TAILIEUCHUNG - Báo cáo sinh học: "Using the Pareto principle in genome-wide breeding value estimation"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Using the Pareto principle in genome-wide breeding value estimation | Genetics Selection Evolution BioMed Central Vz The Open Access Publisher This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Using the Pareto principle in genome-wide breeding value estimation Genetics Selection Evolution 2011 43 35 doi 1297-9686-43-35 Xijiang Yu Theo HE Meuwissen ISSN 1297-9686 Article type Research Submission date 23 September 2010 Acceptance date 1 November 2011 Publication date 1 November 2011 Article URL http content 43 1 35 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . Articles in Genetics Selection Evolution are listed in PubMed and archived at PubMed Central. For information about publishing your research in Genetics Selection Evolution or any BioMed Central journal go to http authors instructions For information about other BioMed Central publications go to http 2011 Yu and Meuwissen licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Using the Pareto principle in genome-wide breeding value estimation Xijiang Yu Theo HE Meuwissen Department of Animal and Aquacultural Sciences Norwegian University of Life Sciences 1432 Ảs Norway Email XY TM Corresponding author Abstract Genome-wide breeding value GWEBV estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance and are computationally fast. In

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