Million Veteran Program: A mega-biobank to study genetic influences on health and disease

Gaziano, J. Michael; Concato, John; Brophy, Mary; Fiore, Louis D.; Pyarajan, Saiju; Breeling, James L.; Whitbourne, Stacey B.; Deen, Jennifer E.; Shannon, Colleen; Humphries, Donald E.; Guarino, Peter; Aslan, Mihaela; Anderson, Daniel R.; LaFleur, Rene; Hammond, Timothy G.; Schaa, Kendra L.; Moser, Jennifer; Huang, Grant D.; Muralidhar, Sumitra; Przygodzki, Ronald; O'Leary, Timothy J.

doi:10.1016/j.jclinepi.2015.09.016

Cited by 860 publications

(772 citation statements)

References 16 publications

Supporting

Mentioning

767

Contrasting

Unclassified

Order By: Relevance

“…Extremely large sample sizes are needed for additional discoveries, given the distribution of effect sizes observed to date for both common and rare variants, as well as the estimated proportion of the heritability of CAD explained by these variants to date. In the coming years, this need should be fulfilled by megabiobanks involving at least a half-million participants, including, but not limited to, the UK Biobank, the China Kadoorie Biobank, the Million Veteran Program, and the soon-to-beestablished NIH Precision Medicine Initiative cohort (161)(162)(163)(164). Such biobanks will likely also be leveraged to gain a better understanding of the clinical utility of genetic risk scores, and to conduct additional, well-powered MR studies to complement studies published to date.…”

Section: Future Directionsmentioning

confidence: 99%

Genetics: Implications for Prevention and Management of Coronary Artery Disease

Assimes

Roberts

2016

Journal of the American College of Cardiology

103

View full text Add to dashboard Cite

An exciting new era has dawned for the prevention and management of CAD utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for coronary artery disease (CAD) confirm not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Lastly, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications. Key Words: genetic variation, Mendelian randomization, risk scores, sequencingAbbreviations and Acronyms CAD = coronary artery disease GRS = genetic risk score GWAS = genome-wide association study HDL = high-density lipoprotein LDL = low-density lipoprotein MI = myocardial infarction MR = Mendelian randomization SNP = single-nucleotide polymorphism WES = whole-exome sequencing WGS = whole-genome sequencing 3

show abstract

Section: Future Directionsmentioning

confidence: 99%

Genetics: Implications for Prevention and Management of Coronary Artery Disease

Assimes

Roberts

2016

Journal of the American College of Cardiology

103

View full text Add to dashboard Cite

show abstract

“…[107][108][109] In Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With PrasugrelThrombolysis in Myocardial Infarction (TRITON-TIMI 38), carriers of low metabolizing CYP2C19 alleles had a 32% reduction in exposure to the active metabolite of clopidogrel compared with noncarriers. 110 The low metabolizing carriers also experienced a 53% increase in the primary composite cardiovascular outcome during treatment with clopidogrel compared with clopidogrel-treated noncarriers. This pharmacogenetic interaction has been questioned 111 but ultimately supported by large meta-analysis.…”

Section: Antiplatelet Therapy With P2y 12 Inhibitorsmentioning

confidence: 98%

Are Genetic Tests for Atherosclerosis Ready for Routine Clinical Use?

Paynter

Ridker

Chasman

2016

Circulation Research

View full text Add to dashboard Cite

T he Presidential Precision Medicine Initiative imagines a future in which medical decisions are increasingly tailored to the clinical profile of each patient.1 Incorporation of genetic information is a key component of the envisioned profile, continuing the goals of integration with patient care set out at the completion of the human genome sequence 15 years ago. As the cost of determining genetic information plummets and point-of-care genetic analysis is increasingly available, it Atherosclerosis Compendium© 2016 American Heart Association, Inc.

show abstract

“…This was often not possible in the early days of GWAS as scarce detailed phenotypic information had been collected in conjunction with genetic data. However, with expansions in data collection and the establishment of electronic health record (EHR)-linked biorepositories[18–22], copious amounts of biomedical data have become available. These data may be used for phenotypic refinement in the development of phenotype algorithms or they may be leveraged in clustering approaches for the stratification of patients into more homogeneous subgroups.…”

Section: The Phenotype In Traditional Genetic Studiesmentioning

confidence: 99%

Informatics and machine learning to define the phenotype

Basile

Ritchie

2018

Expert Review of Molecular Diagnostics

View full text Add to dashboard Cite

Introduction For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed. The authors address approaches for phenotype refinement that can aid in the more accurate characterization of traits. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR) derived data. Expert Commentary The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge, which may be inaccurate. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

show abstract

Million Veteran Program: A mega-biobank to study genetic influences on health and disease

Abstract: By helping to promote the future integration of genetic testing in health care delivery, including clinical decision making, the MVP is designed to contribute to the development of precision medicine.

Cited by 860 publications

References 16 publications

Genetics: Implications for Prevention and Management of Coronary Artery Disease

Genetics: Implications for Prevention and Management of Coronary Artery Disease

Are Genetic Tests for Atherosclerosis Ready for Routine Clinical Use?

Informatics and machine learning to define the phenotype

Contact Info

Product

Resources

About