Literature DB >> 23312993

Contribution of domestic production records, Interbull estimated breeding values, and single nucleotide polymorphism genetic markers to the single-step genomic evaluation of milk production.

J Přibyl1, P Madsen, J Bauer, J Přibylová, M Simečková, L Vostrý, L Zavadilová.   

Abstract

Estimated breeding values (EBV) for first-lactation milk production of Holstein cattle in the Czech Republic were calculated using a conventional animal model and by single-step prediction of the genomic enhanced breeding value. Two overlapping data sets of milk production data were evaluated: (1) calving years 1991 to 2006, with 861,429 lactations and 1,918,901 animals in the pedigree and (2) calving years 1991 to 2010, with 1,097,319 lactations and 1,906,576 animals in the pedigree. Global Interbull (Uppsala, Sweden) deregressed proofs of 114,189 bulls were used in the analyses. Reliabilities of Interbull values were equivalent to an average of 8.53 effective records, which were used in a weighted analysis. A total of 1,341 bulls were genotyped using the Illumina BovineSNP50 BeadChip V2 (Illumina Inc., San Diego, CA). Among the genotyped bulls were 332 young bulls with no daughters in the first data set but more than 50 daughters (88.41, on average) with performance records in the second data set. For young bulls, correlations of EBV and genomic enhanced breeding value before and after progeny testing, corresponding average expected reliabilities, and effective daughter contributions (EDC) were calculated. The reliability of prediction pedigree EBV of young bulls was 0.41, corresponding to EDC=10.6. Including Interbull deregressed proofs improved the reliability of prediction by EDC=13.4 and including genotyping improved prediction reliability by EDC=6.2. Total average expected reliability of prediction reached 0.67, corresponding to EDC=30.2. The combination of domestic and Interbull sources for both genotyped and nongenotyped animals is valuable for improving the accuracy of genetic prediction in small populations of dairy cattle.
Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23312993     DOI: 10.3168/jds.2012-6157

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

1.  Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation.

Authors:  Jérémie Vandenplas; Frederic G Colinet; Nicolas Gengler
Journal:  Genet Sel Evol       Date:  2014-09-30       Impact factor: 4.297

2.  Introduction of the Modern Methods of Assessing the Breeding Value of Cows in the Selection of Dairy Cattle in the Republic of Kazakhstan.

Authors:  S Abugaliev; L Bupebayeva; R Kulbayev; A Baisabyrova
Journal:  Arch Razi Inst       Date:  2021-12-30

3.  Genomic evaluation for a three-way crossbreeding system considering breed-of-origin of alleles.

Authors:  Claudia A Sevillano; Jeremie Vandenplas; John W M Bastiaansen; Rob Bergsma; Mario P L Calus
Journal:  Genet Sel Evol       Date:  2017-10-23       Impact factor: 4.297

4.  Single-step genomic evaluation of Russian dairy cattle using internal and external information.

Authors:  Andrei A Kudinov; Esa A Mäntysaari; Timo J Pitkänen; Ekaterina I Saksa; Gert P Aamand; Pekka Uimari; Ismo Strandén
Journal:  J Anim Breed Genet       Date:  2021-11-28       Impact factor: 3.271

5.  Genomic Prediction Using Bayesian Regression Models With Global-Local Prior.

Authors:  Shaolei Shi; Xiujin Li; Lingzhao Fang; Aoxing Liu; Guosheng Su; Yi Zhang; Basang Luobu; Xiangdong Ding; Shengli Zhang
Journal:  Front Genet       Date:  2021-04-15       Impact factor: 4.599

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.