ArticleBMC medical genomics2016
eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03394859 (Electronic Medical Records and Genomics), which is not on this map. Cited by 22 papers.
What it found
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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.
Electronic Medical Records and Genomics (eMERGE) Phase III
Who cites it
22 citing papers in PubMed, 31 citations in OpenAlex.
- Review
- Psychiatric manifestations of rare variation in medically actionable genes: a PheWAS approach.BMC genomics · 2022Article
- The Role of Electronic Health Records in Advancing Genomic Medicine.Annual review of genomics and human genetics · 2021Review
- ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.Journal of biomedical informatics · 2021Article
- Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics.Nature communications · 2021Article
- Using the electronic health record for genomics research.Current opinion in lipidology · 2020Review
- Unbiased Phenome-Wide Association Studies of Red Cell Distribution Width Identifies Key Associations with Pulmonary Hypertension.Annals of the American Thoracic Society · 2019Article
- The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics.Cold Spring Harbor perspectives in medicine · 2019Review
- The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.Genetic epidemiology · 2019Article
- Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Genomic and Phenomic Research in the 21st Century.Trends in genetics : TIG · 2019Review
- Article
- Article
- The use of electronic health records for psychiatric phenotyping and genomics.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2018Review
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative.American journal of human genetics · 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
- A simulation study investigating power estimates in phenome-wide association studies.BMC bioinformatics · 2018Article
- Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery.Journal of obesity · 2018Article
- Current Scope and Challenges in Phenome-Wide Association Studies.Current epidemiology reports · 2017Article
Corrections and comments
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Authors and funding
17 authors at 9 institutions in 2 countries.
Funding
Abstract
backgroundWe explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.
resultsIn this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).
conclusionIn conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.
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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.