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Machine Learning Approaches to Understanding the Genetic Basis of Complex Traits

In this dissertation, we present machine learning approaches that address these challenges by explicitly modeling an intermediate process between the genotype and phenotype. More specifically, we model the genetic regulatory mechanisms that are induced by sequence variations and that lead to the phenotype, and we learn the model from genome-wide mRNA expression measurements. Using the learned model, we aim to generate a finer-grained hypothesis such as: a sequence variation S induces regulatory interactions R, which lead to changes in the phenotype P.

Chapter 3 Learning the Linear Regulation Network of Modules Data measuring
the expression variation across a population of genetically diverse individuals
provide valuable insight into the regulatory mechanisms underlying complex ...