Abstract Body: Background: Nonischemic sudden cardiac death (SCD) is a catastrophic cardiac rhythm failure. Heritable arrhythmias and cardiomyopathies contribute significantly to SCD risk, and genetic markers can be used to identify and reduce risk, improving odds of survival. Clinically, genetic evaluation of known rare risk variants using gene panels often fails to identify a genetic etiology. These gene panels focus on genes affiliated with monogenic inheritance, and current genetic analysis is agnostic to polygenic disease architectures.

Hypothesis: We hypothesized that cumulative rare and common variation scales with SCD risk.

Approach: We established a diverse cohort of 1042 individuals at low or high SCD risk and classified them as cases (n=505) or controls (n=537). We then performed whole genome sequencing and analyzed variation centered around cardiomyopathy, arrhythmia, and epilepsy panel genes, as well as the regulatory variants that map to these genes. We also performed common variant analysis by computing polygenic scores for cardiac metrics including cardiac morphology, arrhythmia, and EKG intervals. We then created a risk stratification algorithm that incorporated each of those analyses.

Results: We found a 3.4-fold enrichment for pathogenic or likely pathogenic (PLP) cardiomyopathy/arrhythmia variants (P<1e-11) in the high risk group, with 23% of those individuals carrying at least one PLP variant. Rare high-effect epilepsy panel variants were also enriched in the high risk cohort (P=0.006). We also found a greater than two-fold enrichment of rare regulatory variants in the high risk group (P<1e-7). Polygenic scores for blood pressure, QRS interval, and LV ejection fraction were all differentially distributed between the low and high risk groups (P<0.05). The subsequent combined genomic analysis found those scoring in the highest decile of the total genetic risk scale had 13-fold higher odds of being a high-risk case and 73-fold higher odds compared to the lowest decile. Compared to using only the monogenic or only the polygenic analysis, the combined analysis was more predictive.

Conclusions: Incorporating noncoding and polygenic analyses into sudden cardiac death genetic assessment improves risk stratification.