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.

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References

  1. 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.

  2. 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.

  3. 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.

  4. 4.

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

  5. 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.

  6. 6.

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

  7. 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.

  8. 8.

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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 14.

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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. 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.

  27. 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.

  28. 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.

  29. 29.

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

  30. 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.

  31. 31.

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

  32. 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.

  33. 33.

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

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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.

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Correspondence to Tie-Lin Yang.

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The authors declare that they have no conflict of interest.

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