SKAT (version 2.2.5)

SKAT_ChrX: SNP-set (Sequence) Kernel Association Test for X and Y chromosome variables

Description

Test for association between a set of SNPS/genes in the X chromosome and continuous or dichotomous outcomes using the kernel machine.

Usage

SKAT_ChrX(Z, obj, is_X.inact =TRUE
, kernel = "linear.weighted", method="davies", weights.beta=c(1,25)
, weights = NULL, impute.method = "fixed", r.corr=0, is_check_genotype=TRUE
, is_dosage = FALSE, missing_cutoff=0.15, max_maf=1, estimate_MAF=1, SetID=NULL)
SKAT_ChrY(Z, obj, kernel = "linear.weighted", method="davies", weights.beta=c(1,25)
, weights = NULL, impute.method = "fixed", r.corr=0, is_check_genotype=TRUE
, is_dosage = FALSE, missing_cutoff=0.15, max_maf=1, estimate_MAF=1, SetID=NULL)

Value

p.value

p-value of SKAT.

p.value.resampling

p-values from resampled outcome. You can get it when you use obj from SKAT_Null_Model function with resampling. See the SKAT_Null_Model.

p.value.noadj

p-value of SKAT without the small sample adjustment. It only appears when small sample adjustment is applied.

p.value.noadj.resampling

p-values from resampled outcome without the small sample adjustment. It only appears when small sample adjustment is applied.

pval.zero.msg

(only when p.value=0) text message that shows how small the p.value is. ex. "Pvalue < 1.000000e-60" when p.value is smaller than \(10^{-60}\)

Q

the test statistic of SKAT. It has NA when method="optimal.adj" or "optimal".

param

estimated parameters of each method.

param$Is_Converged

(only with method="davies") an indicator of the convergence. When 0 (not converged), "liu" method is used to compute 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.

Arguments

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, where A is a major allele and a is a minor allele. Missing genotypes will be imputed by the simple Hardy-Weinberg equilibrium (HWE) based imputation.

obj

output object of the SKAT_Null_Model_ChrX function. For SKAT_ChrY, SKAT_Null_Model_ChrX function should be used with Model.Y=TRUE

is_X.inact

an indicator variable for the X-inactivation coding (default=TRUE). Male genotypes are coded as g=(0,2) when it is TRUE, and g=(0,1) when it is false.

kernel

a type of kernel (default= "linear.weighted").

method

a method to compute the p-value (default= "davies"). See SKAT page for details.

weights.beta

a numeric vector of parameters of beta weights. See SKAT page for details.

weights

a numeric vector of weights for the weighted kernels. See SKAT page for details.

impute.method

a method to impute missing genotypes (default= "fixed"). "fixed" imputes missing genotypes by assigning the mean genotype value (2p), and "bestguess" uses best guess genotype values.

r.corr

the \(\rho\) parameter for the compound symmetric kernel. See SKAT page for details.

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.

SetID

Internal use only.

Author

Clement Ma and Seunggeun Lee

Details

For details of parameters, please see SKAT page.

Examples

data(SKAT.example.ChrX)
Z<-SKAT.example.ChrX$Z

#############################################################
#	Compute the P-value of SKAT 

# binary trait
obj.x<-SKAT_Null_Model_ChrX(y ~ x1 +x2 + Gender, 
  SexVar="Gender", out_type="D", data=SKAT.example.ChrX)

# SKAT
SKAT_ChrX(Z, obj.x, kernel = "linear.weighted")

# Burden
SKAT_ChrX(Z, obj.x, kernel = "linear.weighted", r.corr=1)

# SKAT-O
SKAT_ChrX(Z, obj.x, kernel = "linear.weighted", method="SKATO")

#############################################################
# Fit the Y chromosome function
# In this example, since male has only one copy of X (and Y), we reuse X chromosome genotype matrix.

# binary trait
obj.x<-SKAT_Null_Model_ChrX(y ~ x1 +x2 + Gender, 
  SexVar="Gender", out_type="D", Model.Y=TRUE, data=SKAT.example.ChrX)

SKAT_ChrY(Z, obj.x, kernel = "linear.weighted", method="SKATO")