Prevalence of Pathogenic Genetic Variants Across Heart Failure Etiologies in an Admixed Population

Heart failure (HF) has multiple etiologies, but genetic testing remains mainly recommended for selected subgroups with suspected inherited cardiomyopathy.

This study identified and classified genetic variants in patients within a HF cohort with reduced ejection fraction or mildly reduced ejection fraction from diverse etiologies in an admixed population.

The authors analyzed whole-genome sequencing data from 1,013 unrelated individuals in the GENIUS-HF (Genetic and ElectroNic medIcal records to predict oUtcomeS in Heart Failure patients) cohort. Rare variants in 123 ClinGen-curated cardiovascular genes were classified according to American College of Medical Genetics and Genomics (ACMG) criteria. Local ancestry inference evaluated associations with variant distribution.

Overall, 6.5% of individuals carried at least one pathogenic/likely pathogenic variant considered causal (no significant difference between HF cohort with reduced ejection fraction and HF with mildly reduced ejection fraction, P = 0.500), 77.7% had at least 1 variant of uncertain significance, and 15.8% had only benign/likely benign variants observed. TTN variants accounted for nearly half of the causal findings, including 13 novel variants, with additional causal variants in MYBPC3, FLNC, BAG3, and DSP. Idiopathic dilated cardiomyopathy showed the highest yield (9.9%), but pathogenic variants were also detected in ischemic (4.6%), Chagasic HF (4.9%), and hypertensive (3.8%). Local ancestry analyses showed that variant of uncertain significance were significantly more frequent in regions with African and Native American ancestry.

These findings replicate a significant diagnostic yield of molecular genetic testing in individuals with dilated cardiomyopathy but extend the relevance of genetic testing beyond traditionally selected HF subgroups. They also underscore the importance of including non-European and admixed populations in cardiovascular genomics to reduce interpretation bias and improve equity in precision medicine.