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Disentangling associations between complex traits and cell types with seismic
Qiliang Lai1, Ruth Dannenfelser1, Jean-Pierre Roussarie2
1Department of Computer Science, Rice University.
Biorxiv : the Preprint Server for Biology
|May 20, 2024
Summary
This study introduces seismic, a new framework for linking complex traits and diseases to specific cell types using single-cell RNA sequencing and Genome-Wide Association Studies. Seismic accurately identifies cell types involved in diseases like Alzheimer's, offering deeper biological insights.
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.
