scGWAS: Landscape of Trait-Cell Type Associations by Integrating Single-Cell Transcriptomics-Wide and Genome-Wide Association Studies

Authors

Peilin Jia
Ruifeng Hu
Fangfang Yan
Yulin Dai
Zhongming Zhao

Language

English

Publication Date

10-17-2022

Journal

Genome Biology

DOI

10.1186/s13059-022-02785-w

PMID

36253801

PMCID

PMC9575201

PubMedCentral® Posted Date

10-17-2022

PubMedCentral® Full Text Version

Post-print

Abstract

BACKGROUND: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.

RESULTS: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.

CONCLUSIONS: We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Keywords

Animals, Genome-Wide Association Study, Humans, Mice, Phenotype, Single-Cell Analysis, Transcriptome, scGWAS, GWAS, Single-cell RNA sequencing, scRNA-seq, Complex diseases, Cell-type specificity

Published Open-Access

yes

Recommended Citation

Jia, Peilin; Hu, Ruifeng; Yan, Fangfang; et al., "scGWAS: Landscape of Trait-Cell Type Associations by Integrating Single-Cell Transcriptomics-Wide and Genome-Wide Association Studies" (2022). Faculty, Staff and Student Publications. 405.
https://digitalcommons.library.tmc.edu/uthshis_docs/405