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Genetics and epigenetics

Identification of a 1p21 independent functional variant for abdominal obesity

Abstract

Objectives

Aiming to uncover the genetic basis of abdominal obesity, we performed a genome-wide association study (GWAS) meta-analysis of trunk fat mass adjusted by trunk lean mass (TFMadj) and followed by a series of functional investigations.

Subjects

A total of 11,569 subjects from six samples were included into the GWAS meta-analysis.

Methods

Meta-analysis was performed by a weighted fixed-effects model. In silico replication analysis was performed in the UK-Biobank (UKB) sample (N = 331,093) and in the GIANT study (N up to 110,204). Cis-expression QTL (cis-eQTL) analysis, dual-luciferase reporter assay and electrophoresis mobility shift assay (EMSA) were conducted to examine the functional relevance of the identified SNPs. At last, differential gene expression analysis (DGEA) was performed.

Results

We identified an independent SNP rs12409479 at 1p21 (MAF = 0.07, p = 7.26 × 10−10), whose association was replicated by the analysis of TFM in the UKB sample (one-sided p = 3.39 × 10−3), and was cross-validated by the analyses of BMI (one-sided p = 0.03) and WHRadj (one-sided p = 0.04) in the GIANT study. Cis-eQTL analysis demonstrated that allele A at rs12409479 was positively associated with PTBP2 expression level in subcutaneous adipose tissue (N = 385, p = 4.15 × 10−3). Dual-luciferase reporter assay showed that the region repressed PTBP2 gene expression by downregulating PTBP2 promoter activity (p < 0.001), and allele A at rs12409479 induced higher luciferase activity than allele G did (p = 4.15 × 10−3). EMSA experiment implied that allele A was more capable of binding to unknown transcription factors than allele G. Lastly, DGEA showed that the level of PTBP2 expression was higher in individuals with obesity than in individuals without obesity (N = 20 and 11, p = 0.04 and 9.22 × 10−3), suggesting a regulatory role in obesity development.

Conclusions

Taken together, we hypothesize a regulating path from rs12409479 to trunk fat mass development through its allelic specific regulation of PTBP2 gene expression, thus providing some novel insight into the genetic basis of abdominal obesity.

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References

  1. 1.

    Haslam DW, James WP. Obesity. Lancet. 2005;366:1197–209.

    PubMed  Google Scholar 

  2. 2.

    Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:766–81.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hammond RA, Levine R. The economic impact of obesity in the United States. Diabetes Metab Syndr Obes. 2010;3:285–95.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Bleich S, Cutler D, Murray C, Adams A. Why is the developed world obese? Annu Rev Public Health. 2008;29:273–95.

    PubMed  Google Scholar 

  5. 5.

    Walley AJ, Blakemore AI, Froguel P. Genetics of obesity and the prediction of risk for health. Hum Mol Genet. 2006;15 Spec No. 2:R124–30.

    PubMed  Google Scholar 

  6. 6.

    Agarwal A, Williams GH, Fisher ND. Genetics of human hypertension. Trends Endocrinol Metab. 2005;16:127–33.

    CAS  PubMed  Google Scholar 

  7. 7.

    Fall T, Ingelsson E. Genome-wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol. 2014;382:740–57.

    CAS  PubMed  Google Scholar 

  8. 8.

    Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27:325–51.

    CAS  PubMed  Google Scholar 

  9. 9.

    Comuzzie AG, Allison DB. The search for human obesity genes. Science. 1998;280:1374–7.

    CAS  PubMed  Google Scholar 

  10. 10.

    Segal NL, Allison DB. Twins and virtual twins: bases of relative body weight revisited. Int J Obes Relat Metab Disord. 2002;26:437–41.

    CAS  PubMed  Google Scholar 

  11. 11.

    Pei YF, Zhang L, Liu Y, Li J, Shen H, Liu YZ, et al. Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet. 2014;23:820–30.

    CAS  PubMed  Google Scholar 

  12. 12.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Srikanthan P, Horwich TB, Tseng CH. Relation of muscle mass and fat mass to cardiovascular disease mortality. Am J Cardiol. 2016;117:1355–60.

    PubMed  Google Scholar 

  14. 14.

    Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ, et al. Abdominal adiposity and coronary heart disease in women. JAMA. 1998;280:1843–8.

    CAS  PubMed  Google Scholar 

  15. 15.

    Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359:2105–20.

    CAS  PubMed  Google Scholar 

  16. 16.

    Miller KK, Biller BM, Lipman JG, Bradwin G, Rifai N, Klibanski A. Truncal adiposity, relative growth hormone deficiency, and cardiovascular risk. J Clin Endocrinol Metab. 2005;90:768–74.

    CAS  PubMed  Google Scholar 

  17. 17.

    Russell M, Mendes N, Miller KK, Rosen CJ, Lee H, Klibanski A, et al. Visceral fat is a negative predictor of bone density measures in obese adolescent girls. J Clin Endocrinol Metab. 2010;95:1247–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Greco EA, Francomano D, Fornari R, Marocco C, Lubrano C, Papa V, et al. Negative association between trunk fat, insulin resistance and skeleton in obese women. World J Diabetes. 2013;4:31–9.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Segura-Jimenez V, Castro-Pinero J, Soriano-Maldonado A, Alvarez-Gallardo IC, Estevez-Lopez F, Delgado-Fernandez M, et al. The association of total and central body fat with pain, fatigue and the impact of fibromyalgia in women; role of physical fitness. Eur J Pain. 2016;20:811–21.

    CAS  PubMed  Google Scholar 

  20. 20.

    Canale MP, Manca di Villahermosa S, Martino G, Rovella V, Noce A, De Lorenzo A, et al. Obesity-related metabolic syndrome: mechanisms of sympathetic overactivity. Int J Endocrinol. 2013;2013:865965.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual-energy X-ray absorptiometry, in children aged 3-19 y. Am J Clin Nutr. 2000;72:490–5.

    CAS  PubMed  Google Scholar 

  22. 22.

    Zhang L, Choi HJ, Estrada K, Leo PJ, Li J, Pei YF, et al. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet. 2014;23:1923–33.

    CAS  PubMed  Google Scholar 

  23. 23.

    Rivadeneira F, Styrkarsdottir U, Estrada K, Halldorsson BV, Hsu YH, Richards JB, et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet. 2009;41:1199–206.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Design of the Women’s Health Initiative clinical trial and observational study. The Women’s Health Initiative Study Group. Control Clin Trials. 1998;19:61–109.

    Google Scholar 

  25. 25.

    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–73.

    PubMed  Google Scholar 

  27. 27.

    Zhang L, Pei YF, Fu X, Lin Y, Wang YP, Deng HW. FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinformatics. 2014;30:1876–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816–34.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Zhang L, Li J, Pei YF, Liu Y, Deng HW. Tests of association for quantitative traits in nuclear families using principal components to correct for population stratification. Ann Hum Genet. 2009;73(Pt 6):601–13.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004.

    CAS  PubMed  Google Scholar 

  31. 31.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 2015;11:e1005378.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Justice AE, Winkler TW, Feitosa MF, Graff M, Fisher VA, Young K, et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun. 2017;8:14977.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Google Scholar 

  36. 36.

    Pei YF, Ren HG, Liu L, Li X, Fang C, Huang Y, et al. Genomic variants at 20p11 associated with body fat mass in the European population. Obesity (Silver Spring). 2017;25:757–64.

    CAS  Google Scholar 

  37. 37.

    Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41:18–24.

    CAS  PubMed  Google Scholar 

  39. 39.

    Graff M, Scott RA, Justice AE, Young KL, Feitosa MF, Barata L, et al. Genome-wide physical activity interactions in adiposity—a meta-analysis of 200,452 adults. PLoS Genet. 2017;13:e1006528.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest. 1995;96:88–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Abate N, Garg A, Peshock RM, Stray-Gundersen J, Adams-Huet B, Grundy SM. Relationship of generalized and regional adiposity to insulin sensitivity in men with NIDDM. Diabetes. 1996;45:1684–93.

    CAS  PubMed  Google Scholar 

  43. 43.

    Grundy SM. Obesity, metabolic syndrome, and cardiovascular disease. J Clin Endocrinol Metab. 2004;89:2595–600.

    CAS  PubMed  Google Scholar 

  44. 44.

    Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond). 2008;32:959–66.

    CAS  Google Scholar 

  45. 45.

    Romanelli MG, Diani E, Lievens PM. New insights into functional roles of the polypyrimidine tract-binding protein. Int J Mol Sci. 2013;14:22906–32.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Lin JC, Lu YH, Liu YR, Lin YJ. RBM4a-regulated splicing cascade modulates the differentiation and metabolic activities of brown adipocytes. Sci Rep. 2016;6:20665.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518:187–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Corradin O, Scacheri PC. Enhancer variants: evaluating functions in common disease. Genome Med. 2014;6:85.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Liu NQ, Ter Huurne M, Nguyen LN, Peng T, Wang SY, Studd JB, et al. The non-coding variant rs1800734 enhances DCLK3 expression through long-range interaction and promotes colorectal cancer progression. Nat Commun. 2017;8:14418.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015;526:112–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32:381–5.

    PubMed  Google Scholar 

  53. 53.

    Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful to Loula M. Burton at the Tulane University for editing the manuscript. We appreciate all the volunteers who participated into this study. We are grateful to the GIANT consortium, the UK-Biobank, and Dr. Neale’s lab researchers for releasing large-scale summary association results for replication. This study was partially supported by the National Natural Science Foundation of China (31771417 and 31501026 to Y-FP, 31571291 to LZ), the Natural Science Foundation of Jiangsu Province of China (BK20150323 to Y-FP), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education (to Y-FP), the NIH (R01 AR069055, U19 AG055373, R01 MH104680, R01 AR059781 and P20 GM109036 to H-WD), the Edward G. Schlieder Endowment (to H-WD), the startup funding project of Soochow University (Q413900214 to LZ and Q413900114 to Y-FP) and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI. Funding for SHARe Affymetrix genotyping was provided by NHLBI Contract N02-HL-64278. SHARe Illumina genotyping was provided under an agreement between Illumina and Boston University. Funding support for the Framingham Whole Body and Regional Dual X-ray Absorptiometry (DXA) dataset was provided by NIH grants R01 AR/AG 41398. The datasets used for the analyses described in this manuscript were obtained from dbGaP (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap) through dbGaP accession phs000342.v14.p10. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, and the US Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221. This manuscript was not prepared in collaboration with investigators of the WHI, has not been reviewed and/or approved by the Women’s Health Initiative (WHI), and does not necessarily reflect the opinions of the WHI investigators or the NHLBI. Funding for WHI SHARe genotyping was provided by NHLBI contractN02-HL-64278.The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000200.v10.p3.

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Correspondence to Hong-Wen Deng or Lei Zhang.

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Samples used in this study are from multiple research and/or clinical centers. All samples were approved by the respective institutional ethics review boards, and all participants signed informed consent documents before being enrolled into the study.

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Liu, L., Pei, YF., Liu, TL. et al. Identification of a 1p21 independent functional variant for abdominal obesity. Int J Obes 43, 2480–2490 (2019). https://doi.org/10.1038/s41366-019-0350-z

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