Abstract 17417: Pathogenic and Likely Pathogenic Missense Variants in Cardiovascular Disease Genes Cluster Around Functional Domains

• Published in: Circulation (New York, N.Y.) Vol. 142; no. Suppl_3 Suppl 3; p. A17417
• Main Authors: Ten, Nicholas S, Grove, Megan, Wand, Hannah, Ison, Hannah, Pariani, Mitchel, Wheeler, Matthew T, Ashley, Euan A, Parikh, Victoria N
• Format: Journal Article
• Language: English
• Published: by the American College of Cardiology Foundation and the American Heart Association, Inc 17.11.2020
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.142.suppl_3.17417

The genetic architecture of inherited cardiomyopathies has mechanistic and potentially therapeutic relevance. We have shown that disease-associated variation in two cardiomyopathy-associated genes clusters in functional protein domains. Here, we extend this hypothesis to a comprehensive list of genes associated with cardiovascular disease. Using ClinVar, we identified 12,043 pathogenic or likely pathogenic (P/LP) variants across 190 cardiovascular disease-associated genes. Only P/LP variants with associated adjudication criteria were included. We interrogated these variants for regional clustering by ranking them by their number of P/LP neighbors within successively larger windows. We found that 87% of variants had at least one nearby variant within a 40 bp neighborhood, and 28% had ≥ 10 neighboring variants (Figure A, p<0.0001 vs. normal distribution). Within variant subtypes, 84% of missense variants and 70% of truncating variants neighbored at least 1 other missense or truncating variant, respectively. 21% of missense and 11% of truncating variants had ≥ 10 neighbors (p<0.0001). LDLR and FBN1 had the highest number of missense P/LP variants within a 40 bp neighborhood. We show that these neighborhoods overlap with functional domains of LDLR and FBN1 (Figure B, C). In summary, we demonstrate that a large portion of P/LP variants are regionally clustered in cardiovascular disease-associated genes, regardless of variant type, and align with known functional domains. Future directions will systematically apply these methods across the genome and explore the potential for novel domain identification and targeting these clusters with genome engineering.