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Learn genetic fine mapping techniques through the Sum of Single Effects (SuSiE) model in this 45-minute conference talk from the Computational Genomics Summer Institute. Explore how SuSiE provides a simple yet powerful approach to variable selection in regression analysis, specifically designed for identifying causal genetic variants from genome-wide association study data. Discover the model's ability to perform fine-mapping from summary statistics data, enabling researchers to pinpoint likely causal variants within associated genomic regions. Understand the mathematical foundations of the SuSiE framework and its advantages over traditional fine-mapping methods, including its capacity for handling linkage disequilibrium and providing credible sets of variants. Examine recent advances in joint fine-mapping of multiple traits using the SuSiE model, which allows for improved statistical power and more comprehensive genetic analysis. Gain insights into practical applications of this methodology in computational genomics research and learn how SuSiE addresses common challenges in genetic association studies, such as distinguishing between causal and correlated variants in regions of high linkage disequilibrium.
