Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic Valve

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 cohorts with late onset sporadic disease (n = 5040). We identified 34 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 8% of EBAV cases, implicating alterations of candidate genes at these loci in the pathogenesis of BAV.

Fig 1.. Overview of Pipeline for CNV Identification and Validation.

SNP, single nucleotide polymorphism. QC, Quality control. CNV, copy number variant. The software and algorithms used for the analysis are provided in boxes to the left of the corresponding steps. Illumina raw signal intensity data was trimmed and exported using GenomeStudio. The intensity data was then analyzed with three different CNV calling algorithms (PennCNV [25], cnvPartition, and QuantiSNP [26]) to generate initial CNV calls and sample-level statistics. Sample-level quality control analysis was performed using PennCNV. PLINK [27] toolset was used to define CNV regions from initial CNV calls for subsequent burden testing, enrichment studies, and replication studies. The initial CNV calls were individually screened for CNVs intersecting with candidate loci, which we defined as genes implicated in bicuspid aortic valve disease and those discovered in our enrichment studies. CNVs of interest were then validated in GenomeStudio.

Fig 2.. UCSC Genome Browser Plots of GATA4 and DSCAM Variants.

(a) Ideogram of Chromosome 8 with view of image in (b) outlined in red box. (b) Plot of GATA4 variants. Each bar represents a copy number variant (CNV). CNVs from the EBAV cohort are in blue and CNVs from the BAVGWAS cohort are in red. The region spanned by GATA4 has been highlighted in light blue. (c) Ideogram of Chromosome 21 with view of image in (d) outlined in red box. (d) Plot of DSCAM variants. EBAV CNVs are in blue and BAVGWAS CNVs are in red. The region spanned by DSCAM is highlighted in light blue. Figures constructed using the UCSC Genome Browser, http://genome.ucsc.edu [29].