RFGLS-package: Rapid Feasible Generalized Least Squares
Description
RFGLS is a family GWAS data-analysis tool that
uses a generalized least squares method to perform single-marker association analysis. When conducting association analysis with a large number of markers, as in GWAS, RFGLS uses rapid feasible generalized least-squares, an approximation to feasible generalized least-squares that considerably reduces computation time with minimal bias in p-values, and with negligible loss in power.
The package includes three functions. Function gls.batch() actually conducts GWAS using the rapid feasible generalized least-squares approximation, under which the residual variance-covariance matrix is estimated once from a regression of the phenotype onto covariates only, and is subsequently "plugged in" for use in all subsequent single-SNP analyses. Function fgls() is called by gls.batch(), and conducts a single feasible generalized least-squares regression. It can be used to simultaneously estimate fixed-effects regression coefficients and the residual variance-covariance matrix. Function gls.batch.get() is useful to restructure data, for use with fgls().
Reference: Li X, Basu S, Miller MB, Iacono WG, McGue M:
A Rapid Generalized Least Squares Model for a Genome-Wide Quantitative Trait Association Analysis in Families.
Hum Hered 2011;71:67-82 (DOI: 10.1159/000324839)Details
ll{
Package: RFGLS
Version: 1.0
Date: 2012/11/29
Depends: R (>= 2.15.0), stats, bdsmatrix, Matrix
License: GPL (>= 2)
}