Artificial Intelligence and Cardiovascular Genetics

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.</jats:p

Abstract 15628: HeartCare: Improving Clinical Practice Through Comprehensive Cardiovascular Genetic Testing

Introduction:Cardiovascular disease (CVD) is the leading cause of mortality in the United States, leading to one in four deaths. The role of inherited susceptibility to CVD is well established, from rare monogenic disorders to polygenic traits. For many of these conditions, guidelines exist for medical interventions and other preventative care that can improve outcomes and quality of life.Methods:We developed a comprehensive genetic screen, "HeartCare", consisting of a 158 gene panel evaluating 1) Mendelian conditions including cardiomyopathies, aortopathies, arrhythmias, and dyslipidemias, 2) a coronary artery disease polygenic risk score (PRS), 3) variants in the LPA gene encoding Lipoprotein(a) that are an independent risk factor for atherosclerotic CVD events, and 4) pharmacogenetic (PGx) variants contributing to simvastatin-induced myopathy and warfarin metabolism. After sequencing in a CAP/CLIA certified laboratory, results were returned to the ordering physician after a multi-disciplinary sign-out conference and uploaded to the EMR.Results:As of June 2020, 678 individuals had completed testing with a 31% overall positive rate for Mendelian genes, elevated polygenic risk, and LPA risk alleles (excluding PGx). Of these, 8.1% had a positive finding for a Mendelian condition, the majority (60%) being dyslipidemias (e.g., FH), followed by 25% cardiomyopathies (e.g., HCM, DCM, ARVC) and 6% aortopathies (e.g., Marfan, Loeys-Dietz). Approximately 20% of individuals carried an LPA risk allele, and 9.3% belonged to the high-risk group according to their PRS. Approximately half had a PGx finding related to simvastatin and/or warfarin metabolism. Nearly one in five individuals had a finding with direct clinical care impact, including referral to specialists, imaging, laboratory studies, therapies/procedures (e.g., PCSK9i, ICD).Conclusions:To our knowledge, this is the first test of its kind assaying four distinct categories of genetic variation related to cardiovascular health. Our results demonstrate that comprehensive testing can be routinely used to identify individuals who may benefit from interventions to improve survival, reduce morbidity, and enhance quality of life

Genetic testing in ambulatory cardiology clinics reveals high rate of findings with clinical management implications

Purpose Cardiovascular disease (CVD) is the leading cause of death in adults in the United States, yet the benefits of genetic testing are not universally accepted. Methods We developed the "HeartCare" panel of genes associated with CVD, evaluating high-penetrance Mendelian conditions, coronary artery disease (CAD) polygenic risk, LPA gene polymorphisms, and specific pharmacogenetic (PGx) variants. We enrolled 709 individuals from cardiology clinics at Baylor College of Medicine, and samples were analyzed in a CAP/CLIA-certified laboratory. Results were returned to the ordering physician and uploaded to the electronic medical record. Results Notably, 32% of patients had a genetic finding with clinical management implications, even after excluding PGx results, including 9% who were molecularly diagnosed with a Mendelian condition. Among surveyed physicians, 84% reported medical management changes based on these results, including specialist referrals, cardiac tests, and medication changes. LPA polymorphisms and high polygenic risk of CAD were found in 20% and 9% of patients, respectively, leading to diet, lifestyle, and other changes. Warfarin and simvastatin pharmacogenetic variants were present in roughly half of the cohort. Conclusion Our results support the use of genetic information in routine cardiovascular health management and provide a roadmap for accompanying research

Additional file 1 of Genetic sex validation for sample tracking in next-generation sequencing clinical testing

Additional file 1: Table S1. 96-SNP panel design—BCM-HGSC-CL

Lessons learned from the eMERGE Network: balancing genomics in discovery and practice

The Electronic Medical Records and Genomics (eMERGE) Network, established in 2007, is a consortium of academic and integrated health systems conducting discovery and implementation research in translational genomics. Here, we outline the history of the network, highlight major impacts and lessons learned, and present the tools and resources developed for large-scale genomic analyses and translation into a clinical setting. The network developed methods to extract phenotypes from the electronic medical record to perform genome-wide and phenome-wide association studies. Recruited cohorts were clinically sequenced off a custom panel for targeted sequencing of variants and monogenic disease risks and returned to participants to investigate the impact of return of genomic results. After generating a 105,000 participant-imputed genome-wide association study (GWAS) dataset for discovery, the network enrolled and sequenced 24,998 participants. Integration of these results into the medical record and the effects of results on participants provided key lessons to the field. These learned lessons inform genetic research in diverse populations and provide insights into the clinical impact of return and implementation of genomic medicine using the electronic medical record. The lessons produced by the eMERGE Network can be utilized by other consortia as translational genomic medicine research evolves