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Health-Universe/scgenetics
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Identifying disease critical cell types and programs from single cell RNAseq. Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease-dependent M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell type programs were associated, whereas disease-dependent epithelial cell programs were most prominent, perhaps suggesting a role in disease response over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease. Here, we present the app required to run this analysis on a new single cell dataset.

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The Single Cell GWAS Explorer app offers researchers an efficient means to investigate genetic influences on cellular traits across diverse tissues and conditions. By leveraging single-cell genomic data and GWAS results, the app enables users to explore associations between genetic variants and cellular phenotypes. Users can select specific tissue categories, traits, and enhancer types to visualize relevant genetic signals through interactive heatmaps. For instance, a genetics researcher studying autoimmune diseases could use the app to explore how genetic variants influence immune cell behavior across different disease states and tissue environments. By uncovering such associations, researchers gain insights into the molecular mechanisms underlying complex diseases and identify potential therapeutic targets.

See owner's GitHub repository for more information: https://github.com/karthikj89/scgenetics.

Jagadeesh, K.A., Dey, K.K., Montoro, D.T. et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet 54, 1479–1492 (2022). https://doi.org/10.1038/s41588-022-01187-9

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/karthikj89/scgenetics.

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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