| SKAT_ChrX {SKAT} | R Documentation |
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)
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 |
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. |
Details
For details of parameters, please see SKAT page.
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 |
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. |
Author(s)
Clement Ma and Seunggeun Lee
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")