medRxiv preprint doi: https://doi.org/10.1101/2021.07.14.21260405; this version posted July 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes Shiyang Ma1, James L. Dalgleish1, Justin Lee2, Chen Wang1, Linxi Liu3, Richard Gill4;5, Joseph D. Buxbaum6, Wendy Chung7, Hugues Aschard8, Edwin K. Silverman9, Michael H. Cho9, Zihuai He2;10, Iuliana Ionita-Laza1;# 1 Department of Biostatistics, Columbia University, New York, NY, 10032, USA 2 Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94305, USA 3 Department of Statistics, University of Pittsburgh, Pittsburgh, PA, 15260, USA 4 Department of Human Genetics, Genentech, South San Francisco, CA, 94080, USA 5 Department of Epidemiology, Columbia University, New York, NY, 10032, USA 6 Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA 7 Department of Pediatrics and Medicine, Herbert Irving Comprehensive Cancer Center, Columbia Uni- versity Irving Medical Center, New York, NY, 10032, USA 8 Department of Computational Biology, Institut Pasteur, Paris, France 9 Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA 10 Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA # e-mail:
[email protected] Abstract Gene-based tests are valuable techniques for identifying genetic factors in complex traits.