The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Extracting research-quality phenotypes from electronic health records to support precision medicine
Open accessAbstract
The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425
Complement factor H variant increases the risk of age-related macular degeneration
Haines, J. L., Hauser, M. A., Schmidt, S., Scott, W. K., Olson, L. M., Gallins, P., Spencer, K. L., Kwan, S. Y., Noureddine, M., Gilbert, J. R., et al. (2005) · Science · doi:10.1126/science.1110359 · Cited by 2,424
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013) · Nature Genetics · doi:10.1038/ng.2711 · Cited by 2,263
PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations
Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., Wang, D., Masys, D. R., Roden, D. M., & Crawford, D. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq126 · Cited by 1,219
Self-citationNext-generation phenotyping of electronic health records
Hripcsak, G., & Albers, D. J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001145 · Cited by 795
CNVs conferring risk of autism or schizophrenia affect cognition in controls
Stefansson, H., Meyer-Lindenberg, A., Steinberg, S., Magnusdottir, B., Morgen, K., Arnarsdottir, S., Bjornsdottir, G., Walters, G. B., Jonsdottir, G. A., Doyle, O. M., et al. (2014) · Nature · doi:10.1038/nature12818 · Cited by 699
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1dbd · Cited by 567 · cited 2×
“EHRs derive from selected populations and their data contain biases [34,45,106]; in particular, they are biased toward sick individuals.”
Background
Cited by
Drug repurposing: progress, challenges and recommendations
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.168 · Cited by 4,718
Clark, M. M., Hildreth, A., Batalov, S., Ding, Y., Chowdhury, S., Watkins, K., Ellsworth, K., Camp, B., Kint, C. I., Yacoubian, C., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aat6177 · Cited by 300
Wei, W.-Q., Teixeira, P. L., Mo, H., Cronin, R. M., Warner, J. L., & Denny, J. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv130 · Cited by 230
Self-citationDeep learning in pharmacogenomics: from gene regulation to patient stratification
Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D. (2018) · Pharmacogenomics · doi:10.2217/pgs-2018-0008 · Cited by 207
Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01931-w · Cited by 154
Metadata
- Authors
- Wei, W.-Q., & Denny, J. C.
- Venue
- Genome Medicine
- Published
- 2015-04-30
- Volume
- 7
- Issue
- 1
- Pages
- 41-41
- ISSN
- 1756-994X (ISSN-L)
- OpenAlex
- W1974660612
- Cited by
- 242
- References
- 17
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Extracting research-quality phenotypes from electronic health records to support precision medicine. https://app.cochat.ai/papers/10.1186/s13073-015-0166-y
- The paper
- Wei, W.-Q., & Denny, J. C. (2015). Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Medicine. https://doi.org/10.1186/s13073-015-0166-y
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
