Abstract
We explore causal relationships between genotype, gene expression and phenotype in the Genetic Analysis Workshop 19 data. We compare the use of structural equation modeling and a Bayesian unified framework approach to infer the most likely causal models that gave rise to the data. Testing an exhaustive set of causal relationships between each single-nucleotide polymorphism, gene expression probe, and phenotype would be computationally infeasible, thus a filtering step is required. In addition to filtering based on pairwise associations, we consider weighted gene correlation network analysis as a method of clustering genes with similar function into a small number of modules. These modules capture the key functional mechanisms of genes while greatly reducing the number of relationships to test for in causal modeling.
Highlights
Even though genome-wide association studies (GWAS) have been very successful over the past decade at identifying genetic variants associated with disease, the mechanism underlying these associations is generally not known
There exist many techniques for causal analysis which could be applied to the Genetic Analysis Workshop 19 (GAW19) data
For the causal analysis we decided to focus on the real phenotypes systolic (SBP) and diastolic blood pressure (DBP) and we only included individuals for whom we have both GWAS and gene expression data
Summary
Even though genome-wide association studies (GWAS) have been very successful over the past decade at identifying genetic variants associated with disease, the mechanism underlying these associations is generally not known. It is hoped that gene expression data could provide the missing link between genotype and phenotype. We are interested in exploring causal relationships between genotype data, gene expression data, and phenotype. We choose to focus on structural equation modeling (SEM) and a Bayesian unified framework (BUF) approach [1]. For both of these methods, graphical models provide a natural framework for describing causal relationships. Suppose we have data from a single single-nucleotide polymorphism (SNP), gene expression measurements from a single probe, and data on a phenotype of interest. In this framework, nodes represent measured variables and the presence of a directed arrow between two nodes implies a causal link
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