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Study of genetic correlation between children’s sleep and obesity

Journal of Human Genetics volume 65, pages 949–959 (2020)Cite this article

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Abstract

Laboratory and epidemiological studies have shown that short sleep time is associated with obesity. In this study, we conducted a post-GWAS analysis to test genetic correlation between children’s sleep and obesity due to linkage disequilibrium (LD) SNPs, shared genes and pathways. Our analysis showed that genetic heritability was 0.14 (p-value = 0.0005) and 0.41 (p-value = 1.18E−24) for children’s sleep and obesity, respectively, but genetic correlation due to LD SNPs was insignificant. Gene associations at children’s GWAS were measured based on SNP associations and ranked by their uniform score (U-score). After adjusting for gene size, measured as the number of independent SNPs, children’s sleep and obesity GWAS had significant gene correlation (r = 0.23). Pathway enrichment analysis showed that “Suz12 target genes” was the significant pathway for both children’s sleep and obesity; pathways were significantly shared among top enriched pathways with an OR of 8.1–59.4; and significant correlation coefficient of pathway U-score was r = 0.36. Analysis of sleep time and obesity GWAS variants for all ages in the NHGRI-EBI GWAS Catalog also presented significant pathway correlation (r = 0.30). The “PAX3-FOXO1 target genes” was the significant pathway for all-age obesity phenotype and ranked as the second top associated pathway for all-age sleep time. Our study suggested that genetic correlation of children’s sleep time and obesity is attributed to genes with pleiotropy effects and common pathway regulations that may contain only weak SNP associations.

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References

  1. Haslam DW, James WPT. Obesity [Review]. Lancet. 2005;366:1197–209.

    Article  Google Scholar 

  2. Kopelman PG. Obesity as a medical problem [Article]. Nature. 2000;404:635–43.

    Article  CAS  Google Scholar 

  3. Must A. Does overweight in childhood have an impact on adult health? Nutr Rev 2003;61:139–42.

    Article  Google Scholar 

  4. Van Cauter E, Knutson KL. Sleep and the epidemic of obesity in children and adults. Eur J Endocrinol/Eur Federation Endocr Societies 2008;159(Suppl 1):S59–66.

    Article  Google Scholar 

  5. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review. Obesity. 2008;16:643–53.

    Article  Google Scholar 

  6. Garaulet M, Ortega FB, Ruiz JR, Rey-Lopez JP, Beghin L, Manios Y, et al. Short sleep duration is associated with increased obesity markers in European adolescents: effect of physical activity and dietary habits. The HELENA study. Int J Obes. 2011;35:1308–17.

    Article  CAS  Google Scholar 

  7. Medic G, Wille M, Hemels ME. Short- and long-term health consequences of sleep disruption. Nat Sci Sleep. 2017;9:151–61.

    Article  Google Scholar 

  8. Maffeis C, Tato L. Long-term effects of childhood obesity on morbidity and mortality. Horm Res. 2001;55(Suppl 1):42–5.

    CAS  PubMed  Google Scholar 

  9. Fabsitz RR, Carmelli D, Hewitt JK. Evidence for independent genetic influences on obesity in middle age. Int J Obes Relat Metab Disord. 1992;16:657–66.

    CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Marinelli M, Pappa I, Bustamante M, Bonilla C, Suarez A, Tiesler CM, et al. Heritability and genome-wide association analyses of sleep duration in children: the EAGLE consortium. Sleep. 2016;39:1859–69.

    Article  Google Scholar 

  12. Watson NF, Harden KP, Buchwald D, Vitiello MV, Pack AI, Weigle DS, et al. Sleep duration and body mass index in twins: a gene–environment interaction. Sleep. 2012;35:597–603.

    Article  Google Scholar 

  13. Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet. 2012;44:526–31.

    Article  CAS  Google Scholar 

  14. Leslie R, O’Donnell CJ, Johnson AD. GRASP: analysis of genotype–phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics. 2014;30:i185–94.

    Article  CAS  Google Scholar 

  15. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45:D896–D901.

    Article  CAS  Google Scholar 

  16. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.

    Article  CAS  Google Scholar 

  17. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J. Schizophrenia Working Group of the Psychiatric Genomics C, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  Google Scholar 

  18. Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ, et al. Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 2011;19:807–12.

    Article  Google Scholar 

  19. Duncan LE, Shen H, Ballon JS, Hardy KV, Noordsy DL, Levinson DF. Genetic correlation profile of schizophrenia mirrors epidemiological results and suggests link between polygenic and rare variant (22q11.2) cases of schizophrenia. Schizophrenia Bull. 2018;44:1350–61.

    Article  Google Scholar 

  20. Mei H, Li L, Jiang F, Simino J, Griswold M, Mosley T. et al. snpGeneSets: R Package Genome-Wide Study annotation. G3. 2016;6:4087–95.

    Article  Google Scholar 

  21. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11.

    Article  CAS  Google Scholar 

  22. Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez gene: gene-centered information at NCBI. Nucleic Acids Res. 2005;33:D54–8.

    Article  CAS  Google Scholar 

  23. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.

    Article  CAS  Google Scholar 

  24. Mei H, Li L, Liu S, Jiang F, Griswold M, Mosley T. The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits. BMC Genom. 2015;16:336.

    Article  Google Scholar 

  25. Shen L, Sinai M. GeneOverlap: test and visualize gene overlaps. R package version 1.18.0. 2018. http://shenlab-sinai.github.io/shenlab-sinai/.

  26. Liberzon A. A description of the Molecular Signatures Database (MSigDB) Web site. Methods Mol Biol. 2014;1150:153–60.

    Article  CAS  Google Scholar 

  27. Holmans P, Green EK, Pahwa JS, Ferreira MA, Purcell SM, Sklar P. et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder [Research Support, Non-U.S. Gov’t]. Am J Hum Genet. 2009;85:13–24.

    Article  CAS  Google Scholar 

  28. Replication DIG, Meta-analysis C. Asian Genetic Epidemiology Network Type 2 Diabetes C, South Asian Type 2 Diabetes C, Mexican American Type 2 Diabetes C, Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in multi-Ethnic Samples C, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44.

    Article  Google Scholar 

  29. Pletscher-Frankild S, Palleja A, Tsafou K, Binder JX, Jensen LJ. DISEASES: text mining and data integration of disease-gene associations. Methods. 2015;74:83–9.

    Article  CAS  Google Scholar 

  30. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–9.

    Article  CAS  Google Scholar 

  31. Spada J, Scholz M, Kirsten H, Hensch T, Horn K, Jawinski P, et al. Genome-wide association analysis of actigraphic sleep phenotypes in the LIFE Adult Study. J Sleep Res. 2016;25:690–701.

    Article  Google Scholar 

  32. Seki N, Hattori A, Hayashi A, Kozuma S, Hori T, Saito T. The human regulator of G-protein signaling protein 6 gene (RGS6) maps between markers WI-5202 and D14S277 on chromosome 14q24.3. J Hum Genet. 1999;44:138–40.

    Article  CAS  Google Scholar 

  33. Demarco GJ, Baghdoyan HA, Lydic R. Differential cholinergic activation of G proteins in rat and mouse brainstem: relevance for sleep and nociception. J Comp Neurol. 2003;457:175–84.

    Article  CAS  Google Scholar 

  34. Kimple ME, Neuman JC, Linnemann AK, Casey PJ. Inhibitory G proteins and their receptors: emerging therapeutic targets for obesity and diabetes. Exp Mol Med. 2014;46:e102.

    Article  CAS  Google Scholar 

  35. Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell. 2006;125:301–13.

    Article  CAS  Google Scholar 

  36. Hayashi Y, Kashiwagi M, Yasuda K, Ando R, Kanuka M, Sakai K, et al. Cells of a common developmental origin regulate REM/non-REM sleep and wakefulness in mice. Science. 2015;350:957–61.

    Article  CAS  Google Scholar 

  37. Mutskov V, Khalyfa A, Wang Y, Carreras A, Nobrega MA, Gozal D. Early-life physical activity reverses metabolic and Foxo1 epigenetic misregulation induced by gestational sleep disturbance. Am J Physiol Regul Integr Comp Physiol. 2015;308:R419–30.

    Article  CAS  Google Scholar 

  38. Vella D, Marini S, Vitali F, Di Silvestre D, Mauri G, Bellazzi R. MTGO: PPI network analysis via topological and functional module identification. Sci Rep. 2018;8:5499.

    Article  Google Scholar 

  39. De Las Rivas J, Fontanillo C. Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol. 2010;6:e1000807.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported in part by grants N01-HC55021 and U01-HL096917 from the National Institutes of Health/National Heart, Lung, and Blood Institute, Mississippi INBRE Grant P20GM103476, and Chinese National Natural Science Foundation (81728017).

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Authors and Affiliations

  1. Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA

    Hao Mei

  2. Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Fan Jiang & Shijian Liu

  3. Department of Biology, Tougaloo College, Jackson, MS, USA

    Lianna Li

  4. Department of Medicine, The MIND Center, University of Mississippi Medical Center, Jackson, MS, USA

    Michael Griswold & Thomas Mosley

  5. Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA

    Thomas Mosley

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  1. Hao Mei
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Correspondence to Hao Mei or Shijian Liu.

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Mei, H., Jiang, F., Li, L. et al. Study of genetic correlation between children’s sleep and obesity. J Hum Genet 65, 949–959 (2020). https://doi.org/10.1038/s10038-020-0791-1

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