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75 – Teaching Online on a Budget

Bayesian Graphical Models for Gene X Environment Interaction

Sponsor: Section on Bayesian Statistical Science
Keywords: Bayes Theorem, Directed Acyclic Graph, Graphical Models, Marginal Independence, Conditional Independence

Paola Sebastiani

Boston University

Harold Bae

Boston University

Avery McIntosh

Boston University

Stefano Monti

Boston University

Bayesian directed graphical models have been described as multivariate models that accommodate many interacting variables and therefore can be useful to describe many interacting genetic and non-genetic variants and their association with a complex genetic trait. We review different concepts of interactions and show that directed graphical models can conveniently represent biological interactions. We show how reading off these relations from a directed graph uses conditional independence between variables, and we review simple algorithms to check if two variables in a directed acyclic graph are conditionally independent given a third variable.

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