SUMMARY Mammalian genomes are organized into megabase-scale topologically associated domains (TADs). We demonstrate that disruption of TADs can rewire long-range regulatory architecture and result in pathogenic phenotypes. We show that distinct human limb malformations are caused by deletions, inversions, or duplications altering the structure of the TAD-spanning WNT6/IHH/EPHA4/PAX3 locus. Using CRISPR/Cas genome editing, we generated mice with corresponding rearrangements. Both in mouse limb tissue and patient-derived fibroblasts, disease-relevant structural changes cause ectopic interactions between promoters and non-coding DNA, and a cluster of limb enhancers normally associated with Epha4 is misplaced relative to TAD boundaries and drives ectopic limb expression of another gene in the locus. This rewiring occurred only if the variant disrupted a CTCF-associated boundary domain. Our results demonstrate the functional importance of TADs for orchestrating gene expression via genome architecture and indicate criteria for predicting the pathogenicity of human structural variants, particularly in non-coding regions of the human genome.

The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders.

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