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Phenome-wide association studies across large population cohorts support drug target validation
Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6191429/ https://www.ncbi.nlm.nih.gov/pubmed/30327483 http://dx.doi.org/10.1038/s41467-018-06540-3 |
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author | Diogo, Dorothée Tian, Chao Franklin, Christopher S. Alanne-Kinnunen, Mervi March, Michael Spencer, Chris C. A. Vangjeli, Ciara Weale, Michael E. Mattsson, Hannele Kilpeläinen, Elina Sleiman, Patrick M. A. Reilly, Dermot F. McElwee, Joshua Maranville, Joseph C. Chatterjee, Arnaub K. Bhandari, Aman Nguyen, Khanh-Dung H. Estrada, Karol Reeve, Mary-Pat Hutz, Janna Bing, Nan John, Sally MacArthur, Daniel G. Salomaa, Veikko Ripatti, Samuli Hakonarson, Hakon Daly, Mark J. Palotie, Aarno Hinds, David A. Donnelly, Peter Fox, Caroline S. Day-Williams, Aaron G. Plenge, Robert M. Runz, Heiko |
author_facet | Diogo, Dorothée Tian, Chao Franklin, Christopher S. Alanne-Kinnunen, Mervi March, Michael Spencer, Chris C. A. Vangjeli, Ciara Weale, Michael E. Mattsson, Hannele Kilpeläinen, Elina Sleiman, Patrick M. A. Reilly, Dermot F. McElwee, Joshua Maranville, Joseph C. Chatterjee, Arnaub K. Bhandari, Aman Nguyen, Khanh-Dung H. Estrada, Karol Reeve, Mary-Pat Hutz, Janna Bing, Nan John, Sally MacArthur, Daniel G. Salomaa, Veikko Ripatti, Samuli Hakonarson, Hakon Daly, Mark J. Palotie, Aarno Hinds, David A. Donnelly, Peter Fox, Caroline S. Day-Williams, Aaron G. Plenge, Robert M. Runz, Heiko |
author_sort | Diogo, Dorothée |
collection | PubMed |
description | Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery. |
format | Online Article Text |
id | pubmed-6191429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61914292018-10-19 Phenome-wide association studies across large population cohorts support drug target validation Diogo, Dorothée Tian, Chao Franklin, Christopher S. Alanne-Kinnunen, Mervi March, Michael Spencer, Chris C. A. Vangjeli, Ciara Weale, Michael E. Mattsson, Hannele Kilpeläinen, Elina Sleiman, Patrick M. A. Reilly, Dermot F. McElwee, Joshua Maranville, Joseph C. Chatterjee, Arnaub K. Bhandari, Aman Nguyen, Khanh-Dung H. Estrada, Karol Reeve, Mary-Pat Hutz, Janna Bing, Nan John, Sally MacArthur, Daniel G. Salomaa, Veikko Ripatti, Samuli Hakonarson, Hakon Daly, Mark J. Palotie, Aarno Hinds, David A. Donnelly, Peter Fox, Caroline S. Day-Williams, Aaron G. Plenge, Robert M. Runz, Heiko Nat Commun Article Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery. Nature Publishing Group UK 2018-10-16 /pmc/articles/PMC6191429/ /pubmed/30327483 http://dx.doi.org/10.1038/s41467-018-06540-3 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Diogo, Dorothée Tian, Chao Franklin, Christopher S. Alanne-Kinnunen, Mervi March, Michael Spencer, Chris C. A. Vangjeli, Ciara Weale, Michael E. Mattsson, Hannele Kilpeläinen, Elina Sleiman, Patrick M. A. Reilly, Dermot F. McElwee, Joshua Maranville, Joseph C. Chatterjee, Arnaub K. Bhandari, Aman Nguyen, Khanh-Dung H. Estrada, Karol Reeve, Mary-Pat Hutz, Janna Bing, Nan John, Sally MacArthur, Daniel G. Salomaa, Veikko Ripatti, Samuli Hakonarson, Hakon Daly, Mark J. Palotie, Aarno Hinds, David A. Donnelly, Peter Fox, Caroline S. Day-Williams, Aaron G. Plenge, Robert M. Runz, Heiko Phenome-wide association studies across large population cohorts support drug target validation |
title | Phenome-wide association studies across large population cohorts support drug target validation |
title_full | Phenome-wide association studies across large population cohorts support drug target validation |
title_fullStr | Phenome-wide association studies across large population cohorts support drug target validation |
title_full_unstemmed | Phenome-wide association studies across large population cohorts support drug target validation |
title_short | Phenome-wide association studies across large population cohorts support drug target validation |
title_sort | phenome-wide association studies across large population cohorts support drug target validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6191429/ https://www.ncbi.nlm.nih.gov/pubmed/30327483 http://dx.doi.org/10.1038/s41467-018-06540-3 |
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