Bruno Dourado Valente

This study integrates longitudinal metabolite profiles into genomic prediction models for pig traits, showing improved prediction accuracy depending on the trait and timepoint of metabolite sampling.

This study evaluates the benefits of using whole-genome sequence data for genomic prediction in pigs, finding small improvements over marker arrays in some scenarios.

The paper identifies rare and population-specific functional variants in commercial pig lines and assesses their impact on production traits and genomic prediction.

The paper applies structural equation models to GWAS in chickens to decompose SNP effects into direct, indirect, and total effects, showing greater insight into causal relationships than traditional multi-trait methods.