ArticleCurrent epidemiology reports2017
Current Scope and Challenges in Phenome-Wide Association Studies.
Article in Current epidemiology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Association Between Clonal Hematopoiesis and Cardiometabolic Disease: A Systematic Review and Meta-Analysis.JACC. CardioOncology · 2026Article
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- Daily Heart Rate per Step: A Wearables Metric Associated With Cardiovascular Disease in a Cross-Sectional Study of theJournal of the American Heart Association · 2025Observational
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Molecular Alterations Caused by Alcohol Consumption in the UK Biobank: A Mendelian Randomisation Study.Nutrients · 2022Article
- Article
- Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.Learning health systems · 2022Article
- Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.Annual review of biomedical data science · 2021Review
- Article
- "Retrofitting" established genetic disorders and diseases through big data and phenomics.Neurology. Clinical practice · 2020Article
- Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology.Nature reviews. Gastroenterology & hepatology · 2020Review
- Turning genome-wide association study findings into opportunities for drug repositioning.Computational and structural biotechnology journal · 2020Review
- Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico.BMC bioinformatics · 2019Article
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.GigaScience · 2019Review
- Fishing forward and reverse: Advances in zebrafish phenomics.Mechanisms of development · 2018Review
- A simulation study investigating power estimates in phenome-wide association studies.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
purpose of reviewOver many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS. RECENT
findingsPheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data. SUMMARY: The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.
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