Luís Felipe V. Ferrão

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

The paper develops improved statistical tests for segregation distortion in tetraploid F1 populations by incorporating double reduction, preferential pairing, and genotype uncertainty, showing better performance than existing methods.

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 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 introduces two prior genotype distribution classes for polyploid genotyping, showing they improve performance compared to flexible or restrictive alternatives.

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 paper develops a method for genotyping polyploid organisms using NGS data, incorporating error modeling and Mendelian segregation, and applies it to hexaploid sweet potatoes.

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.