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The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits
BACKGROUND: Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways fro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415316/ https://www.ncbi.nlm.nih.gov/pubmed/25898945 http://dx.doi.org/10.1186/s12864-015-1515-3 |
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author | Mei, Hao Li, Lianna Liu, Shijian Jiang, Fan Griswold, Michael Mosley, Thomas |
author_facet | Mei, Hao Li, Lianna Liu, Shijian Jiang, Fan Griswold, Michael Mosley, Thomas |
author_sort | Mei, Hao |
collection | PubMed |
description | BACKGROUND: Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis. RESULTS: We proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain. CONCLUSIONS: USGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1515-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4415316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44153162015-05-01 The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits Mei, Hao Li, Lianna Liu, Shijian Jiang, Fan Griswold, Michael Mosley, Thomas BMC Genomics Research Article BACKGROUND: Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis. RESULTS: We proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain. CONCLUSIONS: USGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1515-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-04-23 /pmc/articles/PMC4415316/ /pubmed/25898945 http://dx.doi.org/10.1186/s12864-015-1515-3 Text en © Mei et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Mei, Hao Li, Lianna Liu, Shijian Jiang, Fan Griswold, Michael Mosley, Thomas The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title | The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title_full | The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title_fullStr | The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title_full_unstemmed | The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title_short | The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
title_sort | uniform-score gene set analysis for identifying common pathways associated with different diabetes traits |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415316/ https://www.ncbi.nlm.nih.gov/pubmed/25898945 http://dx.doi.org/10.1186/s12864-015-1515-3 |
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