minP: Minimum p-value using copula
In diptavo/MultiSKAT: Sequence Kernel Association Tests with Multiple phenotypes

Description Usage Arguments Details Value Author(s) Examples

Function to compute the p-value for the minimum p-value test statistic using Copula

minP(obj.res, obj.list, Z,resample = 200,
weights.beta=c(1,25),kernel = "linear.weighted",Is.Common=FALSE,
weights = NULL,r.corr = 0, impute.method = "fixed",
is_check_genotype=TRUE, is_dosage = FALSE, 
missing_cutoff=0.15, estimate_MAF=1,max_maf=1,verbose = TRUE)

`obj.res`	a NULL object from MultiSKAT_NULL or MultiSKAT_NULL_Kins
`obj.list`	A list containing output from MultiSKAT function
`Z`	A genotype matrix
`resample`	Number of resampling iterations to obtain the null p-values(default = 200)
`kernel`	a type of kernel (default= "linear.weighted"). See detail section.
`Is.Common`	a binary variable indiciating whether a variant has the same effect on all the phenotypes (default=FALSE).
`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.
`r.corr`	the ρ parameter for the compound symmetric correlation structure kernels (default=0).
`weights`	a numeric vector of weights for the weighted kernels.
`impute.method`	a method to impute missing genotypes (default= "fixed"). "bestguess" imputes missing genotypes as most likely values (0,1,2), "random" imputes missing genotypes by generating binomial(2,p) random variables (p is the MAF), and "fixed" imputes missing genotypes by assigning the mean genotype values (2p).
`is_check_genotype`	a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). If Z has non-SNP data, please set it FALSE, otherwise you will get an error message. If it is FALSE and you use weighted kernels, the weights should be given through the "weights" parameter.
`is_dosage`	a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, the function will ignore "is_check_genotype".
`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.
`estimate_MAF`	a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. If estimate_MAF=1 (default), it uses all samples to estimate MAFs. If estimate_MAF=2, only samples with non-missing phenotypes and covariates are used to estimate MAFs
`max_maf`	a cutoff of the maximum minor allele frequencies (MAF) (default=1, no cutoff). Any SNPs with MAF > cutoff will be excluded from the analysis
`verbose`	a binary indicator to display messages (default=TRUE, displays messages)

This is a copula based test of association for minimum p-value by combining the MultiSKAT tests of obj.list. Users should not try to combine more than 4 MultiSKAT tests due to numerical instability. The output p-value should be between the minimum p-value of the tests and the same multiplied by the number of tests to be combined.

The function returns a numeric value which is the p-value of the minP test.

Diptavo Dutta

data(MultiSKAT.example.data)
attach(MultiSKAT.example.data)

obj.null <- MultiSKAT_NULL(Phenotypes,Cov)
out1 <- MultiSKAT(obj.null,Genotypes,cov(Phenotypes))
out2 <- MultiSKAT(obj.null,Genotypes,diag(5))
out3 <- MultiSKAT(obj.null,Genotypes,matrix(1,ncol = 5,nrow = 5))

### Combining PhC, Het and Hom
obj.list = list(out1,out2,out3)
obj.minP = minP(obj.null,obj.list,Genotypes)
str(obj.minP)

### Combining Het and Hom
obj.list = list(out2,out3)
obj.minP = minP(obj.null,obj.list,Genotypes)
str(obj.minP)