tailieunhanh - Báo cáo sinh học: "Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model | Wolc et al. Genetics Selection Evolution 2011 43 5 http content 43 1 5 GSE Ge n et i cs Selection Evolution RESEARCH Open Access Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model Atim I Idlr 1 2 Hric Qfrir kd 3 loci IC Amonci4 DotaL Qoi f 2r4 l nof p Pl iliT ir i4 Moil D P VQ illix Tỉn4 D iHrMP Droicinnor5 Anna wolc Chiis Stiickei Jesus Aiango Petek Settai Janet E Fulton Neil P oSullivan Rudoli Pieisingei 2 2 2 2 2 David Habier Rohan Fernando Dorian J Garrick Susan J Lamont Jack CM Dekkers Abstract Background Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection accuracies of estimated breeding values EBV obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. Methods The following traits were analyzed egg production egg weight egg color shell strength age at sexual maturity body weight albumen height and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2 708 birds were genotyped for 23 356 segregating SNP including 1 563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis in total 13 049 production records . The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training these methods were compared by correlating EBV with phenotypes corrected for fixed effects selecting the top 30 individuals based on EBV and evaluating their mean phenotype and by regressing phenotypes on EBV. Results Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection .

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