Simulate population {noia}

Simulates a Population from a Genotype-Phenotype Map

Description

The simulatePop function takes a Genotype-to-Phenotype map (i.e. a vector defining the genotypic value of all possible genotypes) and returns a data frame containing the simulated population.

Usage

simulatePop(gmap, N = 100, sigmaE = 1, type = "F2", freqmat=NULL)

Arguments

gmap

The Genotype-to-phenotype map: a vector of size 3^L, where L is the number of loci. The vector should be named with the code of each genotype (see genNames.

N

Number of individuals.

sigmaE

Standard deviation of the environmental noise (normally distributed).

type

Type of population. "F2", "Finf", "F1", "UWR", "G2A", and "noia" are possible.

freqmat

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

For type="noia": A (nloc x 3) matrix of genotype frequencies such that freqmat[i,]=[frequency(1) frequency(2) frequency(3)] for locus i.

Details

The type of population refers to the expected allelic and genotypic frequences:

• "F1"First generation of an intercross between two parental populations fixed for alleles A and B respectively; expected genotypic frequencies are: AA: 0, AB: 1, BB: 0.

• "F2"Second generation of an intercross between two parental populations fixed for alleles A and B respectively; expected genotypic frequencies are AA: 0.25, AB: 0.5, BB: 0.25.

• "Finf"Theoretical population from an infinite number of generations after an intercross between two parental populations fixed for alleles A and B respectively; expected genotypic frequencies are AA: 0.5, AB: 0, BB: 0.5.

• "UWR"Theoretical population corresponding to ideal (but experimentally unrealistic) equal genotypic frequencies; expected genotypic frequencies are AA: 0.333, AB: 0.333, BB: 0.333. In such a population, the "UnWeighted Regression model" (UWR) by Cheverud and Routman 1995 provides orthogonal estimates.

• "G2A"Population at Hardy-Weinberg frequencies; expected genotypic frequencies are: AA: p*p, AB: 2p(1-p), BB: (1-p)(1-p), the frequency of allele A (p) at locus i being provided by the i-th element of vector freqmat. "G2A" is the name of the statistical model by Zeng et al. (2005) in which genetic effects estimated from such a population are orthogonal.

• "noia"Population in which genotypic frequencies are arbitrary; expected genotypic frequencies are: AA: pAA, AB: pAB, BB: pBB, frequences pAA, pAB, and pBB at locus i being provided by the i-th line of matrix freqmat. "noia" is the name of the statistical model by Alvarez-Castro and Carlborg (2007) in which genetic effects estimated from such a population are orthogonal. In all populations, loci are considered as independent and are at linkage equilibrium.

Value

Returns a data frame, in which the first column ($phen) contains the phenotypes, and the following ones ($Loc1, $loc2, etc) the genotypes of all individuals.

Author(s)

Arnaud Le Rouzic, Arne B. Gjuvsland

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

GPmap, genNames

Examples

set.seed(123456789)

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")
str(pop)

## Create a "noia" population with genotype frequencies 1/3,1/3,1/3 for locus 1 
## and 0.2,0.6,0.2 for locus 2
pop = simulatePop(map, N=1000, sigma=1, type='noia', 
  freqmat=matrix(c(1/3,1/3,1/3,0.2,0.6,0.2),nrow=2, byrow=TRUE))