Methodological challenges in constructing DNA methylation risk scores

Poly-epigenetic scores for cardiometabolic risk factors interact with demographic factors and health behaviors in older US Adults

Poly-epigenetic scores (PEGS) are surrogate measures that help capture individual-level risk. Understanding how the associations between PEGS and cardiometabolic risk factors vary by demographics and health behaviors is crucial for lowering the burden of cardiometabolic diseases. We used results from established epigenome-wide association studies to construct trait-specific PEGS from whole blood DNA methylation for systolic and diastolic blood pressure (SBP, DBP), body mass index (BMI), C-reactive protein (CRP), high- and low-density lipoprotein cholesterol (HDL-C, LDL-C), triglycerides (TG), and fasting glucose. Overall and race-stratified associations between PEGS and corresponding traits were examined in adults >50 years from the Health and Retirement Study (n = 3,996, mean age = 79.5 years). We investigated how demographics (age, sex, educational attainment) and health behaviors (smoking, alcohol consumption, physical activity) modified these associations. All PEGS were positively associated with their corresponding cardiometabolic traits (p < 0.05), and most associations persisted across all racial/ethnic groups. Associations for BMI, HDL-C, and TG were stronger in younger participants, and BMI and HDL-C associations were stronger in females. The CRP association was stronger among those with a high school degree. Finally, the HDL-C association was stronger among current smokers. These findings support PEGS as robust surrogate measures and suggest the associations may differ among subgroups.

Methylation Risk Score Modelling in Endometriosis: Evidence for Non-Genetic DNA Methylation Effects in a Case-Control Study

Endometriosis is a chronic gynaecological disease characterised by endometrial-like tissue found external to the uterus. While several studies have reported strong evidence of a genetic contribution to the disease, studies on the environmental impact on endometriosis are limited. DNA methylation (DNAm) can be influenced by genetic and environmental factors and serves as a useful biological marker of the effects of genetic and environmental exposures on complex diseases. This study aims to develop a methylation risk score (MRS) for endometriosis to increase the power to detect DNAm signals associated with the disease and enhance our understanding of the pathogenesis of the disease. Endometrial methylation and genotype data from 318 controls and 590 cases were analysed. MRSs were developed using several different models. MRS performances were evaluated by splitting samples into training and test sets based on independent cohort institutions, and the area under the receiver-operator curve (AUC) was calculated. The maximum AUC obtained from the best-performing MRS is 0.6748, derived from 746 DNAm sites. The classification performance of MRS and polygenic risk score (PRS) combined was consistently higher than PRS alone. This study demonstrates that there are DNAm signals independent of common genetic variants associated with endometriosis.

Epigenetic biomarkers of socioeconomic status are associated with age-related chronic diseases and mortality in older adults

Later-life health is patterned by socioeconomic influences across the lifecourse. However, the pathways underlying the biological embedding of socioeconomic status (SES) and its consequences on downstream morbidity and mortality are not fully understood. Epigenetic markers like DNA methylation (DNAm) may be promising surrogates of underlying biological processes that can enhance our understanding of how SES shapes population health. Studies have shown that SES is associated with epigenetic aging measures, but few have examined relationships between early and later-life SES and DNAm sites across the epigenome. In this study, we trained and tested DNAm-based surrogates, or "biomarkers," of childhood and adult SES in two large, multiracial/ethnic samples of older adults-the Health and Retirement Study (n = 3,527) and the Multi-Ethnic Study of Atherosclerosis (n = 1,182). Both biomarkers were associated with downstream morbidity and mortality, and these associations persisted after controlling for measured SES, and in some cases, epigenetic aging clocks. Both childhood and adult SES biomarker CpG sites were enriched for genomic features that regulate gene expression (e.g. DNAse hypersensitivity sites and enhancers) and were implicated in prior epigenome-wide studies of inflammation, aging, and chronic disease. Distinct patterns also emerged between childhood CpGs and immune system dysregulation and adult CpGs and metabolic functioning, health behaviors, and cancer. Results suggest DNAm-based surrogate biomarkers of SES may be useful proxies for unmeasured social exposures that can augment our understanding of the biological mechanisms between social disadvantage and downstream health.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

BACKGROUND

Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.

METHODS

Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.

RESULTS

The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.

CONCLUSION

The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

BACKGROUND

Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.

MAIN BODY

For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.

CONCLUSION

DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.

A methylation risk score for chronic kidney disease: a HyperGEN study

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

DNA Methylation at C-Reactive Protein-Associated CpG Sites May Mediate the Pathway Between Educational Attainment and Cognition

Growing evidence has linked inflammatory processes to cognitive decline and dementia. This work examines whether an epigenetic marker of C-reactive protein (CRP), a common clinical inflammatory biomarker, may mediate the relationship between educational attainment and cognition. We first evaluated whether 53 previously reported CRP-associated DNA methylation sites (CpGs) are associated with CRP, both individually and aggregated into a methylation risk score (MRSCRP), in 3 298 participants from the Health and Retirement Study (HRS, mean age = 69.7 years). Forty-nine CpGs (92%) were associated with the natural logarithm of CRP in HRS after adjusting for age, sex, smoking, BMI, genetic ancestry, and white blood cell counts (p < .05), and each standard deviation increase in MRSCRP was associated with a 0.38 unit increase in lnCRP (p = 4.02E-99). In cross-sectional analysis, for each standard deviation increase in MRSCRP, total memory score and total cognitive score decreased, on average, by 0.28 words and 0.43 items, respectively (p < .001). Further, MRSCRP mediated 6.9% of the relationship between high school education and total memory score in a model adjusting for age, sex, and genetic ancestry (p < .05); this was attenuated to 2.4% with additional adjustment for marital status, APOE ε4 status, health behaviors, and comorbidities (p < .05). Thus, CRP-associated methylation may partially mediate the relationship between education and cognition at older ages. Further research is warranted to determine whether DNA methylation at these sites may improve current prediction models for cognitive impairment in older adults.

Decoding the role of DNA methylation in allergic diseases: from pathogenesis to therapy

Allergic diseases, characterized by a broad spectrum of clinical manifestations and symptoms, encompass a significant category of IgE-mediated atopic disorders, including asthma, allergic rhinitis, atopic dermatitis, and food allergies. These complex conditions arise from the intricate interplay between genetic and environmental factors and are known to contribute to socioeconomic burdens globally. Recent advancements in the study of allergic diseases have illuminated the crucial role of DNA methylation (DNAm) in their pathogenesis. This review explores the factors influencing DNAm in allergic diseases and delves into their mechanisms, offering valuable perspectives for clinicians. Understanding these epigenetic modifications aims to lay the groundwork for improved early prevention strategies. Moreover, our analysis of DNAm mechanisms in these conditions seeks to enhance diagnostic and therapeutic approaches, paving the way for more effective management of allergic diseases in the future.

MethParquet: an R package for rapid and efficient DNA methylation association analysis adopting Apache Parquet

SUMMARY

Genome-wide DNA methylation (DNAm) profiling is indispensable for unveiling how DNAm regulates biological pathways and individual phenotypes. However, managing and analyzing extensive DNAm data generated from large cohort studies present computational obstacles. Apache Parquet is a data file format that allows for efficient data storage, retrieval, and manipulation, alleviating computational hurdles associated with conventional row-based formats. We here introduce MethParquet, the first R package leveraging the columnar Parquet format for efficient DNAm data analysis. It can be used for data extraction, methylation risk score calculation, epigenome-wide association analyses, and other standard post-quality control tasks. The package flexibly implements diverse regression models. Via a public methylation dataset, we show the efficiency of this package in reducing running time and RAM usage in large-scale EWAS.

AVAILABILITY AND IMPLEMENTATION

The MethParquet R package is publicly available on the GitHub repository https://github.com/ZWangTen/MethParquet. It includes a vignette and a toy dataset derived from a public resource.

Association of methylation risk score with incident type 2 diabetes mellitus: A nested case-control study

AIMS

To investigate the association of methylation risk score (MRS) and its interactions with environmental factors with type 2 diabetes mellitus (T2DM) risk.

METHODS

We conducted a nested case-control study with 241 onset cases and 241 matched controls. Conditional logistic regression models were employed to identify risk CpG sites. Simple and weighted MRSs were constructed based on the methylation levels of ATP-binding cassette G1 gene, fat mass and obesity associated gene, potassium voltage-gated channel member 1 gene, and thioredoxin-interacting protein gene previously associated with T2DM to estimate the association of MRS with T2DM risk. Stratified analyses were used to investigate interactions between MRS and environmental factors.

RESULTS

A total of 10 CpG loci were identified from the aforementioned genes to calculate MRS. After controlling for potential confounding factors, taking tertile 1 as reference, the odds ratios (ORs) and 95% confidence intervals (CIs) for T2DM of tertile 3 was 2.39 (1.36-4.20) for simple MRS and 2.59 (1.45-4.63) for weighted MRS. With per SD score increment in MRS, the OR (95% CI) was 1.66 (1.29-2.14) and 1.60 (1.24-2.08) for simple and weighted MRSs, respectively. J-curved associations were observed between both simple and weighted MRSs and T2DM risks. Additionally, multiplication interactions for smoking and hypertension with simple MRS on the risk of T2DM were found, similarly for smoking and obesity with weighted MRS on the risk of T2DM (all Pinteraction  < .05).

CONCLUSION

Elevated simple and weighted MRSs were associated with increased risk of T2DM. Environmental risk factors may influence the association between MRS and T2DM.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Background

Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.

Methods

Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.

Results

The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.

Conclusion

Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Prenatal cocaine exposure and its influence on pediatric epigenetic clocks and epigenetic scores in humans

The investigation of the effects of prenatal cocaine exposure (PCE) on offspring has been inconsistent, with few studies investigating biological outcomes in humans. We profiled genome-wide DNA methylation (DNAm) of umbilical cord blood (UCB) from newborns with (n = 35) and without (n = 47) PCE. We used DNAm data to (1) assess pediatric epigenetic clocks at birth and (2) to estimate epigenetic scores (ES) for lifetime disorders. We generated gestational epigenetic age estimates (DNAmGA) based on Knight and Bohlin epigenetic clocks. We also investigated the association between DNAmGA and UCB serum brain-derived neurotrophic factor (BDNF) levels. Considering the large-scale DNAm data availability and existing evidence regarding PCE as a risk for health problems later in life, we generated ES for tobacco smoking, psychosis, autism, diabetes, and obesity. A gene ontology (GO) analysis on the CpGs included in the ES with group differences was performed. PCE was associated with lower DNAmGA in newborns, and this effect remained significant when controlling for potential confounders, such as blood cell type composition predicted by DNAm and obstetric data. DNAmGA was negatively correlated with BDNF levels in the serum of UCB. Higher tobacco smoking, psychosis, and diabetes ES were found in the PCE group. The GO analysis revealed GABAergic synapses as a potential pathway altered by PCE. Our findings of decelerated DNAmGA and ES for adverse phenotypes associated with PCE, suggest that the effects of gestational cocaine exposure on the epigenetic landscape of human newborns are detectable at birth.

Methylation profiles at birth linked to early childhood obesity

Childhood obesity represents a significant global health concern and identifying risk factors is crucial for developing intervention programs. Many 'omics' factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

Pruning and thresholding approach for methylation risk scores in multi-ancestry populations

Recent efforts have focused on developing methylation risk scores (MRS), a weighted sum of the individual's DNA methylation (DNAm) values of pre-selected CpG sites. Most of the current MRS approaches that utilize Epigenome-wide association studies (EWAS) summary statistics only include genome-wide significant CpG sites and do not consider co-methylation. New methods that relax the p-value threshold to include more CpG sites and account for the inter-correlation of DNAm might improve the predictive performance of MRS. We paired informed co-methylation pruning with P-value thresholding to generate pruning and thresholding (P+T) MRS and evaluated its performance among multi-ancestry populations. Through simulation studies and real data analyses, we demonstrated that pruning provides an improvement over simple thresholding methods for prediction of phenotypes. We demonstrated that European-derived summary statistics can be used to develop P+T MRS among other populations such as African populations. However, the prediction accuracy of P+T MRS may differ across multi-ancestry population due to environmental/cultural/social differences.

Genetics and Epigenetics in Obesity: What Do We Know so Far?

PURPOSE OF REVIEW

Enormous progress has been made in understanding the genetic architecture of obesity and the correlation of epigenetic marks with obesity and related traits. This review highlights current research and its challenges in genetics and epigenetics of obesity.

RECENT FINDINGS

Recent progress in genetics of polygenic traits, particularly represented by genome-wide association studies, led to the discovery of hundreds of genetic variants associated with obesity, which allows constructing polygenic risk scores (PGS). In addition, epigenome-wide association studies helped identifying novel targets and methylation sites being important in the pathophysiology of obesity and which are essential for the generation of methylation risk scores (MRS). Despite their great potential for predicting the individual risk for obesity, the use of PGS and MRS remains challenging. Future research will likely discover more loci being involved in obesity, which will contribute to better understanding of the complex etiology of human obesity. The ultimate goal from a clinical perspective will be generating highly robust and accurate prediction scores allowing clinicians to predict obesity as well as individual responses to body weight loss-specific life-style interventions.

Epigenome-wide methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2+): a prospective cohort study in the United States

BACKGROUND

Methylation levels may be associated with and serve as markers to predict risk of progression of precancerous cervical lesions. We conducted an epigenome-wide association study (EWAS) of CpG methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2 +) following an abnormal screening test.

METHODS

A prospective US cohort of 289 colposcopy patients with normal or CIN1 enrollment histology was assessed. Baseline cervical sample DNA was analyzed using Illumina HumanMethylation 450K (n = 76) or EPIC 850K (n = 213) arrays. Participants returned at provider-recommended intervals and were followed up to 5 years via medical records. We assessed continuous CpG M values for 9 cervical cancer-associated genes and time-to-progression to CIN2+. We estimated CpG-specific time-to-event ratios (TTER) and hazard ratios using adjusted, interval-censored Weibull accelerated failure time models. We also conducted an exploratory EWAS to identify novel CpGs with false discovery rate (FDR) < 0.05.

RESULTS

At enrollment, median age was 29.2 years; 64.0% were high-risk HPV-positive, and 54.3% were non-white. During follow-up (median 24.4 months), 15 participants progressed to CIN2+. Greater methylation levels were associated with a shorter time-to-CIN2+ for CADM1 cg03505501 (TTER = 0.28; 95%CI 0.12, 0.63; FDR = 0.03) and RARB Cluster 1 (TTER = 0.46; 95% CI 0.29, 0.71; FDR = 0.01). There was evidence of similar trends for DAPK1 cg14286732, PAX1 cg07213060, and PAX1 Cluster 1. The EWAS detected 336 novel progression-associated CpGs, including those located in CpG islands associated with genes FGF22, TOX, COL18A1, GPM6A, XAB2, TIMP2, GSPT1, NR4A2, and APBB1IP.

CONCLUSIONS

Using prospective time-to-event data, we detected associations between CADM1-, DAPK1-, PAX1-, and RARB-related CpGs and cervical disease progression, and we identified novel progression-associated CpGs.

IMPACT

Methylation levels at novel CpG sites may help identify individuals with ≤CIN1 histology at higher risk of progression to CIN2+ and inform risk-based cervical cancer screening guidelines.

Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective

According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.

Epigenome-wide analysis identifies methylome profiles linked to obsessive-compulsive disorder, disease severity, and treatment response

Obsessive-compulsive disorder (OCD) is a prevalent mental disorder affecting ~2-3% of the population. This disorder involves genetic and, possibly, epigenetic risk factors. The dynamic nature of epigenetics also presents a promising avenue for identifying biomarkers associated with symptom severity, clinical progression, and treatment response in OCD. We, therefore, conducted a comprehensive case-control investigation using Illumina MethylationEPIC BeadChip, encompassing 185 OCD patients and 199 controls recruited from two distinct sites in Germany. Rigorous clinical assessments were performed by trained raters employing the Structured Clinical Interview for DSM-IV (SCID-I). We performed a robust two-step epigenome-wide association study that led to the identification of 305 differentially methylated CpG positions. Next, we validated these findings by pinpointing the optimal set of CpGs that could effectively classify individuals into their respective groups. This approach identified a subset comprising 12 CpGs that overlapped with the 305 CpGs identified in our EWAS. These 12 CpGs are close to or in genes associated with the sweet-compulsive brain hypothesis which proposes that aberrant dopaminergic transmission in the striatum may impair insulin signaling sensitivity among OCD patients. We replicated three of the 12 CpGs signals from a recent independent study conducted on the Han Chinese population, underscoring also the cross-cultural relevance of our findings. In conclusion, our study further supports the involvement of epigenetic mechanisms in the pathogenesis of OCD. By elucidating the underlying molecular alterations associated with OCD, our study contributes to advancing our understanding of this complex disorder and may ultimately improve clinical outcomes for affected individuals.

Epigenetic Association Analyses and Risk Prediction of RLS

BACKGROUND

As opposed to other neurobehavioral disorders, epigenetic analyses and biomarkers are largely missing in the case of idiopathic restless legs syndrome (RLS).

OBJECTIVES

Our aims were to develop a biomarker for RLS based on DNA methylation in blood and to examine DNA methylation in brain tissues for dissecting RLS pathophysiology.

METHODS

Methylation of blood DNA from three independent cohorts (n = 2283) and post-mortem brain DNA from two cohorts (n = 61) was assessed by Infinium EPIC 850 K BeadChip. Epigenome-wide association study (EWAS) results of individual cohorts were combined by random-effect meta-analysis. A three-stage selection procedure (discovery, n = 884; testing, n = 520; validation, n = 879) established an epigenetic risk score including 30 CpG sites. Epigenetic age was assessed by Horvath's multi-tissue clock and Shireby's cortical clock.

RESULTS

EWAS meta-analysis revealed 149 CpG sites linked to 136 genes (P < 0.05 after Bonferroni correction) in blood and 23 CpG linked to 18 genes in brain (false discovery rate [FDR] < 5%). Gene-set analyses of blood EWAS results suggested enrichments in brain tissue types and in subunits of the kainate-selective glutamate receptor complex. Individual candidate genes of the brain EWAS could be assigned to neurodevelopmental or metabolic traits. The blood epigenetic risk score achieved an area under the curve (AUC) of 0.70 (0.67-0.73) in the validation set, comparable to analogous scores in other neurobehavioral disorders. A significant difference in biological age in blood or brain of RLS patients was not detectable.

CONCLUSIONS

DNA methylation supports the notion of altered neurodevelopment in RLS. Epigenetic risk scores are reliably associated with RLS but require even higher accuracy to be useful as biomarkers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

An integrative framework and recommendations for the study of DNA methylation in the context of race and ethnicity

Human social epigenomics research is critical to elucidate the intersection of social and genetic influences underlying racial and ethnic differences in health and development. However, this field faces major challenges in both methodology and interpretation with regard to disentangling confounded social and biological aspects of race and ethnicity. To address these challenges, we discuss how these constructs have been approached in the past and how to move forward in studying DNA methylation (DNAm), one of the best-characterized epigenetic marks in humans, in a responsible and appropriately nuanced manner. We highlight self-reported racial and ethnic identity as the primary measure in this field, and discuss its implications in DNAm research. Racial and ethnic identity reflects the biological embedding of an individual's sociocultural experience and environmental exposures in combination with the underlying genetic architecture of the human population (i.e., genetic ancestry). Our integrative framework demonstrates how to examine DNAm in the context of race and ethnicity, while considering both intrinsic factors-including genetic ancestry-and extrinsic factors-including structural and sociocultural environment and developmental niches-when focusing on early-life experience. We reviewed DNAm research in relation to health disparities given its relevance to race and ethnicity as social constructs. Here, we provide recommendations for the study of DNAm addressing racial and ethnic differences, such as explicitly acknowledging the self-reported nature of racial and ethnic identity, empirically examining the effects of genetic variants and accounting for genetic ancestry, and investigating race-related and culturally regulated environmental exposures and experiences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44155-023-00039-z.

Epigenetics applied to child and adolescent mental health: Progress, challenges and opportunities

Background

Epigenetic processes are fast emerging as a promising molecular system in the search for both biomarkers and mechanisms underlying human health and disease risk, including psychopathology.

Methods

In this review, we discuss the application of epigenetics (specifically DNA methylation) to research in child and adolescent mental health, with a focus on the use of developmentally sensitive datasets, such as prospective, population-based cohorts. We look back at lessons learned to date, highlight current developments in the field and areas of priority for future research. We also reflect on why epigenetic research on child and adolescent mental health currently lags behind other areas of epigenetic research and what we can do to overcome existing barriers.

Results

To move the field forward, we advocate for the need of large-scale, harmonized, collaborative efforts that explicitly account for the time-varying nature of epigenetic and mental health data across development.

Conclusion

We conclude with a perspective on what the future may hold in terms of translational applications as more robust signals emerge from epigenetic research on child and adolescent mental health.

Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes

Background

In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database.

Methods

A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV).

Results

Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking).

Conclusion

Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.

Learning high-order interactions for polygenic risk prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

DNA methylation signatures as biomarkers of socioeconomic position

This review article provides a framework for the use of deoxyribonucleic acid (DNA) methylation (DNAm) biomarkers to study the biological embedding of socioeconomic position (SEP) and summarizes the latest developments in the area. It presents the emerging literature showing associations between individual- and neighborhood-level SEP exposures and DNAm across the life course. In contrast to questionnaire-based methods of assessing SEP, we suggest that DNAm biomarkers may offer an accessible metric to study questions about SEP and health outcomes, acting as a personal dosimeter of exposure. However, further work remains in standardizing SEP measures across studies and evaluating consistency across domains, tissue types, and time periods. Meta-analyses of epigenetic associations with SEP are offered as one approach to confirm the replication of DNAm loci across studies. The development of DNAm biomarkers of SEP would provide a method for examining its impact on health outcomes in a more robust way, increasing the rigor of epidemiological studies.

DNA methylation and aeroallergen sensitization: The chicken or the egg?

Background

DNA methylation (DNAm) is considered a plausible pathway through which genetic and environmental factors may influence the development of allergies. However, causality has yet to be determined as it is unknown whether DNAm is rather a cause or consequence of allergic sensitization. Here, we investigated the direction of the observed associations between well-known environmental and genetic determinants of allergy, DNAm, and aeroallergen sensitization using a combination of high-dimensional and causal mediation analyses.

Methods

Using prospectively collected data from the German LISA birth cohort from two time windows (6–10 years: N = 234; 10–15 years: N = 167), we tested whether DNAm is a cause or a consequence of aeroallergen sensitization (specific immunoglobulin E > 0.35kU/l) by conducting mediation analyses for both effect directions using maternal smoking during pregnancy, family history of allergies, and a polygenic risk score (PRS) for any allergic disease as exposure variables. We evaluated individual CpG sites (EPIC BeadChip) and allergy-related methylation risk scores (MRS) as potential mediators in the mediation analyses. We applied three high-dimensional mediation approaches (HIMA, DACT, gHMA) and validated results using causal mediation analyses. A replication of results was attempted in the Swedish BAMSE cohort.

Results

Using high-dimensional methods, we identified five CpGs as mediators of prenatal exposures to sensitization with significant (adjusted p < 0.05) indirect effects in the causal mediation analysis (maternal smoking: two CpGs, family history: one, PRS: two). None of these CpGs could be replicated in BAMSE. The effect of family history on allergy-related MRS was significantly mediated by aeroallergen sensitization (proportions mediated: 33.7–49.6%), suggesting changes in DNAm occurred post-sensitization.

Conclusion

The results indicate that DNAm may be a cause or consequence of aeroallergen sensitization depending on genomic location. Allergy-related MRS, identified as a potential cause of sensitization, can be considered as a cross-sectional biomarker of disease. Differential DNAm in individual CpGs, identified as mediators of the development of sensitization, could be used as clinical predictors of disease development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01332-5.

Polymethylation scores for prenatal maternal smoke exposure persist until age 15 and are detected in saliva in the Fragile Families and Child Wellbeing cohort

Prenatal maternal smoking is associated with low birthweight, neurological disorders, and asthma in exposed children. DNA methylation signatures can function as biomarkers of prenatal smoke exposure. However, the robustness of DNA methylation signatures across child ages, genetic ancestry groups, or tissues is not clear. Using coefficients from a meta-analysis of prenatal smoke exposure and DNA methylation in newborn cord blood, we created polymethylation scores of saliva DNA methylation from children at ages 9 and 15 in the Fragile Families and Child Wellbeing study. In the full sample at age 9 (n = 753), prenatal smoke exposure was associated with a 0.51 (95%CI: 0.35, 0.66) standard deviation higher polymethylation score. The direction and magnitude of the association was consistent in European and African genetic ancestry samples. In the full sample at age 15 (n = 747), prenatal smoke exposure was associated with a 0.48 (95%CI: 0.32, 0.63) standard deviation higher polymethylation score, and the association was attenuated among the European and Admixed-Latin genetic ancestry samples. The polymethylation score classified prenatal smoke exposure accurately (AUC age 9 = 0.77, age 15 = 0.76). Including the polymethylation score increased the AUC of base model covariates by 5 (95% CI: (2.1, 7.2)) percentage points, while including a single candidate site in the AHRR gene did not (P-value = 0.19). Polymethylation scores for prenatal smoking were portable across genetic ancestries and more accurate than an individual DNA methylation site. Polymethylation scores from saliva samples could serve as robust and practical biomarkers of prenatal smoke exposure.

Epigenetics and ADHD: Reflections on Current Knowledge, Research Priorities and Translational Potential

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder influenced by both genetic and environmental factors, typically identified in the school-age years but hypothesized to have developmental origins beginning in utero. To improve current strategies for prediction, prevention and treatment, a central challenge is to delineate how, at a molecular level, genetic and environmental influences jointly shape ADHD risk, phenotypic presentation, and developmental course. Epigenetic processes that regulate gene expression, such as DNA methylation, have emerged as a promising molecular system in the search for both biomarkers and mechanisms to address this challenge. In this Current Opinion, we discuss the relevance of epigenetics (specifically DNA methylation) for ADHD research and clinical practice, starting with the current state of knowledge, what challenges we have yet to overcome, and what the future may hold in terms of methylation-based applications for personalized medicine in ADHD. We conclude that the field of epigenetics and ADHD is promising but is still in its infancy, and the potential for transformative translational applications remains a distant goal. Nevertheless, rapid methodological advances, together with the rise of collaborative science and increased availability of high-quality, longitudinal data make this a thriving research area that in future may contribute to the development of new tools for improved prediction, management, and treatment of ADHD.

DNA methylation as a potential mediator of the association between prenatal tobacco and alcohol exposure and child neurodevelopment in a South African birth cohort

Prenatal tobacco exposure (PTE) and prenatal alcohol exposure (PAE) have been associated with an increased risk of delayed neurodevelopment in children as well as differential newborn DNA methylation (DNAm). However, the biological mechanisms connecting PTE and PAE, DNAm, and neurodevelopment are largely unknown. Here we aim to determine whether differential DNAm mediates the association between PTE and PAE and neurodevelopment at 6 (N = 112) and 24 months (N = 184) in children from the South African Drakenstein Child Health Study. PTE and PAE were assessed antenatally using urine cotinine measurements and the ASSIST questionnaire, respectively. Cord blood DNAm was measured using the EPIC and 450 K BeadChips. Neurodevelopment (cognitive, language, motor, adaptive behavior, socioemotional) was measured using the Bayley Scales of Infant and Toddler Development, Third Edition. We constructed methylation risk scores (MRS) for PTE and PAE and conducted causal mediation analysis (CMA) with these MRS as mediators. Next, we conducted a high-dimensional mediation analysis to identify individual CpG sites as potential mediators, followed by a CMA to estimate the average causal mediation effects (ACME) and total effect (TE). PTE and PAE were associated with neurodevelopment at 6 but not at 24 months. PTE MRS reached a prediction accuracy (R2) of 0.23 but did not significantly mediate the association between PTE and neurodevelopment. PAE MRS was not predictive of PAE (R2 = 0.006). For PTE, 31 CpG sites and eight CpG sites were identified as significant mediators (ACME and TE P < 0.05) for the cognitive and motor domains at 6 months, respectively. For PAE, 16 CpG sites and 1 CpG site were significant mediators for the motor and adaptive behavior domains at 6 months, respectively. Several of the associated genes, including MAD1L1, CAMTA1, and ALDH1A2 have been implicated in neurodevelopmental delay, suggesting that differential DNAm may partly explain the biological mechanisms underlying the relationship between PTE and PAE and child neurodevelopment.

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways.

Blood-derived DNAm profiles could be exploited as new biomarkers for cancer risk stratification and possibly, early detection. This is of particular interest since blood is a convenient tissue to assay for constitutional methylation and its collection is non-invasive. Exploiting pre-diagnostic blood DNAm data opens the further opportunity to investigate the association of DNAm at baseline on cancer risk, modeling the relationship between sites’ methylation and the Time to Diagnosis. Previous studies mostly provide inconsistent results likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work we argue that an approach to estimate single CpG sites’ effect while modeling their combined effect on the survival outcome is needed, and we claim that such approach should capture the complex (potentially non-linear) relationships interplaying between sites. We prove this concept by developing a novel approach to analyze a prospective case-control study on breast cancer nested in the EPIC Italy cohort. A weighted gene set enrichment analysis confirms that our approach outperforms standard EWAS in identifying biologically meaningful pathways.

Distinct sex-specific DNA methylation differences in Alzheimer's disease

BACKGROUND

Sex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer's disease (AD).

METHODS

We studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset.

RESULTS

In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10^-5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10^-5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65-0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56-0.82).

CONCLUSIONS

Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.

Methylation risk scores for childhood aeroallergen sensitization: Results from the LISA birth cohort

BACKGROUND

Epigenomic (e.g., DNA methylation [DNAm]) changes have been hypothesized as intermediate step linking environmental exposures with allergic disease. Associations between individual DNAm at CpGs and allergic diseases have been reported, but their joint predictive capability is unknown.

METHODS

Data were obtained from 240 children of the German LISA cohort. DNAm was measured in blood clots at 6 (N = 234) and 10 years (N = 227) using the Illumina EPIC chip. Presence of aeroallergen sensitization was measured in blood at 6, 10, and 15 years. We calculated six methylation risk scores (MRS) for allergy-related phenotypes, like total and specific IgE, asthma, or any allergies, based on available publications and assessed their performances both cross-sectionally (biomarker) and prospectively (predictor of the disease). Dose-response associations between aeroallergen sensitization and MRS were evaluated.

RESULTS

All six allergy-related MRS were highly correlated (r > .86), and seven CpGs were included in more than one MRS. Cross-sectionally, we observed an 81% increased risk for aeroallergen sensitization at 6 years with an increased MRS by one standard deviation (best-performing MRS, 95% confidence interval = [43%; 227%]). Significant associations were also seen cross-sectionally at 10 years and prospectively, though the effect of the latter was attenuated when restricted to participants not sensitized at baseline. A clear dose-response relationship with levels of aeroallergen sensitization could be established cross-sectionally, but not prospectively.

CONCLUSION

We found good classification and prediction capabilities of calculated allergy-related MRS cross-sectionally, underlining the relevance of altered gene-regulation in allergic diseases and providing insights into potential DNAm biomarkers of aeroallergen sensitization.

Methylation risk scores are associated with a collection of phenotypes within electronic health record systems

Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort (n = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10-7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.

Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease

To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10^-5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.

In-utero exposure to indoor air pollution or tobacco smoke and cognitive development in a South African birth cohort study

BACKGROUND AND AIMS

There is increasing evidence indicating that air pollution exposure is associated with neuronal damage. Since pregnancy is a critical window of vulnerability, air pollution exposure during this period could have adverse effects on neurodevelopment. This study aims 1) to analyze associations of prenatal exposure to indoor air pollution (particulate matter with diameters ≤10 μm, PM10) and tobacco smoke with neurodevelopment and 2) to determine whether these associations are mediated by deviations of epigenetic gestational age from chronological gestational age (ΔGA).

METHODS

Data of 734 children from the South African Drakenstein Child Health Study were analyzed. Prenatal PM10 exposure was measured using devices placed in the families' homes. Maternal smoking during pregnancy was determined by maternal urine cotinine measures. The Bayley Scales of Infant and Toddler Development III (BSID-III) was used to measure cognition, language and motor development and adaptive behavior at two years of age. Linear regression models adjusted for maternal age, gestational age, sex of child, ancestry, birth weight/length, and socioeconomic status were used to explore associations between air pollutants and BSID-III scores. A mediation analysis was conducted to analyze if these associations were mediated by ΔGA using DNA methylation measurements from cord blood.

RESULTS

An increase of one interquartile range in natural-log transformed PM10 (lnPM10; 1.58 μg/m3) was significantly associated with lower composite scores in cognition, language, and adaptive behavior sub-scores (composite score β-estimate [95%-confidence interval]: -0.950 [-1.821, -0.120]). Maternal smoking was significantly associated with lower adaptive behavior scores (-3.386 [-5.632, -1.139]). Associations were not significantly mediated by ΔGA (e.g., for PM10 and cognition, proportion mediated [p-value]: 4% [0.52]).

CONCLUSION

We found an association of prenatal exposure to indoor air pollution (PM10) and tobacco smoke on neurodevelopment at two years of age, particularly cognition, language, and adaptive behavior. Further research is needed to understand underlying biological mediators.

Sensation-seeking-related DNA methylation and the development of delinquency: A longitudinal epigenome-wide study

Heightened sensation-seeking is related to the development of delinquency. Moreover, sensation-seeking, or biological correlates of sensation-seeking, are suggested as factors linking victimization to delinquency. Here, we focused on epigenetic correlates of sensation-seeking. First, we identified DNA methylation (DNAm) patterns related to sensation-seeking. Second, we investigated the association between sensation-seeking related DNAm and the development of delinquency. Third, we examined whether victimization was related to sensation-seeking related DNAm and the development of delinquency. Participants (N = 905; 49% boys) came from the Avon Longitudinal Study of Parents and Children. DNAm was assessed at birth, age 7 and age 15-17. Sensation-seeking (self-reports) was assessed at age 11 and 14. Delinquency (self-reports) was assessed at age 17-19. Sensation-seeking epigenome-wide association study revealed that no probes reached the critical significance level. However, 20 differential methylated probes reached marginal significance. With these 20 suggestive sites, a sensation-seeking cumulative DNAm risk score was created. Results showed that this DNAm risk score at age 15-17 was related to delinquency at age 17-19. Moreover, an indirect effect of victimization to delinquency via DNAm was found. Sensation-seeking related DNAm is a potential biological correlate that can help to understand the development of delinquency, including how victimization might be associated with adolescent delinquency.

Development and validation of an RNA-seq-based transcriptomic risk score for asthma

Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. The objective of this study was to develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information. We analyzed RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples. Logistic least absolute shrinkage and selection operator (Lasso) regression analysis in the training set identified 73 differentially expressed genes (DEG) to form a weighted RSRS that discriminated asthmatics from healthy subjects with area under the curve (AUC) of 0.80 in the testing set after adjustment for age and gender. The 73-gene RSRS was validated in three independent RNA-seq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively. To explore their biological and molecular functions in asthma phenotype, we examined the 73 genes by enrichment pathway analysis and found that these genes were significantly (p < 0.0001) enriched for DNA replication, recombination, and repair, cell-to-cell signaling and interaction, and eumelanin biosynthesis and developmental disorder. Further in-silico analyses of the 73 genes using Connectivity map shows that drugs (mepacrine, dactolisib) and genetic perturbagens (PAK1, GSR, RBM15 and TNFRSF12A) were identified and could potentially be repurposed for treating asthma. These findings show the promise for RNA-seq risk scores to stratify and predict disease risk.

Complex trait methylation scores in the prediction of major depressive disorder

BACKGROUND

DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions.

METHODS

Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432).

FINDINGS

Each trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089-1.457) and in ALSPAC (βrange=0.078-2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053-0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01-0.5; βrange=-0.069-0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes.

INTERPRETATION

We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes.

FUNDING

Wellcome Trust.

DNA methylation in peripheral tissues and left-handedness

Handedness has low heritability and epigenetic mechanisms have been proposed as an etiological mechanism. To examine this hypothesis, we performed an epigenome-wide association study of left-handedness. In a meta-analysis of 3914 adults of whole-blood DNA methylation, we observed that CpG sites located in proximity of handedness-associated genetic variants were more strongly associated with left-handedness than other CpG sites (P = 0.04), but did not identify any differentially methylated positions. In longitudinal analyses of DNA methylation in peripheral blood and buccal cells from children (N = 1737), we observed moderately stable associations across age (correlation range [0.355-0.578]), but inconsistent across tissues (correlation range [- 0.384 to 0.318]). We conclude that DNA methylation in peripheral tissues captures little of the variance in handedness. Future investigations should consider other more targeted sources of tissue, such as the brain.

Genetic and environment effects on structural neuroimaging endophenotype for bipolar disorder: a novel molecular approach

We investigated gene-environment effects on structural brain endophenotype in bipolar disorder (BD) using a novel method of combining polygenic risk scores with epigenetic signatures since traditional methods of examining the family history and trauma effects have significant limitations. The study enrolled 119 subjects, including 55 BD spectrum (BDS) subjects diagnosed with BD or major depressive disorder (MDD) with subthreshold BD symptoms and 64 non-BDS subjects comprising 32 MDD subjects without BD symptoms and 32 healthy subjects. The blood samples underwent genome-wide genotyping and methylation quantification. We derived polygenic risk score (PRS) and methylation profile score (MPS) as weighted summations of risk single nucleotide polymorphisms and methylation probes, respectively, which were considered as molecular measures of genetic and environmental risks for BD. Linear regression was used to relate PRS, MPS, and their interaction to 44 brain structure measures quantified from magnetic resonance imaging (MRI) on 47 BDS subjects, and the results were compared with those based on family history and childhood trauma. After multiplicity corrections using false discovery rate (FDR), MPS was found to be negatively associated with the volume of the medial geniculate thalamus (FDR = 0.059, partial R² = 0.208). Family history, trauma scale, and PRS were not associated with any brain measures. PRS and MPS show significant interactions on whole putamen (FDR = 0.09, partial R² = 0.337). No significant gene-environment interactions were identified for the family history and trauma scale. PRS and MPS generally explained greater proportions of variances of the brain measures (range of partial R² = [0.008, 0.337]) than the clinical risk factors (range = [0.004, 0.228]).

Age-related DNA methylation on Y chromosome and their associations with total mortality among Chinese males

In view of the sex differences in aging‐related diseases, sex chromosomes may play a critical role during aging process. This study aimed to identify age‐related DNA methylation changes on Y chromosome (ChrY). A two‐stage study design was conducted in this study. The discovery stage contained 419 Chinese males, including 205 from the Wuhan‐Zhuhai cohort panel, 107 from the coke oven workers panel, and 107 from the Shiyan panel. The validation stage contained 587 Chinese males from the Dongfeng‐Tongji sub‐cohort. We used the Illumina HumanMethylation BeadChip to determine genome‐wide DNA methylation in peripheral blood of the study participants. The associations between age and methylation levels of ChrY CpGs were investigated by using linear regression models with adjustment for potential confounders. Further, associations of age‐related ChrY CpGs with all‐cause mortality were tested in the validation stage. We identified the significant associations of 41 ChrY CpGs with age at false discovery rate (FDR) <0.05 in the discovery stage, and 18 of them were validated in the validation stage (p < 0.05). Meta‐analysis of both stages confirmed the robust positive associations of 14 CpGs and negative associations of 4 CpGs with age (FDR<0.05). Among them, cg03441493 and cg17816615 were significantly associated with all‐cause mortality risk [HR(95% CI) = 1.37 (1.04, 1.79) and 0.70 (0.54, 0.93), respectively]. Our results highlighted the importance of ChrY CpGs on male aging.

Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.

Epigenome-wide association studies: current knowledge, strategies and recommendations

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.

A 6-CpG validated methylation risk score model for metabolic syndrome: The HyperGEN and GOLDN studies

There has been great interest in genetic risk prediction using risk scores in recent years, however, the utility of scores developed in European populations and later applied to non-European populations has not been successful. The goal of this study was to create a methylation risk score (MRS) for metabolic syndrome (MetS), demonstrating the utility of MRS across race groups using cross-sectional data from the Hypertension Genetic Epidemiology Network (HyperGEN, N = 614 African Americans (AA)) and the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, N = 995 European Americans (EA)). To demonstrate this, we first selected cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously reported to be significantly associated with MetS and/or component conditions in more than one race/ethnic group (CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Second, we calculated the parameter estimates for the 6 CpGs in the HyperGEN data (AA) and used the beta estimates as weights to construct a MRS in HyperGEN (AA), which was validated in GOLDN (EA). We performed association analyses using logistic mixed models to test the association between the MRS and MetS, adjusting for covariates. Results showed the MRS was significantly associated with MetS in both populations. In summary, a MRS for MetS was a strong predictor for the condition across two race groups, suggesting MRS may be useful to examine metabolic disease risk or related complications across race/ethnic groups.

Epigenetic signature of chronic low back pain in human T cells

Supplemental Digital Content is Available in the Text.

This study reveals sex-specific DNA methylation signatures in human T cells that discriminate chronic low back pain participants from healthy controls.

Objective:

Determine if chronic low back pain (LBP) is associated with DNA methylation signatures in human T cells that will reveal novel mechanisms and potential therapeutic targets and explore the feasibility of epigenetic diagnostic markers for pain-related pathophysiology.

Methods:

Genome-wide DNA methylation analysis of 850,000 CpG sites in women and men with chronic LBP and pain-free controls was performed. T cells were isolated (discovery cohort, n = 32) and used to identify differentially methylated CpG sites, and gene ontologies and molecular pathways were identified. A polygenic DNA methylation score for LBP was generated in both women and men. Validation was performed in an independent cohort (validation cohort, n = 63) of chronic LBP and healthy controls.

Results:

Analysis with the discovery cohort revealed a total of 2,496 and 419 differentially methylated CpGs in women and men, respectively. In women, most of these sites were hypomethylated and enriched in genes with functions in the extracellular matrix, in the immune system (ie, cytokines), or in epigenetic processes. In men, a unique chronic LBP DNA methylation signature was identified characterized by significant enrichment for genes from the major histocompatibility complex. Sex-specific polygenic DNA methylation scores were generated to estimate the pain status of each individual and confirmed in the validation cohort using pyrosequencing.

Conclusion:

This study reveals sex-specific DNA methylation signatures in human T cells that discriminates chronic LBP participants from healthy controls.

Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

Toward personalized medicine in schizophrenia: Genetics and epigenetics of antipsychotic treatment

Schizophrenia is a complex psychiatric disorder where genetic, epigenetic, and environmental factors play a role in disease onset, course of illness, and treatment outcome. Pharmaco(epi)genetic research presents an important opportunity to improve patient care through prediction of medication side effects and response. In this narrative review, we discuss the current state of research and important progress of both genetic and epigenetic factors involved in antipsychotic response, over the past five years. The review is largely focused on the following frequently prescribed antipsychotics: olanzapine, risperidone, aripiprazole, and clozapine. Several consistent pharmacogenetic findings have emerged, in particular pharmacokinetic genes (primarily cytochrome P450 enzymes) and pharmacodynamic genes involving dopamine, serotonin, and glutamate neurotransmission. In addition to studies analysing DNA sequence variants, there are also several pharmacoepigenetic studies of antipsychotic response that have focused on the measurement of DNA methylation. Although pharmacoepigenetics is still in its infancy, consideration of both genetic and epigenetic factors contributing to antipsychotic response and side effects no doubt will be increasingly important in personalized medicine. We provide recommendations for next steps in research and clinical evaluation.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

Differential methylation as a diagnostic biomarker of rare renal diseases: a systematic review

BACKGROUND

The challenges in diagnosis of rare renal conditions can negatively impact patient prognosis, quality of life and result in significant healthcare costs. Differential methylation is emerging as an important biomarker for rare diseases and should be evaluated for rare renal conditions.

METHODS

A comprehensive systematic review of methylation and rare renal disorders was conducted by searching the electronic databases MEDLINE, EMBASE, PubMed, Cochrane Library, alongside grey literature from GreyLit and OpenGrey databases, for publications published before September 2018. Additionally, the reference lists of the included papers were searched. Data was extracted and appraised including the primary focus, measurement and methodological rigour of the source. Eligibility criteria were adapted using the inclusion criteria from 'The 100,000 Genomes Project' and The National Registry of Rare Kidney Diseases, with additional focus on methylation.

RESULTS

Thirteen full text articles were included in the review. Diseases analysed for differential methylation included glomerular disease, IgA nephropathy, ADPKD, rare causes of proteinuria, congenital renal agenesis, and membranous nephropathy.

CONCLUSIONS

Differential methylation has been observed for several rare renal diseases, highlighting its potential for improving molecular characterisation of these disorders. Further investigation of methylation following a standardised reporting structure is necessary to improve research quality. Multi-omic data will provide insights for improved diagnosis, prognosis and support for individuals living and working with rare renal diseases.