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A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways
Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624908/ https://www.ncbi.nlm.nih.gov/pubmed/28970525 http://dx.doi.org/10.1038/s41598-017-12873-8 |
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author | Liu, Yang Zhao, Jing Jiang, Tao Yu, Mei Jiang, Guohua Hu, Yang |
author_facet | Liu, Yang Zhao, Jing Jiang, Tao Yu, Mei Jiang, Guohua Hu, Yang |
author_sort | Liu, Yang |
collection | PubMed |
description | Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D. |
format | Online Article Text |
id | pubmed-5624908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56249082017-10-12 A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways Liu, Yang Zhao, Jing Jiang, Tao Yu, Mei Jiang, Guohua Hu, Yang Sci Rep Article Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D. Nature Publishing Group UK 2017-10-02 /pmc/articles/PMC5624908/ /pubmed/28970525 http://dx.doi.org/10.1038/s41598-017-12873-8 Text en © The Author(s) 2017 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 Liu, Yang Zhao, Jing Jiang, Tao Yu, Mei Jiang, Guohua Hu, Yang A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title | A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title_full | A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title_fullStr | A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title_full_unstemmed | A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title_short | A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
title_sort | pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624908/ https://www.ncbi.nlm.nih.gov/pubmed/28970525 http://dx.doi.org/10.1038/s41598-017-12873-8 |
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