Antônio Augusto Franco Garcia

The paper evaluates long-term genomic selection outcomes in Brazilian cassava breeding programs, showing genetic gains and changes in diversity across four selection cycles.

This study compares single-trait and multi-trait genomic prediction models for cassava yield traits, showing that multi-trait models improve predictive accuracy and reduce phenotyping burden.

This study identifies genomic regions associated with carotenoid and dry matter content in cassava using GWAS models, finding six SNPs explaining most of the phenotypic variance.

The paper identifies four genetic variants associated with post-harvest physiological deterioration in cassava using GWAS and links them to genes involved in PPD pathways.

The paper develops a model for marker-assisted selection in common bean to improve resistance to root-knot nematodes by identifying QTLs and candidate genes.

The paper develops and validates statistical tests for segregation distortion in higher ploidy F1 populations, showing improved type I error control and power.

This study identifies genetic loci associated with flubendiamide resistance in Helicoverpa armigera using GWAS and QTL mapping, revealing an oligogenic architecture and potential molecular markers for resistance monitoring.

This paper develops the highest-density linkage map for hexaploid Urochloa humidicola, revealing genetic organization, apomixis, and polyploid origins using SNP markers.

The paper develops a workflow to improve linkage map construction from GBS data by evaluating and selecting dataset-independent genotype calling and mapping methods.

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

The paper analyzes allele-specific expression in sugarcane hybrids using a Beta-Binomial model, finding higher ASE frequencies in hybrids and genotype-specific ASEGs.

The paper uses Bayesian probability methods to analyze genotype-by-environment interactions in plant breeding, improving cultivar recommendation by quantifying adaptation and stability probabilities.

This study investigates how linkage disequilibrium and epistasis affect genetic variances and covariances in inbred and non-inbred populations using simulated data.

The paper introduces fullsibQTL, an R package for QTL mapping in outcrossing species, which considers multilocus genetic maps and different segregation patterns.

This paper develops Bayesian networks for genomic prediction of developmental traits in biomass sorghum, showing improved accuracy for plant height and biomass yield using multi-time and multi-trait models.

The paper develops a hidden Markov model-based method to estimate recombination fractions and infer linkage phases in autopolyploid species with high ploidy levels using simulated and real data.

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.