Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetics and epigenetics

Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity

Abstract

Background:

Genome-wide association studies have identified many susceptibility loci for obesity. However, missing heritability problem is still challenging and ignorance of genetic interactions is believed to be an important cause. Current methods for detecting interactions usually do not consider regulatory elements in non-coding regions. Interaction analyses within chromatin regulatory circuitry may identify new susceptibility loci.

Methods:

We developed a pipeline named interaction analyses within chromatin regulatory circuitry (IACRC), to identify genetic interactions impacting body mass index (BMI). Potential interacting SNP pairs were obtained based on Hi-C datasets, PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers) algorithm, and super enhancer regions. SNP × SNP analyses were next performed in three GWAS datasets, including 2286 unrelated Caucasians from Kansas City, 3062 healthy Caucasians from the Gene Environment Association Studies initiative, and 3164 Hispanic subjects from the Women’s Health Initiative.

Results:

A total of 16,643,227 SNP × SNP analyses were performed. Meta-analyses showed that two SNP pairs, rs6808450–rs9813534 (combined P = 2.39 × 10−9) and rs6808450–rs3773306 (combined P = 2.89 × 10−9) were associated with BMI after multiple testing corrections. Single-SNP analyses did not detect significant association signals for these three SNPs. In obesity relevant cells, rs6808450 is located in intergenic enhancers, while rs9813534 and rs3773306 are located in the region of strong transcription regions of CAND2 and RPL32, respectively. The expression of CAND2 was significantly downregulated after the differentiation of human Simpson–Golabi–Behmel syndrome (SGBS) preadipocyte cells (P = 0.0241). Functional validation in the International Mouse Phenotyping Consortium database showed that CAND2 was associated with increased lean body mass and decreased total body fat amount.

Conclusions:

Detecting epistasis within chromatin regulatory circuitry identified CAND2 as a novel obesity susceptibility gene. We hope IACRC could facilitate the interaction analyses for complex diseases and offer new insights into solving the missing heritability problem.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes. 2008;32:1431–7.

    Article  CAS  Google Scholar 

  2. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.

    Article  CAS  Google Scholar 

  3. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.

    Article  CAS  Google Scholar 

  4. Stunkard AJ, Foch TT, Hrubec Z. A twin study of human obesity. JAMA. 1986;256:51–4.

    Article  CAS  Google Scholar 

  5. Turula M, Kaprio J, Rissanen A, Koskenvuo M. Body weight in the Finnish Twin Cohort. Diabetes Res Clin Pract. 1990;10(Suppl 1):S33–6.

    Article  Google Scholar 

  6. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009;10:392–404.

    Article  CAS  Google Scholar 

  7. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA. 2012;109:1193–8.

    Article  CAS  Google Scholar 

  8. Wei WH, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15:722–33.

    Article  CAS  Google Scholar 

  9. Dong SS, Hu WX, Yang TL, Chen XF, Yan H, Chen XD, et al. SNP-SNP interactions between WNT4 and WNT5A were associated with obesity related traits in Han Chinese Population. Sci Rep. 2017;7:43939.

    Article  Google Scholar 

  10. Wang MH, Li J, Yeung VS, Zee BC, Yu RH, Ho S, et al. Four pairs of gene-gene interactions associated with increased risk for type 2 diabetes (CDKN2BAS-KCNJ11), obesity (SLC2A9-IGF2BP2, FTO-APOA5), and hypertension (MC4R-IGF2BP2) in Chinese women. Meta Gene. 2014;2:384–91.

    Article  CAS  Google Scholar 

  11. Lucas G, Lluis-Ganella C, Subirana I, Musameh MD, Gonzalez JR, Nelson CP, et al. Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction. PLoS ONE. 2012;7:e41730.

    Article  CAS  Google Scholar 

  12. Yang TL, Guo Y, Li J, Zhang L, Shen H, Li SM, et al. Gene-gene interaction between RBMS3 and ZNF516 influences bone mineral density. J Bone Miner Res. 2013;28:828–37.

    Article  CAS  Google Scholar 

  13. Ma L, Brautbar A, Boerwinkle E, Sing CF, Clark AG, Keinan A. Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations. PLoS Genet. 2012;8:e1002714.

    Article  CAS  Google Scholar 

  14. Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.

    Article  Google Scholar 

  15. Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.

    Article  Google Scholar 

  16. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5.

    Article  CAS  Google Scholar 

  17. Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30.

    Article  CAS  Google Scholar 

  18. Gusev A, Lee SH, Trynka G, Finucane H, Vilhjalmsson BJ, Xu H, et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am J Hum Genet. 2014;95:535–52.

    Article  CAS  Google Scholar 

  19. Krijger PH, de Laat W. Regulation of disease-associated gene expression in the 3D genome. Nat Rev Mol Cell Biol. 2016;17:771–82.

    Article  CAS  Google Scholar 

  20. Ing-Simmons E, Seitan VC, Faure AJ, Flicek P, Carroll T, Dekker J, et al. Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin. Genome Res. 2015;25:504–13.

    Article  CAS  Google Scholar 

  21. Corradin O, Saiakhova A, Akhtar-Zaidi B, Myeroff L, Willis J, Cowper-Sal lari R, et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 2014;24:1–13.

    Article  CAS  Google Scholar 

  22. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-Andre V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–47.

    Article  CAS  Google Scholar 

  23. Yang TL, Guo Y, Li SM, Li SK, Tian Q, Liu YJ, et al. Ethnic differentiation of copy number variation on chromosome 16p12.3 for association with obesity phenotypes in European and Chinese populations. Int J Obes. 2013;37:188–90.

    Article  CAS  Google Scholar 

  24. Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159:1665–80.

    Article  CAS  Google Scholar 

  25. Tang Z, Luo OJ, Li X, Zheng M, Zhu JJ, Szalaj P, et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell. 2015;163:1611–27.

    Article  CAS  Google Scholar 

  26. Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S, Ferreira L, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet. 2015;47:598–606.

    Article  CAS  Google Scholar 

  27. Martin P, McGovern A, Orozco G, Duffus K, Yarwood A, Schoenfelder S, et al. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci. Nat Commun. 2015;6:10069.

    Article  CAS  Google Scholar 

  28. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.

    Article  CAS  Google Scholar 

  29. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  Google Scholar 

  30. Bonferroni CE. Teoria statistica delle classi e calcolo delle probabilità. Pubbl Del R Ist Super di Sci Econ e Commer di Firenze. 1936;8:3–62.

    Google Scholar 

  31. International Mouse Knockout C, Collins FS, Rossant J, Wurst W. A mouse for all reasons. Cell. 2007;128:9–13.

    Article  Google Scholar 

  32. Shiraishi S, Zhou C, Aoki T, Sato N, Chiba T, Tanaka K, et al. TBP-interacting protein 120B (TIP120B)/cullin-associated and neddylation-dissociated 2 (CAND2) inhibits SCF-dependent ubiquitination of myogenin and accelerates myogenic differentiation. J Biol Chem. 2007;282:9017–28.

    Article  CAS  Google Scholar 

  33. Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106:473–81.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the Gene Environment Association Studies initiative (GENEVA) which aimed to identify genetic factors that contribute to type 2 diabetes mellitus. We also thank the Women’s Health Initiative (WHI) project. When we performed the current study, we did not collaborate with the investigators of these projects. Therefore, our study does not necessarily reflect the opinions of them. The datasets we used were obtained through dbGaP authorized access with the accession number of phs000091.v2.p1 and phs000386.v7.p3.

Funding:

This study is supported by National Natural Science Foundation of China (31371278, 81573241, 31701095, and 31771399); China Postdoctoral Science Foundation (2016M602797); Natural Science Basic Research Program Shaanxi Province (2016JQ3026); and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tie-Lin Yang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, SS., Yao, S., Chen, YX. et al. Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity. Int J Obes 43, 450–456 (2019). https://doi.org/10.1038/s41366-018-0069-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-018-0069-2

Search

Quick links