Epistasis regulates genetic control of cardiac hypertrophy.

Autor:	Wang Q; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Tang TM; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA., Youlton N; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Weldy CS; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Kenney AM; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA., Ronen O; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA., Hughes JW; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Chin ET; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Sutton SC; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Agarwal A; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA., Li X; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA., Behr M; Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany., Kumbier K; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA., Moravec CS; Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA., Tang WHW; 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., Margulies KB; Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Hospital of The University of Pennsylvania, Philadelphia, PA, USA., Cappola TP; Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Hospital of The University of Pennsylvania, Philadelphia, PA, USA., Butte AJ; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA., Arnaout R; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA., Brown JB; Department of Statistics, University of California, Berkeley, Berkeley, 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., Priest JR; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.; Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.; Tenaya Therapeutics, San Francisco, CA, USA., Parikh VN; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA., Yu B; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.; Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.; Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA., Ashley EA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.; Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2023 Nov 20. Date of Electronic Publication: 2023 Nov 20.
DOI:	10.21203/rs.3.rs-3509208/v1
Abstrakt:	The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis. Competing Interests: Competing interests E.A.A. is a Founder of Personalis, Deepcell, Svexa, RCD Co, and Parameter Health; Advisor to Oxford Nanopore, SequenceBio, and Pacific Biosciences; and a non-executive director for AstraZeneca. C.S.W. is a consultant for Tensixteen Bio and Renovacor. V.N.P. is an SAB member for and receives research support from BioMarin, Inc, and is a consultant for Constantiam, Inc. and viz.ai. The remaining authors declare no competing interests.
Databáze:	MEDLINE
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