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Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results

Genetics selection evolution (Paris), 2019-04, Vol.51 (1), p.16-16, Article 16 [Peer Reviewed Journal]

COPYRIGHT 2019 BioMed Central Ltd. ;2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ;Distributed under a Creative Commons Attribution 4.0 International License ;The Author(s) 2019 ;Wageningen University & Research ;ISSN: 1297-9686 ;ISSN: 0999-193X ;EISSN: 1297-9686 ;DOI: 10.1186/s12711-019-0460-z ;PMID: 31029078

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Title:
Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results
Author: Gebreyesus, Grum ; Bovenhuis, Henk ; Lund, Mogens S ; Poulsen, Nina A ; Sun, Dongxiao ; Buitenhuis, Bart
Subjects: Accuracy ; Animal Breeding and Genetics ; Animal Breeding and Genomics ; Animal lactation ; Animal Production Systems ; Bos taurus ; Cardiovascular disease ; Cattle ; Chromatography ; Chromosomes ; Composition ; Datasets ; Dierlijke Productiesystemen ; Fatty acid composition ; Fatty acids ; Fokkerij en Genetica ; Fokkerij en Genomica ; Gas chromatography ; Genetic diversity ; Genetic effects ; Genetic research ; Genetic variation ; Genome-wide association studies ; Genomes ; Genomics ; Leerstoelgroep Dierlijke productiesystemen ; Leerstoelgroep Fokkerij en genetica ; Life Sciences ; Mathematical models ; Milk ; Phenotyping ; Polymorphism ; Population ; Population number ; Population studies ; Populations ; Prediction models ; Proteins ; Reliability analysis ; Studies ; WIAS
Is Part Of: Genetics selection evolution (Paris), 2019-04, Vol.51 (1), p.16-16, Article 16
Description: Large-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are expensive/difficult to measure and for breeds with a small reference population size. An effective method to increase reference population size could be to combine datasets from different populations. Prediction models might also benefit from incorporation of information on the biological underpinnings of quantitative traits. Genome-wide association studies (GWAS) show that genomic regions on Bos taurus chromosomes (BTA) 14, 19 and 26 underlie substantial proportions of the genetic variation in milk FA traits. Genomic prediction models that incorporate such results could enable improved prediction accuracy in spite of limited reference population sizes. In this study, we combine gas chromatography quantified FA samples from the Chinese, Danish and Dutch Holstein populations and implement a genomic feature best linear unbiased prediction (GFBLUP) model that incorporates variants on BTA14, 19 and 26 as genomic features for which random genetic effects are estimated separately. Prediction reliabilities were compared to those estimated with traditional GBLUP models. Predictions using a multi-population reference and a traditional GBLUP model resulted in average gains in prediction reliability of 10% points in the Dutch, 8% points in the Danish and 1% point in the Chinese populations compared to predictions based on population-specific references. Compared to the traditional GBLUP, implementation of the GFBLUP model with a multi-population reference led to further increases in prediction reliability of up to 38% points in the Dutch, 23% points in the Danish and 13% points in the Chinese populations. Prediction reliabilities from the GFBLUP model were moderate to high across the FA traits analyzed. Our study shows that it is possible to predict genetic merits for milk FA traits with reasonable accuracy by combining related populations of a breed and using models that incorporate GWAS results. Our findings indicate that international collaborations that facilitate access to multi-population datasets could be highly beneficial to the implementation of genomic selection for detailed milk composition traits.
Publisher: France: BioMed Central Ltd
Language: English;German
Identifier: ISSN: 1297-9686
ISSN: 0999-193X
EISSN: 1297-9686
DOI: 10.1186/s12711-019-0460-z
PMID: 31029078
Source: Hyper Article en Ligne (HAL) (Open Access)
PubMed Central
Alma/SFX Local Collection
Springer OA刊
ProQuest Central
DOAJ Directory of Open Access Journals

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Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results

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