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Inferring causal mediators from omics data in the context of genetic and environmental variations

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A major goal of genomics in the medical context is the identification of functional targets for interventional therapy. Information from interventional data is scarce, and hence the challenge resides in developing analytical strategies to exploit the breadth of population-level genetic and molecular profiling data being generated. Here, we propose new molecular signatures that exploit randomized genetic and external environmental conditions to predict genes with causal roles in phenotype. We propose a simple probabilistic model that builds on these patterns to predict transcripts that mediate the effect of a specific QTL to phenotype. We test this approach in a yeast model, where independent assessment through genome-wide deletion collections confirmed many of the top ranking predictions.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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