Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics.

ABSTRACT: Genome-wide association studies provide a powerful means of identifying 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. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study 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 notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease

SUBMITTER: Jagadeesh KA 

PROVIDER: S-EPMC9910198 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature