Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

ABSTRACT

Livestock multi‐omics integration is key to unraveling complex trait regulation, yet systematic, livestock‐specific strategies remain scarce. This review traces the progression from single‐omics accumulation to multi‐dimensional integration, highlighting how large‐scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi‐omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls—overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome–host integration lacking environmental context, and systematic neglect of metabolic fluxomics—and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock‐adapted three‐tier analytical framework: (1) statistical association of cross‐omics covariation patterns; (2) machine learning‐driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior‐knowledge‐guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single‐cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock‐specific benchmarking, and translational pipelines, charting a path from correlation‐centric reporting to mechanistic causality and precision breeding.