III: Small: Inference of Causal Regulatory Relationships from Genetic Studies

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III:Small: Inference of Causal Regulatory Relationships from Genetic StudiesInference of biological networks from high-throughput genomic data is a central problem in bioinformatics where many different types of methods have been proposed and applied to a wide diversity of datasets. Several recent studies have collected data which contain both genetic variation information as well as gene expression information from a set of genetically distinct strains of an organism which have several advantageous properties for inferring causal regulatory relationships between genes. A principled way of representing causal relationships is using graphical causal models and a rich theory of inference of such models from observational data and interventions has been developed. However, this theory assumes full knowledge of the joint distribution which is equivalent to having very large samples and so is only guaranteed to work asymptotically. In this proposal, the team will extend causal inference methods in several directions motivated by applications to genetic views of genomics datasets where there are relatively small samples. In particular they will apply their new methods to detecting the presence and absence of causal relationships between yeast genes. While the focus of this proposal is on applying the developed techniques to a specific problem in bioinformatics, the causal inference issues addressed in this proposal are the general issues faced when applying causal inference to finite samples. Many of the approaches developed in this proposal will be applicable to a wide range of problems. The resulting methods developed in this proposal will be made available to the scientific community through publicly available software.The project involves the training of a graduate and undergraduate students. The collaborative nature of the project will expose the students to the medical and genetics worlds, and at the same time, it will improve their abilities to design and implement solutions to complex algorithmic and statistical problems. The research will be converted into course materials for the interdisciplinary course, Computational Genetics, which is taken by both undergrad and graduate students as well as students from the medical school.