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Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes
Joint Authors
Wen, Yan
He, Awen
Liang, Xiao
Wang, Wenyu
Liu, Li
Du, Yanan
Fan, Qianrui
Li, Ping
Hao, Jingcan
Guo, Xiong
Zhang, Feng
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-4, 4 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-28
Country of Publication
Egypt
No. of Pages
4
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liang, Xiao& He, Awen& Wang, Wenyu& Liu, Li& Du, Yanan& Fan, Qianrui…[et al.]. 2017. Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes. BioMed Research International،Vol. 2017, no. 2017, pp.1-4.
https://search.emarefa.net/detail/BIM-1134171
Modern Language Association (MLA)
Liang, Xiao…[et al.]. Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes. BioMed Research International No. 2017 (2017), pp.1-4.
https://search.emarefa.net/detail/BIM-1134171
American Medical Association (AMA)
Liang, Xiao& He, Awen& Wang, Wenyu& Liu, Li& Du, Yanan& Fan, Qianrui…[et al.]. Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-4.
https://search.emarefa.net/detail/BIM-1134171
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134171