Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits

Genome-wide association studies have identified many loci for brain disorders, but most non-coding variants fail to colocalize with bulk expression quantitative trait loci. Single-cell expression quantitative trait loci studies capture cell-type-specific regulation but are often underpowered. We developed Bulk And Single cell expression quantitative trait loci Integration across Cell states (BASIC) to combine bulk and single-cell expression quantitative trait loci through "axis-quantitative trait loci," which decompose bulk-tissue effects along orthogonal axes of cell-type expression. BASIC better distinguishes shared versus cell-type-specific effects and increases power. Analyzing single-cell expression quantitative trait loci with cortex bulk data from MetaBrain using BASIC identified 5644 additional gene with quantitative trait loci (74.5%), equivalent to a 76.8% increase in sample size. Integrating axis-quantitative trait loci with 12 brain-related traits improved colocalization by 53.5% versus single-cell studies and 111% versus bulk studies, revealing risk genes such as DEDD for Alzheimer's disease and drug candidates including cabergoline.

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Publication Details

Journal
Nature Communications
Published
2025-11-26
DOI
https://doi.org/10.1038/s41467-025-65643-w
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.81

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article

Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits

Xiang Zhan, Havell Markus, Dajiang J. Liu, Lida Wang et al.
1 citations
Nature Communications
Single-cell and spatial transcriptomics
0.81
article

Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits

Xiang Zhan, Havell Markus, Dajiang J. Liu, Lida Wang, Shuang Gao, Laura Carrel, Siyuan Chen, Gao Wang, Bibo Jiang
article en
1 citations

Abstract

Genome-wide association studies have identified many loci for brain disorders, but most non-coding variants fail to colocalize with bulk expression quantitative trait loci. Single-cell expression quantitative trait loci studies capture cell-type-specific regulation but are often underpowered. We developed Bulk And Single cell expression quantitative trait loci Integration across Cell states (BASIC) to combine bulk and single-cell expression quantitative trait loci through "axis-quantitative trait loci," which decompose bulk-tissue effects along orthogonal axes of cell-type expression. BASIC better distinguishes shared versus cell-type-specific effects and increases power. Analyzing single-cell expression quantitative trait loci with cortex bulk data from MetaBrain using BASIC identified 5644 additional gene with quantitative trait loci (74.5%), equivalent to a 76.8% increase in sample size. Integrating axis-quantitative trait loci with 12 brain-related traits improved colocalization by 53.5% versus single-cell studies and 111% versus bulk studies, revealing risk genes such as DEDD for Alzheimer's disease and drug candidates including cabergoline.

Nature CommunicationsVol. 16(1)
Pennsylvania State University (US), Southeast University (BD), Department of Public Health (MM), Southeast University (CN), Columbia University (US), Penn State Milton S. Hershey Medical Center (US)
National Institutes of Health
Openalex Percentile: Top 21%
Single-cell and spatial transcriptomics
0.81
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