Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health

Abstract

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene-environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

Keywords: bioinformatics; epigenetic regulation; psychiatric conditions; statistical challenges.

Figure 1

Risk of developing poor physical conditions and death from unnatural causes in individuals with mental disorders by age. Data on obesity, hypertension, and diabetes were extracted from The Health Improvement Network in 2018 in England, comprising 1,051,127 patients. Out of them, 9357 (0.9%) had a diagnosis of severe mental illness (SMI). The rate ratios of the prevalence were calculated between the SMI and all patients in three age groups, i.e., 15–24, 25–54, and 55–74 [17]. The standardized mortality ratio from suicide was calculated using 1,003,906 patients with hospital discharge with depression from a dataset of English national hospital episode statistics, linked with data from death records. The ratios were calculated comparing mortality in people with depression with mortality in the general population of England in two age groups: 15–24 and 25–44 years [18]. Reprinted/adapted with permission from ref. [19]. 2022, Diana M. Manu.

Figure 2

Associations between depression, anxiety, and comorbid depression–anxiety and chronic physical condition risk ratios (RR). Analytic sample comprised of 33,242 adults (51% women) with ages between 22 and 64 years, with no self-reported diagnosis of schizophrenia, psychoses, attention deficit hyper activity disorders, adjustment disorders, substance abuse disorders, and personality disorders. COPD = Chronic Obstructive Pulmonary Disease. Individuals were part of the Medical Expenditure Panel Survey annual releases of 2007 and 2009 in the United States. Data were published in [26]. Reprinted/adapted with permission from ref. [19]. 2022, Diana M. Manu.

Figure 3

DNAm and polygenic prediction of the health and lifestyle factors. The proportion of phenotypic variance explained (R2) is plotted for three traits: the body mass index (BMI), smoking, and alcohol consumption (alcohol), based on each trait’s polygenic score (blue), DNA methylation-based score (green), and additive genetic + epigenetic score (yellow). Data were taken from [130]. Reprinted/adapted with permission from ref. [19]. 2022, Diana M. Manu.

Figure 4

The interplay of SNPs, TFs, DNAm, and miRNA binding, leading to differential mRNA gene expression. SNPs, single-nucleotide polymorphisms; TFs, transcription factors; DNAm, DNA methylation; miRNA, microRNA. Reprinted/adapted with permission from ref. [19]. 2022, Diana M. Manu.