75 – Teaching Online on a Budget
Bayesian Graphical Models for Gene X Environment Interaction
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.