Giovanny E. Covarrubias-Pazaran

This paper shows that early-stage sparse testing strategies increase genetic gain in plant breeding by improving selection accuracy and intensity through broader TPE sampling and reduced replication.

The paper introduces lme4breeding, a fast linear mixed model for multi-trait/multi-environment experiments that handles complex covariance structures efficiently.

This paper proposes reciprocal recurrent selection based on genetic complementation to enhance dominance-based heterosis in diploids, showing it is cheaper than GCA-based approaches and effective in hybrid maize.

This paper demonstrates that average semivariance can estimate genetic variance components across different modes of inheritance in complex traits.

This study compares clonal breeding strategies using stochastic simulation to determine their genetic gain per unit cost, finding that reciprocal recurrent selection (RRS) is optimal in diploids with high genomic selection intensity but not in autopolyploids.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

The sommer R package gains a multivariate solver: several random effects at once, with covariance structures set per effect and between traits, for genome-assisted breeding prediction.

SOFIA is an R package that simplifies the creation of publication-quality Circos plots for genetic data by integrating R and Circos with minimal user intervention.

The paper constructs a high-density cranberry composite map using GBS and SSRs to enable molecular-assisted breeding and genomic studies.