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Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes

AIM: To identify novel candidate genes and gene sets for diabetes. METHODS: We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving...

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Autores principales: Liang, Xiao, He, Awen, Wang, Wenyu, Liu, Li, Du, Yanan, Fan, Qianrui, Li, Ping, Wen, Yan, Hao, Jingcan, Guo, Xiong, Zhang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506468/
https://www.ncbi.nlm.nih.gov/pubmed/28744461
http://dx.doi.org/10.1155/2017/1758636
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author Liang, Xiao
He, Awen
Wang, Wenyu
Liu, Li
Du, Yanan
Fan, Qianrui
Li, Ping
Wen, Yan
Hao, Jingcan
Guo, Xiong
Zhang, Feng
author_facet Liang, Xiao
He, Awen
Wang, Wenyu
Liu, Li
Du, Yanan
Fan, Qianrui
Li, Ping
Wen, Yan
Hao, Jingcan
Guo, Xiong
Zhang, Feng
author_sort Liang, Xiao
collection PubMed
description AIM: To identify novel candidate genes and gene sets for diabetes. METHODS: We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. RESULTS: SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10(−8)), MRPL33 (p value = 1.24 × 10(−7)), and FADS1 (p value = 2.39 × 10(−7)). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. CONCLUSION: Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.
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spelling pubmed-55064682017-07-25 Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes Liang, Xiao He, Awen Wang, Wenyu Liu, Li Du, Yanan Fan, Qianrui Li, Ping Wen, Yan Hao, Jingcan Guo, Xiong Zhang, Feng Biomed Res Int Research Article AIM: To identify novel candidate genes and gene sets for diabetes. METHODS: We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. RESULTS: SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10(−8)), MRPL33 (p value = 1.24 × 10(−7)), and FADS1 (p value = 2.39 × 10(−7)). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. CONCLUSION: Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases. Hindawi 2017 2017-06-28 /pmc/articles/PMC5506468/ /pubmed/28744461 http://dx.doi.org/10.1155/2017/1758636 Text en Copyright © 2017 Xiao Liang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liang, Xiao
He, Awen
Wang, Wenyu
Liu, Li
Du, Yanan
Fan, Qianrui
Li, Ping
Wen, Yan
Hao, Jingcan
Guo, Xiong
Zhang, Feng
Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title_full Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title_fullStr Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title_full_unstemmed Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title_short Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
title_sort integrating genome-wide association and eqtls studies identifies the genes and gene sets associated with diabetes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506468/
https://www.ncbi.nlm.nih.gov/pubmed/28744461
http://dx.doi.org/10.1155/2017/1758636
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