Roberto Daniel Lozano

The paper presents a genome-wide variation map of soybean accessions to study domestication-related mutations and finds a reduction in deleterious alleles in domesticated soybean.

This paper introduces a sorghum Practical Haplotype Graph (PHG) that enables accurate SNP calling and imputation from low-coverage sequencing data, improving genomic prediction accuracy and reducing genotyping costs.

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.

The paper identifies beneficial introgressed alleles from M. glaziovii in modern cassava germplasm, showing they improve traits like yield and disease tolerance and are under balancing selection.

The paper identifies enhancer regions in cassava using RNA polymerase mapping and shows they are enriched with causal variants affecting fitness and root composition.

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.

This study identifies genomic regions associated with CBSD resistance in cassava using genome-wide association mapping and genomic prediction models.

The paper evaluates genomic prediction models for Cassava Brown Streak Disease using transcriptomics and GWAS data, finding only marginal improvements in prediction accuracy.

This study evaluates genomic selection accuracy in cassava breeding across different prediction models and scenarios, finding that training population size significantly impacts accuracy.