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

Lookup NU author(s): Dr Holly Fisher, Professor Heather Cordell

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Abstract

© 2016 The Author(s). 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.


Publication metadata

Author(s): Ainsworth HF, Cordell HJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data

Year of Conference: 2016

Pages: -

Online publication date: 18/10/2016

Acceptance date: 01/01/1900

Publisher: BioMed Central Ltd.

URL: http://doi.org/10.1186/s12919-016-0009-x

DOI: 10.1186/s12919-016-0009-x


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