Epistasis regulates genetic control of cardiac hypertrophy.
Authors
Affiliations (23)
Affiliations (23)
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
- Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
- Department of Statistics, University of California Irvine, Irvine, CA, USA.
- Department of Computer Science, Stanford University, Stanford, CA, USA.
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany.
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Tenaya Therapeutics, San Francisco, CA, USA.
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA. [email protected].
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA. [email protected].
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA. [email protected].
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA. [email protected].
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA. [email protected].
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA. [email protected].
Abstract
Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, using deep learning-derived left ventricular mass estimates from 29,661 UK Biobank cardiac magnetic resonance images. We report epistatic variants near CCDC141, IGF1R, TTN and TNKS, identifying loci deemed insignificant in genome-wide association studies. Functional genomic and integrative enrichment analyses reveal that genes mapped from these loci share biological process gene ontologies and myogenic regulatory factors. Transcriptomic network analyses using 313 human hearts demonstrate strong co-expression correlations among these genes in healthy hearts, with significantly reduced connectivity in failing hearts. To assess causality, RNA silencing in human induced pluripotent stem cell-derived cardiomyocytes, combined with novel microfluidic single-cell morphology analysis, confirms that cardiomyocyte hypertrophy is nonadditively modifiable by interactions between CCDC141, TTN and IGF1R. Our results expand the scope of cardiac genetic regulation to epistasis.