Body mass index (BMI) serves as an important measurement of obesity and adiposity, which are highly correlated with cardiometabolic diseases. Although high heritability has been estimated, the identified genetic variants by genetic association studies only explain a small proportion of BMI variation. As an active effort for further exploring the molecular basis of BMI variation, large-scale epigenome-wide association studies have been conducted but with limited number of loci reported, perhaps due to poorly controlled confounding factors, including genetic factors. Being genetically identical, monozygotic twins discordant for BMI are ideal subjects for analyzing the epigenetic association between DNA methylation and BMI, providing perfect control on their genetic makeups largely responsible for BMI variation.
We performed an epigenome-wide association study on BMI using 30 identical twin pairs (15 male and 15 female pairs) with age ranging from 39 to 72 years and degree of BMI discordance ranging from 3–7.5 kg/m2. Methylation data from whole blood samples were collected using the reduced representation bisulfite sequencing technique.
After adjusting for blood cell composition and clinical variables, we identified 136 CpGs with p-value < 1e-4, 30 CpGs with p < 1e-05 but no CpGs reached genome-wide significance. Genomic region-based analysis found 11 differentially methylated regions harboring coding and non-coding genes some of which were validated by gene expression analysis on independent samples.
Our DNA methylation sequencing analysis on identical twins provides new references for the epigenetic regulation on BMI and obesity.
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Di Cesare M, Bentham J, Stevens GA, Zhou B, Danaei G, Lu Y, et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387:1377–96.
Gaziano TA, Opie LH. Body-mass index and mortality. Lancet. 2009;374:113–4.
Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363:2211–9.
Centers of disease control. Body mass index: considerations for practitioners. CDC; 2011. https://www.cdc.gov/obesity/downloads/BMIforPactitioners.pdf.
Eriksen D, Rosthøj S, Burr H, Holtermann A. Sedentary work-Associations between five-year changes in occupational sitting time and body mass index. Prev Med (Baltim). 2015;73:1–5.
García Villar J, Quintana-Domeque C. Income and body mass index in Europe. Econ Hum Biol. 2009;7:73–83.
Elks CE, Hoed M den, Zhao JH, Sharp SJ, Wareham NJ, Loos RJF, et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front Endocrinol (Lausanne). 2012. https://doi.org/10.3389/fendo.2012.00029.
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. https://doi.org/10.1016/j.ajhg.2017.06.005.
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. https://doi.org/10.1038/nature14177.
Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 2016. https://doi.org/10.1038/nrg.2016.59.
Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S, et al. DNA methylation and body-mass index: A genome-wide analysis. Lancet. 2014;383:1990–8.
Wilson LE, Harlid S, Xu Z, Sandler DP, Taylor JA. An epigenome-wide study of body mass index and DNA methylation in blood using participants from the Sister Study cohort. Int J Obes. 2017. https://doi.org/10.1038/ijo.2016.184.
Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017. https://doi.org/10.1038/nature20784.
Tan Q. Epigenetic epidemiology of complex diseases using twins. Med Epigenetics. 2013;1:46–51.
Li W, Christiansen L, Hjelmborg J, Baumbach J, Tan Q. On the power of epigenome-wide association studies using a disease-discordant twin design. Bioinformatics. 2018;34:bty532–bty532.
Kurdyukov S, Bullock M. DNA methylation analysis: choosing the right method. Biology (Basel). 2016. https://doi.org/10.3390/biology5010003.
Duan H, Ning F, Zhang D, Wang S, Zhung D, Tan Q, et al. The qingdao twin registry: a status update. Twin Res Hum Genet. 2013. https://doi.org/10.1017/thg.2012.113.
Qiao Q, Pang Z, Gao W, Wang S, Dong Y, Zhang L, et al. A large-scale diabetes prevention program in real-life settings in Qingdao of CHN (2006-12). Prim. Care Diabetes. 2010. https://doi.org/10.1016/j.pcd.2010.04.003.
Krueger F, Andrews SR. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27:1571–2.
Krueger F. Trim galore. Babraham Bioinforma; 2016 (on line).
Langmead B, Salzberg SL. Langmead. Bowtie2. Nat Methods. 2013;9:357–9.
Hebestreit K, Dugas M, Klein HU. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics. 2013;29:1647–53.
Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods. 2016. https://doi.org/10.1038/nmeth.3809.
Tan Q, Christiansen L, von Bornemann Hjelmborg J, Christensen K. Twin methodology in epigenetic studies. J Exp Biol. 2015;218:134–9.
Hochberg Y, Benjaminit Y. Controlling the false discovery rate: a practical and powerful approach to multiple controlling the false discovery rate: a practical and powerful approach to multiple testing. Source J R Stat Soc Ser B J R Stat Soc Ser B J R Stat Soc B. 1995;57:289–300.
McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010;28:495–501.
Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: Software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012. https://doi.org/10.1093/bioinformatics/bts545.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. BioMart and Bioconductor: A powerful link between biological databases and microarray data analysis. Bioinformatics. 2005. https://doi.org/10.1093/bioinformatics/bti525.
Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/ Bioconductor package biomaRt. Nat Protoc. 2009. https://doi.org/10.1038/nprot.2009.97.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005. https://doi.org/10.1073/pnas.0506580102.
Peng Y, Chen FF, Ge J, Zhu JY, Shi XE, Li X, et al. miR-429 inhibits differentiation and promotes proliferation in porcine preadipocytes. Int J Mol Sci. 2016. https://doi.org/10.3390/ijms17122047.
Magenta A, Ciarapica R, Capogrossi MC. The emerging role of MIR-200 family in cardiovascular diseases. Circ Res. 2017. https://doi.org/10.1161/CIRCRESAHA.116.310274.
Crépin D, Benomar Y, Riffault L, Amine H, Gertler A, Taouis M. The over-expression of miR-200a in the hypothalamus of ob/ob mice is linked to leptin and insulin signaling impairment. Mol Cell Endocrinol. 2014. https://doi.org/10.1016/j.mce.2013.12.016.
Lin D, Chun TH, Kang L. Adipose extracellular matrix remodelling in obesity and insulin resistance. Biochem Pharmacol. 2016. https://doi.org/10.1016/j.bcp.2016.05.005.
Messina G, De Luca V, Viggiano A, Ascione A, Iannaccone T, Chieffi S, et al. Autonomic nervous system in the control of energy balance and body weight: Personal contributions. Neurol Res Int. 2013. https://doi.org/10.1155/2013/639280.
Charrier B, Pilon N. Toward a better understanding of enteric gliogenesis. Neurogenesis 2017. https://doi.org/10.1080/23262133.2017.1293958.
Li J, Tang Y, Purkayastha S, Yan J, Cai D. Control of obesity and glucose intolerance via building neural stem cells in the hypothalamus. Mol Metab. 2014. https://doi.org/10.1016/j.molmet.2014.01.012.
Lorenz C, Prigione A. Mitochondrial metabolism in early neural fate and its relevance for neuronal disease modeling. Curr Opin Cell Biol. 2017. https://doi.org/10.1016/j.ceb.2017.12.004.
Thaler JP, Guyenet SJ, Dorfman MD, Wisse BE, Schwartz MW, Wadden T, et al. Hypothalamic inflammation: marker or mechanism of obesity pathogenesis? Diabetes. 2013. https://doi.org/10.2337/db12-1605.
Bouret SG. Development of Hypothalamic Circuits That ControlFood Intake and Energy Balance. In: Harris RBS (Ed.) Appetiteand Food Intake: Central Control. 2nd Boca Raton (FL): CRCPress/Taylor & Francis; 2017. Chapter 7. Available from: https://www.ncbi.nlm.nih.gov/books/NBK453139/ https://doi.org/10.1201/9781315120171-7.
Godisela KK, Reddy SS, Kumar CU, Saravanan N, Reddy PY, Jablonski MM, et al. Impact of obesity with impaired glucose tolerance on retinal degeneration in a rat model of metabolic syndrome. Mol Vis. 2017;23:263–74.
Hotta K, Kitamoto T, Kitamoto A, Ogawa Y, Honda Y, Kessoku T, et al. Identification of the genomic region under epigenetic regulation during non-alcoholic fatty liver disease progression. Hepatol Res. 2018. https://doi.org/10.1111/hepr.12992.
Sharp GC, Salas LA, Monnereau C, Allard C, Yousefi P, Everson TM, et al. Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium. Hum Mol Genet. 2017. https://doi.org/10.1093/hmg/ddx290.
Feitosa MF, Wojczynski MK, North KE, Zhang Q, Province MA, Carr JJ, et al. The ERLIN1-CHUK-CWF19L1 gene cluster influences liver fat deposition and hepatic inflammation in the NHLBI Family Heart Study. Atherosclerosis. 2013. https://doi.org/10.1016/j.atherosclerosis.2013.01.038.
Day SE, Coletta RL, Kim JY, Garcia LA, Campbell LE, Benjamin TR, et al. Potential epigenetic biomarkers of obesity-related insulin resistance in human whole-blood. Epigenetics. 2017. https://doi.org/10.1080/15592294.2017.1281501.
Xie J-J, Jiang Y-Y, Jiang Y, Li C-Q, Chee L-M, An O, et al. Increased expression of the long non-coding RNA LINC01503, regulated by TP63, in squamous cell carcinoma and effects on oncogenic activities of cancer cell lines. Gastroenterology. 2018. https://doi.org/10.1053/j.gastro.2018.02.018.
Suryavanshi S, Jadhav S, McConnell B. Polymorphisms/mutations in A-kinase anchoring proteins (AKAPs): role in the cardiovascular system. J Cardiovasc Dev Dis. 2018. https://doi.org/10.3390/jcdd5010007.
This project was supported by the EFSD/CDS/Lilly Collaborative Diabetes Research Program (2013), the Lundbeck Foundation [grant number R170-2014-1353]; the DFF research project 1 from the Danish Council for Independent Research, Medical Sciences (DFF-FSS): DFF–6110-00114; the Novo Nordisk Foundation Medical and Natural Sciences Research Grant [grant number NNF13OC0007493, and by the National Natural Science Foundation of China grant # 81773506.
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Li, W., Zhang, D., Wang, W. et al. DNA methylome profiling in identical twin pairs discordant for body mass index. Int J Obes 43, 2491–2499 (2019). https://doi.org/10.1038/s41366-019-0382-4
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