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Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits.
Choe, Eun Kyung; Shivakumar, Manu; Verma, Anurag; Verma, Shefali Setia; Choi, Seung Ho; Kim, Joo Sung; Kim, Dokyoon.
Afiliação
  • Choe EK; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Shivakumar M; Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea.
  • Verma A; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Verma SS; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Choi SH; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Kim JS; Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea.
  • Kim D; Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea. jooskim@snu.ac.kr.
Sci Rep ; 12(1): 1930, 2022 02 04.
Article em En | MEDLINE | ID: mdl-35121771
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.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Variação Genética / Penetrância Tipo de estudo: Clinical_trials / Etiology_studies / Observational_studies / Systematic_reviews Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Variação Genética / Penetrância Tipo de estudo: Clinical_trials / Etiology_studies / Observational_studies / Systematic_reviews Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos