A Method to Detect Differentially Methylated Loci With Next‐Generation Sequencing

Epigenetic changes, especially DNA methylation at CpG loci have important implications in cancer and other complex diseases. With the development of next‐generation sequencing (NGS), it is feasible to generate data to interrogate the difference in methylation status for genome‐wide loci using case‐control design. However, a proper and efficient statistical test is lacking. There are several challenges. First, unlike methylation experiments using microarrays, where there is one measure of methylation for one individual at a particular CpG site, here we have the counts of methylation allele and unmethylation allele for each individual. Second, due to the nature of sample preparation, the measured methylation reflects the methylation status of a mixture of cells involved in sample preparation. Therefore, the underlying distribution of the measured methylation level is unknown, and a robust test is more desirable than parametric approach. Third, currently NGS measures methylation at over 2 million CpG sites. Any statistical tests have to be computationally efficient in order to be applied to the NGS data. Taking these challenges into account, we propose a test for differential methylation based on clustered data analysis by modeling the methylation counts. We performed simulations to show that it is robust under several distributions for the measured methylation levels. It has good power and is computationally efficient. Finally, we apply the test to our NGS data on chronic lymphocytic leukemia. The results indicate that it is a promising and practical test.     

A Novel Method for Detecting Association Between DNA Methylation and Diseases Using Spatial Information

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.     

Accounting for Population Stratification in DNA Methylation Studies

DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome‐wide association studies, issues such as population stratification will need to be addressed. It is well‐documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome‐wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC‐based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single‐nucleotide polymorphism (SNP)‐based PCs, followed by methylation‐based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation‐based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome‐wide SNP data are unavailable.     

Kernel machine methods for integrative analysis of genome‐wide methylation and genotyping studies

Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association. A composite kernel is constructed to measure the similarities in the genotype and methylation values between pairs of samples. We demonstrate through simulations and real data applications that the proposed approach can correctly control type I error, and is more robust and powerful than using only the genotype or methylation data in detecting trait‐associated genes. We applied our method to investigate the genetic and epigenetic regulation of gene expression in response to stressful life events using data that are collected from the Grady Trauma Project. Within the kernel machine testing framework, our methods allow for heterogeneity in effect sizes, nonlinear, and interactive effects, as well as rapid P‐value computation.     

Network‐based regularization for matched case‐control analysis of high‐dimensional DNA methylation data

The matched case‐control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case‐control studies with high‐dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network‐based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non‐tumor tissues or between pre‐treatment and post‐treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network‐based penalty that encourages a grouping effect of (1) linked Cytosine‐phosphate‐Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high‐dimensional variable selection problems for matched case‐control data. We further investigated the benefits of utilizing biological group or graph information for matched case‐control data. We applied the proposed method to a genome‐wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non‐tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.     

Identifying significant gene‐environment interactions using a combination of screening testing and hierarchical false discovery rate control

Although gene‐environment (G× E) interactions play an important role in many biological systems, detecting these interactions within genome‐wide data can be challenging due to the loss in statistical power incurred by multiple hypothesis correction. To address the challenge of poor power and the limitations of existing multistage methods, we recently developed a screening‐testing approach for G× E interaction detection that combines elastic net penalized regression with joint estimation to support a single omnibus test for the presence of G× E interactions. In our original work on this technique, however, we did not assess type I error control or power and evaluated the method using just a single, small bladder cancer data set. In this paper, we extend the original method in two important directions and provide a more rigorous performance evaluation. First, we introduce a hierarchical false discovery rate approach to formally assess the significance of individual G× E interactions. Second, to support the analysis of truly genome‐wide data sets, we incorporate a score statistic‐based prescreening step to reduce the number of single nucleotide polymorphisms prior to fitting the first stage penalized regression model. To assess the statistical properties of our method, we compare the type I error rate and statistical power of our approach with competing techniques using both simple simulation designs as well as designs based on real disease architectures. Finally, we demonstrate the ability of our approach to identify biologically plausible SNP‐education interactions relative to Alzheimer's disease status using genome‐wide association study data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).     

Global test for high-dimensional mediation: Testing groups of potential mediators

We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously. We apply our approach to data from a large Norwegian cohort study, where we look at the hypothesis that smoking increases the risk of lung cancer by modifying the level of DNA methylation.     

Semiparametric regression models for detecting effect modification in matched case-crossover studies

In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity.     

Across‐Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high‐throughput technologies have enabled genome‐wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post‐imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait‐associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome‐wide association study (EWAS).     

Epigenetics,nutrition and mental health. Is there a relationship?

Many aspects of human development and disease are influenced by the interaction between genetic and environmental factors. Understanding how our genes respond to the environment is central to managing health and disease, and is one of the major contemporary challenges in human genetics. Various epigenetic processes affect chromosome structure and accessibility of deoxyribonucleic acid (DNA) to the enzymatic machinery that leads to expression of genes. One important epigenetic mechanism that appears to underlie the interaction between environmental factors, including diet, and our genome, is chemical modification of the DNA. The best understood of these modifications is methylation of cytosine residues in DNA. It is now recognized that the pattern of methylated cytosines throughout our genomes (the ‘methylome’) can change during development and in response to environmental cues, often with profound effects on gene expression. Many dietary constituents may indirectly influence genomic pathways that methylate DNA, and there is evidence for biochemical links between nutritional quality and mental health. Deficiency of both macro- and micronutrients has been associated with increased behavioural problems, and nutritional supplementation has proven efficacious in treatment of certain neuropsychiatric disorders. In this review we examine evidence from the fields of nutrition, developmental biology, and mental health that supports dietary impacts on epigenetic processes, particularly DNA methylation. We then consider whether such processes could underlie the demonstrated efficacy of dietary supplementation in treatment of mental disorders, and whether targeted manipulation of DNA methylation patterns using controlled dietary supplementation may be of wider clinical value.     

Early Life Nutrition and Mental Health: The Role of DNA Methylation

Does the quality of our diet during early life impact our long-term mental health? Accumulating evidence suggests that nutrition interacts with our genes and that there is a strong association between the quality of diet and mental health throughout life. Environmental influences such as maternal diet during pregnancy or offspring diet have been shown to cause epigenetic changes during critical periods of development, such as chemical modifications of DNA or histones by methylation for the regulation of gene expression. One-carbon metabolism, which consists of the folate and methionine cycles, is influenced by the diet and generates S-Adenosylmethinoine (SAM), the main methyl donor for methylation reactions such as DNA and histone methylation. This review provides current knowledge on how the levels of one-carbon metabolism associated micronutrients such as choline, betaine, folate, methionine and B vitamins that play a role in brain function can impact our well-being and mental health across the lifespan. Micronutrients that act as methyl donors for SAM formation could affect global or gene methylation, altering gene expression and phenotype. Strategies should then be adopted to better understand how these nutrients work and their impact at different stages of development to provide individualized dietary recommendations for better mental health outcomes.     

Association between Serum Vitamin B12 and Global DNA Methylation in Colorectal Cancer Patients

Vitamin B12 has been widely related to methionine metabolism, which is an essential component for biological methylation reactions, including DNA methylation. However, the relationship between vitamin B12 and DNA methylation is still controversial. In addition, there is increasing evidence for the association between vitamin B12 and the risk of colorectal cancer (CRC), although results of this association need to be assessed with caution. For this purpose, we hypothesized that serum vitamin B12 could be associated with global DNA methylation in the CRC context. To test this hypothesis, we studied the association between global DNA methylation through long interspersed nuclear element-1 (LINE1) in CRC patients under the 25th percentile of serum vitamin B12. We found that the high vitamin B12 group had low LINE1 methylation in both tumor area and peripheral blood mononuclear cells (PBMCs) than the low serum vitamin B12 group. LINE1 methylation levels were significantly lower in tumor area compared to the adjacent tumor-free area, only in the high vitamin B12 group. LINE1 methylation in visceral adipose tissue (VAT) and PBMCs were correlated with tumoral, inflammatory, and insulin metabolism markers. However, the interaction between LINE1 methylation and vitamin B12 levels was associated with neoadjuvant therapy in the regression analysis only in men, suggesting a beneficial relationship. In conclusion, our results reported an inverse association between DNA methylation and vitamin B12 in the CRC context, which suggests that vitamin B12 may be implicated in an epigenetic state or mediation in CRC.     

Estimating Genome‐Wide Significance for Whole‐Genome Sequencing Studies

Although a standard genome‐wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole‐genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence‐identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome‐wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome‐wide significance thresholds for different analysis choices. Based on UK10K whole‐genome sequence data, we derive genome‐wide significance thresholds ranging between 2.5 × 10^?8 and 8 × 10^?8 for our analytic choices in window‐based testing, and thresholds of 0.6 × 10^?8–1.5 × 10^?8 for a combined analytic strategy of testing common variants using single‐SNP tests together with rare variants analyzed with our sliding‐window test strategy.     

The role of early embryonic environment on epigenotype and phenotype

The influence of epigenetic modifications to the genome on the phenotype of the adult organism is now a tractable problem in biology. This has come about through the development of methods that enable us to study the methylation state of the DNA and the packaging of the chromatin at specific gene loci. It is becoming clear that early embryogenesis is a critical period for the establishment of the epigenotype. Furthermore, it appears that this process is sensitive to environmental conditions. This is a concern in light of the increasing use of artificial reproductive technologies throughout the world.     

Curcumin from Turmeric Rhizome: A Potential Modulator of DNA Methylation Machinery in Breast Cancer Inhibition

One of the most systematically studied bioactive nutraceuticals for its benefits in the management of various diseases is the turmeric-derived compounds: curcumin. Turmeric obtained from the rhizome of a perennial herb Curcuma longa L. is a condiment commonly used in our diet. Curcumin is well known for its potential role in inhibiting cancer by targeting epigenetic machinery, with DNA methylation at the forefront. The dynamic DNA methylation processes serve as an adaptive mechanism to a wide variety of environmental factors, including diet. Every healthy tissue has a precise DNA methylation pattern that changes during cancer development, forming a cancer-specific design. Hypermethylation of tumor suppressor genes, global DNA demethylation, and promoter hypomethylation of oncogenes and prometastatic genes are hallmarks of nearly all types of cancer, including breast cancer. Curcumin has been shown to modulate epigenetic events that are dysregulated in cancer cells and possess the potential to prevent cancer or enhance the effects of conventional anti-cancer therapy. Although mechanisms underlying curcumin-mediated changes in the epigenome remain to be fully elucidated, the mode of action targeting both hypermethylated and hypomethylated genes in cancer is promising for cancer chemoprevention. This review provides a comprehensive discussion of potential epigenetic mechanisms of curcumin in reversing altered patterns of DNA methylation in breast cancer that is the most commonly diagnosed cancer and the leading cause of cancer death among females worldwide. Insight into the other bioactive components of turmeric rhizome as potential epigenetic modifiers has been indicated as well.     

Nutrition and epigenetics: An emerging field

Epigenetics refers to heritable changes to gene expression encoded not by differences in the genetic sequence but by other chemical modifications to chromatin, such as methylation of the DNA backbone, or acetylation and methylation of the histone core. The total set of such epigenetic marks can be referred to as the epigenome, but unlike the genome, epigenetic marks differ between tissues and are modified by metabolic conditions and environmental exposures throughout life. In humans and animal models, key metabolic pathways, such as those of energy metabolism and obesity, are believed to be partly regulated by epigenetic mechanisms and to be subject to metabolic and nutritional modification in utero and throughout life. There is growing interest in the possibility that extremes of energy or micronutrient availability may modulate the epigenome and hence modify the development and disease susceptibility of individuals. Particular interest is evident for methyl donors, including folic acid, which might directly modify DNA methylation in humans.     

Epigenetics and DNA methylation in cancer

Epigenetic is the study of those alterations regulating gene expression without altering DNA sequence and inherited by transmission through cell division. Mutational and epimutational events that alterate cellular growth and division are combined in carcinogenesis. Advances in genome and epigenome-wide analysis identify DNA hypomethylation, hypermethylation of tumor suppressor genes, aberrant histone modifications and/or specific miRNA expression profiles to contribute to tumor initiation and progression. The major challenge for cancer researchers is to enlighten the complex relationship between the epigenetic and genetic machinery in order to optimize combined therapies, reducing chemoresistance and minimizing adverse effects in cancer patients. In this review we will cover many distinct aspects of epigenetic phenomenon. Firstly, we will globally explain the most common epigenetic events and their effects on gene expression regulation. Secondly, we will review the evidence of the correlation between epigenetics and cancer progression, focusing in particular on the effect of aberrant hypo- and hyper-methylation. We will also consider the main methods currently used for methylation analysis, covering both locus-specific technologies and genome-wide analysis. Finally, we will discuss the introduction of novel epigenetic drugs in combination with conventional treatments in order to develop more effective cancer therapies. Such information could help in understanding the important role of epigenetics in cancer.     

Dynamic predictions using flexible joint models of longitudinal and time‐to‐event data

Joint models for longitudinal and time‐to‐event data are particularly relevant to many clinical studies where longitudinal biomarkers could be highly associated with a time‐to‐event outcome. A cutting‐edge research direction in this area is dynamic predictions of patient prognosis (e.g., survival probabilities) given all available biomarker information, recently boosted by the stratified/personalized medicine initiative. As these dynamic predictions are individualized, flexible models are desirable in order to appropriately characterize each individual longitudinal trajectory. In this paper, we propose a new joint model using individual‐level penalized splines (P‐splines) to flexibly characterize the coevolution of the longitudinal and time‐to‐event processes. An important feature of our approach is that dynamic predictions of the survival probabilities are straightforward as the posterior distribution of the random P‐spline coefficients given the observed data is a multivariate skew‐normal distribution. The proposed methods are illustrated with data from the HIV Epidemiology Research Study. Our simulation results demonstrate that our model has better dynamic prediction performance than other existing approaches. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Association of Global DNA Methylation and Global DNA Hydroxymethylation with Metals and Other Exposures in Human Blood DNA Samples

Background: The association between human blood DNA global methylation and global hydroxymethylation has not been evaluated in population-based studies. No studies have evaluated environmental determinants of global DNA hydroxymethylation, including exposure to metals.Objective: We evaluated the association between global DNA methylation and global DNA hydroxymethylation in 48 Strong Heart Study participants for which selected metals had been measured in urine at baseline and DNA was available from 1989–1991 (visit 1) and 1998–1999 (visit 3).Methods: We measured the percentage of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) in samples using capture and detection antibodies followed by colorimetric quantification. We explored the association of participant characteristics (i.e., age, adiposity, smoking, and metal exposure) with both global DNA methylation and global DNA hydroxymethylation.Results: The Spearman’s correlation coefficient for 5-mC and 5-hmC levels was 0.32 (p = 0.03) at visit 1 and 0.54 (p < 0.001) at visit 3. Trends for both epigenetic modifications were consistent across potential determinants. In cross-sectional analyses, the odds ratios of methylated and hydroxymethylated DNA were 1.56 (95% CI: 0.95, 2.57) and 1.76 (95% CI: 1.07, 2.88), respectively, for the comparison of participants above and below the median percentage of dimethylarsinate. The corresponding odds ratios were 1.64 (95% CI: 1.02, 2.65) and 1.16 (95% CI: 0.70, 1.94), respectively, for the comparison of participants above and below the median cadmium level. Arsenic exposure and metabolism were consistently associated with both epigenetic markers in cross-sectional and prospective analyses. The positive correlation of 5-mC and 5-hmC levels was confirmed in an independent study population.Conclusions: Our findings support that both epigenetic measures are related at the population level. The consistent trends in the associations between these two epigenetic modifications and the characteristics evaluated, especially arsenic exposure and metabolism, suggest the need for understanding which of the two measures is a better biomarker for environmental epigenetic effects in future large-scale epidemiologic studies.Citation: Tellez-Plaza M, Tang WY, Shang Y, Umans JG, Francesconi KA, Goessler W, Ledesma M, Leon M, Laclaustra M, Pollak J, Guallar E, Cole SA, Fallin MD, Navas-Acien A. 2014. Association of global DNA methylation and global DNA hydroxymethylation with metals and other exposures in human blood DNA samples. Environ Health Perspect 122:946–954; http://dx.doi.org/10.1289/ehp.1306674     

表观遗传异常与宫颈癌

摘要:癌症是一类遗传及表观遗传异常的复杂疾病。宫颈癌由癌前病变发展为侵入性的宫颈癌是由于人乳头瘤病毒（Human Papilloma Virus,HPV）持续感染引起了宿主基因组及表观遗传基因组的改变。表观遗传改变包括DNA甲基化水平改变、miRNA异常表达、癌基因的激活及抑癌基因沉默等。鉴于miRNA可被表观遗传机制调控,因而其在宫颈癌中的异常表达可能是启动子甲基化水平改变引起的。基于以上判断,本文对HPV感染、DNA甲基化水平改变及miRNA异常表达间的关系进行综述,以期揭示表观遗传异常与宫颈癌发生发展之间的关系。