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Dissecting genotype to phenotype relationships

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If you have a question about this talk, please contact Zoubin Ghahramani.

Individuals differ in important phenotypes, including the risk to develop disease, fitness or intelligence. Understanding the underlying determinants of these phenotypic differences is a central aim of modern biology. For many phenotypes of interest, is has been shown that genotype, and variation in external environments as well as their interactions contribute to phenotype variability. Thanks to technological advances in genome-sequencing, it is now possible to fit large-scale models to population-level data, unraveling the genotype-to-phenotype map at an unprecedented resolution. In this talk, I will discuss current statistical challenges and opportunities in the genetic analyses of global phenotypes and molecular traits. I will discuss suitable machine learning approaches and scalability challenges of latent variable models. Finally, I will conclude with an outlook of how causality regulatory relationships can be detected by combining model comparison techniques with informative experimental designs.

This talk is part of the Machine Learning @ CUED series.

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