SAIGE-QTL analyzes single-cell data directly and can test the combined effects of less common genetic variants.
Gene activity can be influenced by genetic variation, but these effects may differ between cell types. Single-cell RNA sequencing can reveal those differences, yet existing analyses often combine cells into larger groups or do not scale well to large datasets.
SAIGE-QTL is designed to analyze individual-cell profiles without first creating these combined, or “pseudobulk,” measurements. The researchers also used grouped tests to examine the effects of less frequent and rare variants, which are often left out of eQTL studies. In evaluations using real and simulated data, the tool showed greater power to identify eQTLs than existing pipelines.
// Source
Nature Genetics · 2026 · DOI: 10.1038/s41588-026-02798-2
Authors: Wei Zhou, Anna S. E. Cuomo, Linfeng Hu, Angli Xue, Masahiro Kanai, Yijia Christiana Liu, Taeyeop Kang, Youngdae Kim, Alex Rodriguez, Ravi Madduri, Bram L. Gorissen, Grant Chau, Anne Senabouth, M. Grace Gordon, Richard Perez, Pooja Kathail, Min Cheol Kim, Chun Jimmie Ye, Chirag Krishna, Ramnik J. Xavier, Daniel G. MacArthur, Joseph E. Powell, Mark J. Daly, Benjamin M. Neale
Institutions: Harvard University, University of Helsinki, University of California, San Francisco, UNSW Sydney, Broad Institute, Massachusetts General Hospital, Murdoch Children's Research Institute, Garvan Institute of Medical Research, Argonne National Laboratory, Ulsan National Institute of Science and Technology, Institute for Molecular Medicine Finland, Stanley Center for Psychiatric Research, Center for Computational and Integrative Biology, Massachusetts General Hospital
