Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1783249568054378496 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5506468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT liangxiao integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT heawen integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT wangwenyu integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT liuli integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT duyanan integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT fanqianrui integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT liping integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT wenyan integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT haojingcan integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT guoxiong integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes AT zhangfeng integratinggenomewideassociationandeqtlsstudiesidentifiesthegenesandgenesetsassociatedwithdiabetes |