Rare Copy Number Variants Implicated in Bicuspid Aortic Valve

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Carlisle, Steven G.
Albasha, Hasan
Prakash, Siddharth K.

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Background: Bicuspid aortic valve (BAV), the most common congenital heart defect in adults, can lead to many long-term complications. More aggressive complications can manifest in children and adolescents as early onset complications of BAV (EBAV). Rare copy number variants (CNVs) have been implicated in cases of EBAV and related cardiovascular lesions. We hypothesized that rare and highly penetrant CNVs are enriched in EBAV cases. Materials: We developed a computational pipeline to identify rare CNVs in familial EBAV cases (n=394) obtained from the UTHealth BAV Research Registry and BAV probands (n=4216) obtained from the International BAV Consortium. Raw intensity data from Illumina SNP array genotypes was analyzed with three separate computer algorithms (PennCNV, QuantiSNP, and cnvPartition) to generate the initial CNV calls and sample-level quality statistics. CNV calls were merged and refined for case-control analysis with control cohorts (n=16,576) processed using identical methods. A cohort of individuals with left ventricular outflow tract obstruction lesions (LVOTO, n=1561) was used for comparison. Annotation and cataloguing of rare CNVs was performed with PLINK. Results: We identified 308 large (>250 kb) recurrent CNVs in EBAV and BAV cases that are absent or rare (<1:100) in controls. Twenty-five recurrent CNVs intersect with known BAV genes. There were 158 overlapping CNVs between LVOTO and EBAV cases and 90 overlapping CNVs between LVOTO and BAV cases. There were 54 very large (>5000 kb) overlapping CNVs between EBAV and BAV cases and 9 very large overlapping CNVs between LVOTO and BAV cases, 2 of which intersect with known BAV genes. CNVs intersecting with known BAV genes were significantly enriched in cases (P < 1x10-5). Conclusions: We identified rare recurrent CNVs in over 10% of cases, some of which intersect with genes known to be causative of BAV disease. Identification of new candidate genes provides important information for risk stratification.



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