R/variancecomponentFunctions.R
geneticBgEffects.RdgeneticBgEffects 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.
geneticBgEffects(P, N, kinship, phenoID = "Trait_", id_samples = colnames(kinship), shared = TRUE, independent = TRUE, id_phenos = NULL)
| 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=""). |
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.
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.
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)