Research Article: Application of Gene Network Analysis Techniques Identifies AXIN1/PDIA2 and Endoglin Haplotypes Associated with Bicuspid Aortic Valve

Date Published: January 21, 2010

Publisher: Public Library of Science

Author(s): Eric C. Wooten, Lakshmanan K. Iyer, Maria Claudia Montefusco, Alyson Kelley Hedgepeth, Douglas D. Payne, Navin K. Kapur, David E. Housman, Michael E. Mendelsohn, Gordon S. Huggins, Amanda Ewart Toland. http://doi.org/10.1371/journal.pone.0008830

Abstract: Bicuspid Aortic Valve (BAV) is a highly heritable congenital heart defect. The low frequency of BAV (1% of general population) limits our ability to perform genome-wide association studies. We present the application of four a priori SNP selection techniques, reducing the multiple-testing penalty by restricting analysis to SNPs relevant to BAV in a genome-wide SNP dataset from a cohort of 68 BAV probands and 830 control subjects. Two knowledge-based approaches, CANDID and STRING, were used to systematically identify BAV genes, and their SNPs, from the published literature, microarray expression studies and a genome scan. We additionally tested Functionally Interpolating SNPs (fitSNPs) present on the array; the fourth consisted of SNPs selected by Random Forests, a machine learning approach. These approaches reduced the multiple testing penalty by lowering the fraction of the genome probed to 0.19% of the total, while increasing the likelihood of studying SNPs within relevant BAV genes and pathways. Three loci were identified by CANDID, STRING, and fitSNPS. A haplotype within the AXIN1-PDIA2 locus (p-value of 2.926×10−06) and a haplotype within the Endoglin gene (p-value of 5.881×10−04) were found to be strongly associated with BAV. The Random Forests approach identified a SNP on chromosome 3 in association with BAV (p-value 5.061×10−06). The results presented here support an important role for genetic variants in BAV and provide support for additional studies in well-powered cohorts. Further, these studies demonstrate that leveraging existing expression and genomic data in the context of GWAS studies can identify biologically relevant genes and pathways associated with a congenital heart defect.

Partial Text: The aortic valve, as formed during embryonic heart development, is comprised of three cusps divided by three commissures. Cusp fusion, or the failure of the cusps to separate during heart development, can produce a valve with two cusps (bicuspid) or one cusp (unicuspid) [1]. In a bicuspid valve, the two conjoined cusps form a larger cusp that operates with the remaining, normal cusp to perform the valve function [2]. While the prevalence of BAV is approximately one percent of live births (13.7 per 1,000), one third of the aortic valves replaced are found to be bicuspid at the time of valve replacement [3]. Thoracic ascending aortic aneurysms (TAA) are also found in patients with BAV, sometimes even in pre-teen children.

These results demonstrate that mining existing published literature, expression, and genome scan data in a systematic manner can identify biologically plausible genes and pathways that have variants significantly associated with BAV, ultimately a disease of early development. Because we describe a single cohort, we consider the results to be hypothesis generating. The successful identification of candidate SNPs/genes by our prioritization analyses now provides motivation for the collection and testing of additional cohorts for independent analysis.

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http://doi.org/10.1371/journal.pone.0008830

 

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