Regulatory genomic circuitry of human disease loci by integrative epigenomics

TLDR

Annotating the molecular basis of human disease is challenging because 93 % of disease loci are non‑coding and gene‑regulatory annotations are incomplete. The study introduces EpiMap, a compendium of 10,000 epigenomic maps across 800 samples, to define chromatin states, enhancers, modules, regulators, and target genes. Using EpiMap, the authors annotated 30,000 disease‑associated loci linked to 540 traits, predicting trait‑relevant tissues, causal variants in enriched enhancers, and candidate target genes. The analysis partitioned multifactorial traits into tissue‑specific factors, identified monotropic and pleiotropic loci, and showed that top loci often contain multiple driver variants converging on shared targets, underscoring the value of dense epigenomic annotations for complex trait investigation.

Abstract

Abstract Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete 1–3 . Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits 4 , predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.