ArticleBioinformatics (Oxford, England)2015
Application of clinical text data for phenome-wide association studies (PheWASs).
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study.JAMIA open · 2025Review
- Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Estimating the efficacy of pharmacogenomics over a lifetime.Frontiers in medicine · 2023Article
- Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records.Frontiers in digital health · 2023Article
- Druggable Transcriptional Networks in the Human Neurogenic Epigenome.Pharmacological reviews · 2019Review
- Using phenome-wide association studies to examine the effect of environmental exposures on human health.Environment international · 2019Article
- Article
- Disease associations depend on visit type: results from a visit-wide association study.BioData mining · 2019Article
- Genomic and Phenomic Research in the 21st Century.Trends in genetics : TIG · 2019Review
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger.American journal of human genetics · 2018Article
- Variation in Cilia Protein Genes and Progression of Lung Disease in Cystic Fibrosis.Annals of the American Thoracic Society · 2018Observational
- Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.Science (New York, N.Y.) · 2018Article
- Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records.Clinical and translational science · 2018Review
- Applying family analyses to electronic health records to facilitate genetic research.Bioinformatics (Oxford, England) · 2018Article
- Relationship of SULT1A1 copy number variation with estrogen metabolism and human health.The Journal of steroid biochemistry and molecular biology · 2017Article
- Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- RNA-Binding Protein IGF2BP1 in Cutaneous Squamous Cell Carcinoma.The Journal of investigative dermatology · 2017Article
- Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
motivationGenome-wide association studies (GWASs) are effective for describing genetic complexities of common diseases. Phenome-wide association studies (PheWASs) offer an alternative and complementary approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome. International Classification of Disease version 9 (ICD9) codes are used frequently to define the phenome, but using ICD9 codes alone misses other clinically relevant information from the EHR that can be used for PheWAS analyses and discovery.
resultsAs an alternative to ICD9 coding, a text-based phenome was defined by 23 384 clinically relevant terms extracted from Marshfield Clinic's EHR. Five single nucleotide polymorphisms (SNPs) with known phenotypic associations were genotyped in 4235 individuals and associated across the text-based phenome. All five SNPs genotyped were associated with expected terms (P<0.02), most at or near the top of their respective PheWAS ranking. Raw association results indicate that text data performed equivalently to ICD9 coding and demonstrate the utility of information beyond ICD9 coding for application in PheWAS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.