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

Abstract

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.

Harnessing generative AI to annotate the severity of all phenotypic abnormalities within the Human Phenotype Ontology

“…Our findings highlight the potential of this next generation of natural language processing technologies in significantly contributing to the automation and refinement of data curation in biomedical research. These results have a large number of useful real-world applications, such as prioritising gene therapy candidates (Murphy et al, 2023) and guiding clinical decision-making in rare diseases. It may also be used as tool to help inform policy decisions and funding allocation by healthcare or governmental institutions.…”