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

Author

Listed:
  • Lida Wang

    (Pennsylvania State University College of Medicine, Department of Public Health Sciences)

  • Shuang Gao

    (Pennsylvania State University College of Medicine, Department of Public Health Sciences)

  • Siyuan Chen

    (Pennsylvania State University College of Medicine, Department of Public Health Sciences)

  • Havell Markus

    (Pennsylvania State University College of Medicine, Bioinformatics and Genomics PhD Program
    Pennsylvania State University College of Medicine, Institute for Personalized Medicine)

  • Gao Wang

    (Columbia University, Center for Statistical Genetics, The Gertrude H. Sergievsky Center
    Columbia University, Department of Neurology)

  • Laura Carrel

    (Pennsylvania State University College of Medicine, Bioinformatics and Genomics PhD Program
    Pennsylvania State University College of Medicine, Department of Molecular and Precision Medicine)

  • Xiang Zhan

    (Southeast University, School of Statistics and Data Science)

  • Dajiang J. Liu

    (Pennsylvania State University College of Medicine, Department of Public Health Sciences
    Pennsylvania State University College of Medicine, Bioinformatics and Genomics PhD Program
    Pennsylvania State University College of Medicine, Department of Molecular and Precision Medicine)

  • Bibo Jiang

    (Pennsylvania State University College of Medicine, Department of Public Health Sciences)

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.

Suggested Citation

  • Lida Wang & Shuang Gao & Siyuan Chen & Havell Markus & Gao Wang & Laura Carrel & Xiang Zhan & Dajiang J. Liu & Bibo Jiang, 2025. "Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65643-w
    DOI: 10.1038/s41467-025-65643-w
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