Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill
Author(s) -
Andrew Callister,
Ben Bradshaw,
Stephen Elms,
Ross A W Gillies,
Joanna M Sasse,
Jeremy Brawner
Publication year - 2021
Publication title -
g3 genes genomes genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.468
H-Index - 66
ISSN - 2160-1836
DOI - 10.1093/g3journal/jkab253
Subject(s) - eucalyptus globulus , biology , best linear unbiased prediction , eucalyptus , evolutionary biology , selection (genetic algorithm) , ecology , computer science , artificial intelligence
Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.
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