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<p>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 <i>seismic</i>, 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, <i>seismic</i> corroborates known associations and uncovers trait-relevant cell groups not apparent through other methodologies. In Parkinson’s and Alzheimer’s, <i>seismic</i> unveils both cell- and brain-region-specific differences in pathology. Analyzing a pathology-based Alzheimer’s GWAS with <i>seismic</i> enables the identification of vulnerable neuron populations and molecular pathways implicated in their neurodegeneration. In general, <i>seismic</i> 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.</p>

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Disentangling associations between complex traits and cell types with seismic

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月1日

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  • Qiliang Lai,
  • Ruth Dannenfelser,
  • Jean-Pierre Roussarie,
  • Vicky Yao

摘要

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.