Modeling SNP and quantitative trait association from GWAS catalog using CLG Bayesian network

Genome-wide association studies (GWAS) are a type of genetic methods that have recently received intensive attention. In this paper, we study the construction of the Bayesian network from the GWAS catalog for modeling SNP and quantitative trait associations. Existing methods in the literature can only deal with categorical traits. We address this limitation by leveraging the Conditional Linear Gaussian (CLG) Bayesian network, which can handle a mixture of discrete and continuous variables. A two-layered CLG Bayesian network is built where the SNPs are represented as discrete variables in one layer and quantitative traits are represented as continuous variables in another layer. We propose the method for specifying the CLG Bayesian network, focusing on the specification of the CLG distribution for quantitative traits. We empirically evaluate the construction method, and results demonstrate the effectiveness of our method.