Francesco Tiezzi

Widely used GWAS methods can be poorly suited to SNP-level localization under diffuse polygenic architecture in livestock

This study shows that common GWAS methods can produce misleading associations in livestock with polygenic architecture, highlighting the need for methods that control long-range LD spillover.

Gene-environment interactions contribute to blood pressure variation across global populations

The paper evaluates gene-environment interactions in blood pressure traits across diverse populations and finds they explain additional variance, though they do not improve prediction accuracy.

When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan

This study investigates when gene-environment interactions improve complex trait prediction by analyzing Drosophila lifespan data across different cross-validation scenarios.

Fine-mapping methods for complex traits: essential adaptations for samples of related individuals

This paper introduces Bayesian fine-mapping methods adapted for related individuals, showing improved accuracy in pig data compared to existing tools.

Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias

This paper improves polygenic prediction of blood pressure traits by incorporating lifestyle variables and their interactions with genetic data, reducing bias and increasing accuracy.

Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity

The paper uses ROH detection to analyze genetic diversity and inbreeding in eight Italian beef cattle breeds, identifying breed-specific genomic regions and inbreeding patterns.

Microbiability of meat quality and carcass composition traits in swine

The paper examines how gut microbiome composition at different production stages affects meat quality and carcass traits in pigs, finding microbiome contributions vary over time and impact genomic correlations.

Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms

This study uses metagenomic data and machine learning to predict growth and carcass traits in pigs, finding that microbiome data from later stages improves prediction accuracy.

A heuristic method to identify runs of homozygosity associated with reduced performance in livestock.

The paper introduces a heuristic method to identify runs of homozygosity (ROH) linked to reduced performance in livestock and shows it outperforms genome-wide inbreeding metrics.