Multiple Quantitative Trait Analysis in Statistical Genetics with Bayesian Networks

Bayesian networks (BNs) represent a flexible tool for quantitative [9], qualitative and causal [13] reasoning, and are one of the building blocks used to specify complex models and Monte Carlo inference techniques in machine learning [11]. However, BNs can also be approached from a perspective that is much closer to that of classic multivariate statistics by considering Gaussian Bayesian networks (GBNs): • they allow the derivation of many closed form results because of the favourable properties of the multivariate normal distribution; • they are related to such classic techniques as linear regression and covariance matrix decomposition; • and they can be used to extend these techniques beyond their original scopes and definitions. They have widespread applications in life sciences [12] and, as mentioned by Jean-Baptiste, in the upcoming [5, 17