Most biological traits including common diseases have a strong but poorly understood genetic basis. There is general interest in identifying these genetic factors as they can be used to identify individuals that are at risk for a particular disease and as experimental handles to identify novel therapies. Despite an incredible outlay of resources, the majority of causative genetic variants remain unidentified due to the underlying complexity of the genetic architectures of most diseases. Fundamental study of complex genetic traits in model organisms should identify general principles and approaches that can be used to identify causative genetic variants in human traits. In this research proposal, we are developing an unprecedented system in C. elegans to generate multigenic states in model organisms by evolving fluorescent reporters of phenotypes of interest. We will develop an automated microfluidic, fluorescent imaging and computer analysis system to rapidly measure, segment, and describe the expression of a transcriptional reporter in a tissue-specific manner. We will then use this imaging/sorting system to apply selective pressure to evolve multigenic changes to expression over multiple generations. As proof of principal, we will apply this approach to a transcriptional reporter that predicts lifespan in younger animals to evolve longer-lived animals. Causative mutations can then be rapidly identified using next-generation sequencing and carefully studied in the context of known genetic and cellular networks. This work will improve our understanding of aging, and in general transform our approaches in model organisms towards the understanding of biological traits in complex genetic diseases.
Most common diseases have a strong but complex genetic component. By understanding their genetic underpinnings we can predict their likelihood in individuals and identify drug targets for their amelioration. To identify general principals to understand how diseases arise from complex interplays of thousands of genetic variants, we will design a high-throughput system to evolve disease-like states in model organisms to any biological trait of interest.
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