Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus...

Area of Science:

• Forestry science
• Quantitative genetics
• Plant breeding

Background:

• Traditional genetic evaluations often analyze breeding populations separately.
• Integrating genomic data can improve accuracy and connect populations.
• Disjunct breeding populations present challenges for holistic genetic analysis.

Purpose of the Study:

• To implement and evaluate single-step genomic best linear unbiased prediction (HBLUP) for Eucalyptus globulus.
• To compare HBLUP with pedigree-based models using genomic and phenotypic data.
• To assess HBLUP's ability to connect and compare disjunct breeding populations.

Main Methods:

• Combined genomic, pedigree, and phenotypic data from two independent Eucalyptus globulus breeding populations.
• Constructed a unified relationship matrix (H) using genomic relationships to link populations.
• Compared prediction accuracy of HBLUP against pedigree-based models for stem volume and wood quality.

Main Results:

• Genomic relationships successfully connected the two breeding programs, correcting pedigree errors.
• HBLUP demonstrated higher prediction accuracy for parents and genotyped individuals compared to pedigree models.
• Identified cryptic relationships and population structure within native range populations.

Conclusions:

• HBLUP provides a robust framework for holistic genetic analysis of disjunct populations.
• The approach improves breeding value predictions and enables comparisons across programs and regions.
• Incorporating genetic groups into H estimation will further align genetic evaluation pipelines with marker-based approaches.