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Genet Epidemiol. 2015 Jul;39(5):376-84. doi: 10.1002/gepi.21902. Epub 2015 May 17.

Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network.

Author information

1
Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
2
Marshfield Clinic, Marshfield, Wisconsin, United States of America.
3
Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
4
Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America.
5
Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
6
Mayo Clinic, Rochester, Minnesota, United States of America.
7
Center for Inherited Disease Research, IGM, Johns Hopkins University SOM, Baltimore, Maryland, United States of America.
8
Department of Pediatrics, Cincinnati Children's Hospital, University of Cincinnati, Cincinnati, Ohio, United States of America.
9
Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America.
10
Geisinger Health System, Danville, Pennsylvania, United States of America.
11
Group Health Research Institute, Seattle, Washington, United States of America.
12
Essentia Rural Health, Duluth, Minnesota, United States of America.

Abstract

Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)-rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset.

KEYWORDS:

association; complex disease; genetic interaction

PMID:
25982363
PMCID:
PMC4550090
DOI:
10.1002/gepi.21902
[Indexed for MEDLINE]
Free PMC Article

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