assocTestAggregate: Aggregate Association Testing

In GENESIS: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

Description

assocTestAggregate performs aggregate association tests using the null model fit with fitNullModel.

Usage

## S4 method for signature 'SeqVarIterator'
assocTestAggregate(gdsobj, null.model, AF.max=1,
                   weight.beta=c(1,1), weight.user=NULL,
                   test=c("Burden", "SKAT", "fastSKAT", "SMMAT", "SKATO",
		          "BinomiRare", "CMP"),
                   neig = 200, ntrace = 500,
                   rho = seq(from = 0, to = 1, by = 0.1),
                   sparse=TRUE, imputed=FALSE,
                   male.diploid=TRUE, genome.build=c("hg19", "hg38"),
                   verbose=TRUE)
## S4 method for signature 'GenotypeIterator'
assocTestAggregate(gdsobj, null.model, AF.max=1,
                   weight.beta=c(1,1), weight.user=NULL,
                   test=c("Burden", "SKAT", "fastSKAT", "SMMAT", "SKATO",
		          "BinomiRare", "CMP"),
                   neig = 200, ntrace = 500,
                   rho = seq(from = 0, to = 1, by = 0.1),
                   male.diploid=TRUE, verbose=TRUE)

Arguments

gdsobj	An object of class SeqVarIterator from the package SeqVarTools containing the genotype data for the variants and samples to be used for the analysis.
null.model	A null model object returned by fitNullModel.
AF.max	A numeric value specifying the upper bound on the alternate allele frequency for variants to be included in the analysis.
weight.beta	A numeric vector of length two specifying the two parameters of the Beta distribution used to determine variant weights; weights are given by dbeta(MAF, a, b), where MAF is the minor allele frequency, and a and b are the two parameters specified here. weight.beta = c(1,25) gives the Wu weights; weight.beta = c(0.5, 0.5) is proportional to the Madsen-Browning weights; and weight.beta = c(1,1) gives a weight of 1 to all variants. This input is ignored when weight.user is not NULL.
weight.user	A character string specifying the name of a variable to be used as variant weights. This variable can be in either 1) the variantData slot of gdsobj or 2) the mcols of the GRanges or GRangesList object used to create gdsobj (when gdsobj is a link{SeqVarRangeIterator} or link{SeqVarListIterator}). When left NULL (the default), the weights specified by weight.beta will be used.
test	A character string specifying the type of test to be performed. The possibilities are "Burden" (default), "SKAT", "fastSKAT", "SMMAT", "BinomiRare", or "CMP".

neig	The number eigenvalues to approximate by using random projections for calculating p-values with fastSKAT; default is 200. See 'Details' for more information.
ntrace	The number of vectors to sample when using random projections to estimate the trace needed for p-value calculation with fastSKAT; default is 500. See 'Details' for more information.
rho	A numeric value (or vector of numeric values) in [0,1] specifying the rho parameter when using test == "SKATO"; these are the values for which SKAT-O is performed, defining the search space for the optimal rho. If rho = 0, this is equivalent to a standard SKAT test; if rho = 1, this is equivalent to a score burden test.

sparse	Logical indicator of whether to read genotypes as sparse Matrix objects; the default is TRUE. Set this to FALSE if the alternate allele dosage of the genotypes in the test are not expected to be mostly 0.
imputed	Logical indicator of whether to read dosages from the "DS" field containing imputed dosages instead of counting the number of alternate alleles.
male.diploid	Logical for whether males on sex chromosomes are coded as diploid.
genome.build	A character sting indicating genome build; used to identify pseudoautosomal regions on the X and Y chromosomes.
verbose	Logical indicator of whether updates from the function should be printed to the console; the default is TRUE.

Details

The type of aggregate unit tested depends on the class of iterator used for gdsobj. Options include sliding windows, specific ranges of variants or selection of individual variants (ranges with width 1). See SeqVarIterator for more details.

All samples included in null model will be included in the association test, even if a different set of samples is present in the current filter for gdsobj.

The effect size estimate is for each copy of the alternate allele (when gdsobj is a SeqVarIterator object) or the "A" allele (when gdsobj is a GenotypeIterator object). For multiallelic variants, each alternate (or "A") allele is tested separately.

Monomorphic variants (including variants where every sample is a heterozygote) are always omitted from the aggregate unit prior to testing.

Somewhat similarly to SKAT-O, the variant Set Mixed Model Association Test (SMMAT, Chen et al., 2019) combines the burden test p-value with an adjusted SKAT (which is asymptotically independent of the burden test) p-value using a chi-square distribution with 4df from Fisher's method.

SKAT and SMMAT will attempt to use Davies' method (i.e. integration) to calculate p-values; if an error occurs in integration or the reported p-values are too small that they are unreliable (i.e. near machine epsilon), then the saddlepoint approximation will instead be used to calculate the p-values.

The fastSKAT method of Lumley et al. (2018) uses random matrix theory to speed up the computation of SKAT p-values. When test = "fastSKAT", the function attempts to inteligently determine which p-value calculation approach to use for each aggregation unit: (1) if min(number samples, number variants) is small enough, then the standard SKAT p-value calculation is used; (2) if min(number samples, number variants) is too large for standard SKAT, but small enough to explicitly compute the genotype covariance matrix, random projections are used to approximate the eigenvalues of the covariance matrix, and the fastSKAT p-value calculation is used; (3) if min(number samples, number variants) is too big to explicitly compute the genotype covariance matrix, random projections are used to approximate both the eigenvalues and the trace of the covariance matrix, and the fastSKAT p-value calculation is used.)

The BinomiRare test, run by using test = "BinomiRare", and the CMP test, run by using test = "CMP" are carrier-only, robust tests. Only variants where the alternate allele is minor will be tested. Both tests focuse on carriers of the rare variant allele ("carriers"), and use the estimated probabilities of the binary outcome within the carriers, estimated under the null of not association, and the actual number of observed outcomes, to compute p-values. BinomiRare uses the Poisson-Binomial distribution, and the CMP uses the Conway-Maxwell-Poisson distribution, and is specifically designed for mixed models. (If test = "CMP" but null.model$family$mixedmodel = FALSE, the BinomiRare test will be run instead.) These tests provide both a traditional p-value ("pval") and a mid-p-value ("midp"), which is less conservative/more liberal, with the difference being more pronounced for small number of carriers. The BinomiRare test is described in Sofer (2017) and compared to the Score and SPA in various settings in Sofer and Guo (2020).

Value

A list with the following items:

results

A data.frame containing the results from the main analysis. Each row is a separate aggregate test:

If gdsobj is a SeqVarWindowIterator:

chr	The chromosome value
start	The start position of the window
end	The end position of the window

Always:

n.site	The number of variant sites included in the test.
n.alt	The number of alternate alleles included in the test.
n.sample.alt	The number of samples with an observed alternate allele at any variant in the aggregate set.

If test is "Burden":

Score	The value of the score function
Score.SE	The estimated standard error of the Score
Score.Stat	The score Z test statistic
Score.pval	The score p-value
Est	An approximation of the effect size estimate for each additional unit of burden
Est.SE	An approximation of the standard error of the effect size estimate
PVE	An approximation of the proportion of phenotype variance explained

If test is "SKAT" or "fastSKAT":

Q	The SKAT test statistic.
pval	The SKAT p-value.
err	Takes value 1 if there was an error in calculating the p-value; takes the value 2 when multiple random projections were required to get a good approximation from fastSKAT (the reported p-value is likely still reliable); 0 otherwise.
pval.method	The p-value calculation method used. When standard SKAT is used, one of "integration" or "saddlepoint"; when fastSKAT random projections are used to approximate eigenvalues of the genotype covariance matrix, one of "ssvd_integration" or "ssvd_saddlepoint"; when fastSKAT random projections are used to approximate both the eigenvalues and the trace of the genotype covariance matrix, one of "rsvd_integration" or "rsvd_saddlepoint".

If test is "SMMAT":

pval_burden	The burden test p-value
pval_theta	The p-value of the adjusted SKAT test (which is asymptotically independent of the burden test)
pval_SMMAT	The SMMAT p-value after combining pval_burden and pval_theta using Fisher's method.
err	Takes value 1 if there was an error calculating the SMMAT p-value; 0 otherwise. If err=1, pval_SMMAT is set to pval_burden.

If test is "SKATO":

Q_rho	The SKAT test statistic for the value of rho specified. There will be as many of these variables as there are rho values chosen.
pval_rho	The SKAT p-value for the value of rho specified. There will be as many of these variables as there are rho values chosen.
err_rho	Takes value 1 if there was an error in calculating the p-value for the value of rho specified when using the "kuonen" or "davies" methods; 0 otherwise. When there is an error, the p-value returned is from the "liu" method. There will be as many of these variables as there are rho values chosen.
min.pval	The minimum p-value among the p-values calculated for each choice of rho.
opt.rho	The optimal rho value; i.e. the rho value that gave the minimum p-value.
pval_SKATO	The SKAT-O p-value after adjustment for searching across multiple rho values.

If test is "BinomiRare" or "CMP":

n.carrier	Number of individuals with at least one copy of the alternate allele
n.D.carrier	Number of cases with at least one copy of the alternate allele
pval	p-value
mid.pval	mid-p-value
variantInfo	A list with as many elements as aggregate tests performed. Each element of the list is a data.frame providing information on the variants used in the aggregate test with results presented in the corresponding row of results. Each of these data.frames has the following information:
variant.id	The variant ID
chr	The chromosome value
pos	The base pair position
allele.index	The index of the alternate allele. For biallelic variants, this will always be 1.
n.obs	The number of samples with non-missing genotypes
freq	The estimated alternate allele frequency
MAC	The minor allele count. For multiallelic variants, "minor" is determined by comparing the count of the alternate allele specified by allele.index with the sum of all other alleles.
weight	The weight assigned to the variant in the analysis.

Author(s)

Matthew P. Conomos, Stephanie M. Gogarten, Thomas Lumley, Tamar Sofer, Ken Rice, Chaoyu Yu, Han Chen

References

Leal, S.M. & Li, B. (2008). Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data. American Journal of Human Genetics, 83(3): 311-321.

Browning, S.R. & Madsen, B.E. (2009). A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLoS Genetics, 5(2): e1000384.

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. American Journal of Human Genetics, 89(1): 82-93.

Lee, S. et al. (2012). Optimal Unified Approach for Rare-Variant Association Testing with Application to Small-Sample Case-Control Whole-Exome Sequencing Studies. American Journal of Human Genetics, 91(2): 224-237.

Chen, H., Huffman, J. E., Brody, J. A., Wang, C., Lee, S., Li, Z., ... & Blangero, J. (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(2), 260-274.

Lumley, T., Brody, J., Peloso, G., Morrison, A., & Rice, K. (2018). FastSKAT: Sequence kernel association tests for very large sets of markers. Genetic epidemiology, 42(6), 516-527.

Examples

library(SeqVarTools)
library(Biobase)
library(GenomicRanges)

# open a sequencing GDS file
gdsfile <- seqExampleFileName("gds")
gds <- seqOpen(gdsfile)

# simulate some phenotype data
set.seed(4)
data(pedigree)
pedigree <- pedigree[match(seqGetData(gds, "sample.id"), pedigree$sample.id),]
pedigree$outcome <- rnorm(nrow(pedigree))

# construct a SeqVarData object
seqData <- SeqVarData(gds, sampleData=AnnotatedDataFrame(pedigree))

# fit the null model
nullmod <- fitNullModel(seqData, outcome="outcome", covars="sex")

# burden test - Range Iterator
gr <- GRanges(seqnames=rep(1,3), ranges=IRanges(start=c(1e6, 2e6, 3e6), width=1e6))
iterator <- SeqVarRangeIterator(seqData, variantRanges=gr)
assoc <- assocTestAggregate(iterator, nullmod, test="Burden")
assoc$results
lapply(assoc$variantInfo, head)

# SKAT test - Window Iterator
seqSetFilterChrom(seqData, include="22")
iterator <- SeqVarWindowIterator(seqData)
assoc <- assocTestAggregate(iterator, nullmod, test="SKAT")
head(assoc$results)
head(assoc$variantInfo)

# SKAT-O test - List Iterator
seqResetFilter(iterator)
gr <- GRangesList(
  GRanges(seqnames=rep(22,2), ranges=IRanges(start=c(16e6, 17e6), width=1e6)),
  GRanges(seqnames=rep(22,2), ranges=IRanges(start=c(18e6, 20e6), width=1e6)))
iterator <- SeqVarListIterator(seqData, variantRanges=gr)
assoc <- assocTestAggregate(iterator, nullmod, test="SKAT", rho=seq(0, 1, 0.25))
assoc$results
assoc$variantInfo

# user-specified weights - option 1
seqResetFilter(iterator)
variant.id <- seqGetData(gds, "variant.id")
weights <- data.frame(variant.id, weight=runif(length(variant.id)))
variantData(seqData) <- AnnotatedDataFrame(weights)
iterator <- SeqVarListIterator(seqData, variantRanges=gr)
assoc <- assocTestAggregate(iterator, nullmod, test="Burden", weight.user="weight")
assoc$results
assoc$variantInfo

# user-specified weights - option 2
seqResetFilter(iterator)
variantData(seqData)$weight <- NULL
gr <- GRangesList(
  GRanges(seqnames=rep(22,2), ranges=IRanges(start=c(16e6, 17e6), width=1e6), weight=runif(2)),
  GRanges(seqnames=rep(22,2), ranges=IRanges(start=c(18e6, 20e6), width=1e6), weight=runif(2)))
iterator <- SeqVarListIterator(seqData, variantRanges=gr)
assoc <- assocTestAggregate(iterator, nullmod, test="Burden", weight.user="weight")
assoc$results
assoc$variantInfo

seqClose(seqData)

GENESIS documentation built on Jan. 30, 2021, 2:01 a.m.