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Results for “Infinium MethylationEPIC array”

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PubMed2020

Harsh Parenting Predicts Novel HPA Receptor Gene Methylation and NR3C1 Methylation Predicts Cortisol Daily Slope in Middle Childhood.

Adverse experiences in childhood are associated with altered hypothalamic-pituitary-adrenal (HPA) axis function and negative health outcomes throughout life. It is now commonly accepted that abuse and neglect can alter epigenetic regulation of HPA genes. Accumulated evidence suggests harsh parenting practices such as spanking are also strong predictors of negative health outcomes. We predicted harsh parenting at 2.5 years old would predict HPA gene DNA methylation similarly to abuse and neglect, and cortisol output at 8.5 years old. Saliva samples were collected three times a day across 3 days to estimate cortisol diurnal slopes. Methylation was quantified using the Illumina Infinium MethylationEPIC array BeadChip (850 K) with DNA collected from buccal cells. We used principal components analysis to compute a summary statistic for CpG sites across candidate genes. The first and second components were used as outcome variables in mixed linear regression analyses with harsh parenting as a predictor variable. We found harsh parenting significantly predicted methylation of several HPA axis genes, including novel gene associations with AVPRB1, CRHR1, CRHR2, and MC2R (FDR corrected p < 0.05). Further, we found NR3C1 methylation predicted a steeper diurnal cortisol slope. Our results extend the current literature by demonstrating harsh parenting may influence DNA methylation similarly to more extreme early life experiences such as abuse and neglect. Further, we show NR3C1 methylation is associated with diurnal HPA function. Elucidating the molecular consequences of harsh parenting on health can inform best parenting practices and provide potential treatment targets for common complex disorders.

Child↗

PubMed2024

Epigenetic footprints: Investigating placental DNA methylation in the context of prenatal exposure to phenols and phthalates.

BACKGROUND: Endocrine disrupting compounds (EDCs) such as phthalates and phenols can affect placental functioning and fetal health, potentially via epigenetic modifications. We investigated the associations between pregnancy exposure to synthetic phenols and phthalates estimated from repeated urine sampling and genome wide placental DNA methylation. METHODS: The study is based on 387 women with placental DNA methylation assessed with Infinium MethylationEPIC arrays and with 7 phenols, 13 phthalates, and two non-phthalate plasticizer metabolites measured in pools of urine samples collected twice during pregnancy. We conducted an exploratory analysis on individual CpGs (EWAS) and differentially methylated regions (DMRs) as well as a candidate analysis focusing on 20 previously identified CpGs. Sex-stratified analyses were also performed. RESULTS: In the exploratory analysis, when both sexes were studied together no association was observed in the EWAS. In the sex-stratified analysis, 114 individual CpGs (68 in males, 46 in females) were differentially methylated, encompassing 74 genes (36 for males and 38 for females). We additionally identified 28 DMRs in the entire cohort, 40 for females and 42 for males. Associations were mostly positive (for DMRs: 93% positive associations in the entire cohort, 60% in the sex-stratified analysis), with the exception of several associations for bisphenols and DINCH metabolites that were negative. Biomarkers associated with most DMRs were parabens, DEHP, and DiNP metabolite concentrations. Some DMRs encompassed imprinted genes including APC (associated with parabens and DiNP metabolites), GNAS (bisphenols), ZIM2;PEG3;MIMT1 (parabens, monoethyl phthalate), and SGCE;PEG10 (parabens, DINCH metabolites). Terms related to adiposity, lipid and glucose metabolism, and cardiovascular function were among the enriched phenotypes associated with differentially methylated CpGs. The candidate analysis identified one CpG mapping to imprinted LGALS8 gene, negatively associated with ethylparaben. CONCLUSIONS: By combining improved exposure assessment and extensive placental epigenome coverage, we identified several novel genes associated with the exposure, possibly in a sex-specific manner.

Humans↗

PubMed2026

Epigenome-wide placental methylation landscapes in relation to antenatal depressive symptoms.

Antenatal depressive symptoms (ADS) are common during pregnancy and are linked to adverse maternal and offspring neurodevelopmental outcomes. The placenta plays a central role in maternal-fetal communication and may function as an epigenetic sensor of maternal psychological stress. However, placental epigenetic signatures associated with ADS remain poorly understood. This study investigated epigenome-wide placental DNA methylation patterns associated with ADS in an Indian cohort. Placental samples were collected at delivery from women recruited in early pregnancy into the STRiDE cohort. Depressive symptoms were assessed at 24-28 weeks' gestation using the Patient Health Questionnaire-9 (PHQ-9). Participants were classified as controls (PHQ-9 ≤ 4; n = 53) or ADS (PHQ-9 > 4; n = 54). Genome-wide DNA methylation profiling was performed using the Illumina Infinium MethylationEPIC array. Epigenome-wide association analysis identified no CpG sites that remained statistically significant after Benjamini-Hochberg FDR correction. Top nominal CpGs showed medium-to-large effect sizes for ADS. Exploratory analyses of the top nominally associated CpGs annotated to genes including TAP2, LRCH1, SLITRK2, RASSF1 and IL3 implicated in immune regulation, cellular signalling and neurodevelopment. Gene enrichment analysis suggested the involvement of biological processes and pathways related to synaptic organization, ion transport, Hippo signalling, and thyroid hormone regulation. In conclusion, the study findings provide preliminary evidence of DNA methylation signatures linked to potential candidate genes and biological pathways that may be relevant to ADS, supporting the need for validation in larger independent cohorts and functional experimental studies.

Asian Indians↗

PubMed2024

Epigenome-wide Association Study Shows Differential DNA Methylation of MDC1, KLF9, and CUTA in Autoimmune Thyroid Disease.

CONTEXT: Autoimmune thyroid disease (AITD) includes Graves disease (GD) and Hashimoto disease (HD), which often run in the same family. AITD etiology is incompletely understood: Genetic factors may account for up to 75% of phenotypic variance, whereas epigenetic effects (including DNA methylation [DNAm]) may contribute to the remaining variance (eg, why some individuals develop GD and others HD). OBJECTIVE: This work aimed to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) comparing GD to HD. METHODS: Whole-blood DNAm was measured across the genome using the Infinium MethylationEPIC array in 32 Australian patients with GD and 30 with HD (discovery cohort) and 32 Danish patients with GD and 32 with HD (replication cohort). Linear mixed models were used to test for differences in quantile-normalized β values of DNAm between GD and HD and data were later meta-analyzed. Comb-p software was used to identify DMRs. RESULTS: We identified epigenome-wide significant differences (P < 9E-8) and replicated (P < .05) 2 DMPs between GD and HD (cg06315208 within MDC1 and cg00049440 within KLF9). We identified and replicated a DMR within CUTA (5 CpGs at 6p21.32). We also identified 64 DMPs and 137 DMRs in the meta-analysis. CONCLUSION: Our study reveals differences in DNAm between GD and HD, which may help explain why some people develop GD and others HD and provide a link to environmental risk factors. Additional research is needed to advance understanding of the role of DNAm in AITD and investigate its prognostic and therapeutic potential.

Humans↗

PubMed2022

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

DNA Methylation↗

PubMed2026

Epigenetic aging and autosomal methylation remodeling in Anderson-Fabry disease.

Anderson-Fabry disease (AFD) is a rare X-linked lysosomal storage disorder characterized by marked clinical heterogeneity and incompletely understood genotype-phenotype correlations. While X-chromosome inactivation has been extensively investigated, the contribution of autosomal epigenetic mechanisms to phenotypic variability remains poorly defined. Here, we performed an exploratory genome-wide DNA methylation analysis in 32 AFD patients (22 females and 10 males; mean age 51.7 years) recruited within a multicenter regional research project in Calabria (Italy). DNA methylation profiling was conducted using the Infinium MethylationEPIC v2.0 array. The analysis integrated two complementary approaches: differential methylation analysis and evaluation of biological aging through multiple epigenetic clocks, including Horvath, Hannum, PhenoAge, Skin & Blood, GrimAge, and DunedinPACE. Exploratory methylome-wide analysis identified a limited set of CpG loci showing nominal evidence of methylation differences between carriers of pathogenic and non-pathogenic variants; however, none remained statistically significant after correction for multiple testing. Annotation of the top-ranking nominal CpG associations highlighted genes involved in biological processes including vascular regulation, intracellular trafficking, cytoskeletal organization, immune signaling, and lipid metabolism. No significant differences between groups were observed for the conventional epigenetic age-acceleration measures examined. In contrast, carriers of pathogenic variants showed significantly higher DunedinPACE values (p = 0.0328), indicating a faster estimated pace of biological aging. This finding suggests that DunedinPACE may capture aspects of the cumulative systemic burden associated with pathogenic GLA variants, although confirmation in larger independent cohorts is required. Overall, this pilot epigenomic study provides preliminary evidence that autosomal epigenetic remodeling and biological aging acceleration may contribute to phenotypic heterogeneity in AFD.

Anderson-Fabry disease↗

PubMed2026

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying intensified care. The Esketamine multi-omic biomarker evaluation in MDD (EMBER-MDD) is a non-interventional, investigator-initiated, in-vitro study within the EU Psych-STRATA programme, analyzing biospecimens collected in the randomized INTENSIFY study and the mirror OBS-TR cohort after participants complete treatment. EMBER-MDD aims to discover individual-omic and integrated multi-omic (hypothesis-free) biomarkers and signatures associated with TR risk, and molecular correlates of clinical response to esketamine nasal spray versus treatment as usual (TAU). Biomaterials will derive from approximately 420 adults with MDD (estimated n = 210 esketamine; n = 210 TAU) and include whole blood, RNA-stabilized whole blood, plasma and serum, sampled at baseline and, when feasible, during and after treatment (up to ~ 5,040 aliquots stored at - 80 °C). Genomics will use baseline DNA genotyping on Illumina Infinium GSA v3.0+MD arrays; epigenomics will profile genome-wide DNA methylation across time points using MethylationEPIC v2.0; transcriptomics will employ mRNA-seq (NovaSeq X/ X Plus); and proteomics/ metabolomics will be generated using high-throughput Olink and/ or Biocrates platforms. Each layer will undergo state-of-the-art preprocessing and analyses (e.g., GWAS/ PRS, EWAS, differential expression, WGCNA, pathway and network analyses), followed by integrative strategies including QTL mapping (meQTL/ eQTL/ pQTL/ mQTL) and intermediate-fusion machine learning with nested cross-validation, explainable AI (SHAP/ LIME) and treatment-effect modelling. All outputs are research-only and will not support individual efficacy, tolerability, or clinical decision-making. The study will deliver robust biosignatures and mechanistic hypotheses to guide future validation and inform stratified, molecularly guided intervention strategies in subsequent prospective trials. Trial registration number: 2023-506617-21-00 and 2025-178-f-S.

Humans↗