Omics Evidence Chains for Complex Traits in Beef Cattle: From Cross-Layer Colocalization to Genetic Evaluation and...

Area of Science:

• Genetics and Genomics
• Bioinformatics
• Animal Breeding

Background:

• Multi-omics studies offer numerous genetic associations but often lack causal resolution and practical application pathways.
• Bridging the gap between statistical associations and actionable biological insights remains a significant challenge in genetic research.

Purpose of the Study:

• To present a practical, sequential roadmap for translating multi-omics data into robust genetic insights and applications.
• To enhance causal interpretability and cross-population robustness of genetic findings.
• To accelerate the translation of genetic discoveries into industry uptake and practical breeding strategies.

Main Methods:

• A four-step roadmap: signal identification (GWAS), confirmation (regulatory colocalization, TWAS), integration (network analysis, causal inference), and validation (functional, phenotypic).
• Implementation of safeguards including containerized workflows for reproducibility, data harmonization, and consistent identifier mapping for reliability.
• Prioritization of signals robust across ancestries and environments, focusing on regulatory support and functional validation.

Main Results:

• The roadmap successfully prioritized robust genetic signals across diverse traits (growth, carcass, reproduction, adaptation).
• Highlighted modules with strong regulatory support and advanced candidates for functional testing.
• Demonstrated improved causal interpretability and cross-ancestry robustness of genetic associations.

Conclusions:

• The proposed roadmap provides a reliable and reusable framework for advancing multi-omics research from association to application.
• Facilitates the integration of genetic findings into selection indices and informs targeted interventions.
• Shortens the pipeline from statistical association to genetic evaluation and practical industry implementation.