Integration of expression profiles and endo-phenotypes in genetic association studies: A Bayesian approach to determine the path from gene to disease

Sharon M. Lutz, Sunita Sharma, John E. Hokanson, Scott Weiss, Benjamin Raby, Christoph Lange

Department of Biostatistics, Harvard School of Public Health, Boston, USA.
Department of Epidemiology, University of Colorado at Denver, Aurora, USA.
Institute for Genomic Mathematics, University of Bonn, Bonn, Germany.
The Channing Division of Network Medicine, Department of Medicine, Bringham and Women’s Hospital, Boston, USA.

DOI: 10.4236/ojgen.2013.33024                

Abstract

In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the disease. In such situations, in order to establish the path from the gene to the disease, we have to decide whether the gene acts on the disease phenotype primarily through a specific endo-phenotype or whether the gene influences the disease through an unidentified path which is characterized by different intermediate phenotypes. Here, we address the question that a genetic locus, given its effect on an endo-phenotype, influences the trait of interest primarily through the path of the endo-phenotype. We propose a Bayesian approach that can evaluate the genetic association between the genetic locus and the phenotype of interest in the presence of the genetic effect on the endo-phenotype. Using simulation studies, we verify that our approach has the desired properties and compare this approach with a mediation approach. The proposed Bayesian approach is illustrated by an application to genome-wide association study for childhood asthma (CAMP) that contains expression profiles.

Keywords

Expression Profiles; Endo-Phenotypes; Genetic Association Studies; Bayesian Hierarchal Model; Pathway; Mediation

Conflicts of Interest

The authors declare no conflicts of interest.

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