Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States; Department of Genome Sciences, University of Washington, Seattle, United States; Brotman Baty Institute for Precision Medicine, Seattle, United States
Department of Genome Sciences, University of Washington, Seattle, United States; Department of Medicine, University of Washington, Seattle, United States
Frederick Roth
Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Canada; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
Lea M Starita
Department of Genome Sciences, University of Washington, Seattle, United States; Brotman Baty Institute for Precision Medicine, Seattle, United States
Department of Genome Sciences, University of Washington, Seattle, United States; Brotman Baty Institute for Precision Medicine, Seattle, United States; Department of Bioengineering, University of Washington, Seattle, United States
Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes – most notably functional regulatory elements – in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants – at all scales, from other genetic variants and gene regulation to cell biology to organismal environment – are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.