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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: Reducing dimensionality for prediction of genome-wide breeding values | Genetics Selection Evolution BioMed Central Research Reducing dimensionality for prediction of genome-wide breeding values Trygve R Solberg 1 Anna K Sonesson2 John A Woolliams1 3 and Theo HE Meuwissen1 Open Access Address Norwegian University of Life Sciences Department of Animal and Aquacultural Sciences PO Box 5003 N-1432 Ảs Norway 2NOFIMA Marin PO Box 5010 N-1432 Ảs Norway and 3Roslin Institute Edinburgh Roslin Midlothian EH25 9PS UK Email Trygve R Solberg - trygve.roger.solberg@umb.no Anna K Sonesson - anna.sonesson@nofima.no John A Woolliams - john.woolliams@bbsrc.ac.uk Theo HE Meuwissen - theo.meuwissen@umb.no Corresponding author Published 18 March 2009 Received 3 February 2009 Genetics Selection Evolution 2009 41 29 doi l0.ll86 l297-9686-4l -29 Accepted 18 March 2009 This article is available from http www.gsejournal.Org content 4l l 29 2009 Solberg 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 Partial least square regression PLSR and principal component regression PCR are methods designed for situations where the number of predictors is larger than the number of records. The aim was to compare the accuracy of genome-wide breeding values EBV produced using PLSR and PCR with a Bayesian method BayesB . Marker densities of l 2 4 and 8 Ne markers Morgan were evaluated when the effective population size Ne was 100. The correlation between true breeding value and estimated breeding value increased with density from 0.611 to 0.681 and 0.604 to 0.658 using PLSR and PCR respectively with an overall advantage to PLSR of 0.016 s.e 0.008 . Both methods gave a lower accuracy compared to the BayesB for which accuracy increased from 0.690 to 0.860. PLSR and PCR appeared less responsive to increased marker density
