Age-related trajectories of DNA methylation network markers: a parenclitic network approach to a family-based cohort of patients with Down Syndrome

Despite the fact that the cause of Down Syndrome (DS) is well established, the underlying molecular mechanisms that contribute to the syndrome and the phenotype of accelerated aging remain largely unknown. DNA methylation profiles are largely altered in DS, but it remains unclear how different methylation regions and probes are structured into a network of interactions. We develop and generalize the Parenclitic Networks approach that enables finding correlations between distant CpG probes (which are not pronounced as stand-alone biomarkers) and quantifies hidden network changes in DNA methylation. DS and a familybased cohort (including healthy siblings and mothers of persons with DS) are used as a case study. Following this approach, we constructed parenclitic networks and obtained different signatures that indicate (i) differences between individuals with DS and healthy individuals; (ii) differences between young and old healthy individuals; (iii) differences between DS individuals and their age-matched siblings, and (iv) difference between DS and the adult population (their mothers). The Gene Ontology analysis showed that the CpG network approach is more powerful than the single CpG approach in identifying biological processes related to DS phenotype. This includes the processes occurring in the central nervous system, skeletal muscles, disorders in carbohydrate metabolism, cardiopathology, and oncogenes. Our open-source software implementation is accessible to all researchers. The software includes a complete workflow, which can be used to construct Parenclitic Networks with any machine learning algorithm as a kernel to build edges. We anticipate a broad applicability of the approach to other diseases

Estimands in epigenome-wide association studies

Background: In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both. Results: We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results. Conclusions: The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference

Impact of vitamin C supplementation on placental DNA methylation changes related to maternal smoking: association with gene expression and respiratory outcomes

Background: Maternal smoking during pregnancy (MSDP) affects development of multiple organ systems including the placenta, lung, brain, and vasculature. In particular, children exposed to MSDP show lifelong deficits in pulmonary function and increased risk of asthma and wheeze. Our laboratory has previously shown that vitamin C supplementation during pregnancy prevents some of the adverse effects of MSDP on offspring respiratory outcomes. Epigenetic modifications, including DNA methylation (DNAm), are a likely link between in utero exposures and adverse health outcomes, and MSDP has previously been associated with DNAm changes in blood, placenta, and buccal epithelium. Analysis of placental DNAm may reveal critical targets of MSDP and vitamin C relevant to respiratory health outcomes. Results: DNAm was measured in placentas obtained from 72 smokers enrolled in the VCSIP RCT: NCT03203603 (37 supplemented with vitamin C, 35 with placebo) and 24 never-smokers for reference. Methylation at one CpG, cg20790161, reached Bonferroni significance and was hypomethylated in vitamin C supplemented smokers versus placebo. Analysis of spatially related CpGs identified 93 candidate differentially methylated regions (DMRs) between treatment groups, including loci known to be associated with lung function, oxidative stress, fetal development and growth, and angiogenesis. Overlap of nominally significant differentially methylated CpGs (DMCs) in never-smokers versus placebo with nominally significant DMCs in vitamin C versus placebo identified 9059 candidate "restored CpGs" for association with placental transcript expression and respiratory outcomes. Methylation at 274 restored candidate CpG sites was associated with expression of 259 genes (FDR < 0.05). We further identified candidate CpGs associated with infant lung function (34 CpGs) and composite wheeze (1 CpG) at 12 months of age (FDR < 0.05). Increased methylation in the DIP2C, APOH/PRKCA, and additional candidate gene regions was associated with improved lung function and decreased wheeze in offspring of vitamin C-treated smokers. Conclusions: Vitamin C supplementation to pregnant smokers ameliorates changes associated with maternal smoking in placental DNA methylation and gene expression in pathways potentially linked to improved placental function and offspring respiratory health. Further work is necessary to validate candidate loci and elucidate the causal pathway between placental methylation changes and outcomes of offspring exposed to MSDP

Prediction Models for Cancer Risk and Prognosis using Clinical and DNA Methylation Biomarkers: Considerations in Study Design and Model Development

The ability to accurately predict the prognosis for any given disease is of immense value for clinicians and patients. It can dictate and optimize an individual treatment plan for a patient and ultimately improve their quality of life and reduce the financial burden associated with unnecessary treatment. To allow the accurate prediction of disease prognosis, ongoing development of prediction models is of crucial importance. We introduce a novel curated, ad-hoc, feature selection (CAFS) strategy in the context of the Prostate Cancer DREAM Challenge. We demonstrate enhanced prediction performance of overall survival differences in patients with metastatic castration-resistant prostate cancer by applying CAFS and identify clinically important risk-predictors. With ongoing advancements in the omics field promising molecular biomarkers are being identified in order to facilitate disease prognosis beyond the capability of clinical information. The identification of such biomarkers depends on the examination of omic marks in adequately powered studies. With the goal to assist researchers in study design and planning of epigenome wide association studies of DNA methylation, we present a user-friendly tool, pwrEWAS, for comprehensive power estimation for epigenome-wide association studies. The R package for pwrEWAS is publicly available at GitHub (https://github.com/stefangraw/pwrEWAS) and the web interface is available at https://biostats-shinyr.kumc.edu/pwrEWAS/. The enormous volume of omic marks requires stringent evaluation to discover combinations of complementary marks that assemble predictive biomarkers. We therefore present a heuristic feature selection approach that allows one to handle such high-dimensional data. Selection Probability Optimization for Feature Selection (SPOFS) is designed to identify an optimal subset of omic features from among a vast pool of such features, which collectively improves prediction accuracy and form a biomarker. The integration of such biomarkers can then be utilized in the development and improvement of prediction models