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

BMC Proceedings

Proceedings

Holly F. Ainsworth¹  Heather J. Cordell¹ 

[1] Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, NE1 3BZ, Newcastle upon Tyne, UK;

关键词: Structural Equation Modeling;    Causal Model;    Causal Analysis;    Gene Expression Measurement;    Genetic Analysis Workshop;   

DOI  :  10.1186/s12919-016-0009-x

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

Fig. 2