Variance decomposition {noia} | R Documentation |
Variance decomposition in a classical operation in quantitative genetics (e.g. Fisher 1918, Lynch and Walsh 1998). The genetic variance, i.e. the part of phenotypic variance that can be identify as due to genetic factors, can be decomposed into several orthogonal components (generally, the part due to additive factors Var(A), to dominance factors Var(D), and to genetic interactions Var(I)).
varianceDecomposition(obj)
obj |
An object of class "noia.linear" , the output of
linearRegression . |
The details of the varianceDecomposition are provided for all levels of interaction: Var(A) and Var(D) for marginal effects, Var(AA), Var(AD) and Var(DD) for 2nd order interactions, etc.
The function only displays the variances in a nice way and does not return any object.
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.
Fisher RA. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Thans. Roy. Soc. Edinburgh 52:339-433.
Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics, in press.
Lynch M, Walsh B (1998) Genetics and Analysis of Quantitative Traits. Sunderland, MA; Sinauer Associates.
map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25) pop <- simulatePop(map, N=500, sigmaE=0.2, type="F2") # Regression linear <- linearRegression(phen=pop$phen, gen=cbind(pop$Loc1, pop$Loc2)) # Variance decomposition varianceDecomposition(linear)