Utilizing sc-linker to integrate single-cell RNA sequencing and human genetics to identify cell types and driver genes associated with non-small cell lung cancer.

ABSTRACT:

Background

Genome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.

Methods

In this study, we utilized the sc-linker framework developed by Jagadeesh, which is an integrated framework that combines single-cell RNA sequencing (scRNA-seq), epigenomic single nucleotide polymorphism (SNP)-to-gene mapping, and GWAS summary statistics to infer potential cell types and diseases affected by genetic variations.

Results

Using normal cell type programs in the sc-linker, we ide

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11755902 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature