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Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle
Author(s) -
Oliveira H.R.,
Silva F.F.,
Brito L.F.,
Guarini A.R.,
Jamrozik J.,
Schenkel F.S.
Publication year - 2018
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12317
Subject(s) - best linear unbiased prediction , biology , genomic selection , population , statistics , linear regression , dairy cattle , zoology , genetics , selection (genetic algorithm) , mathematics , single nucleotide polymorphism , demography , computer science , machine learning , genotype , sociology , gene
Summary We aimed to investigate the performance of three deregression methods (VanRaden, VR ; Wiggans, WG ; and Garrick, GR ) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value ( REL EBV ) higher than the average of parent reliability ( REL PA ) in the training and validation populations; (ii) including only animals with REL EBV higher than 0.50 in the training and REL EBV higher than REL PA in the validation population; and (iii) including only animals with REL EBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction ( GBLUP ), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in REL EBV between training population animals exist.