Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation | Genet. Sel. Evol. 39 2007 481-494 INRA EDP Sciences 2007 DOI gse 20070016 Available online at Original article Factor analysis models for structuring covariance matrices of additive genetic effects a Bayesian implementation Gustavo de los CAMPOSa Daniel GlANOLAa b c a Department of Animal Sciences University of Wisconsin-Madison WI53706 USA b Department of Dairy Science and Department of Biostatistics and Medical Informatics University of Wisconsin-Madison WI 53706 USA c Department of Animal and Aquacultural Sciences Norwegian University of Life Sciences 1432 As Norway Received 5 January 2006 accepted 28 March 2007 Abstract - Multivariate linear models are increasingly important in quantitative genetics. In high dimensional specifications factor analysis FA may provide an avenue for structuring co variance matrices thus reducing the number of parameters needed for describing co dispersion. We describe how FA can be used to model genetic effects in the context of a multivariate linear mixed model. An orthogonal common factor structure is used to model genetic effects under Gaussian assumption so that the marginal likelihood is multivariate normal with a structured genetic co variance matrix. Under standard prior assumptions all fully conditional distributions have closed form and samples from the joint posterior distribution can be obtained via Gibbs sampling. The model and the algorithm developed for its Bayesian implementation were used to describe five repeated records of milk yield in dairy cattle and a one common FA model was compared with a standard multiple trait model. The Bayesian Information Criterion favored the FA model. factor analysis mixed model co variance structures 1. INTRODUCTION Multivariate mixed models are used in quantitative genetics to describe for example several traits measured on an individual 6-8 or a longitudinal series of measurements of a trait . 23 or observations on the same trait in different .
