The Expansion of Genetic Testing in Cardiovascular Medicine: Preparing the Cardiology Community for the Changing Landscape.

PURPOSE OF REVIEW: Pathogenic DNA variants underlie many cardiovascular disease phenotypes. The most well-recognized of these include familial dyslipidemias, cardiomyopathies, arrhythmias, and aortopathies. The clinical presentations of monogenic forms of cardiovascular disease are often indistinguishable from those with complex genetic and non-genetic etiologies, making genetic testing an essential aid to precision diagnosis. RECENT FINDINGS: Precision diagnosis enables efficient management, appropriate use of emerging targeted therapies, and follow-up of at-risk family members. Genetic testing for these conditions is widely available but under-utilized. In this review, we summarize the potential benefits of genetic testing, highlighting the specific cardiovascular disease phenotypes in which genetic testing should be considered, and how clinicians can integrate guideline-directed genetic testing into their practice.

Evidence review and considerations for use of first line genome sequencing to diagnose rare genetic disorders.

Early use of genome sequencing (GS) in the diagnostic odyssey can reduce suffering and improve care, but questions remain about which patient populations are most amenable to GS as a first-line diagnostic test. To address this, the Medical Genome Initiative conducted a literature review to identify appropriate clinical indications for GS. Studies published from January 2011 to August 2022 that reported on the diagnostic yield (DY) or clinical utility of GS were included. An exploratory meta-analysis using a random effects model evaluated DY based on cohort size and diagnosed cases per cohort. Seventy-one studies met inclusion criteria, comprising over 13,000 patients who received GS in one of the following settings: hospitalized pediatric patients, pediatric outpatients, adult outpatients, or mixed. GS was the first-line test in 38% (27/71). The unweighted mean DY of first-line GS was 45% (12-73%), 33% (6-86%) in cohorts with prior genetic testing, and 33% (9-60%) in exome-negative cohorts. Clinical utility was reported in 81% of first-line GS studies in hospitalized pediatric patients. Changes in management varied by cohort and underlying molecular diagnosis (24-100%). To develop evidence-informed points to consider, the quality of all 71 studies was assessed using modified American College of Radiology (ACR) criteria, with five core points to consider developed, including recommendations for use of GS in the N/PICU, in lieu of sequential testing and when disorders with substantial allelic heterogeneity are suspected. Future large and controlled studies in the pediatric and adult populations may support further refinement of these recommendations.

Development of a comprehensive cardiovascular disease genetic risk assessment test.

Background: 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 interpretation burden. Methods: We developed a comprehensive clinical genome CVD test with semi-automated interpretation. 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 polygenic risk scores (PRS) were assessed for inclusion. Test performance was modeled using 2,594 genomes from the 1000 Genomes Project, and further investigated in 20 previously tested individuals. Results: The CVD genome test is composed of a panel of 215 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 ~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 complete concordance with prior testing. An average of 4 variants were reviewed per case, with interpretation and reporting time ranging from 9-96 min. Conclusions: A genome sequencing based CVD genetic risk assessment can provide comprehensive genetic disease and genetic risk information to patients with CVD. The semi-automated and limited interpretation burden suggest that this testing approach could be scaled to support population-level initiatives.