Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data
Creators
- 1. School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
- 2. School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
- 3. School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
- 4. School of Life Sciences, Westlake University, Hangzhou, 310012, Zhejiang, China
Description
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naïve CD8+ T cells related to COVID-19 severity, and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer’s disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Additional details
Related works
- Is cited by
- ttps://doi.org/10.1101/2023.03.04.23286805 (URL)