| scholarly article | Q13442814 |
| P356 | DOI | 10.1007/S11886-018-0969-8 |
| P2888 | exact match | https://scigraph.springernature.com/pub.10.1007/s11886-018-0969-8 |
| P698 | PubMed publication ID | 29520520 |
| P50 | author | Christopher J O'Donnell | Q89928448 |
| P2093 | author name string | Scott M Damrauer | |
| Aeron M Small | |||
| P2860 | cites work | Prediction of Coronary Heart Disease Using Risk Factor Categories | Q22241923 |
| Measuring diagnoses: ICD code accuracy | Q24537540 | ||
| Genome-wide meta-analyses identify multiple loci associated with smoking behavior | Q24597886 | ||
| Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk | Q24630394 | ||
| Six new loci associated with body mass index highlight a neuronal influence on body weight regulation | Q24646434 | ||
| A common allele on chromosome 9 associated with coronary heart disease | Q24647989 | ||
| Genomewide association analysis of coronary artery disease | Q24658344 | ||
| Biobanks and personalized medicine | Q26866132 | ||
| A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease | Q28267020 | ||
| Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes | Q28272915 | ||
| Extracting research-quality phenotypes from electronic health records to support precision medicine | Q28648163 | ||
| Biobanks and electronic medical records: enabling cost-effective research | Q28652448 | ||
| Secondary use of clinical data: the Vanderbilt approach | Q28654532 | ||
| Discovery and refinement of loci associated with lipid levels | Q28661470 | ||
| Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts | Q28751965 | ||
| Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease | Q29147547 | ||
| Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke | Q29417129 | ||
| Human genetic variation and its contribution to complex traits | Q29614580 | ||
| A common variant on chromosome 9p21 affects the risk of myocardial infarction | Q29614954 | ||
| Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study | Q29615729 | ||
| 'Mendelian randomization': an approach for exploring causal relations in epidemiology. | Q30235019 | ||
| Overview of the BioBank Japan Project: Study design and profile | Q30235963 | ||
| Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US | Q30490882 | ||
| Clinical epidemiology in the era of big data: new opportunities, familiar challenges | Q33627674 | ||
| Mendelian randomization: nature's randomized trial in the post-genome era. | Q42672170 | ||
| Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases. | Q53008274 | ||
| Accelerating the Use of Electronic Health Records in Physician Practices | Q56775282 | ||
| Rare Disease in Cardiovascular Medicine I | Q64046982 | ||
| Where do elderly veterans obtain care for acute myocardial infarction: Department of Veterans Affairs or Medicare? | Q33725969 | ||
| PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations | Q33808357 | ||
| Demographic history and rare allele sharing among human populations | Q34197613 | ||
| Managing incidental findings and research results in genomic research involving biobanks and archived data sets | Q34205284 | ||
| Genetics of human cardiovascular disease | Q34261931 | ||
| Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics | Q34544874 | ||
| Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study: A Genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls | Q34758752 | ||
| Opening of the national biobank of Korea as the infrastructure of future biomedical science in Korea | Q35026168 | ||
| Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data | Q35052005 | ||
| Bias associated with mining electronic health records | Q35145695 | ||
| China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up | Q35605244 | ||
| From Smallpox to Big Data: The Next 100 Years of Epidemiologic Methods | Q35800696 | ||
| Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. | Q36101689 | ||
| Cohort profile: the Mexico City Prospective Study | Q36429900 | ||
| Next-generation phenotyping of electronic health records | Q36563362 | ||
| Return of research results from genomic biobanks: cost matters | Q36792061 | ||
| Electronic medical records for genetic research: results of the eMERGE consortium | Q36948611 | ||
| Rare diseases research: expanding collaborative translational research opportunities | Q36999192 | ||
| Genomics for the world | Q37003231 | ||
| Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance | Q37112170 | ||
| The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research. | Q37168391 | ||
| Using electronic health records to drive discovery in disease genomics | Q37876693 | ||
| Rare-disease genetics in the era of next-generation sequencing: discovery to translation | Q38133704 | ||
| Million Veteran Program: A mega-biobank to study genetic influences on health and disease | Q38386184 | ||
| Biobanking for Personalized Medicine | Q38386392 | ||
| Use of electronic health records to ascertain, validate and phenotype acute myocardial infarction: A systematic review and recommendations | Q38475379 | ||
| Association analyses based on false discovery rate implicate new loci for coronary artery disease | Q38675868 | ||
| Genetic association studies in cardiovascular diseases: Do we have enough power? | Q38689422 | ||
| Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study | Q38829778 | ||
| Associations between single and multiple cardiometabolic diseases and cognitive abilities in 474 129 UK Biobank participants | Q38864388 | ||
| Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease | Q38924697 | ||
| Charting a course for genomic medicine from base pairs to bedside | Q39784592 | ||
| Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms | Q40191164 | ||
| ANGPTL3 Deficiency and Protection Against Coronary Artery Disease | Q40254292 | ||
| Genetic Analysis of Venous Thromboembolism in UK Biobank Identifies the ZFPM2 Locus and Implicates Obesity as a Causal Risk Factor | Q40260213 | ||
| 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study | Q40863323 | ||
| P433 | issue | 4 | |
| P921 | main subject | biobank | Q864217 |
| P304 | page(s) | 22 | |
| P577 | publication date | 2018-03-08 | |
| P1433 | published in | Current cardiology reports | Q26842345 |
| P1476 | title | Large-Scale Genomic Biobanks and Cardiovascular Disease. | |
| P478 | volume | 20 |
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