Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Genomic breeding value estimation using nonparametric additive regression models | Genetics Selection Evolution BioMed Central Research Genomic breeding value estimation using nonparametric additive regression models Jorn Bennewitz 1 2 Trygve Solberg1 and Theo Meuwissen1 Address Department of Animal and Aquacultural Sciences Norwegian University of Life Sciences Box 1432 Ảs Norway and institute of Animal Breeding and Husbandry Christian-Albrechts-University of Kiel 24098 Kiel Germany Email Jorn Bennewitz - j.bennewitz@uni-hohenheim.de Trygve Solberg - trygve.roger.solberg@umb.no Theo Meuwissen - theo.meuwissen@umb.no Corresponding author Open Access Published 27 January 2009 Received 17 December 2008 Genetics Selection Evolution 2009 41 20 doi l0.ll86 l297-9686-4l -20 Accepted 27 January 2009 This article is available from http www.gsejournal.Org content 4l l 20 2009 Bennewitz 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 Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance .
