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.
Authors
- Xiang Zhan (ORCID: https://orcid.org/0000-0001-9650-143X)
- Havell Markus (ORCID: https://orcid.org/0000-0001-7060-4350)
- Dajiang J. Liu (ORCID: https://orcid.org/0000-0001-6553-858X)
- Lida Wang (ORCID: https://orcid.org/0000-0002-7990-7347)
- Shuang Gao (ORCID: https://orcid.org/0000-0002-0191-1522)
- Laura Carrel (ORCID: https://orcid.org/0000-0002-5868-4228)
- Siyuan Chen (ORCID: https://orcid.org/0009-0001-8475-0305)
- Gao Wang (ORCID: https://orcid.org/0000-0001-9336-402X)
- Bibo Jiang (ORCID: https://orcid.org/0000-0002-7985-3780)
Institutions
- 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)
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
Funders
- National Institutes of Health