noia (version 0.94.1)

Simulate population: 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. drawGenotype generates a single-individual genotype, and is a sub-routine of simulatePop.

Usage

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

nloc

Number of loci.

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"

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.

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 & 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.}

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

Run this code

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))

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