SKATBinary_Single {SKAT}

Single variant tests for binary traits with Firth and efficient resampling methods

Description

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

Usage

 
 
	SKATBinary_Single(Z, obj, method.bin="Hybrid"
	, impute.method = "bestguess", is_check_genotype=TRUE, is_dosage = FALSE
	, missing_cutoff=0.15, max_maf=1, estimate_MAF=1
	, N.Resampling=2*10^6, seednum=100, epsilon=10^-6)


 

Arguments

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

output object from SKAT_Null_Model.

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").

is_check_genotype

a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). See SKAT page for details.

is_dosage

a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, SKAT 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.

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.

estimate_MAF

a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. See SKAT page for details

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).

Details

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.

Value

p.value

p-value. It will be the mid p-value if ER is used to compute the p-value.

p.value.standard

(ER only) standard p-value.

p.value.resampling

p-values 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) standard p-values 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.

method.bin

a type of method to be used to compute the p-value.

Author(s)

Seunggeun Lee

References

Lee, S., Fuchsberger, C., Kim, S., Scott, L. (2015) An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies. Biostatistics, in press.

Examples

data(SKATBinary.example)
Z<-SKATBinary.example$Z


obj<-SKAT_Null_Model(y ~ x1 + x2, out_type="D", data=SKATBinary.example)
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