Sign in
Genetic & Genomic MedicineGenome Medicine
Peer-reviewed journal article

Cell type-specific contextualisation of the human phenome: towards the systematic treatment of all rare diseases

Brian M. SchilderiD, Kitty B. MurphyiD, Hiranyamaya DashiD, Yichun Zhang, Robert Gordon-Smith, Jai Chapman, Momoko Otani, Nathan SkeneiD

Abstract

Rare diseases (RDs) are an extremely heterogeneous and underserved category of medical conditions. While the majority of RDs are strongly genetic, it remains largely unknown via which physiological mechanisms genetics cause RD. Therefore, we sought to systematically characterise the cell type-specific mechanisms underlying all RD phenotypes with a known genetic cause by leveraging the Human Phenotype Ontology and transcriptomic single-cell atlases of the entire human body from embryonic, foetal, and adult samples. In total we identified significant associations between 201 cell types and 9,575/11,028 (86.7%) unique phenotypes across 8,628 RDs. This greatly the collective knowledge of RD phenotype-cell type mechanisms. Next, we sought to systematically identify phenotypes in which the application of these results would have the greatest clinical impact based on metrics of severity (e.g. lethality, motor/mental impairment) and compatibility with gene therapy (e.g. filtering out physical malformations). Furthermore, we have made these results entirely reproducible and freely accessible to the global community to maximise their impact, including an interactive web portal (https://neurogenomics-ukdri.dsi.ic.ac.uk/). To summarise, this work represents a significant step forward in the mission to treat patients across an extremely diverse spectrum of serious RDs.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISAlías-Segur '24Novoa '26Uría-Regojo '26Pontikos '20Wang '26Hekselman '24
65% semantic matchBioinformatics
Sergio Alías-Segura, Florencio Pazos, Mónica Chagoyen·Bioinformatics·Oct 24, 2024· 3 citations