Disentangling associations between complex traits and cell types with seismic

Qiliang Lai, Ruth Dannenfelser, Jean-Pierre Roussarie () and Vicky Yao ()
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Qiliang Lai: Rice University
Ruth Dannenfelser: Rice University
Jean-Pierre Roussarie: Boston University Chobanian & Avedisian School of Medicine
Vicky Yao: Rice University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Integrating single-cell RNA sequencing with Genome-Wide Association Studies (GWAS) can uncover cell types involved in complex traits and disease. However, current methods often lack scalability, interpretability, and robustness. We present seismic, a framework that computes a novel specificity score capturing both expression magnitude and consistency across cell types and introduces influential gene analysis, an approach to identify genes driving each cell type-trait association. Across over 1000 cell-type characterizations at different granularities and 28 polygenic traits, seismic corroborates known associations and uncovers trait-relevant cell groups not apparent through other methodologies. In Parkinson’s and Alzheimer’s, seismic unveils both cell- and brain-region-specific differences in pathology. Analyzing a pathology-based Alzheimer’s GWAS with seismic enables the identification of vulnerable neuron populations and molecular pathways implicated in their neurodegeneration. In general, seismic is a computationally efficient, powerful, and interpretable approach for mapping the relationships between polygenic traits and cell-type-specific expression, offering new insights into disease mechanisms.

Date: 2025
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DOI: 10.1038/s41467-025-63753-z

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