Disentangling associations between complex traits and cell types with seismic

Area of Science:

• Genomics
• Computational Biology
• Translational Medicine

Background:

• Integrating single-cell RNA sequencing (scRNA-seq) with Genome-Wide Association Studies (GWAS) is crucial for understanding complex traits and diseases at a cell-type-specific level.
• Current methods face limitations in systematically, scalably, and interpretably associating diseases with specific cell types.
• Identifying cell-type-specific biological processes underlying complex traits and diseases remains a significant challenge.

Purpose of the Study:

• To develop a novel, scalable, and interpretable framework called seismic for pinpointing associations between complex traits/diseases and cell types.
• To enhance the understanding of cell-type-specific biological mechanisms in complex diseases.
• To provide a method for accessing and analyzing genes driving cell type-trait associations.

Main Methods:

• Developed the seismic framework, which utilizes a novel specificity score to characterize cell types.
• Compared seismic with alternative methods across over 1,000 cell type characterizations and 28 traits.
• Applied seismic to neurodegenerative diseases, including Alzheimer's disease, considering different GWAS endpoints.

Main Results:

• Seismic demonstrates superior performance in corroborating existing findings and identifying novel trait-relevant cell groups compared to other methodologies.
• The framework facilitates easy access and analysis of specific genes driving cell type-trait associations, enabling deeper biological insights.
• Analysis of Alzheimer's disease revealed that GWAS endpoints (clinical diagnosis vs. tau biomarker) influence the identification of associated cell types (microglia vs. neurons).

Conclusions:

• Seismic is a computationally efficient, powerful, and interpretable approach for identifying associations between complex traits and cell type-specific gene expression.
• The framework offers significant advantages in studying diseases with cell-type and brain region-specific manifestations, such as neurodegenerative disorders.
• Considering specific GWAS endpoints is critical for accurate cell-type association studies, particularly in complex diseases.