From Single Nucleotide Polymorphism to Transcriptional Mechanism
A Model for FRMD3 in Diabetic Nephropathy
- Sebastian Martini1,
- Viji Nair1,
- Sanjeevkumar R. Patel1,
- Felix Eichinger1,
- Robert G. Nelson2,
- E. Jennifer Weil2,
- Marcus G. Pezzolesi3,
- Andrzej S. Krolewski3,
- Ann Randolph1,
- Benjamin J. Keller4,
- Thomas Werner1,5 and
- Matthias Kretzler1⇑
- 1Departments of Internal Medicine and Nephrology, University of Michigan, Ann Arbor, Michigan
- 2National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
- 3Research and Clinic Divisions, Joslin Diabetes Center, Boston, Massachusetts
- 4Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan
- 5Genomatix Software GmbH, Munich, Germany
- Corresponding author: Matthias Kretzler, .
Genome-wide association studies have proven to be highly effective at defining relationships between single nucleotide polymorphisms (SNPs) and clinical phenotypes in complex diseases. Establishing a mechanistic link between a noncoding SNP and the clinical outcome is a significant hurdle in translating associations into biological insight. We demonstrate an approach to assess the functional context of a diabetic nephropathy (DN)-associated SNP located in the promoter region of the gene FRMD3. The approach integrates pathway analyses with transcriptional regulatory pattern-based promoter modeling and allows the identification of a transcriptional framework affected by the DN-associated SNP in the FRMD3 promoter. This framework provides a testable hypothesis for mechanisms of genomic variation and transcriptional regulation in the context of DN. Our model proposes a possible transcriptional link through which the polymorphism in the FRMD3 promoter could influence transcriptional regulation within the bone morphogenetic protein (BMP)-signaling pathway. These findings provide the rationale to interrogate the biological link between FRMD3 and the BMP pathway and serve as an example of functional genomics-based hypothesis generation.
This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1416/-/DC1.
- Received October 12, 2012.
- Accepted February 17, 2013.
- © 2013 by the American Diabetes Association.
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