Bayesian Sparse Factor Analysis of Genetic Covariance Matrices
Bayesian Sparse Factor Analysis of Genetic Covariance Matrices (BSFG) is a genetic sparse factor model that inferences the matrix of genetic covariances among traits. The code implementing the model uses a Gibbs sampler to draw samples from the posterior distribution of a multivariate linear mixed effect model, where the random effects are generally unobserved genetic values (breeding values) with known covariance (ex. based on a pedigree). The focus of the model is on estimating the matrix of genetic (and residual) covariances among traits, called the G-matrix.
Instructions: Instructions describing BSFG.
Software: Implementations of the method plus some explanation.