Polygenic Background and Penetrance of Pathogenic Variants in Hypertrophic and Dilated Cardiomyopathies

Abstract

Importance: Polygenic background modifies variant penetrance in hypertrophic (HCM) and dilated (DCM) cardiomyopathy, diseases with opposing morphologic characteristics and inversely related genetic pathways. Whether polygenic susceptibility for one disease protects against monogenic risk for the other remains unexplored.

Objective: To characterize if polygenic background bidirectionally modifies pathogenicity of established rare variants associated with HCM and DCM.

Design: Cross-sectional study.

Setting: The Penn Medicine BioBank (PMBB).

Participants: Volunteers enrolled in PMBB with available electronic health record and genotyping data.

Exposures: Normalized polygenic scores (PGS) for HCM and DCM, as well as carrier status of pathogenic variants in established HCM or DCM genes.

Main outcomes: HCM and DCM defined using electronic health record diagnosis and procedure code, as well as echocardiogram measurements derived from medical records.

Results: This study included 49,434 PMBB participants. An increased HCM PGS was associated with significantly increased left ventricular ejection fraction (LVEF), decreased left ventricular internal diameter at end-diastole (LVIDd), and increased interventricular septal thickness (IVS) (p<0.001). An increased DCM PGS was significantly (p<0.001) associated with decreased LVEF and increased LVIDd, but was not associated with IVS. A one standard deviation increase in HCM PGS was associated with increased risk of HCM (OR 1.8; 95% CI 1.6-2.0; p=9.6×10^-25) and decreased risk of DCM (OR 0.69; 95% CI 0.64-0.74; p=4.3×10^-22). A one standard deviation increase in DCM PGS was associated with an increased risk of DCM (OR 1.6; 95% CI 1.5-1.7; p=1.7×10^-40) and decreased risk of HCM (OR 0.69; 95% CI 0.63-0.76; p=3.0×10^Ȓ13). Monogenic and polygenic risk terms had significant, independent effects when combined in models of disease status and echocardiographic measurements; the additional inclusion of either an HCM or DCM PGS improved the discrimination of models of HCM and DCM that included age, sex, and monogenic variant status (>95% probability of difference in AUROC).

Conclusions and relevance: HCM and DCM risk are markedly modified by polygenic background which exists on an overlapping spectrum. Consideration of polygenic background may offer clinical value through improving understanding and prediction of these inherited cardiomyopathies.

Figure 1:. Sample filtering and workflow.

From an original sample of unrelated participants, the subset of patients with echocardiogram data was identified. Individuals were separately classified for HCM or DCM case and non-case status, excluding individuals with conflicting diagnosis codes, heart failure, or an HCM phenocopy such as amyloidosis. After validating case definitions with echocardiogram data, associations of genetics with clinical and echocardiogram data were separately tested.

Figure 2:. Effects of polygenic and monogenic risk on HCM and DCM probability.

Logistic regression models including include polygenic risk, monogenic risk, a polygenic:monogenic interaction term, sex, and age at time of biobank enrollment were used for marginal prediction of clinical cardiomyopathy outcomes. A) Prediction of DCM in carriers of a DCM pathogenic variant, using a DCM (left) and HCM (right) PGS. B) Prediction of HCM in carriers of a HCM pathogenic variant, using a DCM (left) and HCM (right) PGS.

Figure 3:. Contributions of monogenic and polygenic risk terms to A) DCM and B) HCM model discrimination.

HCM_P and DCM_P indicate the presence of a pathogenic variant in an HCM and DCM gene respectively.

Figure 4:. Effects of polygenic and monogenic risk on echocardiogram measurements.

Linear regression models including polygenic risk, monogenic risk, a polygenic:monogenic interaction term, sex, and age at time of echocardiogram were used for marginal prediction of echocardiogram measurements. A) Predicted Minimum EF and B) Predicted Maximal LVIDd stratified by DCM pathogenic variant status using DCM (left panel) and HCM (right panel) PGSs. C) Predicted Maximum EF and D) Predicted Maximal IVS stratified by HCM pathogenic variant status using DCM (left panel) and HCM (right panel) PGSs.