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.
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.
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