Two articles written by UW-Madison faculty and researchers that tackle the genomic prediction of traits were selected to be Issue Highlights in the July 2013 issue of Genetics. Valente et al. (and see Commentary by Rousset) assess whether use of structural equation models, which can convey causal relationships among traits, improves predictions. Gianola explores the role of prior assumptions about the distribution of marker effects in Bayesian whole-genome regression models, finding that claims made about genetic architecture using these methods must be taken with caution.
Is structural squation modeling advantageous for the genetic improvement of multiple traits?, pp. 561–572
Bruno D. Valente, Guilherme J. Rosa, Daniel Gianola, Xiao-Lin Wu, and Kent A. Weigel
Priors in whole-genome regression: the Bayesian alphabet returns, pp. 573–596