Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: A proposed framework

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  • Gustavo Glusman, Institute for Systems Biology
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  • Peter W. Rose, San Diego Supercomputer Center
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  • Andreas Prlić, San Diego Supercomputer Center, University of California, San Diego
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  • Jennifer Dougherty, Institute for Systems Biology
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  • José M. Duarte, University of California, San Diego
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  • Andrew S. Hoffman, University of Washington, Seattle
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  • Geoffrey J. Barton, Dundee University
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  • Emøke Bendixen
  • Timothy Bergquist, University of Washington, Seattle
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  • Christian Bock, University of Washington, Seattle
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  • Elizabeth Brunk, University of California, San Diego
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  • Marija Buljan, Eidgenossische Technische Hochschule Zurich
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  • Stephen K. Burley, San Diego Supercomputer Center, University of California, San Diego, Rutgers, the State University of New Jersey
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  • Binghuang Cai, University of Washington, Seattle
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  • Hannah Carter, University of California, San Diego
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  • Jian Jiong Gao, Memorial Sloan Kettering Cancer Center
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  • Adam Godzik, SBP Medical Discovery Institute
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  • Michael Heuer, University of California System
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  • Michael Hicks, Human Longevity, Inc.
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  • Thomas Hrabe, SBP Medical Discovery Institute
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  • Rachel Karchin, Johns Hopkins University Hospital, Johns Hopkins Medicine
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  • Julia Koehler Leman, Simons Foundation, New York University
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  • Lydie Lane, SIB Swiss Institute of Bioinformatics and University of Geneva
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  • David L. Masica, Johns Hopkins University Hospital
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  • Sean D. Mooney, University of Washington, Seattle
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  • John Moult, Maryland University
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  • Gilbert S. Omenn, Institute for Systems Biology, University of Michigan, Ann Arbor, Michigan.
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  • Frances Pearl, Sussex University
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  • Vikas Pejaver, University of Washington, Seattle
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  • Sheila M. Reynolds, Institute for Systems Biology
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  • Ariel Rokem, University of Washington, Seattle
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  • Torsten Schwede, Biozentrum University of Basel
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  • Sicheng Song, University of Washington, Seattle
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  • Hagen Tilgner, Brain and Mind Research Institute
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  • Yana Valasatava, University of California, San Diego
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  • Yang Zhang, University of Michigan, University of Michigan, Ann Arbor, Michigan.
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  • Eric W. Deutsch, Institute for Systems Biology

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

Original languageEnglish
Article number113
JournalGenome Medicine
Volume9
Issue1
Number of pages10
ISSN1756-994X
DOIs
Publication statusPublished - 18 Dec 2017

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