Patricio Muńoz

This paper develops a high-efficiency Rhizobium rhizogenes hairy root transformation system for Vaccinium, achieving up to 80% transformation efficiency and enabling gene validation and genome editing.

This study identifies candidate loci for anthracnose resistance in blueberries using genome-wide association analysis and image-based disease scoring.

This study presents the first haplotype-phased reference genome of V. stamineum, a blueberry wild relative, revealing genetic traits useful for breeding programs.

SimpleMating predicts and optimizes breeding crosses using genomic selection to maximize genetic gain while minimizing inbreeding in simulated maize breeding programs.

This paper constructs and analyzes the first pangenomes for blueberry and cranberry, revealing high genetic diversity and identifying genes associated with disease resistance and fruit quality.

This study evaluates methods for genomic prediction using categorical data in breeding programs, finding that mixed models and Bayesian approaches perform well despite normality violations.

This study identifies terpene volatiles as key contributors to blueberry aroma and consumer preference using metabolomics and sensory analysis.

This paper develops software for QTL mapping in tetraploid and diploid diallel populations, demonstrating its effectiveness in detecting QTLs and improving model fit through non-additive effects.

The paper develops a statistical framework for haplotype reconstruction in tetraploid F1 populations and implements it in the software PolyOrigin, demonstrating its effectiveness on a potato dataset.

This study evaluates genomic prediction in family bulks for loblolly pine, finding that GWFP outperforms individual GEBV predictions and is suitable for breeding programs using family-level selection.

This paper summarizes genomic prediction strategies for autotetraploid blueberry, showing the importance of model parametrization, sequence depth, and genotype calling for improving breeding efficiency.

This study creates a high-resolution linkage map for autotetraploid blueberry and identifies QTLs for machine harvest traits like firmness and detachment force.

This paper evaluates genomic prediction methods for autotetraploid blueberry using continuous genotyping and relationship matrices, finding marker-based models improve prediction accuracy and reduce computational time.

This study improves genomic prediction models for maize hybrids by incorporating dominance effects and genotype-by-environment interactions, enhancing accuracy for grain yield and moisture.