NOIA package {noia} | R Documentation |
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of geneticEffects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations, estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is extensively described in Le Rouzic & Alvarez-Castro (2008).
Package: | noia |
Type: | Package |
Version: | 0.92 |
Date: | 2008-07-03 |
License: | GPL-2 |
Data set: The user must provide (i) The vector of phenotypes
of all individuals measured in
the population, and (ii) The matrix of the genotypes. There are two input
formats for the genotype, see linearRegression
.
Regression functions: linearRegression
and
multilinearRegression
.
Change of reference: geneticEffects
.
Genotype-to-phenotype map: GPmap
.
Decomposition of genetic variance: varianceDecomposition
.
Arnaud Le Rouzic
Maintainer: Arnaud Le Rouzic <a.p.s.lerouzic@bio.uio.no>
Alvarez-Castro JM, Carlborg O. (2007). A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 176(2):1151-1167.
Alvarez-Castro JM, Le Rouzic A, Carlborg O. (2008). How to perform meaningful estimates of genetic effects. PLoS Genetics 4(5):e1000062.
Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics 4.
set.seed(123456789) map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25) names(map) <- genNames(2) pop <- simulatePop(map, N=500, sigmaE=0.2, type="F2") # Regressions linear <- linearRegression(phen=pop$phen, gen=pop[2:3]) multilinear <- multilinearRegression(phen=pop$phen, gen=cbind(pop$Loc1, pop$Loc2)) # Linear effects, associated variances and stderr linear # Multilinear effects multilinear # Change of reference: geneticEffects in the "11" genotype (parental 1) geneticEffects(linear, ref.genotype="P1") # Variance decomposition varianceDecomposition(linear) # GP maps maps <- cbind(map, GPmap(linear)[,1], GPmap(multilinear)[,1]) colnames(maps) <- c("Actual", "Linear", "Multilinear") maps