Cacilda Borges do Valle

This study uses random regression models to analyze longitudinal dry matter yield data from perennial forage species, identifying genotype adaptability and stability through reaction norms.

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

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

This paper introduces an automated SNP-based method to identify contaminants in polyploid plant progenies using PCA, Mendelian segregation analysis, and clustering, validated on simulated and real data.

The paper develops a method to convert tetraploid GBS data into diploidized genotype calls and evaluates its performance using Urochloa hybrids.