seqAssocGLMM_spaACAT_V: ACAT-V tests
In SAIGEgds: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/assoc_aggregate.r

ACAT-V p-value calculations using mixed models and the Saddlepoint approximation method for case-control imbalance.

seqAssocGLMM_spaACAT_V(gdsfile, modobj, units, wbeta=AggrParamBeta,
    burden.mac=10, burden.summac=3, dsnode="", spa.pval=0.05, var.ratio=NaN,
    res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=TRUE)

`gdsfile`	a SeqArray GDS filename, or a GDS object
`modobj`	an R object for SAIGE model parameters
`units`	a list of units of selected variants, with S3 class `"SeqUnitListClass"` defined in the SeqArray package
`wbeta`	weights for per-variant effect, using beta distribution `dbeta()` according to variant's MAF; a length-two vector, or a matrix with two rows for multiple beta parameters; by default, using beta(1,1) and beta(1,25) both
`burden.mac`	a threshold of minor allele count for using burden test instead of single variant test if `mac < burden.mac`
`burden.summac`	a threshold for the weighted sum of minor allele counts in burden test (checking `>= burden.summac`)
`dsnode`	"" for automatically searching the GDS nodes "genotype" and "annotation/format/DS", or use a user-defined GDS node in the file
`spa.pval`	the p-value threshold for SPA adjustment, 0.05 by default
`var.ratio`	`NaN` for using the estimated variance ratio in the model fitting, or a user-defined variance ratio
`res.savefn`	an RData or GDS file name, "" for no saving
`res.compress`	the compression method for the output file, it should be one of LZMA, LZMA_RA, ZIP, ZIP_RA and none
`parallel`	`FALSE` (serial processing), `TRUE` (multicore processing), a numeric value for the number of cores, or other value; `parallel` is passed to the argument `cl` in `seqParallel`, see `seqParallel` for more details
`verbose`	if `TRUE`, show information
`verbose.maf`	if `TRUE`, show summary of MAFs in units

Liu Y., Chen S., Li Z., Morrison A.C., Boerwinkle E., Lin X. ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. Am J Hum Genetics 104, 410-421 (2019).

Return a data.frame with the following components if not saving to a file: chr, chromosome; start, a starting position; end, an ending position; numvar, the number of variants in a window; summac, the weighted sum of minor allele counts; beta, beta coefficient, odds ratio if binary outcomes); SE, standard error for beta coefficient; pval, adjusted p-value with Saddlepoint approximation;

p.norm

p-values based on asymptotic normality (could be 0 if it is too small, e.g., pnorm(-50) = 0 in R; used for checking only

cvg, whether the SPA algorithm converges or not for adjusted p-value.

Xiuwen Zheng

XX

seqAssocGLMM_spaBurden, seqAssocGLMM_spaACAT_O

# open a GDS file
fn <- system.file("extdata", "grm1k_10k_snp.gds", package="SAIGEgds")
gdsfile <- seqOpen(fn)

# load phenotype
phenofn <- system.file("extdata", "pheno.txt.gz", package="SAIGEgds")
pheno <- read.table(phenofn, header=TRUE, as.is=TRUE)
head(pheno)

# fit the null model
glmm <- seqFitNullGLMM_SPA(y ~ x1 + x2, pheno, gdsfile, trait.type="binary")

# get a list of variant units for burden tests
units <- seqUnitSlidingWindows(gdsfile, win.size=500, win.shift=250)

assoc <- seqAssocGLMM_spaACAT_V(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)