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Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Deregressing estimated breeding values and weighting information for genomic regression analyses | Genetics Selection Evolution BioMed Zentral Research Open Access Deregressing estimated breeding values and weighting information for genomic regression analyses Dorian J Garrick 1 2 Jeremy F Taylor3 and Rohan L Fernando1 Addresses Department of Animal Science Iowa State University Ames IA 50011 USA institute of Veterinary Animal Biomedical Sciences Massey University Palmerston North New Zealand and 3Division of Animal Sciences University of Missouri Columbia 65201 USA E-mail Dorian J Garrick - dorian@iastate.edu Jeremy F Taylor - taylorjerr@missouri.edu Rohan L Fernando - rohan@iastate.edu Corresponding author Published 31 December 2009 Received 2 July 2009 Genetics Selection Evolution 2009 41 55 doi 10.1186 1297-9686-41-55 Accepted 3 I December 2009 This article is available from http www.gsejournal.Org content 41 1 55 2009 Garrick et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes repeated observations records on close family members such as progeny estimated breeding values EBV or their deregressed counterparts from genetic evaluations. The literature indicates that researchers are inconsistent in their approach to using EBV or deregressed data and as to using the appropriate methods for weighting some data sources to account for heterogeneous variance. Methods A logical approach to using information for genomic prediction is introduced which demonstrates the appropriate weights for analyzing observations with
