The American Journal of Human GeneticsGenomicsPeer-reviewed journal article
Topologically associating domain (TAD) boundaries stable across diverse cell types are evolutionarily constrained and enriched for heritability
Abstract
Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs--TAD boundaries--contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD boundary disruption have been implicated in rare disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common trait-associated variants. Here, we investigate an attribute of 3D genome architecture--the stability of TAD boundaries across cell types--and demonstrate its relevance to understanding how genetic variation in TADs contribute to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWAS), we investigate the differences in functionality and evolutionary pressure on variation in TADs versus TAD boundaries. We quantify their contribution to trait heritability and sequence-level evolutionary constraint and demonstrate that genetic variation in TAD boundaries contributes more to complex trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial differences. Boundaries stable across cell types are further enriched for complex trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes compared to boundaries unique to a specific cell type. This suggests greater functional importance for stable boundaries. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genomes functional landscape and enabling 3D-structure aware variant interpretation.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: eQTL / molecular QTL mapping
- research focus: GWAS / association mapping
- research focus: variant effect prediction
- research focus: benchmarking
- approach: statistical model
- Citations
- 206
- FWCI
- 10.98
- Field percentile
- top 1%
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