Rare genomic copy number variants implicate new candidate genes for bicuspid aortic valve

EBAV Investigators, BAVCon Investigators

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Bicuspid aortic valve (BAV), the most common congenital heart defect, is a major cause of aortic valve disease requiring valve interventions and thoracic aortic aneurysms predisposing to acute aortic dissections. The spectrum of BAV ranges from early onset valve and aortic complications (EBAV) to sporadic late onset disease. Rare genomic copy number variants (CNVs) have previously been implicated in the development of BAV and thoracic aortic aneurysms. We determined the frequency and gene content of rare CNVs in EBAV probands (n = 272) using genome-wide SNP microarray analysis and three complementary CNV detection algorithms (cnvPartition, PennCNV, and QuantiSNP). Unselected control genotypes from the Database of Genotypes and Phenotypes were analyzed using identical methods. We filtered the data to select large genic CNVs that were detected by multiple algorithms. Findings were replicated in a BAV cohort with late onset sporadic disease (n = 5040). We identified 3 large and rare (< 1,1000 in controls) CNVs in EBAV probands. The burden of CNVs intersecting with genes known to cause BAV when mutated was increased in case-control analysis. CNVs intersecting with GATA4 and DSCAM were enriched in cases, recurrent in other datasets, and segregated with disease in families. In total, we identified potentially pathogenic CNVs in 9% of EBAV cases, implicating alterations of candidate genes at these loci in the pathogenesis of BAV.

Original languageEnglish (US)
Article numbere0304514
JournalPloS one
Volume19
Issue number9
DOIs
StatePublished - Sep 2024

Funding

Funding: This study was supported in part by grants R01HL137028 (SP), R01HL114823 (SCB), and R21HL150373 (SCB) from the National Heart, Lung, and Blood Institute (NHLBI). The funder did not play any role in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript. We thank Joana Castillo and Jacqueline Jennings for sample preparation, William J. Allen for computational support, and Gladys Zapata, Nitesh Mehta, and the Laboratory for Translational Genomics at Baylor College of Medicine for microarray genotyping. Figs 1 and 2 were created using BioRender.com. S1 Fig was created using CeGaT Pedigree Chart Designer. The Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http://www.tacc.utexas.edu) provided high-performance computing resources for data analysis.

ASJC Scopus subject areas

  • General

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