GenABEL (version 1.8-0)

grammar: GRAMMAR test for association in samples with genetic structure

Description

Fast approximate test for association between a trait and genetic polymorphisms, in samples with genetic sub-structure (e.g. relatives). The function implements several varieties of GRAMMAR ('gamma','gc', and 'raw').

Usage

grammar(polyObject, data, method = c("gamma", "gc", "raw"), propPs = 1, ...)

Arguments

polyObject

object returned by polygenic function

data

object of gwaa.data-class

method

to be used, one of 'gamma','gc', or 'raw'

propPs

proportion of non-corrected P-values used to estimate the inflation factor Lambda, passed directly to the estlambda

...

arguments passed to the function used for computations, (qtscore)

Value

Object of scan.gwaa-class

Details

With 'raw' argument, the original GRAMMAR (Aulchenko et al., 2007) is implemented. This method is conservative and generates biased estimates of regression coefficients.

With 'gc' argument, the GRAMMAR-GC (Amin et al., 2007) is implemented. This method solves the conservativity of the test, but the Genomic Control (GC) lambda is by definition "1" and can not serve as an indicator of goodness of the model; also, the estimates of regression coefficients are biased (the same as in 'raw' GRAMMAR).

GRAMMAR-Gamma (default 'gamma' argument) solves these problems, producing a correct distribution of the test statistic, interpretable value of GC Lambda, and unbiased estimates of the regression coefficients. All together, the default 'gamma' method is recommended for use.

References

GRAMMAR-Raw: Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics. 2007 Sep;177(1):577-85.

GRAMMAR-GC: Amin N, van Duijn CM, Aulchenko YS. A genomic background based method for association analysis in related individuals. PLoS One. 2007 Dec 5;2(12):e1274.

GRAMMAR-Gamma: Svischeva G, Axenovich TI, Belonogova NM, van Duijn CM, Aulchenko YS. Rapid variance components-based method for whole-genome association analysis. Nature Genetics. 2012 44:1166-1170. doi:10.1038/ng.2410

See Also

polygenic, mmscore, qtscore

Examples

# Using clean ge03d2 data
require(GenABEL.data)
data(ge03d2.clean)
# take only a small piece for speed
ge03d2.clean <- ge03d2.clean[1:200,]
# estimate genomic kinship
gkin <- ibs(ge03d2.clean[,sample(autosomal(ge03d2.clean),1000)], w="freq")
# perform polygenic analysis
h2ht <- polygenic(height ~ sex + age, kin=gkin, ge03d2.clean)
h2ht$est
# compute mmscore stats
mm <- mmscore(h2ht, data=ge03d2.clean)
# compute grammar-gc
grGc <- grammar(h2ht, data=ge03d2.clean, method="gc")
# compute grammar-gamma
grGamma <- grammar(h2ht, data=ge03d2.clean, method="gamma")
# compare lambdas
lambda(mm)
estlambda(mm[,"chi2.1df"])
lambda(grGamma)
estlambda(grGamma[,"chi2.1df"])
lambda(grGc)
estlambda(grGc[,"chi2.1df"])
# compare top results
summary(mm)
summary(grGamma)
summary(grGc)