Integration of expression profiles and endo-phenotypes in genetic association studies: A Bayesian approach to determine the path from gene to disease ()
Affiliation(s)
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.
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.
Share and Cite:
Lutz, S. , Sharma, S. , Hokanson, J. , Weiss, S. , Raby, B. and Lange, C. (2013) Integration of expression profiles and endo-phenotypes in genetic association studies: A Bayesian approach to determine the path from gene to disease.
Open Journal of Genetics,
3, 216-223. doi:
10.4236/ojgen.2013.33024.
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