SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips

Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood

Puberty marks numerous physiological processes which are initiated by central activation of the hypothalamic–pituitary–gonadal axis, followed by development of secondary sexual characteristics. To a large extent, pubertal timing is heritable, but current knowledge of genetic polymorphisms only explains few months in the large inter-individual variation in the timing of puberty. We have analysed longitudinal genome-wide changes in DNA methylation in peripheral blood samples (n = 102) obtained from 51 healthy children before and after pubertal onset. We show that changes in single methylation sites are tightly associated with physiological pubertal transition and altered reproductive hormone levels. These methylation sites cluster in and around genes enriched for biological functions related to pubertal development. Importantly, we identified that methylation of the genomic region containing the promoter of TRIP6 was co-ordinately regulated as a function of pubertal development. In accordance, immunohistochemistry identified TRIP6 in adult, but not pre-pubertal, testicular Leydig cells and circulating TRIP6 levels doubled during puberty. Using elastic net prediction models, methylation patterns predicted pubertal development more accurately than chronological age. We demonstrate for the first time that pubertal attainment of secondary sexual characteristics is mirrored by changes in DNA methylation patterns in peripheral blood. Thus, modulations of the epigenome seem involved in regulation of the individual pubertal timing.

An evaluation of processing methods for HumanMethylation450 BeadChip data

Background

Illumina’s HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis. As with other types of microarray platforms, technical artifacts are a concern, including background fluorescence, dye-bias from the use of two color channels, bias caused by type I/II probe design, and batch effects. Several approaches and pipelines have been developed, either targeting a single issue or designed to address multiple biases through a combination of methods. We evaluate the effect of combining separate approaches to improve signal processing.

Results

In this study nine processing methods, including both within- and between- array methods, are applied and compared in four datasets. For technical replicates, we found both within- and between-array methods did a comparable job in reducing variance across replicates. For evaluating biological differences, within-array processing always improved differential DNA methylation signal detection over no processing, and always benefitted from performing background correction first. Combinations of within-array procedures were always among the best performing methods, with a slight advantage appearing for the between-array method Funnorm when batch effects explained more variation in the data than the methylation alterations between cases and controls. However, when this occurred, RUVm, a new batch correction method noticeably improved reproducibility of differential methylation results over any of the signal-processing methods alone.

Conclusions

The comparisons in our study provide valuable insights in preprocessing HumanMethylation450 BeadChip data. We found the within-array combination of Noob + BMIQ always improved signal sensitivity, and when combined with the RUVm batch-correction method, outperformed all other approaches in performing differential DNA methylation analysis. The effect of the data processing method, in any given data set, was a function of both the signal and noise.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2819-7) contains supplementary material, which is available to authorized users.

Methods for identifying differentially methylated regions for sequence- and array-based data

DNA methylation is one of the most important epigenetic mechanisms, and participates in the pathogenic processes of many diseases. Differentially methylated regions (DMRs) in the genome have been reported and implicated in a number of different diseases, tissues and cell types, and are associated with gene expression levels. Therefore, identification of DMRs is one of the most critical and fundamental issues in dissecting the disease etiologies. Based on bisulfite conversion, advances in sequence- and array-based technologies have helped investigators study genome-wide DNA methylation. Many methods have been developed to detect DMRs, and they have revolutionized our understanding of DNA methylation and provided new insights into its role in diverse biological functions. According to data and region types, we discuss various methods in detecting DMRs, their utility and limitations comprehensively. We recommend using a few of the methods in the same data and region type to detect DMRs because they could be complementary to one another.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

Longitudinal study of DNA methylation during the first 5 years of life

BACKGROUND

Early life epigenetic programming influences adult health outcomes. Moreover, DNA methylation levels have been found to change more rapidly during the first years of life. Our aim was the identification and characterization of the CpG sites that are modified with time during the first years of life. We hypothesize that these DNA methylation changes would lead to the detection of genes that might be epigenetically modulated by environmental factors during early childhood and which, if disturbed, might contribute to susceptibility to diseases later in life.

METHODS

The study of the DNA methylation pattern of 485577 CpG sites was performed on 30 blood samples from 15 subjects, collected both at birth and at 5 years old, using Illumina(®) Infinium 450 k array. To identify differentially methylated CpG (dmCpG) sites, the methylation status of each probe was examined using linear models and the Empirical Bayes Moderated t test implemented in the limma package of R/Bioconductor. Surogate variable analysis was used to account for batch effects.

RESULTS

DNA methylation levels significantly changed from birth to 5 years of age in 6641 CpG sites. Of these, 36.79 % were hypermethylated and were associated with genes related mainly to developmental ontology terms, while 63.21 % were hypomethylated probes and associated with genes related to immune function.

CONCLUSIONS

Our results suggest that DNA methylation alterations with age during the first years of life might play a significant role in development and the regulation of leukocyte-specific functions. This supports the idea that blood leukocytes experience genome remodeling related to their interaction with environmental factors, underlining the importance of environmental exposures during the first years of life and suggesting that new strategies should be take into consideration for disease prevention.

Imprinted genes and imprinting control regions show predominant intermediate methylation in adult somatic tissues

Genomic imprinting is an epigenetic feature characterized by parent-specific monoallelic gene expression. The aim of this study was to compare the DNA methylation status of imprinted genes and imprinting control regions (ICRs), harboring differentially methylated regions (DMRs) in a comprehensive panel of 18 somatic tissues. The germline DMRs analyzed were divided into ubiquitously imprinted and placenta-specific DMRs, which show identical and different methylation imprints in adult somatic and placental tissues, respectively. We showed that imprinted genes and ICR DMRs maintain methylation patterns characterized by intermediate methylation levels in somatic tissues, which are pronounced in a specific region of the promoter area, located 200-1500 bp from the transcription start site. This intermediate methylation is concordant with gene expression from a single unmethylated allele and silencing of a reciprocal parental allele through DNA methylation. The only exceptions were seen for ICR DMRs of placenta-specific imprinted genes, which showed low levels of methylation, suggesting that these genes escape parent-specific epigenetic regulation in somatic tissues.

Tumor purity and differential methylation in cancer epigenomics

DNA methylation is an epigenetic modification of DNA molecule that plays a vital role in gene expression regulation. It is not only involved in many basic biological processes, but also considered an important factor for tumorigenesis and other human diseases. Study of DNA methylation has been an active field in cancer epigenomics research. With the advances of high-throughput technologies and the accumulation of enormous amount of data, method development for analyzing these data has gained tremendous interests in the fields of computational biology and bioinformatics. In this review, we systematically summarize the recent developments of computational methods and software tools in high-throughput methylation data analysis with focus on two aspects: differential methylation analysis and tumor purity estimation in cancer studies.

A Molecular Clock Infers Heterogeneous Tissue Age Among Patients with Barrett's Esophagus

Biomarkers that drift differentially with age between normal and premalignant tissues, such as Barrett’s esophagus (BE), have the potential to improve the assessment of a patient’s cancer risk by providing quantitative information about how long a patient has lived with the precursor (i.e., dwell time). In the case of BE, which is a metaplastic precursor to esophageal adenocarcinoma (EAC), such biomarkers would be particularly useful because EAC risk may change with BE dwell time and it is generally not known how long a patient has lived with BE when a patient is first diagnosed with this condition. In this study we first describe a statistical analysis of DNA methylation data (both cross-sectional and longitudinal) derived from tissue samples from 50 BE patients to identify and validate a set of 67 CpG dinucleotides in 51 CpG islands that undergo age-related methylomic drift. Next, we describe how this information can be used to estimate a patient’s BE dwell time. We introduce a Bayesian model that incorporates longitudinal methylomic drift rates, patient age, and methylation data from individually paired BE and normal squamous tissue samples to estimate patient-specific BE onset times. Our application of the model to 30 sporadic BE patients’ methylomic profiles first exposes a wide heterogeneity in patient-specific BE onset times. Furthermore, independent application of this method to a cohort of 22 familial BE (FBE) patients reveals significantly earlier mean BE onset times. Our analysis supports the conjecture that differential methylomic drift occurs in BE (relative to normal squamous tissue) and hence allows quantitative estimation of the time that a BE patient has lived with BE.

Barrett’s Esophagus (BE) is a metaplastic precursor to esophageal adenocarcinoma (EAC). When a patient is diagnosed with BE, it is generally not known how long he/she has had this condition because BE is asymptomatic. While the question of how long a premalignant tissue or lesion has been resident in an organ (dwell time) may not be of importance for cases where curative interventions are readily available (such as adenomas in the colon), for BE, curative interventions are either costly or carry patient risks. Knowledge of a precursor’s dwell time may therefore be advantageous in determining the cancer risk due to the stepwise accumulation of critical mutations in the precursor. In this study, we create a molecular clock model that infers patient-specific BE onsets from DNA methylation data. We show that there is considerable variation in the predicted BE onset times which translates, using mathematical modeling of EAC, into large variation in individual EAC risks. We make the case that, notwithstanding other known risk factors such as chronological age, gender, reflux status, etc., knowledge of biological tissue age can provide valuable patient-specific risk information when a patient is first diagnosed with BE.

Inhalation of diesel exhaust and allergen alters human bronchial epithelium DNA methylation

BACKGROUND

Allergic disease affects 30% to 40% of the world's population, and its development is determined by the interplay between environmental and inherited factors. Air pollution, primarily consisting of diesel exhaust emissions, has increased at a similar rate to allergic disease. Exposure to diesel exhaust may play a role in the development and progression of allergic disease, in particular allergic respiratory disease. One potential mechanism underlying the connection between air pollution and increased allergic disease incidence is DNA methylation, an epigenetic process with the capacity to integrate gene-environment interactions.

OBJECTIVE

We sought to investigate the effect of allergen and diesel exhaust exposure on bronchial epithelial DNA methylation.

METHODS

We performed a randomized crossover-controlled exposure study to allergen and diesel exhaust in humans, and measured single-site (CpG) resolution global DNA methylation in bronchial epithelial cells.

RESULTS

Exposure to allergen alone, diesel exhaust alone, or allergen and diesel exhaust together (coexposure) led to significant changes in 7 CpG sites at 48 hours. However, when the same lung was exposed to allergen and diesel exhaust but separated by approximately 4 weeks, significant changes in more than 500 sites were observed. Furthermore, sites of differential methylation differed depending on which exposure was experienced first. Functional analysis of differentially methylated CpG sites found genes involved in transcription factor activity, protein metabolism, cell adhesion, and vascular development, among others.

CONCLUSIONS

These findings suggest that specific exposures can prime the lung for changes in DNA methylation induced by a subsequent insult.

RCP: a novel probe design bias correction method for Illumina Methylation BeadChip

MOTIVATION

The Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis.

RESULTS

Here, we developed a novel method, called Regression on Correlated Probes (RCP), which uses the existing correlation between pairs of nearby type I and II probes to adjust the beta values of all type II probes. We evaluate the effect of this adjustment on reducing probe design type bias, reducing technical variation in duplicate samples, improving accuracy of measurements against known standards, and retention of biological signal. We find that RCP is statistically significantly better than unadjusted data or adjustment with alternative methods including SWAN and BMIQ.

AVAILABILITY

We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html).

CONTACT

niulg@ucmail.uc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prostate tumor DNA methylation is associated with cigarette smoking and adverse prostate cancer outcomes

BACKGROUND

DNA methylation has been hypothesized as a mechanism for explaining the association between smoking and adverse prostate cancer (PCa) outcomes. This study was aimed at assessing whether smoking is associated with prostate tumor DNA methylation and whether these alterations may explain in part the association of smoking with PCa recurrence and mortality.

METHODS

A total of 523 men had radical prostatectomy as their primary treatment, detailed smoking history data, long-term follow-up for PCa outcomes, and tumor tissue profiled for DNA methylation. Ninety percent of the men also had matched tumor gene expression data. A methylome-wide analysis was conducted to identify differentially methylated regions (DMRs) by smoking status. To select potential functionally relevant DMRs, their correlation with the messenger RNA (mRNA) expression of corresponding genes was evaluated. Finally, a smoking-related methylation score based on the top-ranked DMRs was created to assess its association with PCa outcomes.

RESULTS

Forty DMRs were associated with smoking status, and 10 of these were strongly correlated with mRNA expression (aldehyde oxidase 1 [AOX1], claudin 5 [CLDN5], early B-cell factor 1 [EBF1], homeobox A7 [HOXA7], lectin galactoside-binding soluble 3 [LGALS3], microtubule-associated protein τ [MAPT], protocadherin γ A [PCDHGA]/protocadherin γ B [PCDHGB], paraoxonase 3 [PON3], synaptonemal complex protein 2 like [SYCP2L], and zinc finger and SCAN domain containing 12 [ZSCAN12]). Men who were in the highest tertile for the smoking-methylation score derived from these DMRs had a higher risk of recurrence (odds ratio [OR], 2.29; 95% confidence interval [CI], 1.42-3.72) and lethal disease (OR, 4.21; 95% CI, 1.65-11.78) in comparison with men in the lower 2 tertiles.

CONCLUSIONS

This integrative molecular epidemiology study supports the hypothesis that smoking-associated tumor DNA methylation changes may explain at least part of the association between smoking and adverse PCa outcomes. Future studies are warranted to confirm these findings and understand the implications for improving patient outcomes. Cancer 2016;122:2168-77. © 2016 American Cancer Society.

Single base resolution analysis of 5-hydroxymethylcytosine in 188 human genes: implications for hepatic gene expression

To improve the epigenomic analysis of tissues rich in 5-hydroxymethylcytosine (hmC), we developed a novel protocol called TAB-Methyl-SEQ, which allows for single base resolution profiling of both hmC and 5-methylcytosine by targeted next-generation sequencing. TAB-Methyl-SEQ data were extensively validated by a set of five methodologically different protocols. Importantly, these extensive cross-comparisons revealed that protocols based on Tet1-assisted bisulfite conversion provided more precise hmC values than TrueMethyl-based methods. A total of 109 454 CpG sites were analyzed by TAB-Methyl-SEQ for mC and hmC in 188 genes from 20 different adult human livers. We describe three types of variability of hepatic hmC profiles: (i) sample-specific variability at 40.8% of CpG sites analyzed, where the local hmC values correlate to the global hmC content of livers (measured by LC-MS), (ii) gene-specific variability, where hmC levels in the coding regions positively correlate to expression of the respective gene and (iii) site-specific variability, where prominent hmC peaks span only 1 to 3 neighboring CpG sites. Our data suggest that both the gene- and site-specific components of hmC variability might contribute to the epigenetic control of hepatic genes. The protocol described here should be useful for targeted DNA analysis in a variety of applications.

Establishing an analytic pipeline for genome-wide DNA methylation

The need for research investigating DNA methylation (DNAm) in clinical studies has increased, leading to the evolution of new analytic methods to improve accuracy and reproducibility of the interpretation of results from these studies. The purpose of this article is to provide clinical researchers with a summary of the major data processing steps routinely applied in clinical studies investigating genome-wide DNAm using the Illumina HumanMethylation 450K BeadChip. In most studies, the primary goal of employing DNAm analysis is to identify differential methylation at CpG sites among phenotypic groups. Experimental design considerations are crucial at the onset to minimize bias from factors related to sample processing and avoid confounding experimental variables with non-biological batch effects. Although there are currently no de facto standard methods for analyzing these data, we review the major steps in processing DNAm data recommended by several research studies. We describe several variations available for clinical researchers to process, analyze, and interpret DNAm data. These insights are applicable to most types of genome-wide DNAm array platforms and will be applicable for the next generation of DNAm array technologies (e.g., the 850K array). Selection of the DNAm analytic pipeline followed by investigators should be guided by the research question and supported by recently published methods.

Comprehensive DNA Methylation and Gene Expression Profiling in Differentiating Human Adipocytes

Insight into the processes controlling adipogenesis is important in the battle against the obesity epidemic and its related disorders. The transcriptional regulatory cascade involved in adipocyte differentiation has been extensively studied, however, the mechanisms driving the transcription activation are still poorly understood. In this study, we explored the involvement of DNA methylation in transcriptional regulation during adipocyte differentiation of primary human mesenchymal stem cells (hMSCs). Genome-wide changes in DNA methylation were measured using the Illumina 450K BeadChip. In addition, expression of 84 adipogenic genes was determined, of which 43 genes showed significant expression changes during the differentiation process. Among these 43 differentially expressed genes, differentially methylated regions (DMRs) were detected in only three genes. By comparing genome-wide DNA methylation profiles in undifferentiated and differentiated adipocytes 793 significant DMRs were detected. Pathway analysis revealed the adipogenesis pathway as the most statistically significant, although only a small number of genes were differentially methylated. Genome-wide DNA methylation changes for single probes were most often located in intergenic regions, and underrepresented close to the transcription start site. In conclusion, DNA methylation remained relatively stable during adipocyte differentiation, implying that changes in DNA methylation are not the underlying mechanism regulating gene expression during adipocyte differentiation. J. Cell. Biochem. 117: 2707-2718, 2016. © 2016 Wiley Periodicals, Inc.

Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis

Objective

Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.

Methods

An epigenome‐wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis‐acting single‐nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients.

Results

Five positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P = 1.39 × 10⁻⁸ and cg26401028, P = 1.69 × 10⁻⁸). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P = 2.63 × 10⁻⁷ and P = 1.05 × 10⁻⁶, respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P = 0.03; n = 56) and in the independent replication cohort (P = 0.003; n = 1,204).

Conclusion

We identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low‐density lipoprotein receptor–related protein 1. Additional replication experiments in independent sample collections are now needed.

Methods for genome-wide DNA methylation analysis in human cancer

Aberrant DNA methylation is considered to be one of the most common hallmarks of cancer. Several recent advances in assessing the DNA methylome provide great promise for deciphering the cancer-specific DNA methylation patterns. Herein, we present the current key technologies used to detect high-throughput genome-wide DNA methylation, and the available cancer-associated methylation databases. Additionally, we focus on the computational methods for preprocessing, analyzing and interpreting the cancer methylome data. It not only discusses the challenges of the differentially methylated region calling and the prediction model construction but also highlights the biomarker investigation for cancer diagnosis, prognosis and response to treatment. Finally, some emerging challenges in the computational analysis of cancer methylome data are summarized.

Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies

In epigenome-wide association studies (EWAS), different methylation profiles of distinct cell types may lead to false discoveries. We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in EWAS. ReFACTor does not require knowledge of cell counts, and it provides improved estimates of cell type composition, resulting in improved power and control for false positives in EWAS. Corresponding software is available at http://www.cs.tau.ac.il/~heran/cozygene/software/refactor.html.

Genome-wide methylation analysis demonstrates that 5-aza-2-deoxycytidine treatment does not cause random DNA demethylation in fragile X syndrome cells

Background

Fragile X syndrome (FXS) is caused by CGG expansion over 200 repeats at the 5′ UTR of the FMR1 gene and subsequent DNA methylation of both the expanded sequence and the CpGs of the promoter region. This epigenetic change causes transcriptional silencing of the gene. We have previously demonstrated that 5-aza-2-deoxycytidine (5-azadC) treatment of FXS lymphoblastoid cell lines reactivates the FMR1 gene, concomitant with CpG sites demethylation, increased acetylation of histones H3 and H4 and methylation of lysine 4 on histone 3.

Results

In order to check the specificity of the 5-azadC-induced DNA demethylation, now we performed bisulphite sequencing of the entire methylation boundary upstream the FMR1 promoter region, which is preserved in control wild-type cells. We did not observe any modification of the methylation boundary after treatment. Furthermore, methylation analysis by MS-MLPA of PWS/AS and BWS/SRS loci demonstrated that 5-azadC treatment has no demethylating effect on these regions. Genome-wide methylation analysis through Infinium 450K (Illumina) showed no significant enrichment of specific GO terms in differentially methylated regions after 5-azadC treatment. We also observed that reactivation of FMR1 transcription lasts up to a month after a 7-day treatment and that maximum levels of transcription are reached at 10–15 days after last administration of 5-azadC.

Conclusions

Taken together, these data demonstrate that the demethylating effect of 5-azadC on genomic DNA is not random, but rather restricted to specific regions, if not exclusively to the FMR1 promoter. Moreover, we showed that 5-azadC has a long-lasting reactivating effect on the mutant FMR1 gene.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0060-x) contains supplementary material, which is available to authorized users.

A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data

DNA methylation plays an important role in disease etiology. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a widely used platform in large-scale epidemiologic studies. This platform can efficiently and simultaneously measure methylation levels at ∼480,000 CpG sites in the human genome in multiple study samples. Due to the intrinsic chip design of 2 types of chemistry probes, data normalization or preprocessing is a critical step to consider before data analysis. To date, numerous methods and pipelines have been developed for this purpose, and some studies have been conducted to evaluate different methods. However, validation studies have often been limited to a small number of CpG sites to reduce the variability in technical replicates. In this study, we measured methylation on a set of samples using both whole-genome bisulfite sequencing (WGBS) and 450K chips. We used WGBS data as a gold standard of true methylation states in cells to compare the performances of 8 normalization methods for 450K data on a genome-wide scale. Analyses on our dataset indicate that the most effective methods are peak-based correction (PBC) and quantile normalization plus β-mixture quantile normalization (QN.BMIQ). To our knowledge, this is the first study to systematically compare existing normalization methods for Illumina 450K data using novel WGBS data. Our results provide a benchmark reference for the analysis of DNA methylation chip data, particularly in white blood cells.

Epigenome-wide DNA methylation patterns associated with fatigue in primary Sjögren's syndrome

OBJECTIVE

Chronic fatigue is a common, disabling and poorly understood phenomenon. Recent studies indicate that epigenetic mechanisms may be involved in the expression of fatigue, a prominent feature of primary SS (pSS). The aim of this study was to investigate whether DNA methylation profiles of whole blood are associated with fatigue in patients with pSS.

METHODS

Forty-eight pSS patients with high (n = 24) or low (n = 24) fatigue as measured by a visual analogue scale were included. Genome-wide DNA methylation was investigated using the Illumina HumanMethylation450 BeadChip array. After quality control, a total of 383 358 Cytosine-phosphate-Guanine (CpG) sites remained for further analysis. Age, sex and differential cell count estimates were included as covariates in the association model. A false discovery rate-corrected P < 0.05 was considered significant, and a cut-off of 3% average difference in methylation levels between high- and low-fatigue patients was applied.

RESULTS

A total of 251 differentially methylated CpG sites were associated with fatigue. The CpG site with the most pronounced hypomethylation in pSS high fatigue annotated to the SBF2-antisense RNA1 gene. The most distinct hypermethylation was observed at a CpG site annotated to the lymphotoxin alpha gene. Functional pathway analysis of genes with differently methylated CpG sites in subjects with high vs low fatigue revealed enrichment in several pathways associated with innate and adaptive immunity.

CONCLUSION

Some genes involved in regulation of the immune system and in inflammation are differently methylated in pSS patients with high vs low fatigue. These findings point to functional networks that may underlie fatigue. Epigenetic changes could constitute a fatigue-regulating mechanism in pSS.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)

Background

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.

Results

Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R²>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R²>0.90 and RMSE<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.

Conclusions

Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0943-7) contains supplementary material, which is available to authorized users.

DNA methylation profiling in human Huntington's disease brain

Despite extensive progress in Huntington's disease (HD) research, very little is known about the association of epigenetic variation and HD pathogenesis in human brain tissues. Moreover, its contribution to the tissue-specific transcriptional regulation of the huntingtin gene (HTT), in which HTT expression levels are highest in brain and testes, is currently unknown. To investigate the role of DNA methylation in HD pathogenesis and tissue-specific expression of HTT, we utilized the Illumina HumanMethylation450K BeadChip array to measure DNA methylation in a cohort of age-matched HD and control human cortex and liver tissues. In cortex samples, we found minimal evidence of HD-associated DNA methylation at probed sites after correction for cell heterogeneity but did observe an association with the age of disease onset. In contrast, comparison of matched cortex and liver samples revealed tissue-specific DNA methylation of the HTT gene region at 38 sites (FDR < 0.05). Importantly, we identified a novel differentially methylated binding site in the HTT proximal promoter for the transcription factor CTCF. This CTCF site displayed increased occupancy in cortex, where HTT expression is higher, compared with the liver. Additionally, CTCF silencing reduced the activity of an HTT promoter-reporter construct, suggesting that CTCF plays a role in regulating HTT promoter function. Overall, although we were unable to detect HD-associated DNA methylation alterations at queried sites, we found that DNA methylation may be correlated to the age of disease onset in cortex tissues. Moreover, our data suggest that DNA methylation may, in part, contribute to tissue-specific HTT transcription through differential CTCF occupancy.

Variations in 5-methylcytosine and 5-hydroxymethylcytosine among human brain, blood, and saliva using oxBS and the Infinium MethylationEPIC array

Investigating 5-methylcytosine (5mC) has led to many hypotheses regarding molecular mechanism underlying human diseases and disorders. Many of these studies, however, utilize bisulfite conversion alone, which cannot distinguish 5mC from its recently discovered oxidative product, 5-hydroxymethylcytosine (5hmC). Furthermore, previous array-based technologies do not have the necessary probes to adequately investigate both modifications simultaneously. In this manuscript, we used technical replicates of DNA from human brain, human blood, and human saliva, in combination with oxidative bisulfite conversion and Illumina's Infinium MethylationEPIC array, to analyze 5mC and 5hmC at more than 650 000 and 450 000 relevant loci, respectively, in the human genome. We show the presence of loci with detectable 5mC and 5hmC to be equally distributed across chromosomes and genomic features, while also being present in genomic regions with transcriptional regulatory properties. We also describe 2528 5hmC sites common across tissue types that show a strong association with immune-related functions. Lastly, in human brain, we show that 5hmC accounts for one-third of the total signal from bisulfite-converted data. As such, not only do our results confirm the efficacy and sensitivity of pairing oxidative bisulfite conversion and the EPIC array to detect 5mC and 5hmC in all three tissue types, but they also highlight the importance of dissociating 5hmC from 5mC in future studies related to cytosine modifications.

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other Tumors

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Our model MGMT-STP27 allows prediction of the methylation status of the MGMT promoter using data from the Illumina's Human Methylation BeadChips (HM-27K and HM-450K) that is publically available for many cancer data sets. Here, we investigate the impact of the context of genetic and epigenetic alterations and tumor type on the classification and report on technical aspects, such as robustness of cutoff definition and preprocessing of the data. The association between gene copy number variation, predicted MGMT methylation, and MGMT expression revealed a gene dosage effect on MGMT expression in lower grade glioma (World Health Organization grade II/III) that in contrast to glioblastoma usually carry two copies of chromosome 10 on which MGMT resides (10q26.3). This implies some MGMT expression, potentially conferring residual repair function blunting the therapeutic effect of alkylating agents. A sensitivity analyses corroborated the performance of the original cutoff for various optimization criteria and for most data preprocessing methods. Finally, we propose an R package mgmtstp27 that allows prediction of the methylation status of the MGMT promoter and calculation of appropriate confidence and/or prediction intervals. Overall, MGMT-STP27 is a robust model for MGMT classification that is independent of tumor type and is adapted for single sample prediction.

Profiling placental and fetal DNA methylation in human neural tube defects

Background

The incidence of neural tube defects (NTDs) declined by about 40 % in Canada with the introduction of a national folic acid (FA) fortification program. Despite the fact that few Canadians currently exhibit folate deficiency, NTDs are still the second most common congenital abnormality. FA fortification may have aided in reducing the incidence of NTDs by overcoming abnormal one carbon metabolism cycling, the process which provides one carbon units for methylation of DNA. We considered that NTDs persisting in a folate-replete population may also occur in the context of FA-independent compromised one carbon metabolism, and that this might manifest as abnormal DNA methylation (DNAm). Second trimester human placental chorionic villi, kidney, spinal cord, brain, and muscle were collected from 19 control, 22 spina bifida, and 15 anencephalic fetuses in British Columbia, Canada. DNA was extracted, assessed for methylenetetrahydrofolate reductase (MTHFR) genotype and for genome-wide DNAm using repetitive elements, in addition to the Illumina Infinium HumanMethylation450 (450k) array.

Results

No difference in repetitive element DNAm was noted between NTD status groups. Using a false discovery rate <0.05 and average group difference in DNAm ≥0.05, differentially methylated array sites were identified only in (1) the comparison of anencephaly to controls in chorionic villi (n = 4 sites) and (2) the comparison of spina bifida to controls in kidney (n = 3342 sites).

Conclusions

We suggest that the distinctive DNAm of spina bifida kidneys may be consequent to the neural tube defect or reflective of a common etiology for abnormal neural tube and renal development. Though there were some small shifts in DNAm in the other tested tissues, our data do not support the long-standing hypothesis of generalized altered genome-wide DNAm in NTDs. This finding may be related to the fact that most Canadians are not folate deficient, but it importantly opens the field to the investigation of other epigenetic and non-epigenetic mechanisms in the etiology of NTDs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0054-8) contains supplementary material, which is available to authorized users.

Genome-Wide Methylation Study Identifies an IL-13-induced Epigenetic Signature in Asthmatic Airways

RATIONALE

Epigenetic changes to airway cells have been proposed as important modulators of the effects of environmental exposures on airway diseases, yet no study to date has shown epigenetic responses to exposures in the airway that correlate with disease state. The type 2 cytokine IL-13 is a key mediator of allergic airway diseases, such as asthma, and is up-regulated in response to many asthma-promoting exposures.

OBJECTIVES

To directly study the epigenetic response of airway epithelial cells (AECs) to IL-13 and test whether IL-13-induced epigenetic changes differ between individuals with and without asthma.

METHODS

Genome-wide DNA methylation and gene expression patterns were studied in 58 IL-13-treated and untreated primary AEC cultures and validated in freshly isolated cells of subjects with and without asthma using the Illumina Human Methylation 450K and HumanHT-12 BeadChips. IL-13-mediated comethylation modules were identified and correlated with clinical phenotypes using weighted gene coexpression network analysis.

MEASUREMENTS AND MAIN RESULTS

IL-13 altered global DNA methylation patterns in cultured AECs and were significantly enriched near genes associated with asthma. Importantly, a significant proportion of this IL-13 epigenetic signature was validated in freshly isolated AECs from subjects with asthma and clustered into two distinct modules, with module 1 correlated with asthma severity and lung function and module 2 with eosinophilia.

CONCLUSIONS

These results suggest that a single exposure of IL-13 may selectively induce long-lasting DNA methylation changes in asthmatic airways that alter specific AEC pathways and contribute to asthma phenotypes.

Genome-wide DNA methylation analysis in multiple tissues in primary Sjögren's syndrome reveals regulatory effects at interferon-induced genes

Objectives

Increasing evidence suggests an epigenetic contribution to the pathogenesis of autoimmune diseases, including primary Sjögren's Syndrome (pSS). The aim of this study was to investigate the role of DNA methylation in pSS by analysing multiple tissues from patients and controls.

Methods

Genome-wide DNA methylation profiles were generated using HumanMethylation450K BeadChips for whole blood, CD19+ B cells and minor salivary gland biopsies. Gene expression was analysed in CD19+ B cells by RNA-sequencing. Analysis of genetic regulatory effects on DNA methylation at known pSS risk loci was performed.

Results

We identified prominent hypomethylation of interferon (IFN)-regulated genes in whole blood and CD19+ B cells, including at the genes MX1, IFI44L and PARP9, replicating previous reports in pSS, as well as identifying a large number of novel associations. Enrichment for genomic overlap with histone marks for enhancer and promoter regions was observed. We showed for the first time that hypomethylation of IFN-regulated genes in pSS B cells was associated with their increased expression. In minor salivary gland biopsies we observed hypomethylation of the IFN-induced gene OAS2. Pathway and disease analysis resulted in enrichment of antigen presentation, IFN signalling and lymphoproliferative disorders. Evidence for genetic control of methylation levels at known pSS risk loci was observed.

Conclusions

Our study highlights the role of epigenetic regulation of IFN-induced genes in pSS where replication is needed for novel findings. The association with altered gene expression suggests a functional mechanism for differentially methylated CpG sites in pSS aetiology.

DNA methylation age is associated with mortality in a longitudinal Danish twin study

An epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. In this study, we estimated the DNAm age of 378 Danish twins, age 30–82 years, and furthermore included a 10‐year longitudinal study of the 86 oldest‐old twins (mean age of 86.1 at follow‐up), which subsequently were followed for mortality for 8 years. We found that the DNAm age is highly correlated with chronological age across all age groups (r = 0.97), but that the rate of change of DNAm age decreases with age. The results may in part be explained by selective mortality of those with a high DNAm age. This hypothesis was supported by a classical survival analysis showing a 35% (4–77%) increased mortality risk for each 5‐year increase in the DNAm age vs. chronological age. Furthermore, the intrapair twin analysis revealed a more‐than‐double mortality risk for the DNAm oldest twin compared to the co‐twin and a ‘dose–response pattern’ with the odds of dying first increasing 3.2 (1.05–10.1) times per 5‐year DNAm age difference within twin pairs, thus showing a stronger association of DNAm age with mortality in the oldest‐old when controlling for familial factors. In conclusion, our results support that DNAm age qualifies as a biomarker of aging.

Non-CG DNA methylation is a biomarker for assessing endodermal differentiation capacity in pluripotent stem cells

Non-CG methylation is an unexplored epigenetic hallmark of pluripotent stem cells. Here we report that a reduction in non-CG methylation is associated with impaired differentiation capacity into endodermal lineages. Genome-wide analysis of 2,670 non-CG sites in a discovery cohort of 25 phenotyped human induced pluripotent stem cell (hiPSC) lines revealed unidirectional loss (Δβ=13%, P<7.4 × 10(-4)) of non-CG methylation that correctly identifies endodermal differentiation capacity in 23 out of 25 (92%) hiPSC lines. Translation into a simplified assay of only nine non-CG sites maintains predictive power in the discovery cohort (Δβ=23%, P<9.1 × 10(-6)) and correctly identifies endodermal differentiation capacity in nine out of ten pluripotent stem cell lines in an independent replication cohort consisting of hiPSCs reprogrammed from different cell types and different delivery systems, as well as human embryonic stem cell (hESC) lines. This finding infers non-CG methylation at these sites as a biomarker when assessing endodermal differentiation capacity as a readout.

Genetic Variation, Not Cell Type of Origin, Underlies the Majority of Identifiable Regulatory Differences in iPSCs

The advent of induced pluripotent stem cells (iPSCs) revolutionized human genetics by allowing us to generate pluripotent cells from easily accessible somatic tissues. This technology can have immense implications for regenerative medicine, but iPSCs also represent a paradigm shift in the study of complex human phenotypes, including gene regulation and disease. Yet, an unresolved caveat of the iPSC model system is the extent to which reprogrammed iPSCs retain residual phenotypes from their precursor somatic cells. To directly address this issue, we used an effective study design to compare regulatory phenotypes between iPSCs derived from two types of commonly used somatic precursor cells. We find a remarkably small number of differences in DNA methylation and gene expression levels between iPSCs derived from different somatic precursors. Instead, we demonstrate genetic variation is associated with the majority of identifiable variation in DNA methylation and gene expression levels. We show that the cell type of origin only minimally affects gene expression levels and DNA methylation in iPSCs, and that genetic variation is the main driver of regulatory differences between iPSCs of different donors. Our findings suggest that studies using iPSCs should focus on additional individuals rather than clones from the same individual.

Induced pluripotent stem cells (iPSCs) are a new and powerful cell type that provides scientists the ability to model complex human diseases in vitro. These cells can be cryopreserved and later expanded, providing a renewable source of cells from the same individual. iPSCs can be made from a variety of somatic cells in the body and many labs have created them from blood and skin cells. We asked whether the cell type of origin impacts methylation and gene expression patterns in the reprogrammed iPSCs. Our findings indicate that there are remarkably few regulatory remnants of the cell type of origin in the iPSCs. In other words, most of the variation between iPSCs can be attributed to individual genetics. Our findings suggest that studies using iPSCs should focus on obtaining additional individuals rather than additional clones from the same individual. We caution that our current findings are limited to iPSCs and further studies are needed to address the question of somatic memory in differentiated cell types.

Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ_adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12–1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ_adjusted = 1.00–1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.

Pervasive polymorphic imprinted methylation in the human placenta

The maternal and paternal copies of the genome are both required for mammalian development, and this is primarily due to imprinted genes, those that are monoallelically expressed based on parent-of-origin. Typically, this pattern of expression is regulated by differentially methylated regions (DMRs) that are established in the germline and maintained after fertilization. There are a large number of germline DMRs that have not yet been associated with imprinting, and their function in development is unknown. In this study, we developed a genome-wide approach to identify novel imprinted DMRs in the human placenta and investigated the dynamics of these imprinted DMRs during development in somatic and extraembryonic tissues. DNA methylation was evaluated using the Illumina HumanMethylation450 array in 134 human tissue samples, publicly available reduced representation bisulfite sequencing in the human embryo and germ cells, and targeted bisulfite sequencing in term placentas. Forty-three known and 101 novel imprinted DMRs were identified in the human placenta by comparing methylation between diandric and digynic triploid conceptions in addition to female and male gametes. Seventy-two novel DMRs showed a pattern consistent with placental-specific imprinting, and this monoallelic methylation was entirely maternal in origin. Strikingly, these DMRs exhibited polymorphic imprinted methylation between placental samples. These data suggest that imprinting in human development is far more extensive and dynamic than previously reported and that the placenta preferentially maintains maternal germline-derived DNA methylation.

Post-transcriptional down regulation of ICAM-1 in feto-placental endothelium in GDM

Maternal gestational diabetes (GDM) is associated with hyperglycaemia and hyperinsulinemia in the fetal circulation which consequently may induce endothelial dysfunction in the feto-placental vasculature. In fact, feto-placental vasculature reveals various morphological changes in response to GDM. The cell adhesion molecules (CAMs) ICAM-1, VCAM-1 and E-selectin promote attachment and trans-endothelial migration of leukocytes, and are up regulated in inflammation and endothelial dysfunction. Thus, we hypothesized that the GDM environment upregulates ICAM-1, VCAM-1 and E-selectin in the feto-placental endothelium. We isolated primary feto-placental endothelial cells (fpEC) after normal (n=18) and GDM pregnancy (n=11) and analyzed mRNA (RT-qPCR) and protein expression (Immunoblot) of ICAM-1, VCAM-1 and E-selectin. While other CAMs were unchanged on mRNA and protein levels, ICAM-1 protein was decreased by GDM. Further analysis revealed also a decrease in the release of soluble ICAM-1 (sICAM-1), whose levels correlated negatively with maternal BMI. We conclude that this reduction of ICAM-1 protein species is the result of post-translational regulation, since ICAM-1 mRNA expression was unchanged. In fact, miRNAs targeting ICAM-1 were upregulated in GDM fpEC. Immunohistochemistry showed weaker ICAM-1 staining in the placental endothelium after GDM pregnancies, and demonstrated ICAM-1 binding partners CD11a and CD18 expressed on leukocytes in fetal circulation and on placental tissue macrophages. This study identified reduction of ICAM-1 protein in fpEC in GDM pregnancy, which was regulated post-transcriptionally. Low ICAM-1 protein production may represent a protective, placenta-specific mechanism to avoid leukocyte transmigration into the placenta in response to GDM.

Altered DNA methylation of glycolytic and lipogenic genes in liver from obese and type 2 diabetic patients

OBJECTIVE

Epigenetic modifications contribute to the etiology of type 2 diabetes.

METHOD

We performed genome-wide methylome and transcriptome analysis in liver from severely obese men with or without type 2 diabetes and non-obese men to discover aberrant pathways underlying the development of insulin resistance. Results were validated by pyrosequencing.

RESULT

We identified hypomethylation of genes involved in hepatic glycolysis and insulin resistance, concomitant with increased mRNA expression and protein levels. Pyrosequencing revealed the CpG-site within ATF-motifs was hypomethylated in four of these genes in liver of severely obese non-diabetic and type 2 diabetic patients, suggesting epigenetic regulation of transcription by altered ATF-DNA binding.

CONCLUSION

Severely obese non-diabetic and type 2 diabetic patients have distinct alterations in the hepatic methylome and transcriptome, with hypomethylation of several genes controlling glucose metabolism within the ATF-motif regulatory site. Obesity appears to shift the epigenetic program of the liver towards increased glycolysis and lipogenesis, which may exacerbate the development of insulin resistance.

Global measures of peripheral blood-derived DNA methylation as a risk factor in the development of mature B-cell neoplasms

AIM

To examine whether peripheral blood methylation is associated with risk of developing mature B-cell neoplasms (MBCNs).

MATERIALS & METHODS

We conducted a case-control study nested within a large prospective cohort. Peripheral blood was collected from healthy participants. Cases of MBCN were identified by linkage to cancer registries. Methylation was measured using the Infinium(®) HumanMethylation450.

RESULTS

During a median of 10.6-year follow-up, 438 MBCN cases were evaluated. Global hypomethylation was associated with increased risk of MBCN (odds ratio: 2.27, [95% CI: 1.59-3.25]). Within high CpG promoter regions, hypermethylation was associated with increased risk (odds ratio: 1.76 [95% CI: 1.25-2.48]). Promoter hypermethylation was observed in HOXA9 and CDH1 genes.

CONCLUSION

Aberrant global DNA methylation is detectable in peripheral blood collected years before diagnosis and is associated with increased risk of MBCN, suggesting changes to DNA methylation are an early event in MBCN development.

Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts

DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case-control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31-0.54, P-value=3.3 × 10(-11)) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31-0.56, P-value=3.9 × 10(-10)), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case-control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.

Epigenomic profiling of prostate cancer identifies differentially methylated genes in TMPRSS2:ERG fusion-positive versus fusion-negative tumors

Background

About half of all prostate cancers harbor the TMPRSS2:ERG (T2E) gene fusion. While T2E-positive and T2E-negative tumors represent specific molecular subtypes of prostate cancer (PCa), previous studies have not yet comprehensively investigated how these tumor subtypes differ at the epigenetic level. We therefore investigated epigenome-wide DNA methylation profiles of PCa stratified by T2E status.

Results

The study included 496 patients with clinically localized PCa who had a radical prostatectomy as primary treatment for PCa. Fluorescence in situ hybridization (FISH) “break-apart” assays were used to determine tumor T2E-fusion status, which showed that 266 patients (53.6 %) had T2E-positive PCa. The study showed global DNA methylation differences between tumor subtypes. A large number of differentially methylated CpG sites were identified (false-discovery rate [FDR] Q-value <0.00001; n = 27,876) and DNA methylation profiles accurately distinguished between tumor T2E subgroups. A number of top-ranked differentially methylated CpGs in genes (FDR Q-values ≤1.53E−29) were identified: C3orf14, CACNA1D, GREM1, KLK10, NT5C, PDE4D, RAB40C, SEPT9, and TRIB2, several of which had a corresponding alteration in mRNA expression. These genes may have various roles in the pathogenesis of PCa, and the calcium-channel gene CACNA1D is a known ERG-target. Analysis of The Cancer Genome Atlas (TCGA) data provided confirmatory evidence for our findings.

Conclusions

This study identified substantial differences in DNA methylation profiles of T2E-positive and T2E-negative tumors, thereby providing further evidence that different underlying oncogenic pathways characterize these molecular subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0161-6) contains supplementary material, which is available to authorized users.

Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia

Type 2 diabetes mellitus (DM) is an established risk factor for a wide range of vascular diseases, including ischemic stroke (IS). Glycated hemoglobin A1c (HbA1c), a marker for average blood glucose levels over the previous 12 weeks, is used as a measure of glycemic control and also as a diagnostic criterion for diabetes (HbA1c levels ≥ 6.5%). Epigenetic mechanisms, such as DNA methylation, may be associated with aging processes and with modulation of the risk of various pathologies, such as DM. Specifically, DNA methylation could be one of the mechanisms mediating the relation between DM and environmental exposures. Our goal was to identify new CpG methylation sites associated with DM. We performed a genome-wide methylation study in whole-blood DNA from an IS patient cohorts. Illumina HumanMethylation450 BeadChip array was used to measure DNA methylation in CpG sites. All statistical analyses were adjusted for sex, age, hyperlipidemia, body mass index (BMI), smoking habit and cell count. Findings were replicated in two independent cohorts, an IS cohort and a population-based cohort, using the same array. In the discovery phase (N = 355), we identified a CpG site, cg19693031 (located in the TXNIP gene) that was associated with DM (P = 1.17 × 10(-12)); this CpG was replicated in two independent cohorts (N = 167 and N = 645). Methylation of TXNIP was inversely and intensely associated with HbA1c levels (P = 7.3 × 10(-16)), specifically related to diabetic patients with poor control of glucose levels. We identified an association between the TXNIP gene and DM through epigenetic mechanisms, related to sustained hyperglycemia levels (HbA1c ≥ 7%).

Associative DNA methylation changes in children with prenatal alcohol exposure

Aim: Prenatal alcohol exposure (PAE) can cause fetal alcohol spectrum disorders (FASD). Previously, we assessed PAE in brain tissue from mouse models, however whether these changes are present in humans remains unknown. Materials & methods: In this report, we show some identical changes in DNA methylation in the buccal swabs of six children with FASD using the 450K array. Results: The changes occur in genes related to protocadherins, glutamatergic synapses, and hippo signaling. The results were found to be similar in another heterogeneous replication group of six FASD children. Conclusion: The replicated results suggest that children born with FASD have unique DNA methylation defects that can be influenced by sex and medication exposure. Ultimately, with future clinical development, assessment of DNA methylation from buccal swabs can provide a novel strategy for the diagnosis of FASD.

Epigenomic profiling of DNA methylation in paired prostate cancer versus adjacent benign tissue

BACKGROUND

Aberrant DNA methylation may promote prostate carcinogenesis. We investigated epigenome-wide DNA methylation profiles in prostate cancer (PCa) compared to adjacent benign tissue to identify differentially methylated CpG sites.

METHODS

The study included paired PCa and adjacent benign tissue samples from 20 radical prostatectomy patients. Epigenetic profiling was done using the Infinium HumanMethylation450 BeadChip. Linear models that accounted for the paired study design and False Discovery Rate Q-values were used to evaluate differential CpG methylation. mRNA expression levels of the genes with the most differentially methylated CpG sites were analyzed.

RESULTS

In total, 2,040 differentially methylated CpG sites were identified in PCa versus adjacent benign tissue (Q-value < 0.001), the majority of which were hypermethylated (n = 1,946; 95%). DNA methylation profiles accurately distinguished between PCa and benign tissue samples. Twenty-seven top-ranked hypermethylated CpGs had a mean methylation difference of at least 40% between tissue types, which included 25 CpGs in 17 genes. Furthermore, for 10 genes over 50% of promoter region CpGs were hypermethylated in PCa versus benign tissue. The top-ranked differentially methylated genes included three genes that were associated with both promoter hypermethylation and reduced gene expression: SCGB3A1, HIF3A, and AOX1. Analysis of The Cancer Genome Atlas (TCGA) data provided confirmatory evidence for our findings.

CONCLUSIONS

This study of PCa versus adjacent benign tissue showed many differentially methylated CpGs and regions in and outside gene promoter regions, which may potentially be used for the development of future epigenetic-based diagnostic tests or as therapeutic targets.

The epigenomic landscape of African rainforest hunter-gatherers and farmers

The genetic history of African populations is increasingly well documented, yet their patterns of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA sequence variation and temporal changes in lifestyle and habitat on the human epigenome remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for 362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and historical lifestyle of a population have similarly critical impacts on the methylome, but the biological functions affected strongly differ. Specifically, methylation variation associated with recent changes in habitat mostly concerns immune and cellular functions, whereas that associated with historical lifestyle affects developmental processes. Furthermore, methylation variation—particularly that correlated with historical lifestyle—shows strong associations with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our work provides new insight into the genetic and environmental factors affecting the epigenomic landscape of human populations over time.

Genetic and environmental factors affect genome-wide patterns of epigenetic variation. Here, the authors show that while current habitat and historical lifestyle impact the methylome of rainforest hunter-gatherers and sedentary farmers, the biological functions affected and the degree of genetic control differ.

Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies

Background

Infinium HumanMethylation 450 BeadChip Arrays by Illumina (Illumina HM450K) are among the most popular CpG microarray platforms widely used in biological and medical research. Several recent studies highlighted the potentially confounding impact of the genomic variation on the results of methylation studies performed using Illumina’s Infinium methylation probes. However, the complexity of SNPs impact on the methylation level measurements (β values) has not been comprehensively described.

Results

In our comparative study of European and Asian populations performed using Illumina HM450K, we found that the majority of Infinium probes, which differentiated two examined groups, had SNPs in their target sequence. Characteristic tri-modal or bi-modal patterns of β values distribution among individual samples were observed for CpGs with SNPs in the first and second position, respectively. To better understand how SNPs affect methylation readouts, we investigated their impact in the context of SNP position and type, and of the Illumina probe type (Infinium I or II).

Conclusions

Our study clearly demonstrates that SNP variation existing in the genome, if not accounted for, may lead to false interpretation of the methylation signal differences suggested by some of the Illumina Infinium probes. In addition, it provides important practical clues for discriminating between differences due to the methylation status and to the genomic polymorphisms, based on the inspection of methylation readouts in individual samples. This approach is of special importance when Illumina Infinium assay is used for any comparative population studies, whether related to cancer, disease, ethnicity where SNP frequencies differentiate the studied groups.

The effects of long-term daily folic acid and vitamin B12 supplementation on genome-wide DNA methylation in elderly subjects

BACKGROUND

Folate and its synthetic form folic acid function as donor of one-carbon units and have been, together with other B-vitamins, implicated in programming of epigenetic processes such as DNA methylation during early development. To what extent regulation of DNA methylation can be altered via B-vitamins later in life, and how this relates to health and disease, is not exactly known. The aim of this study was to identify effects of long-term supplementation with folic acid and vitamin B12 on genome-wide DNA methylation in elderly subjects. This project was part of a randomized, placebo-controlled trial on effects of supplemental intake of folic acid and vitamin B12 on bone fracture incidence (B-vitamins for the PRevention Of Osteoporotic Fractures (B-PROOF) study). Participants with mildly elevated homocysteine levels, aged 65-75 years, were randomly assigned to take 400 μg folic acid and 500 μg vitamin B12 per day or a placebo during an intervention period of 2 years. DNA was isolated from buffy coats, collected before and after intervention, and genome-wide DNA methylation was determined in 87 participants (n = 44 folic acid/vitamin B12, n = 43 placebo) using the Infinium HumanMethylation450 BeadChip.

RESULTS

After intervention with folic acid and vitamin B12, 162 (versus 14 in the placebo group) of the 431,312 positions were differentially methylated as compared to baseline. Comparisons of the DNA methylation changes in the participants receiving folic acid and vitamin B12 versus placebo revealed one single differentially methylated position (cg19380919) with a borderline statistical significance. However, based on the analyses of differentially methylated regions (DMRs) consisting of multiple positions, we identified 6 regions that differed statistically significantly between the intervention and placebo group. Pronounced changes were found for regions in the DIRAS3, ARMC8, and NODAL genes, implicated in carcinogenesis and early embryonic development. Furthermore, serum levels of folate and vitamin B12 or plasma homocysteine were related to DNA methylation of 173, 425, and 11 regions, respectively. Interestingly, for several members of the developmental HOX genes, DNA methylation was related to serum levels of folate.

CONCLUSIONS

Long-term supplementation with folic acid and vitamin B12 in elderly subjects resulted in effects on DNA methylation of several genes, among which genes implicated in developmental processes.

DNA Methylation Changes in the IGF1R Gene in Birth Weight Discordant Adult Monozygotic Twins

Low birth weight (LBW) can have an impact on health outcomes in later life, especially in relation to pre-disposition to metabolic disease. Several studies suggest that LBW resulting from restricted intrauterine growth leaves a footprint on DNA methylation in utero, and this influence likely persists into adulthood. To investigate this further, we performed epigenome-wide association analyses of blood DNA methylation using Infinium HumanMethylation450 BeadChip profiles in 71 adult monozygotic (MZ) twin pairs who were extremely discordant for birth weight. A signal mapping to the IGF1R gene (cg12562232, p = 2.62 × 10−8), was significantly associated with birth weight discordance at a genome-wide false-discovery rate (FDR) of 0.05. We pursued replication in three additional independent datasets of birth weight discordant MZ pairs and observed the same direction of association, but the results were not significant. However, a meta-analysis across the four independent samples, in total 216 birth-weight discordant MZ twin pairs, showed a significant positive association between birth weight and DNA methylation differences at IGF1R (random-effects meta-analysis p = .04), and the effect was particularly pronounced in older twins (random-effects meta-analysis p = .008, 98 older birth-weight discordant MZ twin pairs). The results suggest that severe intra-uterine growth differences (birth weight discordance >20%) are associated with methylation changes in the IGF1R gene in adulthood, independent of genetic effects.

Genome-wide profiling in treatment-naive early rheumatoid arthritis reveals DNA methylome changes in T and B lymphocytes

AIM

Although aberrant DNA methylation has been described in rheumatoid arthritis (RA), no studies have interrogated this epigenetic modification in early disease. Following recent investigations of T and B lymphocytes in established disease, we now characterize in these cell populations genome-wide DNA methylation in treatment-naive patients with early RA.

PATIENTS & METHODS

HumanMethylation450 BeadChips were used to examine genome-wide DNA methylation in lymphocyte populations from 23 early RA patients and 11 healthy individuals.

RESULTS

Approximately 2000 CpGs in each cell type were differentially methylated in early RA. Clustering analysis identified a novel methylation signature in each cell type (150 sites in T lymphocytes, 113 sites in B lymphocytes) that clustered all patients separately from controls. A subset of sites differentially methylated in early RA displayed similar changes in established disease.

CONCLUSION

Treatment-naive early RA patients display novel disease-specific DNA methylation aberrations, supporting a potential role for these changes in the development of RA.

Genetic and Environmental Causes of Variation in the Difference Between Biological Age Based on DNA Methylation and Chronological Age for Middle-Aged Women

The disease- and mortality-related difference between biological age based on DNA methylation and chronological age (Δage) has been found to have approximately 40% heritability by assuming that the familial correlation is only explained by additive genetic factors. We calculated two different Δage measures for 132 middle-aged female twin pairs (66 monozygotic and 66 dizygotic twin pairs) and their 215 sisters using DNA methylation data measured by the Infinium HumanMethylation450 BeadChip arrays. For each Δage measure, and their combined measure, we estimated the familial correlation for MZ, DZ and sibling pairs using the multivariate normal model for pedigree analysis. We also pooled our estimates with those from a former study to estimate weighted average correlations. For both Δage measures, there was familial correlation that varied across different types of relatives. No evidence of a difference was found between the MZ and DZ pair correlations, or between the DZ and sibling pair correlations. The only difference was between the MZ and sibling pair correlations (p < .01), and there was marginal evidence that the MZ pair correlation was greater than twice the sibling pair correlation (p < .08). For weighted average correlation, there was evidence that the MZ pair correlation was greater than the DZ pair correlation (p < .03), and marginally greater than twice the sibling pair correlation (p < .08). The varied familial correlation of Δage is not explained by additive genetic factors alone, implying the existence of shared non-genetic factors explaining variation in Δage for middle-aged women.