A robust GDAG among 13 cardiovascular risk factor phenotypes using information across the genome embodied in 130 principal components remained in the model. Only some of them are depicted here (numbered nodes) so as to better highlight the structure among the phenotypes.Display Omitted A framework is introduced to integrate multi-omics and identify robust networks.A principle component (PC) approach is used to extract information across the genome.Genome-wide PCs facilitates identifying a causal network over phenotypes.The resulting network identifies candidate targets for intervention and prediction. Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk.
['Azam Yazdani', 'Akram Yazdani', 'Ahmad Samiei', 'Eric Boerwinkle']
Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data