p.value.all<-rep(0,3)
p.value.internal.all<-rep(0,3)
for(i in 1:3){

	re<-iECAT(Z.list[[i]], obj, tbl.external.all.list[[i]], method="optimal")
	p.value.all[i]<-re$p.value
	p.value.internal.all[i]<-re$p.value.internal

}

# iECAT-O p-values
p.value.all

# SKAT-O p-values
p.value.internal.all