SynthesisScientific reports2022
Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits.
Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Genetic Architecture of Trans-Laminar Cribrosa Pressure Difference and Primary Open-Angle Glaucoma.Investigative ophthalmology & visual science · 2026Article
- Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.Journal of Korean medical science · 2026Article
- Genetic Determinants of Leisure-Time Physical Activity in the Taiwanese Population: A Genome-Wide Association Study.Medicine and science in sports and exercise · 2025Article
- Genome-Wide Association Study of Cognitive Function in Population-Based Cohorts in Japan: The Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study.Genes, brain, and behavior · 2025Article
- Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.Current osteoporosis reports · 2025Review
- Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups.GigaScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.
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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.