Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits

Eun Kyung Choe, Manu Shivakumar, Anurag Verma, Shefali Setia Verma, Seung Ho Choi, Joo Sung Kim, Dokyoon Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number1930
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

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