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The term phenotype is so commonly used that we often assume that we each mean the same thing. The general definition, the set of observable characteristics of an individual resulting from the interaction of their genotype with the environment, is often left to the eye of the beholder. Whether applied to the multiple levels of biological phenomena or the intact human being, our ability to characterize, classify, and analyze phenotype has been limited by measurement deficits, computing limitations, and a culture that avoids the generalizable. With the advent of modern technology, there is the potential for a revolution in phenotyping, which incorporates old and new in structured ways to dramatically advance basic understanding of biology and behavior and to lead to major improvements in clinical care and public health. This revolution in how we think about phenotypes will require a radical change in the scale at which biomedicine operates with significant changes in the unit of action, which will have far-reaching implications for how care, translation, and discovery are implemented.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition., Competing Interests: The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest., (© 2018 American Heart Association, Inc.)

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing., (Published by Elsevier Inc.)

The field of genetics and genomics has advanced considerably with the achievement of recent milestones encompassing the identification of many loci for cardiovascular disease and variable drug responses. Despite this achievement, a gap exists in the understanding and advancement to meaningful translation that directly affects disease prevention and clinical care. The purpose of this scientific statement is to address the gap between genetic discoveries and their practical application to cardiovascular clinical care. In brief, this scientific statement assesses the current timeline for effective translation of basic discoveries to clinical advances, highlighting past successes. Current discoveries in the area of genetics and genomics are covered next, followed by future expectations, tools, and competencies for achieving the goal of improving clinical care., (© 2015 American Heart Association, Inc.)

As genome sequencing technologies increasingly enter medical practice, genetics laboratories must communicate sequencing results effectively to nongeneticist physicians. We describe the design and delivery of a clinical genome sequencing report, including a one-page summary suitable for interpretation by primary care physicians. To illustrate our preliminary experience with this report, we summarize the genomic findings from 10 healthy participants in a study of genome sequencing in primary care., (© 2015 S. Karger AG, Basel.)

Background: Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits., Results: To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM., Conclusion: Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Despite increasing ability to understand and correct molecular derangements in disease, genomics and novel phenotypic assays are unevenly deployed in clinical practice. This has hampered translational research and our ability to identify clinically actionable subtypes of disease. Historic examples illustrate how the perspectives of stakeholders across the healthcare ecosystem can influence adoption of innovations in healthcare. Consideration of these factors, from discovery to implementation, can accelerate adoption of new molecular and digital phenotypes in a 'learning' healthcare ecosystem. [ABSTRACT FROM AUTHOR]

Background--As DNA sequencing costs decline, genetic testing options have expanded. Whole exome sequencing and whole genome sequencing (WGS) are entering clinical use, posing questions about their incremental value compared with disease-specific multigene panels that have been the cornerstone of genetic testing. Methods and Results--Forty-one patients with hypertrophic cardiomyopathy who had undergone targeted hypertrophic cardiomyopathy genetic testing (either multigene panel or familial variant test) were recruited into the MedSeq Project, a clinical trial of WGS. Results from panel genetic testing and WGS were compared. In 20 of 41 participants, panel genetic testing identified variants classified as pathogenic, likely pathogenic, or uncertain significance. WGS identified 19 of these 20 variants, but the variant detection algorithm missed a pathogenic 18 bp duplication in myosin binding protein C (MYBPC3) because of low coverage. In 3 individuals, WGS identified variants in genes implicated in cardiomyopathy but not included in prior panel testing: a pathogenic protein tyrosine phosphatase, non-receptor type 11 (PTPN11) variant and variants of uncertain significance in integrin-linked kinase (ILK) and filamin-C (FLNC). WGS also identified 84 secondary findings (mean=2 per person, range=0-6), which mostly defined carrier status for recessive conditions. Conclusions--WGS detected nearly all variants identified on panel testing, provided 1 new diagnostic finding, and allowed interrogation of posited disease genes. Several variants of uncertain clinical use and numerous secondary genetic findings were also identified. Whereas panel testing and WGS provided similar diagnostic yield, WGS offers the advantage of reanalysis over time to incorporate advances in knowledge, but requires expertise in genomic interpretation to appropriately incorporate WGS into clinical care. [ABSTRACT FROM AUTHOR]

The article projects the future of cardiovascular treatment options in the U.S. Topics discussed include the concepts of cardiovascular disease and their association with therapy, the target population of cardiovascular community, various drug discovery approaches and their driving factors, and the emergence of different classes of therapeutics.

Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ∼100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease. [ABSTRACT FROM AUTHOR]

An editorial is presented which discusses the state-of-the-art strategies to assess the causality of newly identified variants in potential cardiovascular disease genes. It mentions the discovery of numerous disease genes for cardiomyopathies, arrhythmias, aortopathies, and other disorders through genetic and genomic studies which demontsrate complex relationships between genotype and phenotype.

As the current paradigms of drug discovery evolve, it has become clear that a more comprehensive understanding of the interactions between small molecules and organismal biology will be vital. The zebrafish is emerging as a complement to existing in vitro technologies and established preclinical in vivo models that can be scaled for high-throughput. In this review, we highlight the current status of zebrafish toxicology studies, identify potential future niches for the model in the drug development pipeline, and define the hurdles that must be overcome as zebrafish technologies are refined for systematic toxicology. [ABSTRACT FROM AUTHOR]

The authors reflect on the insights from genome-wide associations (GWAs). Topics include correlation between common variants and peripheral or tissue-specific expression quantitative trait loci (eQTL), a study by N. A. M. Almontashiri and colleagues on disruption of specific transcription factor (TF) binding sites, and a phenotype gap in modern human genetics.

The article focuses on the projected future of the U.S. cardiovascular biomedicine. Topics discussed include the application of molecular medicine and its mechanistic insights to clinical medicine, the generation of recombinant proteins, and the significance of cardiovascular biomedicine in transformation biomedicine.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries—through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists—and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition. [ABSTRACT FROM AUTHOR]

The article discusses research concerning the need for quantitative tools in clinical genomic studies to determine the association of genotype to phenotype. An overview on the establishment of initial disease gene in segregation studies of multiple independent families and specific variant in the gene, is provided. Also outlined is the opportunities for genomic medicine and the hurdles with combination of remarkable degradation in pedigree information.