Overview

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R/qtlCurve

QTL mapping for function-valued traits using estimating equations.

Citation

Xiong H, Goulding EH, Carlson EJ, Tecott LH, McCulloch CE, Sen S (2011) "A flexible estimating equations approach for mapping function-valued traits" Genetics 189:305-316.

Installation

Stable branch

Install the most current stable version using the following commands which will install the stable branch; this should run on all operating systems.


install.packages("devtools")
library(devtools)
install_bitbucket("linen/qtlcurve",ref="stable")

Development branch

The default branch contains code under development. You likely don't want this branch, but if you do, use:

install_bitbucket("linen/qtlcurve",ref="default")

C++ branch

The withcpp branch of the package contains C++ code that makes the functions a lot faster. Currently, that feature is in development, and compiles reliably on Linux only. To get that branch use:

install_bitbucket("linen/qtlcurve",ref="withcpp")

Usage

Read data

Read in the example data from Xiong et. al. (2011).

 # get name of genotype/phenotype and phenotype covariate files
 # from system location
 genofile <- system.file("extdata","xiong11.csv",package="qtlCurve")
 pcovfile <- system.file("extdata","xiong11phenocov.csv",
                         package="qtlCurve")
 # read in data and assign as cross object
 cr <- readCross(genoPhenoFile=genofile,phenoCovFile=pcovfile)

Genome scan

First we have to calculate the genotype probabilities for Haley-Knott regression.

 # calculate genotype probabilities for haley-knott regression
 cr <- calc.genoprob(cr,step=1)
 # calculate one-dimensional genome scan
 # use b-spline basis matrix of dimension 6
 # select out the activity data columns
 out1 <- scanOne(cr,pheno.col=3:224,
                basisMatrix=basisMatrix("bs",x=1:222,dim=6))
 # plot genome scan                 
 plot(out1)