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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

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 Phe...

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Autores principales: Choe, Eun Kyung, Shivakumar, Manu, Verma, Anurag, Verma, Shefali Setia, Choi, Seung Ho, Kim, Joo Sung, Kim, Dokyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817039/
https://www.ncbi.nlm.nih.gov/pubmed/35121771
http://dx.doi.org/10.1038/s41598-021-04580-2
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author Choe, Eun Kyung
Shivakumar, Manu
Verma, Anurag
Verma, Shefali Setia
Choi, Seung Ho
Kim, Joo Sung
Kim, Dokyoon
author_facet Choe, Eun Kyung
Shivakumar, Manu
Verma, Anurag
Verma, Shefali Setia
Choi, Seung Ho
Kim, Joo Sung
Kim, Dokyoon
author_sort Choe, Eun Kyung
collection PubMed
description 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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spelling pubmed-88170392022-02-09 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 Sci Rep Article 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. Nature Publishing Group UK 2022-02-04 /pmc/articles/PMC8817039/ /pubmed/35121771 http://dx.doi.org/10.1038/s41598-021-04580-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Choe, Eun Kyung
Shivakumar, Manu
Verma, Anurag
Verma, Shefali Setia
Choi, Seung Ho
Kim, Joo Sung
Kim, Dokyoon
Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title_full Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title_fullStr Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title_full_unstemmed Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title_short Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits
title_sort leveraging deep phenotyping from health check-up cohort with 10,000 korean individuals for phenome-wide association study of 136 traits
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817039/
https://www.ncbi.nlm.nih.gov/pubmed/35121771
http://dx.doi.org/10.1038/s41598-021-04580-2
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