The genetic component of bicuspid aortic valve and aortic dilation. An exome-wide association study

- The first bicuspid aortic valve exome-wide association study was performed.
- Bicuspid aortic valve patients were reclassified based on thoracic aorta status.
- No association signal was consistent across series.
- Bicuspid aortic valve seemed more genetically complex than previously believed.
- Phenotypic variability should be further considered in future research.

BackgroundBicuspid aortic valve is the most common cardiovascular congenital malformation affecting 2% of the general population. The incidence of life-threatening complications, the high heritability, and familial clustering rates support the interest in identifying risk or protective genetic factors. The main objective of the present study was to identify population-based genetic variation associated with bicuspid aortic valve and concomitant ascending aortic dilation.Materials and methodsA cross-sectional exome-wide association study was conducted in 565 Spanish cases and 484 controls. Single-marker and gene-based association analyses enriched for low frequency and rare genetic variants were performed on this discovery stage cohort and for the subsets of cases with and without ascending aortic dilation. Discovery-stage association signals and additional markers indirectly associated with bicuspid aortic valve, were genotyped in a replication cohort that comprised 895 Caucasian cases and 1483 controls.ResultsAlthough none of the association signals were consistent across series, the involvement of HMCN2 in calcium metabolism and valve degeneration caused by calcium deposit, and a nominal but not genome-wide significant association, supported it as an interesting gene for follow-up studies on the genetic susceptibility to bicuspid aortic valve.ConclusionsThe absence of a genome-wide significant association signal shows this valvular malformation may be more genetically complex than previously believed. Exhaustive phenotypic characterization, even larger datasets, and collaborative efforts are needed to detect the combination of rare variants conferring risk which, along with specific environmental factors, could be causing the development of this disease.