SKAT_CommonRare: SKAT for the combined effect of common and rare variants

Description

Sequence Kernel association test for the combined effect of common and rare variants.

Usage

SKAT_CommonRare(Z, obj, weights.beta.rare=c(1,25)
	, weights.beta.common=c(0.5,0.5), weights=NULL
	, method="C", r.corr.rare=0, r.corr.common=0, CommonRare_Cutoff=NULL
	, test.type="Joint", is_dosage=FALSE, missing_cutoff=0.15
	, estimate_MAF=1, SetID1=NULL)

	SKAT_CommonRare.SSD.OneSet(SSD.INFO
	, SetID, obj, ..., obj.SNPWeight=NULL)
	SKAT_CommonRare.SSD.OneSet_SetIndex(SSD.INFO
	, SetIndex, obj, ..., obj.SNPWeight=NULL )

Value

p.value: p-value.
p.value.resampling: p-values from resampled phenotypes. You can get it when you use obj from SKAT_Null_Model function with resampling. See the SKAT_Null_Model.
n.rare: the number of rare variants used for the test
n.common: the number of common variants used for the test
Cutoff: the MAF cut-off to divide common and rare variants
Q: the test statistic of SKAT. It has NA when method="A" or "AR".
param: estimated parameters of each method.
param$Is_Converged: an indicator of the convergence. 1 indicates the method is converged, and 0 indicates the method is not converged. When 0 (not converged), "liu.mod" method is used to compute p-value.
param$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.
test.snp.mac: a vector of minor allele count (MAC) of the snps tested. The name is SNP-ID.

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: an output object of the SKAT_Null_Model function.
weights.beta.rare: a numeric vector of parameters of beta weights for rare variants (default=c(1,25)).
weights.beta.common: a numeric vector of parameters of beta weights for common variants (default=c(0.5,0.5)).
weights: a numeric vector of weights for both common and rare variants. When it is NULL, the beta weight with the ``weights.beta.rare'' and ``weights.beta.common'' parameter are used. When method ="C", the coefficient to combine common and rare variants test statistics will be calculated after applying the weights to variants.
method: a method to combine common and rare variant effects (default="C"). "C" represents the combined sum test, and "A" represents the adaptive sum test. "AR" represents a different type of adaptive test in which common variants are projected over rare variants.
r.corr.rare: the $\rho$ parameter for rare variants (default= 0). $\rho$ =0 and 1 indicate SKAT and Burden test, respectively
r.corr.common: the $\rho$ parameter for common variants (default= 0). $\rho$ =0 and 1 indicate SKAT and Burden test, respectively
CommonRare_Cutoff: MAF cutoff for common vs rare variants (default=NULL). It should be a numeric value between 0 and 0.5, or NULL. When it is NULL, $1/ \sqrt{2 SampleSize }$ will be used.
test.type: a string to indicate test type (default="Joint"). "Joint" indicates the joint test of the combined effects of common and rare variants. "Rare.Only" and "Common.Only" will conduct test only with rare and common variants, respectively.
is_dosage: see SKAT
missing_cutoff: see SKAT
estimate_MAF: see SKAT
SetID1: internal use only
SSD.INFO: an SSD_INFO object returned from Open_SSD.
SetID: a character value of Set ID. You can find a set ID of each set from SetInfo object of SSD.INFO
SetIndex: a numeric value of Set index. You can find a set index of each set from SetInfo object of SSD.INFO
obj.SNPWeight: output object from Read_SNP_WeightFile (default=NULL). If NULL, the beta weight with the ``weights.beta'' parameter will be used.
...: furthuer arguments to be passed to ``SKAT_CommonRare''

Author

Seunggeun Lee

Details

The small sample adjustment for binary traits is not implemented for "A" and "AR".

References

Ionita-Laza, I.*, Lee, S.*, Makarov, V., Buxbaum, J. Lin, X. (2013). Sequence kernel association tests for the combined effect of rare and common variants. American Journal of Human Genetics, 92, 841-853. * contributed equally.

Examples

Run this code



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


# continuous trait
obj<-SKAT_Null_Model(y.c ~ X, out_type="C", data=SKAT.example)
SKAT_CommonRare(Z, obj)$p.value
SKAT_CommonRare(Z, obj, method="A")$p.value
SKAT_CommonRare(Z, obj, method="AR")$p.value


# dichotomous trait 
obj<-SKAT_Null_Model(y.b ~ X, out_type="D", data=SKAT.example)

# Combined sum test in the manuscript (SKAT-C and Burden-C)
SKAT_CommonRare(Z, obj)$p.value
SKAT_CommonRare(Z, obj, r.corr.rare=1, r.corr.common=1 )$p.value


# Test only with common variant
SKAT_CommonRare(Z, obj, test.type="Common.Only")$p.value

# Test only with rare variant
SKAT_CommonRare(Z, obj, test.type="Rare.Only")$p.value


# Use CommonRare_Cutoff=0.01 instead of CommonRare_Cutoff = NULL
SKAT_CommonRare(Z, obj, CommonRare_Cutoff=0.01)$p.value

# Use custom weights; the first 10 variants have higher weights 
weights<-rep(1,67); weights[1:10]<-2
SKAT_CommonRare(Z, obj, weights=weights)$p.value

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