Samuel B. Fernandes

This study evaluates genomic prediction models for maize hybrids using GBLUP-based multi-kernel models to estimate genetic variance components and predict hybrid performance.

This study identifies the polygenic architecture of photoprotection in sorghum through a high-throughput screen of natural genetic variation in non-photochemical quenching traits.

This paper improves simulation accuracy for digital breeding by integrating GWAS-derived genetic architecture into trait simulations, achieving high concordance with empirical data.

This paper improves genomic prediction accuracy by using synthetic traits derived from hyperspectral data, showing higher accuracy than single-trait models for leaf traits in sorghum.

This paper integrates environmental data with genetic data using machine learning to improve genomic prediction models for maize grain yield, showing increased predictive ability and computational efficiency.

This study performs GWAS on macauba palms to identify candidate genes related to oil production traits using single-trait and multi-trait analyses.

The paper develops a method to enhance forest productivity by analyzing indirect genetic effects in clonal composites of Eucalyptus hybrids.

This study simulates traits controlled by variance quantitative trait nucleotides in Arabidopsis and maize to evaluate the performance of two statistical methods for identifying variance quantitative trait loci.

simplePHENOTYPES simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in genetic architectures to aid in testing multivariate statistical analyses for GWAS and GS approaches.

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.

This study compares deleterious mutation patterns between sorghum and maize, finding sorghum diverges from the domestication cost hypothesis and develops a gene deleterious index with accuracy over 0.5.

This study estimates deleterious mutation burden in sorghum and assesses its impact on agronomic traits, finding moderate genetic variance explanation but limited improvement in breeding accuracy due to linkage.