Literature DB >> 31301840

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle.

A R Guarini1, D A L Lourenco2, L F Brito3, M Sargolzaei4, C F Baes1, F Miglior5, S Tsuruta2, I Misztal2, F S Schenkel6.   

Abstract

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUPIM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUPIM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.
Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  genomic evaluation; multi-country genomic information; multiple across-country evaluation; single-step genomic best linear unbiased predictor

Mesh:

Year:  2019        PMID: 31301840     DOI: 10.3168/jds.2018-15819

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


  5 in total

1.  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

Review 2.  Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.

Authors:  Fabiana F Moreira; Hinayah R Oliveira; Jeffrey J Volenec; Katy M Rainey; Luiz F Brito
Journal:  Front Plant Sci       Date:  2020-05-26       Impact factor: 5.753

Review 3.  Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review.

Authors:  Miguel A Gutierrez-Reinoso; Pedro M Aponte; Manuel Garcia-Herreros
Journal:  Animals (Basel)       Date:  2021-02-25       Impact factor: 3.231

4.  Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.

Authors:  Fernando L Macedo; Ole F Christensen; Jean-Michel Astruc; Ignacio Aguilar; Yutaka Masuda; Andrés Legarra
Journal:  Genet Sel Evol       Date:  2020-08-12       Impact factor: 4.297

5.  Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle.

Authors:  Menghua Zhang; Hanpeng Luo; Lei Xu; Yuangang Shi; Jinghang Zhou; Dan Wang; Xiaoxue Zhang; Xixia Huang; Yachun Wang
Journal:  Animals (Basel)       Date:  2022-01-07       Impact factor: 2.752

  5 in total

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