Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle

Abstract Background Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices... Mehr ...

Verfasser: José Luis Gualdrón Duarte
Ann-Stephan Gori
Xavier Hubin
Daniela Lourenco
Carole Charlier
Ignacy Misztal
Tom Druet
Dokumenttyp: Artikel
Erscheinungsdatum: 2020
Reihe/Periodikum: BMC Genomics, Vol 21, Iss 1, Pp 1-18 (2020)
Verlag/Hrsg.: BMC
Schlagwörter: Genomic prediction / Genomic selection / Genome-wide association study / Bovine genomics / Beef cattle / Biotechnology / TP248.13-248.65 / Genetics / QH426-470
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-26500832
Datenquelle: BASE; Originalkatalog
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Link(s) : https://doi.org/10.1186/s12864-020-06921-3