Epigenetics of Addiction: Current Knowledge, Challenges, and Future Directions

Cecil, Charlotte A. M.; Walton, Esther; Viding, Essi

doi:10.15288/jsad.2016.77.688

Cited by 31 publications

(7 citation statements)

References 24 publications

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“…Despite numerous successful findings of disease related biomarkers [2,8,9] and the enthusiasm within this novel and promising field of study, it is important to obtain a more nuanced picture of the biological processes related to the biomarkers being assessed, and to develop a more comprehensive and commonly accepted framework for their analysis. At present, researchers in this field have to cope with new technological possibilities and an insufficient understanding of the methylome at the same time [4,10,11].…”

Section: Introductionmentioning

confidence: 99%

Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies

et al. 2020

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Background: Systematic technical effects-also called batch effects-are a considerable challenge when analyzing DNA methylation (DNAm) microarray data, because they can lead to false results when confounded with the variable of interest. Methods to correct these batch effects are error-prone, as previous findings have shown. Results: Here, we demonstrate how using the R function ComBat to correct simulated Infinium HumanMethylation450 BeadChip (450 K) and Infinium MethylationEPIC BeadChip Kit (EPIC) DNAm data can lead to a large number of false positive results under certain conditions. We further provide a detailed assessment of the consequences for the highly relevant problem of p-value inflation with subsequent false positive findings after application of the frequently used ComBat method. Using ComBat to correct for batch effects in randomly generated samples produced alarming numbers of false discovery rate (FDR) and Bonferroni-corrected (BF) false positive results in unbalanced as well as in balanced sample distributions in terms of the relation between the outcome of interest variable and the technical position of the sample during the probe measurement. Both sample size and number of batch factors (e.g. number of chips) were systematically simulated to assess the probability of false positive findings. The effect of sample size was simulated using n = 48 up to n = 768 randomly generated samples. Increasing the number of corrected factors led to an exponential increase in the number of false positive signals. Increasing the number of samples reduced, but did not completely prevent, this effect.

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Section: Introductionmentioning

confidence: 99%

Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies

et al. 2020

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“…Whether similar conclusion can be proposed for the other drugs of abuse has still not been explored. The human studies are confronted with the constraint of analysis of DNAm at a unique time point in postmortem tissue (Cecil et al, 2016b). Whether extrapolation of data from studies examining DNA methylation in blood samples would be feasible is still questionable.…”

Section: 4-dnam At Splicing Sitesmentioning

confidence: 99%

Neuroepigenetics and addictive behaviors: Where do we stand?

Nogueira

Mérienne

Befort

2019

Neuroscience & Biobehavioral Reviews

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Substance use disorders involve long-term changes in the brain that lead to compulsive drug seeking, craving, and a high probability of relapse. Recent findings have highlighted the role of epigenetic regulations in controlling chromatin access and regulation of gene expression following exposure to drugs of abuse. In the present review, we focus on data investigating genome-wide epigenetic modifications in the brain of addicted patients or in rodent models exposed to drugs of abuse, with a particular focus on DNA methylation and histone modifications associated with transcriptional studies. We highlight critical factors for epigenomic studies in addiction. We discuss new findings related to psychostimulants, alcohol, opiate, nicotine and cannabinoids. We examine the possible transmission of these changes across generations. We highlight developing tools, specifically those that allow investigation of structural reorganization of the chromatin. These have the potential to increase our understanding of alteration of chromatin architecture at gene regulatory regions.

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“…There have been many hypotheses proposed to explain why some people develop SUD while others do not. Animal and human studies have investigated epigenetic changes 16 and novelty-seeking behaviour traits as risk factors for addiction 17,18 as well as a disproportionately strong response to dopamine-releasing drugs, whether preexisting or drug-induced. 19,20 Numerous studies have reported that self-medication of symptoms associated with comorbid psychiatric disorders is a risk factor for development of SUD, 1,14,[21][22][23] and this may be more of a risk factor for female healthcare practitioners than for their male counterparts.…”

Section: Risk Factorsmentioning

confidence: 99%

“…Plusieurs hypothèses ont été proposées pour expliquer pourquoi certaines personnes sont victimes de TCS et d'autres pas. Les études chez l'animal et l'humain ont examiné les changements épigénétiques 16 et les traits comportementaux qui poussent à la recherche de nouveauté en tant que facteurs de risque de dépendance, 17,18 ainsi que les réponses disproportionnées aux médicaments libérant de la dopamine, que ces réponses soient préexistantes ou induites par un médicament. 19,20 Plusieurs études ont rapporté que l'auto-médication pour le traitement de symptômes associés à des troubles psychiatriques comorbides était un facteur de risque de manifester un TCS, 1,14,[21][22][23] et ce facteur de risque pourrait jouer un rôle plus important chez les professionnelles de la santé de sexe féminin que chez leurs pendants masculins.…”

Section: Facteurs De Risqueunclassified