PhenotypeSimulator (version 0.3.4)

geneticBgEffects: Simulate infinitesimal genetic effects (reflecting sample kinship).

Description

geneticBgEffects simulates an infinitesimal genetic effects with a proportion of the effect shared across samples and a proportion independent across samples; they are based on the kinship estimates of the (simulated) samples.

Usage

geneticBgEffects(
  P,
  N,
  kinship,
  phenoID = "Trait_",
  id_samples = colnames(kinship),
  shared = TRUE,
  independent = TRUE,
  id_phenos = NULL
)

Arguments

P

Number [integer] of phenotypes to simulate .

N

Number [integer] of samples to simulate; has to be provided as a dimnesionality check for kinship and downstream analyses; nrow(kinship) has to be equal to N.

kinship

[N x N] Matrix of kinship estimates [double].

phenoID

Prefix [string] for naming traits.

id_samples

Vector of [NrSamples] sample IDs [string]; if not provided colnames(kinship) are used.

shared

[bool] shared effect simulated if set to TRUE; at least one of shared or independent has to be set to TRUE.

independent

[bool] independent effect simulated if set to TRUE.

id_phenos

Vector of [NrTraits] phenotype IDs [string]; if not provided constructed by paste(phenoID, 1:P, sep="").

Value

Named list of shared infinitesimal genetic effects (shared: [N x P] matrix) and independent infinitesimal genetic effects (independent: [N x P] matrix), the covariance term of the shared effect (cov_shared: [P x P] matrix), the covariance term of the independent effect (cov_independent: [P x P] matrix), the eigenvectors (eigenvec_kinship: [N x N]) and eigenvalues (eigenval_kinship: [N]) of the kinship matrix.

Details

For the simulation of the infinitesimal genetic effects, three matrix components are used: i) the kinship matrix K [N x N] which is treated as the sample design matrix, ii) matrix B [N x P] with vec(B) drawn from a normal distribution and iii) the trait design matrix A [P x P]. For the independent effect, A is a diagonal matrix with normally distributed values. A for the shared effect is a matrix of rowrank one, with normally distributed entries in row 1 and zeros elsewhere. To construct the final effects, the three matrices are multiplied as: E = cholesky(K)BA^T.

Examples

# NOT RUN {
genotypes <- simulateGenotypes(N=100, NrSNP=400, verbose=FALSE)
kinship <- getKinship(N=100, X=genotypes$genotypes, standardise=TRUE, 
verbose=FALSE)
geneticBg <- geneticBgEffects(N=100, P=10, kinship=kinship)
# }