Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

ABSTRACT: Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the

SUBMITTER: Runcie DE 

PROVIDER: S-EPMC3697978 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature