BGData: GWAS – R documentation

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GWAS

Performs Single Marker Regressions Using BGData Objects

Description

Implements single marker regressions. The regression model includes all the covariates specified in the right-hand-side of the formula plus one column of the genotypes at a time. The data from the association tests is obtained from a BGData object.

Usage

GWAS(formula, data, method = "lsfit", i = seq_len(nrow(geno(data))),
  j = seq_len(ncol(geno(data))), chunkSize = 5000L,
  nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)

Arguments

`formula`	The formula for the GWAS model without including the marker, e.g. `y ~ 1` or `y ~ factor(sex) + age`. The variables included in the formula must be in the sample information of the `BGData`.
`data`	A `BGData` object.
`method`	The regression method to be used. Currently, the following methods are implemented: `rayOLS`, `lsfit`, `lm`, `lm.fit`, `glm`, `lmer`, and `SKAT`. Defaults to `lsfit`.
`i`	Indicates which rows of the genotypes should be used. Can be integer, boolean, or character. By default, all rows are used.
`j`	Indicates which columns of the genotypes should be used. Can be integer, boolean, or character. By default, all columns are used.
`chunkSize`	The number of columns of the genotypes that are brought into physical memory for processing per core. If `NULL`, all elements in `j` are used. Defaults to 5000.
`nCores`	The number of cores (passed to `mclapply`). Defaults to the number of cores as detected by `detectCores`.
`verbose`	Whether progress updates will be posted. Defaults to `FALSE`.
`...`	Additional arguments for chunkedApply and regression method.

Value

The same matrix that would be returned by coef(summary(model)).

Examples

# Restrict number of cores to 1 on Windows
if (.Platform$OS.type == "windows") {
    options(mc.cores = 1)
}

# Load example data
bg <- BGData:::loadExample()

# Perform a single marker regression
res1 <- GWAS(formula = FT10 ~ 1, data = bg)

# Draw a Manhattan plot
plot(-log10(res1[, 4]))

# Use lm instead of lsfit (the default)
res2 <- GWAS(formula = FT10 ~ 1, data = bg, method = "lm")

# Use glm instead of lsfit (the default)
y <- pheno(bg)$FT10
pheno(bg)$FT10.01 <- y > quantile(y, 0.8, na.rm = TRUE)
res3 <- GWAS(formula = FT10.01 ~ 1, data = bg, method = "glm")

# Perform a single marker regression on the first 50 markers (useful for
# distributed computing)
res4 <- GWAS(formula = FT10 ~ 1, data = bg, j = 1:50)

BGData

A Suite of Packages for Analysis of Big Genomic Data

v2.2.0

MIT + file LICENSE

Authors

Gustavo de los Campos [aut], Alexander Grueneberg [aut, cre], Paulino Perez [ctb], Ana Vazquez [ctb]

Initial release