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American Heart Association

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Final ID: BCVS22

Combining Noncoding and Coding Genetic Variation Improves Arrhythmia Risk Prediction

Abstract Body (Do not enter title and authors here): Background Ventricular arrythmias are highly heritable disorders and can cause sudden cardiac death (SCD). However, clinical genetic testing identifies pathogenic variants in less than 20% of nonischemic SCD cases. Additionally, there is considerable overlap in cardiomyopathy and arrhythmia (CMAR) genetics. Identifying genetic risk is important for predicting who might benefit from clinical intervention. We hypothesized that combining common, regulatory, and coding genetic variation could improve genetic risk prediction.

Methods: We established a cohort of 993 individuals with nonischemic arrhythmia risk (n=456, mean age 37) or controls, lacking cardiac or arrhythmia related diagnostic codes (n=537, mean age 58). Genome sequencing was analyzed for rare variants in CMAR and epilepsy genes, and extended to include noncoding variants in the regulatory regions of these genes. We also included common variants derived from polygenic scores for 18 cardiac rhythm and myopathic disorders. We then created a risk stratification algorithm that incorporated all 3 analyses.

Results: When comparing the top quintile to the lowest quintile of scores, the 18 polygenic scores had an odds ratio of 6.3 [95%CI 4.7-8.5], indicating significant contribution from common variation. Coding region analysis demonstrated an enrichment of ultrarare and pathogenic CMAR and epilepsy gene variants in the arrhythmia cases compared to controls. The high risk arrhythmia cases also had a >1.5-fold enrichment of ultrarare regulatory variants mapping to CMAR/epilepsy genes. As a predictor, the combination of common, coding, and regulatory variant analysis performed best, with the top 20% of the cumulative risk score carrying an odds ratio of 23.5 [95%CI 16.1-234.3], outperforming any subset of variant classes. A replication cohort confirmed these results. Importantly, this risk analysis performed well in individuals without monogenic arrhythmia risk (genotype negative cases).

Conclusions: A comprehensive genetic risk score that includes noncoding and polygenic analyses can improve arrhythmia risk stratification, including individuals who would otherwise be considered "genotype negative."
  • Monroe, Tanner  ( Northwestern University , Chicago , Illinois , United States )
  • Pesce, Lorenzo  ( Northwestern University , Chicago , Illinois , United States )
  • Kearns, Samuel  ( Northwestern University , Chicago , Illinois , United States )
  • Page, Patrick  ( Northwestern University , Chicago , Illinois , United States )
  • Dellefave-castillo, Lisa  ( Northwestern University , Chicago , Illinois , United States )
  • Webster, Gregory  ( Northwestern University , Chicago , Illinois , United States )
  • Puckelwartz, Megan  ( Northwestern University , Chicago , Illinois , United States )
  • Mcnally, Elizabeth  ( Northwestern University , Chicago , Illinois , United States )
  • Author Disclosures:
    Tanner Monroe: DO NOT have relevant financial relationships | Lorenzo pesce: DO NOT have relevant financial relationships | Samuel Kearns: DO NOT have relevant financial relationships | Patrick Page: DO NOT have relevant financial relationships | Lisa Dellefave-Castillo: DO NOT have relevant financial relationships | Gregory Webster: DO NOT have relevant financial relationships | Megan Puckelwartz: No Answer | Elizabeth McNally: DO have relevant financial relationships ; Consultant:Amgen:Active (exists now) ; Speaker:Regeneron:Past (completed) ; Ownership Interest:Ikaika Therapeutics:Active (exists now) ; Advisor:PepGen:Active (exists now) ; Advisor:Tenaya:Active (exists now) ; Consultant:Cytokinetics:Past (completed)
Meeting Info:

Scientific Sessions 2024

2024

Chicago, Illinois

Session Info:

Best of AHA Specialty Conferences: BCVS 2024

Saturday, 11/16/2024 , 02:00PM - 03:00PM

Best of Specialty Conferences

More abstracts on this topic:
More abstracts from these authors:
Combining Monogenic and Polygenic Analysis Improves Sudden Cardiac Death Risk Prediction

Monroe Tanner, Pesce Lorenzo, Kearns Samuel, Dellefave-castillo Lisa, Webster Gregory, Puckelwartz Megan, Mcnally Elizabeth

Polygenic Assessment for Atrial Fibrillation Liability Predicts Ventricular Arrhythmia Risk

Puckelwartz Megan, Acciani Daniel, Monroe Tanner, Pesce Lorenzo, Kearns Samuel, Dellefave-castillo Lisa, Webster Gregory, Mcnally Elizabeth

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