Using gene expression data to identify causal pathways between genotype and phenotype in a complex disease: application to Genetic Analysis Workshop 19

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• 作者：Holly F. Ainsworth ; Heather J. Cordell
• 刊名：BMC Proceedings
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：10
• 期：7-supp
• 页码：79-84
• 全文大小：957 KB
• 刊物主题：Medicine/Public Health, general;
• 出版者：BioMed Central
• ISSN：1753-6561
• 卷排序：10

文摘

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.