Literature DB >> 22717310

Single-step methods for genomic evaluation in pigs.

O F Christensen1, P Madsen, B Nielsen, T Ostersen, G Su.   

Abstract

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

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Year:  2012        PMID: 22717310     DOI: 10.1017/S1751731112000742

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  94 in total

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Journal:  Heredity (Edinb)       Date:  2016-12-28       Impact factor: 3.821

3.  Genomic selection in American mink (Neovison vison) using a SSGBLUP model for size and quality traits graded on live mink.

Authors:  Trine M Villumsen; Guosheng Su; Bernt Guldbrandtsen; Torben Asp; Mogens S Lund
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4.  The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction.

Authors:  Jeremy T Howard; Tom A Rathje; Caitlyn E Bruns; Danielle F Wilson-Wells; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2018-11-21       Impact factor: 3.159

5.  Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending.

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6.  Factors affecting GEBV accuracy with single-step Bayesian models.

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Journal:  Heredity (Edinb)       Date:  2017-11-23       Impact factor: 3.821

7.  Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework.

Authors:  Johnna L Baller; Stephen D Kachman; Larry A Kuehn; Matthew L Spangler
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

8.  Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information.

Authors:  Bjarke G Poulsen; Birgitte Ask; Hanne M Nielsen; Tage Ostersen; Ole F Christensen
Journal:  Genet Sel Evol       Date:  2020-10-07       Impact factor: 4.297

9.  Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes.

Authors:  Albert Wilhelm Schulthess; Yu Wang; Thomas Miedaner; Peer Wilde; Jochen C Reif; Yusheng Zhao
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10.  Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle.

Authors:  Egill Gautason; Goutam Sahana; Guosheng Su; Baldur Helgi Benjamínsson; Guðmundur Jóhannesson; Bernt Guldbrandtsen
Journal:  J Anim Sci       Date:  2021-07-01       Impact factor: 3.159

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