Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.

ABSTRACT: 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 159

SUBMITTER: Yazdani A 

PROVIDER: S-EPMC4886234 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature