SKATBinary_Single: Single variant tests for binary traits with Firth and...
In ttimbers/SKAT: SNP-Set (Sequence) Kernel Association Test

Description Usage Arguments Details Value Author(s) Examples

This function computes p-values of single variant test using the firth and efficient resampling methods.

 
 
	SKATBinary_Single(Z, obj, method.bin="Hybrid"
	, impute.method = "bestguess", is_check_genotype=TRUE, is_dosage = FALSE
	, estimate_MAF=1, N.Resampling=2*10^6, seednum=100, epsilon=10^-6)

`Z`	a numeric genotype vector. Each genotype should be coded as 0, 1, 2, and 9 (or NA) for AA, Aa, aa, and missing, where A is a major allele and a is a minor allele.
`obj`	an output object of the SKAT_Null_Model function.
`method.bin`	a type of method to compute a p-value (default="Hybrid"). See details.
`impute.method`	a method to impute missing genotypes (default= "bestguess"). "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 value (2p).
`is_check_genotype`	a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). If you use non-SNP type data and want to run kernel machine test, please set it FALSE, otherwise you will get an error message. If you use SNP data or imputed data, please set it TRUE. If it is FALSE, and you use weighted kernels, the weights should be given through “weights” parameter.
`is_dosage`	a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, SKAT will ignore “is_check_genotype”.
`estimate_MAF`	a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. If estimate_MAF=1 (default), SKAT uses all samples to estimate MAFs. If estimate_MAF=2, only samples with non-missing phenotypes and covariates are used to estimate MAFs.
`N.Resampling`	a number of resampling to be conducted to get p-values (default=2 *10^6).
`seednum`	a seed number for random number generation (default=100). If NULL, no seed number will be assigned.
`epsilon`	a precision level (default=10^-6).

This function implements three methods (method.bin) to compute p-values: 1) Efficient resampling (ER); 2) Firth biased adjusted likelihood ratio test (Firth); and 3) Hybrid. "Hybrid" selects a method based on the total minor allele count (MAC), the number of individuals with minor alleles (m), and the degree of case-control imbalance.

Adaptive ER (ER.A) is not implemented yet.

If seednum is not NULL, set.seed(seednum) function is used to specify seeds to get the same p-values of ER based methods for different runs. Therefore, please set seednum=NULL, if you do not want to set seeds.

`p.value`	the p-value. It will be the mid p-value if ER is used to compute the p-value.
`p.value.standard`	(ER only) the standard p-value.
`p.value.resampling`	the p-value from resampled outcome. You can obtain it when n.Resampling in SKAT_Null_Model was > 0. See the SKAT_Null_Model.
`p.value.standard.resampling`	(ER only)the standard p-value from resampled outcome.
`m`	the number of individuals with minor alleles.
`MAP`	the minimum possible p-values. It is available when the method.bin="ER" and m is sufficiently small.
`MAC`	the total minor allele count (MAC).
`n.total`	(ER only) the number of resampling to be generated to get the p-value. It can be smaller than N.Resampling when the total number of configurations of case-controls among individuals with minor alleles are smaller than N.Resampling.
`is.accurate`	logical value for the accuracy of the p-value. If it is false, more resampling is needed to accurately estimate the p-value.
`param$n.marker`	a number of SNPs in the genotype matrix
`param$n.marker.test`	a number of SNPs used for the test. It can be different from param$n.marker when some markers are monomorphic or have higher missing rates than the missing_cutoff.
`method.bin`	a type of method to be used to compute the p-value.

Seunggeun Lee

data(SKATBinary.example)
attach(SKATBinary.example)


obj<-SKAT_Null_Model(y ~ x1 + x2, out_type="D")
out = SKATBinary_Single(Z[,1], obj)

# p-value
out$p.value

# MAP
out$MAP

# method used to compute p-value (method.bin)
out$method.bin


#
#	Use firth method to compute p-value
SKATBinary_Single(Z[,1], obj, method.bin="Firth")$p.value