DNA modification study of major depressive disorder: Beyond locus-by-locus comparisons

Gabriel Oh, Sun Chong Wang, Mrinal Pal, Zheng Fei Chen, Tarang Khare, Mamoru Tochigi, Catherine Ng, Yeqing A. Yang, Andrew Kwan, Zachary A. Kaminsky, Jonathan Mill, Cerisse Gunasinghe, Jennifer L. Tackett, Irving I. Gottesman, Gonneke Willemsen, Eco J C De Geus, Jacqueline M. Vink, P. Eline Slagboom, Naomi R. Wray, Andrew C. HeathGrant W. Montgomery, Gustavo Turecki, Nicholas G. Martin, Dorret I. Boomsma, Peter McGuffin, Rafal Kustra, Art Petronis

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Background: Major depressive disorder (MDD) exhibits numerous clinical and molecular features that are consistent with putative epigenetic misregulation. Despite growing interest in epigenetic studies of psychiatric diseases, the methodologies guiding such studies have not been well defined. Methods: We performed DNA modification analysis in white blood cells from monozygotic twins discordant for MDD, in brain prefrontal cortex, and germline (sperm) samples from affected individuals and control subjects (total N = 304) using 8.1K CpG island microarrays and fine mapping. In addition to the traditional locus-by-locus comparisons, we explored the potential of new analytical approaches in epigenomic studies. Results: In the microarray experiment, we detected a number of nominally significant DNA modification differences in MDD and validated selected targets using bisulfite pyrosequencing. Some MDD epigenetic changes, however, overlapped across brain, blood, and sperm more often than expected by chance. We also demonstrated that stratification for disease severity and age may increase the statistical power of epimutation detection. Finally, a series of new analytical approaches, such as DNA modification networks and machine-learning algorithms using binary and quantitative depression phenotypes, provided additional insights on the epigenetic contributions to MDD. Conclusions: Mapping epigenetic differences in MDD (and other psychiatric diseases) is a complex task. However, combining traditional and innovative analytical strategies may lead to identification of disease-specific etiopathogenic epimutations.

Original languageEnglish (US)
Pages (from-to)246-255
Number of pages10
JournalBiological psychiatry
Volume77
Issue number3
DOIs
StatePublished - Feb 1 2015

Bibliographical note

Funding Information:
This research has been supported by the Canadian Institutes of Health Research (CIHR) (Grant 77689 ) and the US National Institutes of Health (Grants MH074127 and MH088413 ) to AP. AP is Tapscott Chair in Schizophrenia Studies at the University of Toronto and a senior fellow of the Ontario Mental Health Foundation. GO was supported by the CIHR Vanier Canada Graduate Scholarship and the CIHR Collaborative Program – Molecular Medicine Research Award. Collection of Queensland Institute of Medical Research samples was funded by grants from the National Institutes of Health ( AA07535 and AA07728 ) and the Australian National Health and Medical Research Council ( 241944 , 339462 , 389927 , 389875 , 389891 , 389892 , 389938 , 442915 , 442981 , 496675 , 496739 , 552485 , 552498 ). Data collection for The Netherlands twin samples was funded by The Netherlands Organization for Scientific Research (MagW/ZonMW Grants 904-61-090 , 985-10-002 , 904-61-193 , 480-04-004 , 400-05-717 , Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI–NL, 184.021.007 ), the VU University Institute for Health and Care Research and Neuroscience Campus Amsterdam, and the European Science Council (ERC-230374).

Publisher Copyright:
© 2015 Society of Biological Psychiatry.

Keywords

  • DNA modification
  • Epigenetic outliers
  • Epigenetics
  • Heteroscedasticity
  • Major depressive disorder
  • Molecular networks

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