The interplay of DNA methylation over time with Th2 pathway genetic variants on asthma risk and temporal asthma transition

BackgroundGenetic effects on asthma of genes in the T-helper 2 (Th2) pathway may interact with epigenetic factors including DNA methylation. We hypothesized that interactions between genetic variants and methylation in genes in this pathway (IL4, IL4R, IL13, GATA3, and STAT6) influence asthma risk, that such influences are age-dependent, and that methylation of some CpG sites changes over time in accordance with asthma transition. We tested these hypotheses in subsamples of girls from a population-based birth cohort established on the Isle of Wight, UK, in 1989.ResultsLogistic regression models were applied to test the interaction effect of DNA methylation and SNP on asthma within each of the five genes. Bootstrapping was used to assess the models identified. From 1,361 models fitted at each age of 10 and 18 years, 8 models, including 4 CpGs and 8 SNPs, showed potential associations with asthma risk. Of the 4 CpGs, methylation of cg26937798 (IL4R) and cg23943829 (IL4) changes between ages 10 and 18 (both higher at 10; P?=?9.14?×?10?6 and 1.07?×?10?5, respectively).At age 10, the odds of asthma tended to decrease as cg12405139 (GATA3) methylation increased (log-OR?=??12.15; P?=?0.049); this effect disappeared by age 18. At age 18, methylation of cg09791102 (IL4R) was associated with higher risk of asthma among subjects with genotype GG compared to AG (P?=?0.003), increased cg26937798 methylation among subjects with rs3024685 (IL4R) genotype AA (P?=?0.003) or rs8832 (IL4R) genotype GG (P?=?0.01) was associated with a lower asthma risk; these CpGs had no effect at age 10. Increasing cg26937798 methylation over time possibly reduced the risk of positive asthma transition (asthma-free at age 10???asthma at age 18; log-OR?=??3.11; P?=?0.069) and increased the likelihood of negative transition (asthma at age 10???asthma-free at age 18; log-OR?=?3.97; P?=?0.074).ConclusionsThe interaction of DNA methylation and SNPs in Th2 pathway genes is likely to contribute to asthma risk. This effect may vary with age. Methylation of some CpGs changed over time, which may influence asthma transition

Neonatal Lead (Pb) Exposure and DNA Methylation Profiles in Dried Bloodspots

Lead (Pb) exposure remains a major concern in the United States (US) and around the world, even following the removal of Pb from gasoline and other products. Environmental Pb exposures from aging infrastructure and housing stock are of particular concern to pregnant women, children, and other vulnerable populations. Exposures during sensitive periods of development are known to influence epigenetic modifications which are thought to be one mechanism of the Developmental Origins of Health and Disease (DOHaD) paradigm. To gain insights into early life Pb exposure-induced health risks, we leveraged neonatal dried bloodspots in a cohort of children from Michigan, US to examine associations between blood Pb levels and concomitant DNA methylation profiles (n = 96). DNA methylation analysis was conducted via the Infinium MethylationEPIC array and Pb levels were assessed via high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS). While at-birth Pb exposure levels were relatively low (average 0.78 µg/dL, maximum of 5.27 ug/dL), we identified associations between DNA methylation and Pb at 33 CpG sites, with the majority (82%) exhibiting reduced methylation with increasing Pb exposure (q \u3c 0.2). Biological pathways related to development and neurological function were enriched amongst top differentially methylated genes by p-value. In addition to increases/decreases in methylation, we also demonstrate that Pb exposure is related to increased variability in DNA methylation at 16 CpG sites. More work is needed to assess the accuracy and precision of metals assessment using bloodspots, but this study highlights the utility of this unique resource to enhance environmental epigenetics research around the world

Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring

GENOME-SCALE METHYLATION ANALYSIS IN BLOOD AND TUMOR IDENTIFIES IMMUNE PROFILE, AGE ACCELERATION, AND DNA METHYLATION ALTERATIONS ASSOCIATED WITH BLADDER CANCER OUTCOMES

Bladder cancer patients receive frequent screening due to the high tumor recurrence rate (more than 60%). Nowadays, the conventional monitoring method relies on cystoscopy which is highly invasive and increases patient morbidity and burden to the health care system with frequent follow-up. As a result, it is urgent to explore novel markers related to the outcomes of bladder cancer. Immune profiles have been associated with cancer outcomes and may have the potential to be biomarkers for outcomes management. However, little work has been conducted to investigate the associations of immune cell profiles with bladder cancer outcomes. Here, I utilized the Illumina HumanMethylationEPIC array to measure DNA methylation profiles of peripheral blood and matched tumor tissues of bladder cancer cases recruited in a population-based cohort study in New Hampshire. Then, cell-type deconvolution was applied to quantify immune cell-type proportions, and three epigenetic clocks were estimated for calculating age acceleration. Cox proportional hazard models were performed to test the association of methylation-derived profiles with bladder cancer outcomes. The partDSA algorithm and a semi-supervised recursively partitioned mixture model were conducted to determine overall survival groups based on immune cell profiles, clinical variables, and DNA methylation level. We used an epigenome‐wide association study approach adjusting for immune cell profiles to identify CpG sites associated with the hazard of bladder cancer outcomes, and then, those identified CpG sites were used for enrichment analyses. Finally, we evaluated the association between circulating immune cell-type proportions with the cell-type proportions in the tumor microenvironment. We demonstrated that highly circulating CD4T memory and CD8T memory cell proportions were significantly associated with a decreased hazard of tumor recurrence or death, whereas high neutrophil cell proportion, NLR, and age acceleration were associated with an increased hazard of tumor recurrence or death. Collectively, we identified associations of methylation-derived immune profiles and age acceleration with bladder cancer outcomes that may facilitate the development of bladder cancer prognostic biomarkers

Influence of Maternal BMI on Offspring BMI from In-utero to Adolescence and offspring BMI trajectories via Epigenetic Changes

The impact of maternal body mass index (BMI) on offspring health outcomes such as obesity has been widely investigated, with evidence suggesting that in-utero conditions may influence DNA methylation (DNAm) and BMI developmental trajectories. However, it is unclear whether differential DNAm on sites with a cytosine followed by a guanine linked by phosphate dinucleotide (CpG) related to gestational BMI are linked to BMI changes in offspring. Using data from the Isle of Wight birth cohort, UK, this study aims to investigate BMI trajectories and address the role of DNAm. Using trajectory analysis four distinct BMI developmental trajectories - ‘normal’ (n= 1042), ‘early persistent obesity’ (EPO, n=61), ‘early transient overweight’ (ETO, n=185), and ‘delayed overweight’ (DOW, n=149) - that spanned first 26 year of life were identified. The trajectories were found to be influenced by gestational BMI. To identify CpGs related to gestational BMI academic database were explored and 1,090 CpGs were found in the IOW cohort data as candidate CpGs for specific aims 2 and 3 out of 1,773 differentially methylated CpGs reported in prior investigations. Fourteen of these candidate CpGs were found to be significantly associated with BMI trajectories, two survived multiple testing. Higher methylation of cg23089913 (NANOS1 gene) was associated with decreased odds of being in the EPO trajectory with an odds ratio of 0.84 (95%CI: 0.76-0.93). In contrast, increased methylation of cg13217064 (SOX14) was associated with a 1.4 times higher odds (95%CI: 1.13-1.67) of being in the DOW compared to the \u27normal\u27 trajectory. Finally, associations between candidate CpGs and repeated BMI measurements from infancy to 26 years of age were investigated.Five CpGs - cg00488692 (SP3), cg14434213 (RNF5P1), cg23089913 (NANOS1), cg26862527 (BAI3), and cg17812850 (TMEM184C) were found to be statistically significantly linked with BMI. Female participants exposed to prenatal paternal smoking and mixed feeding during infancy had a higher BMI, while male participants with lower birth weight had 0.4 kg/m2 higher BMI. The study identified candidate CpGs on genes critical to metabolic disorders and provides a basis for further investigations to understand the biological role of DNAm sites in BMI development

Regulatory genomic consequences of polygenic risk burden for Alzheimer’s disease

Dementia is an umbrella term used to describe a group of symptoms associated with global cognitive impairment and is a major contributor to the global burden of disease; currently there are over 50 million individuals affected world-wide. Due to the ageing population and lack of effective disease-modifying treatments, this number is expected to triple by 2050. Dementia encompasses a number of neurological diseases, including Alzheimer’s disease (AD), which accounts for 60-80% of cases. There is a well-established genetic component to AD and genome wide-association studies have identified >75 variants robustly associated with disease. Little is known about the functional mechanisms by which risk variants mediate disease susceptibility; as the majority of these variants do not index coding variants affecting protein structure they are hypothesised to influence gene regulation, supported by the observation that they are enriched in regulatory domains including enhancers. The primary aim of this thesis was to assess whether genetic liability for AD is associated with regulatory genomic variation (i.e. epigenetic and transcriptomic) in whole blood and the human cortex. Epigenome-wide association studies and multi-omic methods were utilised to explore the molecular mechanisms leading to disease. The results from this thesis indicate that epigenetic mechanisms are involved in AD pathogenesis and provide further support for several established AD pathways such as lipid and cholesterol metabolism, Aβ, tau and APP processing as well as a role for the immune system. The analyses incorporating AD genetic variation with DNA methylation infer that there are both direct cis genetic effects and indirect polygenic effects on regulatory processes which are involved in the aetiology of AD. Although there were consistencies at some loci across the whole blood and cortex analyses, there was also evidence for heterogeneity across tissues which might represent tissue specific effects in areas primarily affected in AD (e.g. the cortex) in comparison to peripheral tissues. In summary, using multiple approaches, I characterised the complex relationship between genetic and epigenetic variation, enabling the exploration of molecular genomic mechanisms driving AD pathogenesis in both peripheral and brain tissues and prioritised genes which could be targeted in future functional studies

Epigenetic Marker Identification and Assessment of Methods on Cell Type Compositions at the Epigenome-Scale

Epigenetics is the study of heritable changes in genes which are caused by chemical compounds derived from natural and man-made sources. DNA methylation an epigenetic phenomenon, is most vulnerable to environmental factors during embryogenesis, which is a period of rapid cell division and epigenetic remodeling. Given the recent increase in the incidence of childhood diseases, it is crucial to understand the role of environmental factor through epigenetic study in causing adverse health effects. This dissertation revolves around three major hypotheses. In hypothesis one we evaluated the association between in utero arsenic exposure and genome-wide DNA methylation in cord blood from the birth cohort data of Taiwan. The identified CpG sites were replicated in an independent birth cohort (New Hampshire birth cohort study; NHBCS) and further assessed longitudinal associations of DNA methylation with disease biomarkers measured at later ages in our cohort from Taiwan. In second hypotheses we assessed the association between Immunoglobulin E (IgE) production and DNA methylation at birth via cord blood in a longitudinal study. The study was conducted from the birth cohort data of Taiwan and the findings were replicated in an independent birth cohort (Isle of Wight; IoW), and further the stability of identified CpG sites was assessed based on intra-class (ICC) correlation measure. In the third hypotheses we assessed the confounding effect in epigenome wide association study due to underlying cell composition and evaluated several methods and algorithms proposed to adjust for this confounding effect

Graph structure inference for high-throughput genomic data

Recent advances in high-throughput sequencing technologies enable us to study a large number of biomarkers and use their information collectively. Based on high-throughput experiments, there are many genome-wide networks constructed to characterize the complex physical or functional interactions between the biomarkers. To identify outcome-related biomarkers, it is often advantageous to make use of the known relational structure, because graph structured inference introduces smoothness and reduces complexity in modelling. In this dissertation, we propose models for high-dimensional epigenetic and genomic data that incorporate the network structure and update the network structure based on empirical evidence. In the first part of this dissertation, we propose a penalized conditional logistic regression model for high dimensional DNA methylation data. DNA methylation of CpG sites within genes are often correlated and the number of CpG sites typically far outnumbers the sample size. The new penalty function combines the truncated lasso penalty and a graph fuse-lasso penalty to induce parsimonious and consistent models, and to incorporate the CpG sites network structure without introducing extra bias. An efficient minorization-maximization algorithm that utilizes difference of convex programming and alternating direction method of multipliers is presented. Extensive simulations demonstrated superior performance of the proposed method compared to several existing methods in both model selection consistency and parameter estimation accuracy. We also applied the proposed method to a matched case-control breast invasive carcinoma methylation data from the Cancer Genome Atlas (TCGA), generated from both Illumina Infinium HumanMethylation27 (HM27) and HumanMethylation450 (HM450) Beadchip. The proposed method identified several outcome-related CpG sites that have been missed by the existing methods. In the latter part of this dissertation, we propose a Bayesian hierarchical graph-structured model that integrates {\em a priori} network information with empirical evidence. Empirical data may suggest modifications to the given network structure, which could lead to new and interesting biological findings when the prior knowledge on the graphical structure among the variables is limited or partial. We present the full hierarchical model along with the Markov Chain Monte Carlo sampling inference procedure. Using both simulations and brain aging gene pathway data, we showed that the new method can identify discrepancy between data and a prior known graph structure and suggest modifications and updates. Motivated by methylation and gene expression data, the two models we propose in this thesis make use of the available structure in the data and produce better inferential results. The proposed methods can be applied to a wider range of problems