# GWAS and Machine Learning Screening of Genomic Determinants Underlying Host Adaptation in Swine and Chicken Salmonella Typhimurium Isolates

## TL;DR

This study identifies genetic features in Salmonella Typhimurium that help it adapt to pigs and chickens, using genomics and machine learning.

## Contribution

The study combines GWAS and interpretable machine learning to reveal host-specific genomic signatures in Salmonella Typhimurium.

## Key findings

- Chicken-adapted S. Typhimurium subpopulations show lower genetic diversity and distinct virulence factors.
- GWAS identified 1878 host-associated genes and SNPs/indels linked to metabolism and regulation.
- A Random Forest model accurately distinguished swine and chicken origins with an AUC of 0.974.

## Abstract

Salmonella Typhimurium is a major zoonotic pathogen, with pigs and chickens serving as key reservoirs for human infection, yet the genomic determinants of its host adaptation remain incompletely understood. This study integrated comparative genomics, genome-wide association studies (GWASs), and interpretable machine learning on 1654 high-quality genomes of swine- and chicken-origin S. Typhimurium isolates to identify host-associated genetic features. Phylogenetic analysis revealed host-preferred lineages and significantly lower genetic diversity within chicken-adapted subpopulations. Meta-analysis identified distinct host-associated profiles of antimicrobial resistance genes (e.g., higher prevalence of floR and blaTEM-1 in swine) and virulence factors (e.g., enrichment of allB and the yersiniabactin system in chickens). GWASs pinpointed 1878 host-associated genes and multiple SNPs/indels, functionally enriched in metabolism, regulation, and cell processes. A two-stage Random Forest model, built using the most contributory features, accurately discriminated between swine and chicken origins (AUC = 0.974). These findings systematically revealed the genomic signatures of host adaptation in S. Typhimurium, providing a prioritized set of candidate markers for experimental validation.