Daniela A. L. Lourenco

This paper estimates genetic parameters and performs GWAS to analyze service sire effects on litter size and its relationship with semen quality in three terminal sire lines.

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 paper introduces an algorithm to approximate p-values for single-step GWAS in large populations, reducing computational costs while maintaining detection power.

This study evaluates the impact of using whole-genome sequence data for genomic predictions in pig lines, finding modest improvements in accuracy for maternal lines but mixed results for terminal lines.

This study simulates GWA and variant selection to determine optimal sample sizes and variant inclusion strategies based on genomic dimensionality and effective population size.

The paper investigates SNP profiles around QTN in small Ne populations and shows how these profiles affect genomic prediction accuracy and QTN detection.

This study investigates whether using the APY algorithm with truncated pedigree and phenotypes reduces computational costs in ssGREML for variance component estimation, finding that it slightly decreases ordering and symbolic factorization time without affecting estimates.

PATRIOT improves genomic prediction accuracy in self-pollinating crops by tracing identical-by-descent segments, increasing accuracy by 16.6% over traditional methods.

This study identifies the effective start of genomic selection in pigs, beef cattle, and broilers by analyzing divergence between genetic and RMS trends from BLUP and ssGBLUP.

The paper implements exact p-values for GWAS within the SSGBLUP framework, demonstrating its efficiency and application to a large beef cattle dataset.

The paper identifies QTL affecting muscle yield in rainbow trout using a 50K transcribed gene SNP-chip, finding larger genetic variance explained compared to previous studies.