One of the fundamental challenges in contemporary genomics lies in understanding how genomic alterations produce disease. An increasing urgency to meet this challenge has arisen owing to several factors. First, we have learned that every individual harbors a surprisingly large number of rare, protein-coding variants whose functional consequences will be difficult to address using association-based methods. Second, we have made incredible strides in understanding the genes and pathways involved in many diseases. As a result, we are tantalizingly close to being able to offer personalized, genomically-based advice to physicians, patients and casual users of genetic tests. However, we are hampered by our lack of effective methods for determining the functional consequences of the ~300 rare variants we find in the protein-coding regions of a typical human genome. Current methods for assessing the consequences of rare protein-coding variants are either experimental or computational. Experimental methods generally involve cellular or biochemical assays for protein function. Though these methods are effective, they are used on a case-by-case basis, which cannot be scaled to address the rare variants we find in each human genome. Computational methods for determining the impact of protein variants, though easily scalable, generally produce a large number of false positive and negative results. Thus, a novel approach to studying the functional consequences of protein-coding variation is needed. We propose to address this need by developing methods for directly measuring the functional consequences of all possible single mutations in a protein simultaneously using eukaryotic model systems. We can use these data to create sequence-function maps for disease-related proteins, which will enable more effective genetic diagnosis. To accomplish this goal, we will draw on our expertise in combining assays for protein function with high-throughput DNA sequencing to measure the functional consequences of hundreds of thousands of variants of a protein simultaneously. Furthermore, we will begin to dissect the complexity of mutational effects on proteins by studying the impact of mutagenesis on multiple cellular phenotypes simultaneously.

Public Health Relevance

Genome sequencing has the power to revolutionize medicine, but in order to provide actionable information for patients, physicians and other individuals we need to understand the consequences of mutations in genomes. This proposal describes the development of technology aimed at making it possible to understand the consequences of mutations, thereby realizing the promise of personalized, genomic medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM109110-03
Application #
9120379
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Krasnewich, Donna M
Project Start
2014-09-01
Project End
2018-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Washington
Department
Genetics
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Gray, Vanessa E; Hause, Ronald J; Luebeck, Jens et al. (2018) Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data. Cell Syst 6:116-124.e3
Matreyek, Kenneth A; Starita, Lea M; Stephany, Jason J et al. (2018) Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet 50:874-882
Rose, John C; Stephany, Jason J; Wei, Cindy T et al. (2018) Rheostatic Control of Cas9-Mediated DNA Double Strand Break (DSB) Generation and Genome Editing. ACS Chem Biol 13:438-442
McDonald, Matthew G; Ray, Sutapa; Amorosi, Clara J et al. (2017) Expression and Functional Characterization of Breast Cancer-Associated Cytochrome P450 4Z1 in Saccharomyces cerevisiae. Drug Metab Dispos 45:1364-1371
Taskinen, Barbara; Ferrada, Evandro; Fowler, Douglas M (2017) Early emergence of negative regulation of the tyrosine kinase Src by the C-terminal Src kinase. J Biol Chem 292:18518-18529
Starita, Lea M; Ahituv, Nadav; Dunham, Maitreya J et al. (2017) Variant Interpretation: Functional Assays to the Rescue. Am J Hum Genet 101:315-325
Rubin, Alan F; Gelman, Hannah; Lucas, Nathan et al. (2017) A statistical framework for analyzing deep mutational scanning data. Genome Biol 18:150
Manolio, Teri A; Fowler, Douglas M; Starita, Lea M et al. (2017) Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell 169:6-12
Weile, Jochen; Sun, Song; Cote, Atina G et al. (2017) A framework for exhaustively mapping functional missense variants. Mol Syst Biol 13:957
Relling, M V; Krauss, R M; Roden, D M et al. (2017) New Pharmacogenomics Research Network: An Open Community Catalyzing Research and Translation in Precision Medicine. Clin Pharmacol Ther 102:897-902

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