Childhood maltreatment and DNA methylation: A systematic review.

Child maltreatment (CM) encompasses sexual abuse, physical abuse, emotional abuse, neglect, and exposure to domestic and family violence. Epigenetic research investigating CM has focused on differential DNA methylation (DNAm) in genes associated with the stress response, but there has been limited evaluation of the specific effects of subtypes of CM. This systematic review of literature investigating DNAm associated with CM in non-clinical populations aimed to summarise the approaches currently used in research, how the type of maltreatment and age of exposure were encoded via methylation, and which genes have consistently been associated with CM. A total of fifty-four papers were eligible for review, including forty-one candidate gene studies, eight epigenome-wide association studies, and five studies with a mixed design. The ways in which the various forms of CM were conceptualised and measured varied between papers. Future studies would benefit from assessments that employ conceptually robust definitions of CM, and that capture important contextual information such as age of exposure and subtype of CM.

Multi-omics data integration methods and their applications in psychiatric disorders.

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Shared genetic influences between blood analyte levels and risk of severe COVID-19.

Genome-wide association studies (GWASs) show that genetic factors contribute to the risk of severe coronavirus disease 2019 (COVID-19) and blood analyte levels. Here, we utilize GWAS summary statistics to study the shared genetic influences (pleiotropy) between severe COVID-19 and 344 blood analytes at the genome, gene, and single-nucleotide polymorphism (SNP) levels. Our pleiotropy analyses genetically link blood levels of 71 analytes to severe COVID-19 in at least one of the three levels of investigation-suggesting shared biological mechanisms or causal relationships. Six analytes (alanine aminotransferase, alkaline phosphatase, apolipoprotein B, C-reactive protein, triglycerides, and urate) display evidence of pleiotropy with severe COVID-19 at all three levels. Causality analyses indicate that higher triglycerides levels causally increase the risk of severe COVID-19, thereby providing important support for the use of lipid-lowering drugs such as statins and fibrates to prevent severe COVID-19.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (NBrCa = 228,951, NPrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (NBrCa = 621, NPrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (Pcond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)-a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Recalibrating the epigenetic clock after exposure to trauma: The role of risk and protective psychosocial factors.

BACKGROUND: Epigenetic aging is associated with a plethora of negative health outcomes and increased mortality. Yet, the dynamicity of epigenetic age after exposure to trauma and the factors that influence epigenetic age are not fully understood. This research evaluated longitudinal changes in epigenetic age before and after exposure to work-related trauma among paramedicine students. We further investigated psychological and social risk (psychological distress, posttraumatic stress disorder/PTSD symptom severity, professional quality of life) and protective factors (social support and organisational membership) that drive epigenetic aging at both time points. METHODS: The study comprised of 80 samples of University paramedicine students including 40 individuals at two time points - t0 (baseline) and t1 (post-trauma exposure). Epigenome-wide analysis was performed from t0 and t1 saliva using the Illumina EPIC arrays that cover >860k probes. Data analysis was performed using R via generalized regression models. The epigenetic age was calculated based on the Horvath algorithm, GrimAge and SkinBloodAge were calculated using the Horvath online calculator, and p-value for significance was corrected using the FDR method for multiple testing corrections. RESULTS: The epigenetic age at t0 and t1 were highly correlated with chronological age and with each other (r = 0.84-0.94). Baseline epigenetic age and follow-up epigenetic age were significantly associated with risk factors of psychological distress and PTSD symptom severity. Among the protective factors, a sense of psychological organisational membership at the start of the paramedicine course as measured at baseline significantly reduced epigenetic age at baseline and post-trauma exposure. On the other hand, receiving social support acted as a protective factor only after exposure to trauma (follow-up), decreasing epigenetic aging at follow-up. GrimAge acceleration at follow-up was significantly associated with increased PTSD symptom severity at baseline and follow-up. Moreover, increased social support at baseline and follow-up was associated with reduced follow-up GrimAge acceleration. CONCLUSION: These results demonstrate that epigenetic aging is dynamic and changes after exposure to trauma. Additionally, results demonstrate that different risk and protective factors influence epigenetic aging at different times. In conclusion, the research identified risk and protective factors associated with epigenetic aging pre- and post-trauma exposure, with implications for health and well-being among individuals exposed to trauma.

Genetic overlap and causality between blood metabolites and migraine.

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping.

Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.