Identification of cell type-specific gene targets underlying thousands of rare diseases and subtraits

Rare diseases (RDs) are uncommon as individual diagnoses, but as a group contribute to an enormous disease burden globally. However, partly due the low prevalence and high diversity of individual RDs, this category of diseases is understudied and under-resourced. The advent of large, standardised genetics databases has enabled high-throughput, comprehensive approaches that uncover new insights into the multi-scale aetiology of thousands of diseases. Here, using the Human Phenotype Ontology (9,677 annotated phenotypes) and multiple single-cell transcriptomic atlases (77 human cell types and 38 mouse cell types), we conducted >688,000 enrichment tests (x100,000 bootstrap iterations each) to identify >13,888 genetically supported cell type-phenotype associations. Our results recapitulate well-known cell type-phenotype relationships, and extend our understanding of these diseases by pinpointing the genes linking phenotypes to specific cell (sub)types. We also reveal novel cell type-phenotype relationships across disparate branches of clinical disease (e.g. the nervous, cardiovascular, and immune systems). Next, we introduce a computational pipeline to prioritise gene targets with high cell type-specificity to minimise off-target effects and maximise therapeutic potential. To broaden the impact of our study, we have released two R packages to fully replicate our analyses, as well as a series of interactive web apps so that stakeholders from a variety of backgrounds may further explore and utilise our findings. Together, we present a promising avenue for systematically and robustly uncovering the multi-scale aetiology of RDs at scale. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by a UK Dementia Research Institute (UK DRI) Future Leaders Fellowship [MR/T04327X/1] and the UK DRI which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Human Phenotype Ontology Descartes scRNA-seq atlas I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data and code is made freely available through preexisting databases and/or GitHub repositories / software associated with this publication. [https://neurogenomics.github.io/rare\_disease\_celltyping_apps/home][1] [https://github.com/neurogenomics/rare\_disease\_celltyping][2] [https://github.com/neurogenomics/rare\_disease\_celltyping/tree/master/results][3] [1]: https://neurogenomics.github.io/rare_disease_celltyping_apps/home [2]: https://github.com/neurogenomics/rare_disease_celltyping [3]: https://github.com/neurogenomics/rare_disease_celltyping/tree/master/results