SMMAT: Variant Set Mixed Model Association Tests (SMMAT)

In GMMAT: Generalized Linear Mixed Model Association Tests

Variant Set Mixed Model Association Tests (SMMAT)

Description

Variant Set Mixed Model Association Tests (SMMAT-B, SMMAT-S, SMMAT-O and SMMAT-E) for multiple user-defined test units and a null generalized linear mixed model. SMMAT.prep and SMMAT.lowmem are the two-step low-memory version of SMMAT. SMMAT.lowmem takes the returned R object from SMMAT.prep and uses less memory (if the returned R object from SMMAT.prep is saved to an R data file, the R session is terminated, and this R object is loaded into a new R session for running SMMAT.lowmem), especially when group.file contains only a subset of variants from geno.file.

Usage

SMMAT(null.obj, geno.file, group.file, group.file.sep = "\t", 
	meta.file.prefix = NULL, MAF.range = c(1e-7, 0.5), 
	MAF.weights.beta = c(1, 25), miss.cutoff = 1, 
	missing.method = "impute2mean", method = "davies", 
	tests = "E", rho = c(0, 0.1^2, 0.2^2, 0.3^2, 0.4^2, 
	0.5^2, 0.5, 1), use.minor.allele = FALSE, 
	auto.flip = FALSE, Garbage.Collection = FALSE,
	is.dosage = FALSE, ncores = 1, verbose = FALSE)
SMMAT.prep(null.obj, geno.file, group.file, group.file.sep = "\t", 
	auto.flip = FALSE)
SMMAT.lowmem(SMMAT.prep.obj, geno.file = NULL, meta.file.prefix = NULL,
        MAF.range = c(1e-7, 0.5), MAF.weights.beta = c(1, 25),
	miss.cutoff = 1, missing.method = "impute2mean",
	method = "davies", tests = "E", rho = c(0, 0.1^2,
	0.2^2, 0.3^2, 0.4^2, 0.5^2, 0.5, 1),
	use.minor.allele = FALSE, Garbage.Collection = FALSE,
	is.dosage = FALSE, ncores = 1, verbose = FALSE)

Arguments

null.obj	a class glmmkin or a class glmmkin.multi object, returned by fitting the null GLMM using glmmkin.
geno.file	the .gds file name or an object of class SeqVarGDSClass for the full genotypes. The sample.id in geno.file should overlap id_include in null.obj. It is recommended that sample.id in geno.file include the full samples (at least all samples as specified in id_include of null.obj). It is not necessary for the user to take a subset of geno.file before running the analysis. If geno.file is an object of class SeqVarGDSClass, the .gds file will be closed upon successful completion of the function.
group.file	a plain text file with 6 columns defining the test units. There should be no headers in the file, and the columns are group name, chromosome, position, reference allele, alternative allele and weight, respectively.
group.file.sep	the delimiter in group.file (default = "\t").
meta.file.prefix	prefix of intermediate files (.score.* and .var.*) required in a meta-analysis. If NULL, such intermediate files are not generated (default = NULL).
MAF.range	a numeric vector of length 2 defining the minimum and maximum minor allele frequencies of variants that should be included in the analysis (default = c(1e-7, 0.5)).
MAF.weights.beta	a numeric vector of length 2 defining the beta probability density function parameters on the minor allele frequencies. This internal minor allele frequency weight is multiplied by the external weight given by the group.file. To turn off internal minor allele frequency weight and only use the external weight given by the group.file, use c(1, 1) to assign flat weights (default = c(1, 25)).
miss.cutoff	the maximum missing rate allowed for a variant to be included (default = 1, including all variants).
missing.method	method of handling missing genotypes. Either "impute2mean" or "impute2zero" (default = "impute2mean").
method	a method to compute p-values for SKAT-type test statistics (default = "davies"). "davies" represents an exact method that computes a p-value by inverting the characteristic function of the mixture chisq distribution, with an accuracy of 1e-6. When "davies" p-value is less than 1e-5, it defaults to method "kuonen". "kuonen" represents a saddlepoint approximation method that computes the tail probabilities of the mixture chisq distribution. When "kuonen" fails to compute a p-value, it defaults to method "liu". "liu" is a moment-matching approximation method for the mixture chisq distribution.
tests	a character vector indicating which SMMAT tests should be performed ("B" for the burden test, "S" for SKAT, "O" for SKAT-O and "E" for the efficient hybrid test of the burden test and SKAT). The burden test and SKAT are automatically included when performing "O", and the burden test is automatically included when performing "E" (default = "E").
rho	a numeric vector defining the search grid used in SMMAT-O for SKAT-O (see the SKAT-O paper for details). Not used for SMMAT-B for the burden test, SMMAT-S for SKAT or SMMAT-E for the efficient hybrid test of the burden test and SKAT (default = c(0, 0.1^2, 0.2^2, 0.3^2, 0.4^2, 0.5^2, 0.5, 1)).
use.minor.allele	a logical switch for whether to use the minor allele (instead of the alt allele) as the coding allele (default = FALSE). It does not change SMMAT-S results, but SMMAT-B (as well as SMMAT-O and SMMAT-E) will be affected. Along with the MAF filter, this option is useful for combining rare mutations, assuming rare allele effects are in the same direction.
auto.flip	a logical switch for whether to enable automatic allele flipping if a variant with alleles ref/alt is not found at a position, but a variant at the same position with alleles alt/ref is found (default = FALSE). Use with caution for whole genome sequence data, as both ref/alt and alt/ref variants at the same position are not uncommon, and they are likely two different variants, rather than allele flipping.
Garbage.Collection	a logical switch for whether to enable garbage collection in each test (default = FALSE). Pay for memory efficiency with slower computation speed.
is.dosage	a logical switch for whether imputed dosage should be used from geno.file (default = FALSE).
ncores	a positive integer indicating the number of cores to be used in parallel computing (default = 1).
verbose	a logical switch for whether a progress bar should be shown (default = FALSE).
SMMAT.prep.obj	a class SMMAT.prep object, returned by SMMAT.prep.

Value

SMMAT and SMMAT.lowmem return a data frame with the following components:

group	name of the test unit group.
n.variants	number of variants in the test unit group that pass the missing rate and allele frequency filters.
miss.min	minimum missing rate for variants in the test unit group.
miss.mean	mean missing rate for variants in the test unit group.
miss.max	maximum missing rate for variants in the test unit group.
freq.min	minimum coding allele frequency for variants in the test unit group.
freq.mean	mean coding allele frequency for variants in the test unit group.
freq.max	maximum coding allele frequency for variants in the test unit group.
B.score	burden test score statistic.
B.var	variance of burden test score statistic.
B.pval	burden test p-value.
S.pval	SKAT p-value.
O.pval	SKAT-O p-value.
O.minp	minimum p-value in the SKAT-O search grid.
O.minp.rho	rho value at the minimum p-value in the SKAT-O search grid.
E.pval	SMMAT efficient hybrid test of the burden test and SKAT p-value.

SMMAT.prep return a list with the following components:

null.obj	a class glmmkin or a class glmmkin.multi object from the null model, after pre-processing.
geno.file	the name of the .gds file for the full genotypes.
group.file	the name of the plain text file with 6 columns defining the test units.
group.file.sep	the delimiter in group.file.
auto.flip	a logical indicator showing whether automatic allele flipping is enabled in pre-processing if a variant with alleles ref/alt is not found at a position, but a variant at the same position with alleles alt/ref is found.
residuals	residuals from the null model, after pre-processing.
sample.id	sample.id from geno.file, after pre-processing.
group.info	group.info read from group.file, after pre-processing.
groups	unique groups in group.info, after pre-processing.
group.idx.start	a vector of the start variant index for each group, after pre-processing.
group.idx.end	a vector of the end variant index for each group, after pre-processing.

Author(s)

Han Chen

References

Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. The American Journal of Human Genetics 89, 82-93.

Lee, S., Wu, M.C., Lin, X. (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762-775.

Sun, J., Zheng, Y., Hsu, L. (2013) A unified mixed-effects model for rare-variant association in sequencing studies. Genetic Epidemiology 37, 334-344.

Chen, H., Huffman, J.E., Brody, J.A., Wang, C., Lee, S., Li, Z., Gogarten, S.M., Sofer, T., Bielak, L.F., Bis, J.C., et al. (2019) Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. The American Journal of Human Genetics 104, 260-274.

See Also

glmmkin, SMMAT.meta

Examples

if(requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
        quietly = TRUE)) {
data(example)
attach(example)
model0 <- glmmkin(disease ~ age + sex, data = pheno, kins = GRM, id = "id",
        family = binomial(link = "logit"))
geno.file <- system.file("extdata", "geno.gds", package = "GMMAT")
group.file <- system.file("extdata", "SetID.withweights.txt",
	package = "GMMAT")
out <- SMMAT(model0, geno.file, group.file, MAF.range = c(0, 0.5), 
       		miss.cutoff = 1, method = "davies")
print(out)
}

## Not run: 
obj <- SMMAT.prep(model0, geno.file, group.file)
save(obj, file = "SMMAT.prep.tmp.Rdata")
# quit R session
# open a new R session
obj <- get(load("SMMAT.prep.tmp.Rdata"))
out <- SMMAT.lowmem(obj, MAF.range = c(0, 0.5), miss.cutoff = 1,
        method = "davies")
print(out)
unlink("SMMAT.prep.tmp.Rdata")

## End(Not run)

GMMAT documentation built on Nov. 17, 2023, 5:07 p.m.