Disentangling the multi-omics layers of complex traits: Celiac disease as a model.

The post-genomic era has ushered in a transformative shift in biomedical research, driven by the integration of multi-omics technologies and advanced computational tools. While genome-wide association studies (GWAS) have identified thousands of variants linked to complex traits and diseases, the majority of these lie in non-coding regions, where their functional roles remain elusive. This chapter explores how fine-mapping, functional genomics, and systems biology are converging to bridge this gap, moving from statistical associations to mechanistic insights. Using celiac disease as a model, we illustrate how genomic, transcriptomic, epigenomic, and proteomic data can be harmonized to identify causal variants, prioritize candidate genes, and map regulatory networks that drive disease pathogenesis. We highlight the power of fine-mapping in refining GWAS signals and the importance of integrating chromatin accessibility, QTL colocalization, and single-cell omics to contextualize genetic risk within specific cellular environments. The chapter also discusses the promise of polygenic risk scores, the role of metabolomics in capturing functional phenotypes, and the emergence of single-cell and spatial technologies in revealing disease heterogeneity. Despite these advances, challenges remain-including data heterogeneity, computational complexity, and the underrepresentation of non-European populations in genomic studies. Addressing these issues will be critical for ensuring the equity and clinical utility of precision medicine. Ultimately, this chapter underscores the transformative potential of translational genomics. By connecting genetic variation to molecular function and clinical outcome, multi-omics approaches are paving the way for more predictive, preventive, and personalized healthcare-particularly in the context of autoimmune and other complex diseases.