Integrating Machine Learning and Genomic Data to Study Microbiome-Associated SNPs in Beef Cattle

Abstract

The relationship between the microbiome and single-nucleotide polymorphisms (SNPs) in beef cattle could help improve animal health, productivity, and sustainability. In this study, we use computational analysis to exam-ine how the microbiome and SNPs are connected. The primary objective is to identify significant associations that could inform breeding strategies and disease management practices within the context of beef cattle pro-duction. We use computational biology techniques, focusing on feature selection methods such as the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm, to identify important SNPs associated with micro-bial composition. By combining genomic and microbiome data, we aim to clarify the interactions that affect the health and productivity of cattle. This research adds to our understanding of how hosts and their microbiomes interact, which could help us develop more personalized methods for managing livestock in the future.