Abstract
Abstract Germline genetic variation can strongly influence lifetime cancer risk and somatic variation can influence prognosis and treatment. However, variants are difficult to interpret, even when they occur in cancer-associated genes. Multiplexed assays of variant effect leverage deep sequencing to enable the experimental assessment of thousands of genetic variants simultaneously. The resulting large-scale variant functional data can be used to drive variant interpretation, empowering the use of genetic information in cancer prevention and treatment. We are developing multiplexed assays of variant effect that can be deployed widely, with the ultimate goal of measuring the effect of most variants at most disease-related genes in the human genome. Citation Format: Douglas Fowler. Combining multiplexed assays of variant effect and modeling to drive variant interpretation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-11.
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