Package: BayesQTLBIC
Version: 1.0-0
Date: 2008-10-15
Title: Bayesian multi-locus QTL analysis based on the BIC criterion
Author: Rod Ball <rod.ball@ATscionresearch.com>
Maintainer: Rod Ball <rod.ball@ATscionresearch.com>
Description: R package for a non-MCMC approximate multilocus Bayesian
model selection approach to the analysis of quantitative trait
loci (QTL).  The method and models are described in (Ball, R.
D. Genetics 159: 1351--1364, 2001;
http://www.genetics.org/cgi/content/abstract/159/3/1351).  Data
is assumed to be from a QTL mapping family with DNA markers
genotyped along the genome. The QTL mapping problem is
represented as a model selection problem, where each model is a
linear regression of the trait on a selected set of marker
values. The main function bicreg.qtl() is based on the S
function bicreg()--- posterior probabilities for models are
approximated from the BIC criterion, calculated for each model
in a search of model space using leaps or regsubsets.
Additionally, we allow for prior probabilities based on
expected numbers of QTL per genome and options to control the
size of models considered, and to allow for selectivly
genotyping from the tails of the phenotypic distribution.
Missing values are estimated by multiple imputation, and
estimates of marker effects can be obtained conditional on
selection or unconditional and free of selection bias.  The
method relies on 3 approximations: (1.) QTL configuration is
represented approximately by configurations with QTL located at
marker positions; (2.) Posterior probabilities are given
approximately in terms of the BIC criterion; and (3.) The
distribution of missing marker values is approximated by
multiple imputation, sampling from the distribution of missing
values conditional on non-missing values. We have found these
are good approximations provided (1.) the marker spacing is
reasonable (less than 30cM); (2.) the sample size is 100 or
more for fully genotyped populations; and (3.) around 10
imputations are used and the effect of any given QTL on the
trait is not large. Due to limits on the number of markers that
can be considered simultaneously the method is generally
applied separately to each chromosome or could be iteratively
applied to sets of chromosomes using fixed sets of predictors
from other chromsomes when analysing a given chromosome.
Depends: leaps
License: GPL (>= 2)
URL: mailto:rod.ball@scionresearch.com www.scionresearch.com/
Packaged: Fri Oct 30 13:38:09 2009; rod
Repository: CRAN
Date/Publication: 2009-10-30 07:56:55