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

Area of Science:

• Livestock genomics and systems biology.
• Multi-omics data integration strategies.

Background:

• Livestock multi-omics integration is vital for complex trait regulation but lacks livestock-specific strategies.
• Current approaches often face challenges like data heterogeneity, limited samples, and reductionist analyses.

Purpose of the Study:

• To review the progression of multi-omics integration in livestock.
• To identify key challenges and pitfalls in current methodologies.
• To propose a novel analytical framework for livestock multi-omics data.

Main Methods:

• Systematic review of single-omics to multi-omics integration.
• Critical appraisal of common pitfalls (correlation vs. causation, omics limitations).
• Proposal of a three-tier analytical framework incorporating statistical association, machine learning, and causal inference.

Main Results:

• Identified major impediments: species diversity, data heterogeneity, small sample sizes, and oversimplification of data.
• Highlighted the need for exposomics and fluxomics for causal and dynamic insights.
• Proposed a framework for statistical association, machine learning, and causal interpretation.

Conclusions:

• Multimodal sequencing and generative AI can address heterogeneity and strengthen causal evidence.
• Future priorities include database standardization, livestock-specific benchmarking, and translational pipelines.
• The proposed framework facilitates a shift from correlation-based reporting to mechanistic causality for precision breeding.