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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: Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens | Genetics Selection Evolution BioMed Central Research Genome-assisted prediction of a quantitative trait measured in parents and progeny application to food conversion rate in chickens Oscar Gonzalez-Recio 1 Daniel Gianola1 2 Guilherme JM Rosa1 Kent A Weigel1 and Andreas Kranis3 Address Department of Dairy Science University of Wisconsin Madison WI 53706 USA 2Department of Animal Sciences University of Wisconsin Madison WI 53706 USA and 3Aviagen Ltd. Newbridge Scotland UK Email Oscar Gonzalez-Recio - gonzalez.oscar@inia.es Daniel Gianola - gianola@ansci.wisc.edu Guilherme JM Rosa - grosa@wisc.edu Kent A Weigel - kweigel@facstaff.wisc.edu Andreas Kranis - akranis@aviagen.com Corresponding author Open Access Published 5 January 2009 Received 16 December 2008 Genetics Selection Evolution 2009 41 3 doi l0.ll86 l297-9686-4l-3 Accepted 5 January 2009 This article is available from http www.gsejournal.Org content 4l l 3 2009 González-Recio 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 Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate FCR in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model Bayes A and a semiparametric approach Reproducing kernel Hilbert Spaces regression RKHS using all available SNPs p 348l were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs p 400 using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set and
