RFGLS-package: Rapid Feasible Generalized Least Squares

Description

RFGLS uses a generalized least-squares method to perform single-marker association analysis, in datasets of nuclear families containing parents, twins, and/or adoptees. It is designed for families of no greater than four members. 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 (FGLS) that considerably reduces computation time with minimal bias in p-values, and with negligible loss in power. The package includes four 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 FGLS regression. It can be used to simultaneously estimate fixed-effects regression coefficients and the residual covariance matrix. Function gls.batch.get() is useful to restructure data, for use with fgls(). Function FSV.frompedi() creates family-structure variables based upon available information in a pedigree file. Functions gls.batch() and gls.batch.get() use these family-structure variables, which represent the type of family to which each participant belongs and how s/he fits into that family.

Arguments

Details

Package:

RFGLS

Version:

1.1

Date:

2013/8/29

Depends:

R (>= 2.15.0), stats, bdsmatrix, Matrix

License:

GPL (>= 2)

References

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. Human Heredity 2011;71:67-82 (DOI: 10.1159/000324839)