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BioMed Research International
Volume 2017 (2017), Article ID 1758636, 4 pages
https://doi.org/10.1155/2017/1758636
Research Article

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

Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, China

Correspondence should be addressed to Feng Zhang

Received 29 March 2017; Accepted 24 May 2017; Published 28 June 2017

Academic Editor: Rosaria Scudiero

Copyright © 2017 Xiao Liang et al. 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.

Linked References

  1. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Grinder-Hansen, R. Ribel-Madsen, J. F. P. Wojtaszewski, P. Poulsen, L. G. Grunnet, and A. Vaag, “A common variation of the PTEN gene is associated with peripheral insulin resistance,” Diabetes and Metabolism, vol. 42, no. 4, pp. 280–284, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Grarup, K. L. Stender-Petersen, E. A. Andersson et al., “Association of variants in the sterol regulatory element-binding factor 1 (SREBF1) gene with type 2 diabetes, glycemia, and insulin resistance A study of 15,734 danish subjects,” Diabetes, vol. 57, no. 4, pp. 1136–1142, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Zeggini, L. J. Scott, and R. Saxena, “Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes,” Nature Genetics, vol. 40, no. 5, pp. 638–645, 2008. View at Publisher · View at Google Scholar
  5. G. A. Walford et al., “Genome-wide association study of the modified Stumvoll Insulin Sensitivity Index identifies BCL2 and FAM19A2 as novel insulin sensitivity loci,” Diabetes, vol. 65, no. 10, Article ID db160199, pp. 3200–3211, 2016. View at Publisher · View at Google Scholar
  6. D. L. Nicolae, E. Gamazon, W. Zhang, S. Duan, M. Eileen Dolan, and N. J. Cox, “Trait-associated SNPs are more likely to be eQTLs: Annotation to enhance discovery from GWAS,” PLoS Genetics, vol. 6, no. 4, Article ID e1000888, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Yang, Y. Liu, N. Jiang et al., “Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals,” BMC Genomics, vol. 15, no. 1, article 13, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. E. Petretto, “Single cell expression quantitative trait loci and complex traits,” Genome Medicine, vol. 5, no. 8, article 72, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Zhu, F. Zhang, H. Hu et al., “Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets,” Nature Genetics, vol. 48, no. 5, pp. 481–487, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. A. K. Manning, “A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance,” Nature Genetics, vol. 44, no. 6, pp. 659–669, 2012. View at Publisher · View at Google Scholar
  11. Y. Li, C. J. Willer, J. Ding, P. Scheet, and G. R. Abecasis, “MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes,” Genetic Epidemiology, vol. 34, no. 8, pp. 816–834, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Marchini, B. Howie, S. Myers, G. McVean, and P. Donnelly, “A new multipoint method for genome-wide association studies by imputation of genotypes,” Nature Genetics, vol. 39, no. 7, pp. 906–913, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. A. K. Manning, M. LaValley, C.-T. Liu et al., “Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients,” Genetic Epidemiology, vol. 35, no. 1, pp. 11–18, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. H. J. Westra et al., “Systematic identification of trans eQTLs as putative drivers of known disease associations,” Nature Genetics, vol. 45, no. 10, pp. 1238–1243, 2013. View at Google Scholar
  15. A. Subramanian, P. Tamayo, V. K. Mootha et al., “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 43, pp. 15545–15550, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Wang, M. Li, and M. Bucan, “Pathway-based approaches for analysis of genomewide association studies,” American Journal of Human Genetics, vol. 81, no. 6, pp. 1278–1283, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Wen, W. Wang, X. Guo, and F. Zhang, “PAPA: A flexible tool for identifying pleiotropic pathways using genome-wide association study summaries,” Bioinformatics, vol. 32, no. 6, pp. 946–948, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Bochenek, R. Häsler, N.-E. E. Mokhtari et al., “The large non-coding RNA ANRIL, which is associated with atherosclerosis, periodontitis and several forms of cancer, regulates ADIPOR1, VAMP3 and C11ORF10,” Human Molecular Genetics, vol. 22, no. 22, Article ID ddt299, pp. 4516–4527, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Zabaneh and D. J. Balding, “A genome-wide association study of the metabolic syndrome in Indian Asian men,” PLoS ONE, vol. 5, no. 8, Article ID e11961, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. D. R. Powell, J. P. Gay, M. Smith et al., “Fatty acid desaturase 1 knockout mice are lean with improved glycemic control and decreased development of atheromatous plaque,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 9, pp. 185–199, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Yao, J. Li, T. Xie et al., “Polymorphisms of rs174616 in the FADS1-FADS2 gene cluster is associated with a reduced risk of type 2 diabetes mellitus in northern Han Chinese people,” Diabetes Research and Clinical Practice, vol. 109, no. 1, pp. 206–212, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Cormier, I. Rudkowska, E. Thifault, S. Lemieux, P. Couture, and M.-C. Vohl, “Polymorphisms in Fatty Acid Desaturase (FADS) gene cluster: Effects on glycemic controls following an omega-3 Polyunsaturated Fatty Acids (PUFA) supplementation,” Genes, vol. 4, no. 3, pp. 485–498, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. S. P. Sajuthi, N. K. Sharma, J. W. Chou et al., “Mapping adipose and muscle tissue expression quantitative trait loci in African Americans to identify genes for type 2 diabetes and obesity,” Human Genetics, vol. 135, no. 8, pp. 869–880, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Adachi and M. Tsujimoto, “Adaptor protein sorting nexin 17 interacts with the scavenger receptor FEEL-1/stabilin-1 and modulates its expression on the cell surface,” Biochimica et Biophysica Acta - Molecular Cell Research, vol. 1803, no. 5, pp. 553–563, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. W. Gao, Y. Zhou, Q. Li et al., “Analysis of global gene expression profiles suggests a role of acute inflammation in type 3C diabetes mellitus caused by pancreatic ductal adenocarcinoma,” Diabetologia, vol. 58, no. 4, pp. 835–844, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. N. Rabhi, P.-D. Denechaud, X. Gromada et al., “KAT2B Is Required for Pancreatic Beta Cell Adaptation to Metabolic Stress by Controlling the Unfolded Protein Response,” Cell Reports, vol. 15, no. 5, pp. 1051–1061, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Hoffjan, A. Okur, J. T. Epplen, S. Wieczorek, A. Chan, and D. A. Akkad, “Association of TNFAIP3 and TNFRSF1A variation with multiple sclerosis in a German case-control cohort,” International Journal of Immunogenetics, vol. 42, no. 2, pp. 106–110, 2015. View at Publisher · View at Google Scholar · View at Scopus