scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.

Journal: Cell genomics
Published Date: May 14, 2025

Abstract

Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here, we propose scPrediXcan, which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our prediction approach, ctPred, predicts cell-type-specific expression with high accuracy and captures complex gene-regulatory grammar that linear models overlook. Applied to type 2 diabetes (T2D) and systemic lupus erythematosus (SLE), scPrediXcan outperformed the canonical TWAS framework by identifying more candidate causal genes, explaining more genome-wide association study (GWAS) loci and providing insights into the cellular specificity of TWAS hits. Overall, our results demonstrate that scPrediXcan represents a significant advance, promising to deepen our understanding of the cellular mechanisms underlying complex diseases.

Authors

  • Yichao Zhou
    Committee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
  • Temidayo Adeluwa
    Committee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
  • Lisha Zhu
    School of Biomedical Informatics, University of Texas Health Science Center, Houston.
  • Sofia Salazar-Magaña
    Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
  • Sarah Sumner
    Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
  • Hyunki Kim
    Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea.
  • Saideep Gona
    Committee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
  • Festus Nyasimi
    Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
  • Rohit Kulkarni
    Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
  • Joseph E Powell
    UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia; Translational Genomics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
  • Ravi Madduri
    Data Science and Learning Department, Argonne National Laboratory, Lemont, IL, United States of America.
  • Boxiang Liu
    Institute of Deep Learning, Baidu Research, Sunnyvale, USA. jollier.liu@gmail.com.
  • Mengjie Chen
    Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Hae Kyung Im
    Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.

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