noia (version 0.94.1)

GP map analysis: Noia analysis of genotype-to-phenotype (GP) maps in ideal populations

Description

Functions for doing a NOIA analysis of a GP map for $L$ loci in a population where the loci are in complete linkage equilibrium.

Usage

linearGPmapanalysis(gmap, reference="F2", freqmat=NULL, 
                    max.level=NULL , S_full=NULL)
preparelinearGPmap(gmap, reference="F2", freqmat=NULL, S_full=TRUE)

Arguments

gmap

Vector of length $3^L$ with genotypic values for all possible genotypes in the order defined by genNames.

reference

The reference population in which the analysis is done. By default, the "F2" population is used. Other possibilities are "noia", "G2A", "UWR".

freqmat

For reference="G2A": A vector of length $L$ containing allele frequencies such that freqmat[i]=frequency(allele 1) for locus i. For reference="noia"

max.level

Maximum level of interactions.

S_full

Boolean argument indicating whether to keep full S matrix $(3^L\times3^L)$ in memory or alternatively to keep $L$ single locus S matrices $(3\times3)$ and compute single row and columns of the full matrix.

Value

• linearGPmapanalysis returns an object of class "noia.linear.gpmap" , with its own print method: print.noia.linear.gpmap.

Details

The algebraic framework is described extensively in Alvarez-Castro & Carlborg 2007. When analysing GP maps in ideal populations we can work directly with the S matrix and do not have to consider the X and Z matrices used in linearRegression. When it comes to the S_full argument keeping the multilocus S matrix in memory is generally fastest for computing all $3^L$ genetic effects. However it does not allow for computing only a subset of the effects and also runs out of memory for $L>8$ on a typical desktop machine. For S_full=NULL in linearGPmapanalysis a full S matrix is used if $L<=8$ and="" max.level="NULL," while="" $l$="" single="" locus="" S matrices are used otherwise. prepareGPmapanalysis performs all preliminary calculation on the dataset, but does not solve the linear system.

References

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. Cheverud JM, Routman, EJ. (1995). Epistasis and its contribution to genetic variance components. Genetics 139:1455-1461. Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics 4. Zeng ZB, Wang T, Zou W. (2005). Modelling quantitative trait loci and interpretation of models. Genetics 169: 1711-1725.

See Also

varianceDecomposition

Examples

map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25)

# Genotype-to-phenotype map analysis
linearGP <- linearGPmapanalysis(map, reference="F2")

# Linear effects in ideal F2 population
linearGP