IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0065648.html
   My bibliography  Save this article

Profiling of Childhood Adversity-Associated DNA Methylation Changes in Alcoholic Patients and Healthy Controls

Author

Listed:
  • Huiping Zhang
  • Fan Wang
  • Henry R Kranzler
  • Hongyu Zhao
  • Joel Gelernter

Abstract

The increased vulnerability to alcohol dependence (AD) seen in individuals with childhood adversity (CA) may result in part from CA-induced epigenetic changes. To examine CA-associated DNA methylation changes in AD patients, we examined peripheral blood DNA methylation levels of 384 CpGs in promoter regions of 82 candidate genes in 279 African Americans [AAs; 88 with CA (70.5% with AD) and 191 without CA (38.2% with AD)] and 239 European Americans [EAs; 61 with CA (86.9% with AD) and 178 without CA (46.6% with AD)] using Illumina GoldenGate Methylation Array assays. The effect of CA on methylation of individual CpGs and overall methylation in promoter regions of genes was evaluated using a linear regression analysis (with consideration of sex, age, and ancestry proportion of subjects) and a principal components-based analysis, respectively. In EAs, hypermethylation of 10 CpGs in seven genes (ALDH1A1, CART, CHRNA5, HTR1B, OPRL1, PENK, and RGS19) were cross validated in AD patients and healthy controls who were exposed to CA. P values of two CpGs survived Bonferroni correction when all EA samples were analyzed together to increase statistical power [CHRNA5_cg17108064: Padjust = 2.54×10−5; HTR1B_cg06031989: Padjust = 8.98×10−5]. Moreover, overall methylation levels in the promoter regions of three genes (ALDH1A1, OPRL1 and RGS19) were elevated in both EA case and control subjects who were exposed to CA. However, in AAs, CA-associated DNA methylation changes in AD patients were not validated in healthy controls. Our findings suggest that CA could induce population-specific methylation alterations in the promoter regions of specific genes, thus leading to changes in gene transcription and an increased risk for AD and other disorders.

Suggested Citation

  • Huiping Zhang & Fan Wang & Henry R Kranzler & Hongyu Zhao & Joel Gelernter, 2013. "Profiling of Childhood Adversity-Associated DNA Methylation Changes in Alcoholic Patients and Healthy Controls," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0065648
    DOI: 10.1371/journal.pone.0065648
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065648
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0065648&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0065648?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Afifi, T.O. & Enns, M.W. & Cox, B.J. & Asmundson, G.J.G. & Stein, M.B. & Sareen, J., 2008. "Population attributable fractions of psychiatric disorders and suicide ideation and attempts associated with adverse childhood experiences," American Journal of Public Health, American Public Health Association, vol. 98(5), pages 946-952.
    3. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ming Yuan & Christina Kendziorski, 2006. "A Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification," Biometrics, The International Biometric Society, vol. 62(4), pages 1089-1098, December.
    2. Leek Jeffrey T & Storey John D., 2011. "The Joint Null Criterion for Multiple Hypothesis Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, June.
    3. Hongkai Ji & Wing Hung Wong, 2006. "Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes," Biometrics, The International Biometric Society, vol. 62(3), pages 645-663, September.
    4. N. Bochkina & S. Richardson, 2007. "Tail Posterior Probability for Inference in Pairwise and Multiclass Gene Expression Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1117-1125, December.
    5. Ji Tieming & Liu Peng & Nettleton Dan, 2012. "Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-29, May.
    6. Santu Ghosh & Alan M. Polansky, 2022. "Large-Scale Simultaneous Testing Using Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 808-843, August.
    7. Simina M. Boca & Héctor Céorrada Bravo & Brian Caffo & Jeffrey T. Leek & Giovanni Parmigiani, 2013. "A Decision-Theory Approach to Interpretable Set Analysis for High-Dimensional Data," Biometrics, The International Biometric Society, vol. 69(3), pages 614-623, September.
    8. Marko Sysi-Aho & Andrey Ermolov & Peddinti V Gopalacharyulu & Abhishek Tripathi & Tuulikki Seppänen-Laakso & Johanna Maukonen & Ismo Mattila & Suvi T Ruohonen & Laura Vähätalo & Laxman Yetukuri & Tain, 2011. "Metabolic Regulation in Progression to Autoimmune Diabetes," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
    9. Hwang J.T. Gene & Liu Peng, 2010. "Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-35, October.
    10. Dørum Guro & Snipen Lars & Solheim Margrete & Sæbø Solve, 2009. "Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, July.
    11. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    12. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    13. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    14. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    15. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    16. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    17. repec:plo:pone00:0103514 is not listed on IDEAS
    18. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
    19. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    20. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    21. Ambroise Jérôme & Bearzatto Bertrand & Robert Annie & Macq Benoit & Gala Jean-Luc, 2012. "Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-20, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0065648. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.