ArticleGenetics in medicine open2026
Development of a comprehensive cardiovascular disease genetic risk assessment test.
Article in Genetics in medicine open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Envisioning a more equitable and economical future for genomic testing.Genetics in medicine open · 2026Article
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30 authors.
Funding
Abstract
Purpose: Despite monogenic and polygenic contributions to cardiovascular disease (CVD), genetic testing is not widely adopted, and current tests are limited by the breadth of surveyed conditions and variant interpretation burden. To address these limitations, a comprehensive clinical genome CVD test with semiautomated interpretation was developed. Methods: Monogenic conditions and risk alleles were selected based on the strength of disease association and evidence for increased disease risk, respectively. Non-CVD secondary findings genes, pharmacogenomic (PGx) variants, and CVD-associated polygenic risk scores (PRS) were also assessed for inclusion. Test performance was modeled using 2594 genomes from the 1000 Genomes Project and further investigated in 20 previously tested individuals. Results: The CVD genome test comprises a panel of 215 high-confidence CVD gene-disease pairs, 35 non-CVD secondary findings genes, 4 risk alleles or genotypes, 10 PGx genes, and a PRS for coronary artery disease. Modeling of test performance using samples from the 1000 Genomes Project revealed approximately 6% of individuals with a monogenic finding in a CVD-associated gene, 6% with a risk allele finding, 1% with a non-CVD secondary finding, and 93% with CVD-associated PGx variants. Assessment of blinded clinical samples showed concordance with prior testing. An average of 4 variants were reviewed per case, with interpretation and reporting time ranging from 9 to 96 minutes. Conclusion: A genome-sequencing-based CVD genetic risk assessment test can provide comprehensive genetic disease and genetic risk information to patients with CVD. The semiautomated and limited interpretation burden suggest that this testing approach can be scaled to support population-level initiatives in phenotypically enriched populations.
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