Package 'SAIGEgds' reference manual

Title:	Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies
Description:	Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package.
Authors:	Xiuwen Zheng [aut, cre] , Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb]
Maintainer:	Xiuwen Zheng <[email protected]>
License:	GPL-3
Version:	2.7.1
Built:	2024-12-21 03:41:04 UTC
Source:	https://github.com/bioc/SAIGEgds

Scalable Implementation of Generalized mixed models in Phenome-Wide Association Studies using GDS files

Description

Scalable and accurate implementation of generalized mixed mode with the support of Genomic Data Structure (GDS) files and highly optimized C++ implementation. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and hundreds of thousands of samples, e.g., UK Biobank genotype data, controlling for case-control imbalance and sample structure in single variant association studies.

The implementation of SAIGEgds is based on the original SAIGE R package (v0.29.4.4) [Zhou et al. 2018] https://github.com/weizhouUMICH/SAIGE/releases/tag/v0.29.4.4. All of the calculation with single-precision floating-point numbers in SAIGE are replaced by the double-precision calculation in SAIGEgds. SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint Approximation.

Details

Package:	SAIGEgds
Type:	Package
License:	GPL version 3

Author(s)

Xiuwen Zheng [email protected], Wei Zhou (the original author of the SAIGE R package, https://github.com/weizhouUMICH/SAIGE)

References

Zheng X, Davis J.Wade. SAIGEgds – an efficient statistical tool for large-scale PheWAS with mixed models. *Bioinformatics* (2020). DOI: 10.1093/bioinformatics/btaa731.

Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, LeFaive J, VandeHaar P, Gagliano SA, Gifford A, Bastarache LA, Wei WQ, Denny JC, Lin M, Hveem K, Kang HM, Abecasis GR, Willer CJ, Lee S. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. *Nat Genet* (2018). Sep;50(9):1335-1341.

Zhou W, Zhao Z, Nielsen JB, Fritsche LG, LeFaive J, Taliun SAG, Bi W, Gabrielsen ME, Daly MJ, Neale BM, Hveem K, Abecasis GR, Willer CJ, et al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nat Genet. 2020; 52: 634-9.

Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D. SeqArray – A storage-efficient high-performance data format for WGS variant calls. *Bioinformatics* (2017). DOI: 10.1093/bioinformatics/btx145.

Examples

# open the 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")

# p-value calculation
assoc <- seqAssocGLMM_SPA(gdsfile, glmm, mac=10)

head(assoc)

# close the GDS file
seqClose(gdsfile)
# open the 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")

# p-value calculation
assoc <- seqAssocGLMM_SPA(gdsfile, glmm, mac=10)

head(assoc)

# close the GDS file
seqClose(gdsfile)

Heritability estimation

Description

Get the heritability estimate from the SAIGE model.

Usage

glmmHeritability(modobj, adjust=TRUE)
glmmHeritability(modobj, adjust=TRUE)

Arguments

`modobj`	an R object for SAIGE model parameters
`adjust`	if `TRUE` and binary outcomes, uses adjusted tau estimate for the heritability estimation

Details

In SAIGE, penalized quasi-likelihood (PQL) is used to estimate the variance component parameter tau. It is known to produce biased estimate of the variance component tau using PQL. If adjust=TRUE for binary outcomes, tau is adjusted based prevalence and observed tau using the data in Supplementary Table 7 (Zhou et al. 2018) to reduce the bias of PQL estimate of variance component.

Value

Return a liability scale heritability.

Author(s)

Xiuwen Zheng

Examples

# 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")

glmmHeritability(glmm)

seqClose(gdsfile)
# 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")

glmmHeritability(glmm)

seqClose(gdsfile)

Cauchy Combination Test

Description

P-value calculation from Cauchy combination test.

Usage

pACAT(p, w=NULL)
pACAT2(p, maf, wbeta=c(1,25))
pACAT(p, w=NULL)
pACAT2(p, maf, wbeta=c(1,25))

Arguments

`p`	a numeric vector for p-values
`w`	weight for each p-value
`maf`	minor allele frequency for each p-value
`wbeta`	weights for per-variant effect, using beta distribution `dbeta()` according to variant's MAF

Value

Return a single number for the combined p-value.

References

Liu Y., Cheng 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).

Examples

p1 <- 10^-4
p2 <- 10^-5
p3 <- 10^-(3:20)
sapply(p3, function(p) pACAT(c(p1, p2, p)))

pACAT2(c(10^-4, 10^-6), c(0.01, 0.005))
p1 <- 10^-4
p2 <- 10^-5
p3 <- 10^-(3:20)
sapply(p3, function(p) pACAT(c(p1, p2, p)))

pACAT2(c(10^-4, 10^-6), c(0.01, 0.005))

ACAT-V tests

Description

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

Usage

seqAssocGLMM_ACAT_O(gdsfile, modobj, units, wbeta=AggrParamBeta,
    acatv.collapse.mac=10, skat.collapse.mac=10,
    skat.collapse.method=c("PA", "PA_int", "SumG"), burden.summac=3, dsnode="",
    res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)
seqAssocGLMM_ACAT_O(gdsfile, modobj, units, wbeta=AggrParamBeta,
    acatv.collapse.mac=10, skat.collapse.mac=10,
    skat.collapse.method=c("PA", "PA_int", "SumG"), burden.summac=3, dsnode="",
    res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)

Arguments

`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
`acatv.collapse.mac`	a threshold of minor allele count for collapsing ultra rare variants used in burden tests if `mac < acatv.collapse.mac`, 10 by default
`skat.collapse.mac`	a threshold of minor allele count for collapsing ultra rare variants used in SKAT tests if `mac < skat.collapse.mac`, 10 by default
`skat.collapse.method`	presence or absence ("PA", by default), "PA_int" or average genotypes ("SumG")
`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
`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

Details

seqUnitFilterCond() in the SeqArray package can be used to restrict the variant sets units to a range of MAC and/or MAF. ACAT-O combines the p-values from ACAT-V, burden and SKAT together to calculate the final p-value via Cauchy distribution.

For more details of the ACAT-O method, please refer to the ACAT paper [Liu et al. 2019] (see the reference section).

Value

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.

Author(s)

Xiuwen Zheng

References

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).

Examples

# 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_ACAT_O(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)
# 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_ACAT_O(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)

ACAT-V tests

Description

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

Usage

seqAssocGLMM_ACAT_V(gdsfile, modobj, units, wbeta=AggrParamBeta,
    ccimb.adj=TRUE, collapse.mac=10, burden.summac=3, dsnode="", res.savefn="",
    res.compress="LZMA", parallel=FALSE, verbose=TRUE, verbose.maf=FALSE)
seqAssocGLMM_ACAT_V(gdsfile, modobj, units, wbeta=AggrParamBeta,
    ccimb.adj=TRUE, collapse.mac=10, burden.summac=3, dsnode="", res.savefn="",
    res.compress="LZMA", parallel=FALSE, verbose=TRUE, verbose.maf=FALSE)

Arguments

`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
`ccimb.adj`	whether adjusting for case-control imbalance or not
`collapse.mac`	a threshold of minor allele count for collapsing ultra rare variants used in burden tests if `mac <= collapse.mac`, 10 by default
`burden.summac`	a threshold for the sum of minor allele counts (MAC) in the burden test for collapsing ultra rare variants (checking `>= burden.summac`); no calculation if the sum of MAC `< burden.summac`
`dsnode`	"" for automatically searching the GDS nodes "genotype" and "annotation/format/DS", or use a user-defined GDS node in the file
`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

Details

seqUnitFilterCond() in the SeqArray package can be used to restrict the variant sets units to a range of MAC and/or MAF.

For more details of the ACAT-V method, please refer to the ACAT paper [Liu et al. 2019] (see the reference section).

Value

Return a data.frame with the following components if not saving to a file: chr, chromosome; start, the starting position; end, the ending position; numvar, the number of variants in the window; maf.avg, the average of MAFs in the window; maf.sd, the standard deviation of MAFs in the window; maf.min, the minimum of MAFs in the window; maf.max, the maximum of MAFs in the window; mac.avg, the average of MACs in the window; mac.sd, the standard deviation of MACs in the window; mac.min, the minimum of MACs in the window; mac.max, the maximum of MACs in the window; n.single, the number of single variant tests; n.burden, the number of ultra rare variants in the burden test; pval, ACAT-V p-value; p.med, the median of the list of p-value; p.min, the minimum of the list of p-values; p.max, the maximum of the list of p-values.

Author(s)

Xiuwen Zheng

References

Examples

# 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_ACAT_V(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)
# 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_ACAT_V(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)

Burden tests

Description

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

Usage

seqAssocGLMM_Burden(gdsfile, modobj, units, wbeta=AggrParamBeta, ccimb.adj=TRUE,
    summac=3, dsnode="", res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)
seqAssocGLMM_Burden(gdsfile, modobj, units, wbeta=AggrParamBeta, ccimb.adj=TRUE,
    summac=3, dsnode="", res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)

Arguments

`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
`ccimb.adj`	whether adjusting for case-control imbalance or not
`summac`	a threshold for the sum of minor allele counts (MAC) (checking `>= summac`); no calculation if the sum of MAC `< summac`
`dsnode`	"" for automatically searching the GDS nodes "genotype" and "annotation/format/DS", or use a user-defined GDS node in the file
`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

Details

seqUnitFilterCond() in the SeqArray package can be used to restrict the variant sets units to a range of MAC and/or MAF.

Value

Return a data.frame with the following components if not saving to a file: chr, chromosome; start, the starting position; end, the ending position; numvar, the number of variants in the window; maf.avg, the average of MAFs in the window; maf.sd, the standard deviation of MAFs in the window; maf.min, the minimum of MAFs in the window; maf.max, the maximum of MAFs in the window; mac.avg, the average of MACs in the window; mac.sd, the standard deviation of MACs in the window; mac.min, the minimum of MACs in the window; mac.max, the maximum of MACs in the window; summac, the sum of minor allele counts; beta, beta coefficient, log odds ratio if binary outcomes; SE, standard error for beta coefficient; pval, p-value (adjusted p-value with Saddlepoint approximation if ccimb.adj=TRUE and binary outcomes);

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.

Author(s)

Xiuwen Zheng

Examples

# 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_Burden(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)
# 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_Burden(gdsfile, glmm, units)
head(assoc)

# close the GDS file
seqClose(gdsfile)

SKAT tests

Description

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

Usage

seqAssocGLMM_SKAT(gdsfile, modobj, units, wbeta=AggrParamBeta,
    collapse.mac=10, collapse.method=c("PA", "PA_int", "SumG"), dsnode="",
    ccimb.adj=TRUE, res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)
seqAssocGLMM_SKAT(gdsfile, modobj, units, wbeta=AggrParamBeta,
    collapse.mac=10, collapse.method=c("PA", "PA_int", "SumG"), dsnode="",
    ccimb.adj=TRUE, res.savefn="", res.compress="LZMA", parallel=FALSE,
    verbose=TRUE, verbose.maf=FALSE)

Arguments

`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
`collapse.mac`	the mac threshold for collapsing ultra rare variants
`collapse.method`	presence or absence ("PA", by default), "PA_int" or average genotypes ("SumG")
`dsnode`	"" for automatically searching the GDS nodes "genotype" and "annotation/format/DS", or use a user-defined GDS node in the file
`res.savefn`	an RData or GDS file name, "" for no saving
`ccimb.adj`	whether adjusting for case-control imbalance
`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

Details

seqUnitFilterCond() in the SeqArray package can be used to restrict the variant sets units to a range of MAC and/or MAF.

For more details of the SAIGE-GENE method, please refer to the SAIGE paper [Zhou et al. 2020] (see the reference section).

Value

Return a data.frame with the following components if not saving to a file: chr, chromosome; start, the starting position; end, the ending position; numvar, the number of variants in the window; maf.avg, the average of MAFs in the window; maf.sd, the standard deviation of MAFs in the window; maf.min, the minimum of MAFs in the window; maf.max, the maximum of MAFs in the window; mac.avg, the average of MACs in the window; mac.sd, the standard deviation of MACs in the window; mac.min, the minimum of MACs in the window; mac.max, the maximum of MACs in the window; ncol_g, the actual number of variants used in the calculation including collapsed variants; n_collapse, the number of ultra rare variants collapsing; pval, adjusted p-value with Saddlepoint approximation;

Author(s)

Xiuwen Zheng

References

Examples

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

sp_grm_fn <- system.file("extdata", "grm1k_10k_sp_grm.rds", package="SAIGEgds")
sp_grm <- readRDS(sp_grm_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, sp_grm,
    trait.type="binary")

# get a list of variant units for burden tests
units <- seqUnitSlidingWindows(gdsfile, win.size=50, win.shift=250)

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

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

sp_grm_fn <- system.file("extdata", "grm1k_10k_sp_grm.rds", package="SAIGEgds")
sp_grm <- readRDS(sp_grm_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, sp_grm,
    trait.type="binary")

# get a list of variant units for burden tests
units <- seqUnitSlidingWindows(gdsfile, win.size=50, win.shift=250)

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

# close the GDS file
seqClose(gdsfile)

P-value calculation

Description

P-value calculations using variance approximation and an adjustment of Saddlepoint approximation in the mixed model framework.

Usage

seqAssocGLMM_SPA(gdsfile, modobj, maf=NaN, mac=10, missing=0.05, spa=TRUE,
    dsnode="", geno.ploidy=2L, res.savefn="", res.compress="ZIP",
    parallel=FALSE, load.balancing=TRUE, verbose=TRUE)
seqAssocGLMM_SPA(gdsfile, modobj, maf=NaN, mac=10, missing=0.05, spa=TRUE,
    dsnode="", geno.ploidy=2L, res.savefn="", res.compress="ZIP",
    parallel=FALSE, load.balancing=TRUE, verbose=TRUE)

Arguments

`gdsfile`	a SeqArray GDS filename, or a GDS object
`modobj`	an R object for SAIGE model parameters
`maf`	minor allele frequency threshold (checking >= maf), `NaN` for no filter
`mac`	minor allele count threshold (checking >= mac), `NaN` for no filter
`missing`	missing threshold for variants (checking <= missing), `NaN` for no filter
`spa`	`TRUE` for using the Saddlepoint approximation method for adjusting case-control imbalance
`dsnode`	"" for automatically searching the GDS nodes "genotype" and "annotation/format/DS", or use a user-defined GDS node in the file
`geno.ploidy`	specify the ploidy (2 by default); 0 or NA used for non-genotype data (e.g., CNV and `dsnode` should be specified for CNVs meanwhile)
`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 ZIP, ZIP_RA, LZMA, LZMA_RA and none; see `compression.gdsn` for more details
`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
`load.balancing`	load balancing for the calculation in parallel if `TRUE`
`verbose`	if `TRUE`, show information

Details

For more details of SAIGE algorithm, please refer to the SAIGE paper [Zhou et al. 2018] (see the reference section).

Value

Return a data.frame with the following components if not saving to a file:

`id`	variant ID in the GDS file;
`chr`	chromosome;
`pos`	position;
`rs.id`	the RS IDs if it is available in the GDS file;
`ref`	the reference allele;
`alt`	the alternative allele;
`AF.alt`	allele frequency for the alternative allele; the minor allele frequency is `pmin(AF.alt, 1-AF.alt)`;
`mac`	minor allele count; the allele count for the alternative allele is `ifelse(AF.alt<=0.5, mac, 2*num-mac)`;
`num`	the number of samples with non-missing genotypes;
`beta`	beta coefficient, odds ratio if binary outcomes (alternative allele vs. reference allele);
`SE`	standard error for beta coefficient;
`pval`	adjusted p-value with the Saddlepoint approximation method;
`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
`converged`	whether the SPA algorithm converges or not for adjusted p-values.

Author(s)

Xiuwen Zheng

References

Examples

# 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")

# p-value calculation
assoc <- seqAssocGLMM_SPA(gdsfile, glmm, mac=10)
head(assoc)

# close the GDS file
seqClose(gdsfile)
# 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")

# p-value calculation
assoc <- seqAssocGLMM_SPA(gdsfile, glmm, mac=10)
head(assoc)

# close the GDS file
seqClose(gdsfile)

Linkage disequilibrium pruning

Description

Construct LD-pruned SNP sets for genetic relationship matrix (GRM).

Usage

seqFitLDpruning(gdsfile, sample.id=NULL, variant.id=NULL,
    ld.threshold=0.1, maf=0.01, missing.rate=0.005, autosome.only=TRUE,
    use.cateMAC=TRUE, num.marker=100L, num.total=100000L, save.gdsfn=NULL,
    seed=200L, parallel=FALSE, parallel.multi=NULL, verbose=TRUE)
seqFitLDpruning(gdsfile, sample.id=NULL, variant.id=NULL,
    ld.threshold=0.1, maf=0.01, missing.rate=0.005, autosome.only=TRUE,
    use.cateMAC=TRUE, num.marker=100L, num.total=100000L, save.gdsfn=NULL,
    seed=200L, parallel=FALSE, parallel.multi=NULL, verbose=TRUE)

Arguments

`gdsfile`	a SeqArray GDS file name, a GDS object for genotypes, or a character vector for a list of GDS file names when genotyeps are split by chromosomes
`sample.id`	NULL for all samples, or sample IDs in GRM
`variant.id`	a list of variant IDs, used to construct GRM
`ld.threshold`	the LD threshold; see `snpgdsLDpruning()`
`maf`	minor allele frequency threshold for genotypes in `gdsfile` (checking >= maf), if `variant.id=NULL`; `NaN` for no filter
`missing.rate`	threshold of missing rate (checking <= missing.rate), if `variant.id=NULL`; `NaN` for no filter
`autosome.only`	`TRUE` for using autosomes only
`use.cateMAC`	`FALSE`, to use a single global variance ratio; `TRUE` (equal to `use.cateMAC=c(1.5, 2.5, 3.5, 4.5, 5.5, 10.5, 20.5)`) for MAC categories (0, 1.5), [1.5, 2.5), ... [10.5, 20.5) and [20.5, Inf); or a numeric vector (strictly increasing) for unique cut points
`num.marker`	the number of SNPs used to calculate the variance ratio in each MAC category
`num.total`	the total number of LD-pruned variants excluding ultra rare variants; if the number of variants selected by the process of LD pruning is larger than `num.total`, the random set is used
`save.gdsfn`	if a file name is specified, construct a GDS genotype file to include all selected variants
`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
`parallel.multi`	only applicable when there are multiple input files in `gdsfile` and `save.gdsfn` is used; multiple GDS files can be extracted to create a sub GDS file in parallel via `parallel.multi`; if `NULL`, use `parallel` instead; `parallel.multi` could have less CPU cores than `parallel` to reduce memory usage
`seed`	an integer passed to `set.seed()` as a seed for random numbers, or `NULL` for no action
`verbose`	if `TRUE`, show information

Details

This function calls snpgdsLDpruning in the SNPRelate package to perform linkage disequilibrium (LD) pruning. When use.cateMAC is not FALSE, the ultra rare variants will be selected according to the MAC categories, which could be used in the null model fitting.

Value

Returns variant IDs or a list of variant IDs for multiple input GDS files.

Author(s)

Xiuwen Zheng

Examples

library(SeqArray)
library(SAIGEgds)

# open a GDS file
gds_fn <- seqExampleFileName("KG_Phase1")

seqFitLDpruning(gds_fn, save.gdsfn="grm.gds")

# delete the temporary file
unlink("grm.gds", force=TRUE)
library(SeqArray)
library(SAIGEgds)

# open a GDS file
gds_fn <- seqExampleFileName("KG_Phase1")

seqFitLDpruning(gds_fn, save.gdsfn="grm.gds")

# delete the temporary file
unlink("grm.gds", force=TRUE)

Fit the null model with GRM

Description

Fit the null model in the mixed model framework with genetic relationship matrix (GRM).

Usage

seqFitNullGLMM_SPA(formula, data, gdsfile=NULL, grm.mat=NULL,
    trait.type=c("binary", "quantitative"), sample.col="sample.id", maf=0.01,
    missing.rate=0.01, max.num.snp=1000000L, variant.id=NULL,
    variant.id.varratio=NULL, nsnp.sub.random=2000L, rel.cutoff=0.125,
    inv.norm=c("residuals", "quant", "none"), use.cateMAC=FALSE,
    cateMAC.inc.maf=TRUE, cateMAC.simu=FALSE, X.transform=TRUE, tol=0.02,
    maxiter=20L, nrun=30L, tolPCG=1e-5, maxiterPCG=500L, num.marker=30L,
    tau.init=c(0,0), traceCVcutoff=0.0025, ratioCVcutoff=0.001,
    geno.sparse=TRUE, num.thread=1L, model.savefn="", seed=200L,
    fork.loading=FALSE, verbose=TRUE)
seqFitNullGLMM_SPA(formula, data, gdsfile=NULL, grm.mat=NULL,
    trait.type=c("binary", "quantitative"), sample.col="sample.id", maf=0.01,
    missing.rate=0.01, max.num.snp=1000000L, variant.id=NULL,
    variant.id.varratio=NULL, nsnp.sub.random=2000L, rel.cutoff=0.125,
    inv.norm=c("residuals", "quant", "none"), use.cateMAC=FALSE,
    cateMAC.inc.maf=TRUE, cateMAC.simu=FALSE, X.transform=TRUE, tol=0.02,
    maxiter=20L, nrun=30L, tolPCG=1e-5, maxiterPCG=500L, num.marker=30L,
    tau.init=c(0,0), traceCVcutoff=0.0025, ratioCVcutoff=0.001,
    geno.sparse=TRUE, num.thread=1L, model.savefn="", seed=200L,
    fork.loading=FALSE, verbose=TRUE)

Arguments

`formula`	an object of class `formula` (or one that can be coerced to that class), e.g., `y ~ x1 + x2`, see `lm`
`data`	a data frame for the formulas
`gdsfile`	a SeqArray GDS filename or a GDS object for genotypes used in the GRM calculation; see details
`grm.mat`	NULL, a user-defined GRM or an object of 'snpgdsGRMClass' returned from `SNPRelate::snpgdsGRM()`: a dense numeric matrix or a sparse symmetric matrix 'sparseMatrix' defined in `Matrix`; `colnames` and `rownames` should be sample IDs; or `TRUE`, call `seqFitSparseGRM()` to get a sparse GRM; see details
`trait.type`	"binary" for binary outcomes, "quantitative" for continuous outcomes
`sample.col`	the column name of sample IDs corresponding to the GDS file
`maf`	minor allele frequency threshold for genotypes in `gdsfile` (checking >= maf), if `variant.id=NULL`; `NaN` for no filter
`missing.rate`	threshold of missing rate (checking <= missing.rate), if `variant.id=NULL`; `NaN` for no filter
`max.num.snp`	the maximum number of SNPs used, or -1 for no limit
`variant.id`	a list of variant IDs, considered to be used in GRM; or `NULL`, all variants in the GDS file to be the candidate variants
`variant.id.varratio`	a list of variant IDs, to be used in the variance ratio estimation; or `NULL` to use the candidate variants according to `variant.id`
`nsnp.sub.random`	used in `seqFitSparseGRM()` when `grm.mat=TRUE`: the number of SNP markers randomly selected from the candidate variants for the initial scan of relatedness
`rel.cutoff`	relatedness threshold for treating two individuals as unrelated (check >= rel.cutoff), `NaN` or `-Inf` for no threshold; only applicable when `grm.mat=TRUE`
`use.cateMAC`	`FALSE`, to use a single global variance ratio; `TRUE` (equal to `use.cateMAC=c(1.5, 2.5, 3.5, 4.5, 5.5, 10.5, 20.5)`) for MAC categories (0, 1.5), [1.5, 2.5), ... [10.5, 20.5) and [20.5, Inf); or a numeric vector (strictly increasing) for unique cut points
`cateMAC.inc.maf`	`TRUE` to include a MAC cut point according to MAF=`maf`, or a numeric vector for MAF; only applicable if `use.cateMAC` is not `FALSE`
`cateMAC.simu`	`TRUE` for using simulated independent genotypes under Hardy-Weinberg equilibrium if there are no enough SNP markers in the MAC category; only applicable if `use.cateMAC` is not `FALSE`
`inv.norm`	`"residuals"` (by default), perform the inverse normal transformation on the residuals after fitting the fixed effects; `"quant"`, perform the inverse normal transformation on the outcome variable directly (the same behavior as the SAIGE package); `"none"`, no transformation. If `inv.norm=TRUE`, perform `inv.norm="residuals"`; if `inv.norm=FALSE`, it is as the same as `inv.norm="none"`; See the reference for more discussion [Sofer et al., 2019]
`X.transform`	if `TRUE`, perform QR decomposition on the design matrix
`tol`	overall tolerance for model fitting
`maxiter`	the maximum number of iterations for model fitting
`nrun`	the number of random vectors in the trace estimation
`tolPCG`	tolerance of PCG iterations
`maxiterPCG`	the maximum number of PCG iterations
`num.marker`	the number of SNPs used to calculate the variance ratio
`tau.init`	a 2-length numeric vector, the initial values for variance components, tau; for binary traits, the first element is always be set to 1. if `tau.init` is not specified, the second element will be 0.5 for binary traits
`traceCVcutoff`	the threshold for coefficient of variation (CV) for the trace estimator, and the number of runs for trace estimation will be increased until the CV is below the threshold
`ratioCVcutoff`	the threshold for coefficient of variation (CV) for estimating the variance ratio, and the number of randomly selected markers will be increased until the CV is below the threshold
`geno.sparse`	if `TRUE` (by default), store the sparse structure for genotypes; otherwise, save genotypes in a 2-bit dense matrix; see details
`num.thread`	the number of threads, 1 by default
`model.savefn`	the filename of model output, R data file '.rda', '.RData', or '.rds'
`seed`	an integer passed to `set.seed()` as a seed for random numbers, or `NULL` for no action
`fork.loading`	load genotypes via parallel or not; multiple processes can reduce loading time of genotypes, but may double the memory usage
`verbose`	if `TRUE`, show information

Details

Utilizing the sparse structure of genotypes could significantly improve the computational efficiency of model fitting, but it also increases the memory usage. For more details of SAIGE algorithm, please refer to the SAIGE paper [Zhou et al., 2018] (see the reference section).

Value

Returns a list with the following components:

`coefficients`	the beta coefficients for fixed effects;
`tau`	a numeric vector of variance components 'Sigma_E' and 'Sigma_G';
`linear.predictors`	the linear fit on link scale;
`fitted.values`	fitted values from objects returned by modeling functions using `glm.fit`;
`residuals`	residuals;
`cov`	covariance matrix of beta coefficients;
`converged`	whether the model is fitted or not;
`obj.noK`	internal use, returned object from the SPAtest package;
`var.ratio`	a data.frame with columns 'id' (variant.id), 'maf' (minor allele frequency), 'mac' (minor allele count), 'var1' (the variance of score statistic), 'var2' (a variance estimate without accounting for estimated random effects) and 'ratio' (var1/var2, estimated variance ratio for variance approximation);
`trait.type`	either "binary" or "quantitative";
`sample.id`	the sample IDs used in the model fitting;
`variant.id`	the variant IDs used in the model fitting.

Author(s)

Xiuwen Zheng

References

Examples

# 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")
glmm

# close the GDS file
seqClose(gdsfile)
# 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")
glmm

# close the GDS file
seqClose(gdsfile)

Sparse & dense genetic relationship matrix

Description

Construct sparse and dense genetic relationship matrix (GRM).

Usage

seqFitSparseGRM(gdsfile, sample.id=NULL, variant.id=NULL, nsnp.sub.random=2000L,
    rel.cutoff=0.125, maf=0.01, missing.rate=0.005, num.thread=1L,
    return.ID=FALSE, seed=200L, verbose=TRUE)
seqFitDenseGRM(gdsfile, sample.id=NULL, variant.id=NULL, maf=0.01,
    missing.rate=0.005, num.thread=1L, use.double=TRUE, return.ID=FALSE,
    verbose=TRUE)
seqFitSparseGRM(gdsfile, sample.id=NULL, variant.id=NULL, nsnp.sub.random=2000L,
    rel.cutoff=0.125, maf=0.01, missing.rate=0.005, num.thread=1L,
    return.ID=FALSE, seed=200L, verbose=TRUE)
seqFitDenseGRM(gdsfile, sample.id=NULL, variant.id=NULL, maf=0.01,
    missing.rate=0.005, num.thread=1L, use.double=TRUE, return.ID=FALSE,
    verbose=TRUE)

Arguments

`gdsfile`	a SeqArray GDS file name or a GDS object for genotypes used in the GRM calculation; see details
`sample.id`	NULL for all samples, or sample IDs in GRM
`variant.id`	candidate variant IDs used for constructing GRM; or `NULL` for using all available candidate variants
`nsnp.sub.random`	the number of SNP markers randomly selected from the candidate variants for the initial scan of relatedness; if `nsnp.sub.random=0`, use all candidate SNPs
`rel.cutoff`	relatedness threshold for treating two individuals as unrelated (check >= rel.cutoff); `NaN` or `-Inf` for no threshold
`maf`	minor allele frequency threshold for genotypes in `gdsfile` (checking >= maf), if `variant.id=NULL`; `NaN` for no filter
`missing.rate`	threshold of missing rate (checking <= missing.rate), if `variant.id=NULL`; `NaN` for no filter
`num.thread`	the number of threads, 1 by default
`use.double`	`TRUE` for using 64-bit double precision to calculate GRM; otherwise, to use 32-bit single precision
`return.ID`	if `TRUE`, return the IDs for samples, the full variant set and the subset of variants
`seed`	an integer passed to `set.seed()` as a seed for random numbers, or `NULL` for no action
`verbose`	if `TRUE`, show information

Details

The genetic relationship matrix (GRM) is defined as $grm_ij = avg_l [(g_il - 2*p_l)*(g_jl - 2*p_l) / 2*p_l*(1 - p_l)]$ for individuals i,j and locus l, where $g_il$ is 0, 1 or 2, and $p_l$ is the allele frequency at locus l. The missing genotypes are dropped from the calculation.

Value

If return.ID=TRUE, returns a list with sample.id for sample IDs, variant.id for the full set of variants, variant.sub.id for the subset of variants, and the GRM matrix. Otherwise, it returns a sparse or dense symmetric matrix for GRM, with sample IDs in colnames() and rownames().

Author(s)

Xiuwen Zheng

Examples

library(Matrix)

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

seqSetFilter(gdsfile, variant.sel=1:100)
m <- seqFitSparseGRM(gdsfile, rel.cutoff=0.125)
is(m)
nnzero(m)                 # num of non-zero
nnzero(m) / prod(dim(m))  # percentage of non-zero

m <- seqFitDenseGRM(gdsfile)
str(m)

# close the GDS file
seqClose(gdsfile)
library(Matrix)

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

seqSetFilter(gdsfile, variant.sel=1:100)
m <- seqFitSparseGRM(gdsfile, rel.cutoff=0.125)
is(m)
nnzero(m)                 # num of non-zero
nnzero(m) / prod(dim(m))  # percentage of non-zero

m <- seqFitDenseGRM(gdsfile)
str(m)

# close the GDS file
seqClose(gdsfile)

Load the association results

Description

Load the association results from an RData, RDS or GDS file.

Usage

seqSAIGE_LoadPval(fn, varnm=NULL, index=NULL, verbose=TRUE)
seqSAIGE_LoadPval(fn, varnm=NULL, index=NULL, verbose=TRUE)

Arguments

`fn`	RData, RDS or GDS file names, merging datasets if multiple files
`varnm`	NULL, or a character vector to include the column names; e.g., `c("chr", "position", "rs.id", "ref", "alt", "pval")`
`index`	NULL, or a logical/numeric vector for a set of rows
`verbose`	if `TRUE`, show information

Value

Return a data.frame including p-values.

Author(s)

Xiuwen Zheng

Examples

(fn <- system.file("unitTests", "saige_pval.rds", package="SAIGEgds"))
pval <- seqSAIGE_LoadPval(fn)

names(pval)
#  [1] "id"         "chr"        "pos"        "rs.id"      "ref"
#  [6] "alt"        "AF.alt"     "AC.alt"     "num"        "beta"
# [11] "SE"         "pval"       "pval.noadj" "converged"

head(pval)
(fn <- system.file("unitTests", "saige_pval.rds", package="SAIGEgds"))
pval <- seqSAIGE_LoadPval(fn)

names(pval)
#  [1] "id"         "chr"        "pos"        "rs.id"      "ref"
#  [6] "alt"        "AF.alt"     "AC.alt"     "num"        "beta"
# [11] "SE"         "pval"       "pval.noadj" "converged"

head(pval)

Package 'SAIGEgds'

Help Index

Scalable Implementation of Generalized mixed models in Phenome-Wide Association Studies using GDS files

Description

Details

Author(s)

References

Examples

Heritability estimation

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Cauchy Combination Test

Description

Usage

Arguments

Value

References

See Also

Examples

ACAT-V tests

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

ACAT-V tests

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Burden tests

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

SKAT tests

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

P-value calculation

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Linkage disequilibrium pruning

Description

Usage

Arguments

Details

Value