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Background: Sleep is important for healthy functioning in children. Numerous genetic and environmental factors, from conception onwards, may influence this phenotype. Epigenetic mechanisms such as DNA methylation have been proposed to underlie variation in sleep or may be an early-life marker of sleep disturbances. We examined if DNA methylation at birth or in school age is associated with parent-reported and actigraphy-estimated sleep outcomes in children., Methods: We meta-analysed epigenome-wide association study results. DNA methylation was measured from cord blood at birth in 11 cohorts and from peripheral blood in children (4-13 years) in 8 cohorts. Outcomes included parent-reported sleep duration, sleep initiation and fragmentation problems, and actigraphy-estimated sleep duration, sleep onset latency and wake-after-sleep-onset duration., Results: We found no associations between DNA methylation at birth and parent-reported sleep duration (n = 3658), initiation problems (n = 2504), or fragmentation (n = 1681) (p values above cut-off 4.0 × 10 -8 ). Lower methylation at cg24815001 and cg02753354 at birth was associated with longer actigraphy-estimated sleep duration (p = 3.31 × 10 -8 , n = 577) and sleep onset latency (p = 8.8 × 10 -9 , n = 580), respectively. DNA methylation in childhood was not cross-sectionally associated with any sleep outcomes (n = 716-2539)., Conclusion: DNA methylation, at birth or in childhood, was not associated with parent-reported sleep. Associations observed with objectively measured sleep outcomes could be studied further if additional data sets become available., (© 2022. The Author(s).)

Altered maternal haemoglobin levels during pregnancy are associated with pre-clinical and clinical conditions affecting the fetus. Evidence from animal models suggests that these associations may be partially explained by differential DNA methylation in the newborn with possible long-term consequences. To test this in humans, we meta-analyzed the epigenome-wide associations of maternal haemoglobin levels during pregnancy with offspring DNA methylation in 3,967 newborn cord blood and 1,534 children and 1,962 adolescent whole-blood samples derived from 10 cohorts. DNA methylation was measured using Illumina Infinium Methylation 450K or MethylationEPIC arrays covering 450,000 and 850,000 methylation sites, respectively. There was no statistical support for the association of maternal haemoglobin levels with offspring DNA methylation either at individual methylation sites or clustered in regions. For most participants, maternal haemoglobin levels were within the normal range in the current study, whereas adverse perinatal outcomes often arise at the extremes. Thus, this study does not rule out the possibility that associations with offspring DNA methylation might be seen in studies with more extreme maternal haemoglobin levels.

Saliva is a widely used biological sample, especially in pediatric research, containing a heterogenous mixture of immune and epithelial cells. Associations of exposure or disease with saliva DNA methylation can be influenced by cell-type proportions. Here, we developed a saliva cell-type DNA methylation reference panel to estimate interindividual cell-type heterogeneity in whole saliva studies. Saliva was collected from 22 children (7-16 years) and sorted into immune and epithelial cells, using size exclusion filtration and magnetic bead sorting. DNA methylation was measured using the Illumina MethylationEPIC BeadChip. We assessed cell-type differences in DNA methylation profiles and tested for enriched biological pathways. Immune and epithelial cells differed at 181,577 (22.8%) DNA methylation sites (t-test p < 6.28 × 10 -8 ). Immune cell hypomethylated sites are mapped to genes enriched for immune pathways (p < 3.2 × 10 -5 ). Epithelial cell hypomethylated sites were enriched for cornification (p = 5.2 × 10 -4 ), a key process for hard palette formation. Saliva immune and epithelial cells have distinct DNA methylation profiles which can drive whole-saliva DNA methylation measures. A primary saliva DNA methylation reference panel, easily implemented with an R package, will allow estimates of cell proportions from whole saliva samples and improve epigenetic epidemiology studies by accounting for measurement heterogeneity by cell-type proportions.

The effects of prenatal lead exposure on child development include impaired growth and cognitive function. DNA methylation might be involved in the underlying mechanisms and previous epigenome-wide association studies reported associations between lead exposure during pregnancy and cord blood methylation levels. However, it is unclear during which developmental stage lead exposure is most harmful. Cord blood methylation levels were assayed in 420 children from a Mexican pre-birth cohort using the Illumina Infinium MethylationEPIC microarray. Lead concentrations were measured in umbilical cord blood as well as in blood samples from the mothers collected at 2nd and 3rd trimester and delivery using inductively coupled plasma-mass spectrometry. In addition, maternal bone lead levels were measured in tibia and patella using X-ray fluorescence. Comprehensive quality control and preprocessing of microarray data was followed by an unbiased restriction to methylation sites with substantial variance. Methylation levels at 202 111 cytosine-phosphate-guanine sites were regressed on each exposure adjusting for child sex, leukocyte composition, batch variables, gestational age, birthweight-for-gestational-age, maternal age, maternal education and mode of delivery. We find no association between prenatal lead exposure and cord blood methylation. This null result is strengthened by a sensitivity analysis showing that in the same dataset known biomarkers for birthweight-for-gestational-age can be recovered and the fact that phenotypic associations with lead exposure have been described in the same cohort., (© The Author(s) 2020. Published by Oxford University Press.)

DNA methylation microarrays have been the platform of choice for epigenome-wide association studies in epidemiology, but declining costs have rendered targeted bisulphite sequencing a feasible alternative. Nonetheless, the literature for researchers seeking guidance on which platform to choose is sparse. To fill this gap, we conducted a comparison study in which we processed cord blood samples from four newborns in duplicates using both the Illumina HumanMethylationEPIC BeadChip and the Illumina TruSeq Methyl Capture EPIC Kit, and evaluated both platforms in regard to coverage, reproducibility, and identification of differential methylation. We conclude that with current analytic goals microarrays still outperform bisulphite sequencing for precise quantification of DNA methylation.

Background: DNA methylation microarrays are popular for epigenome-wide association studies (EWAS), but spurious values complicate downstream analysis and threaten replication. Conventional cut-offs for detection p values for filtering out undetected probes were demonstrated in a single previous study as insufficient leading to many apparent methylation calls in samples from females in probes targeting the Y-chromosome. We present an alternative approach to calculate more accurate detection p values utilizing non-specific background fluorescence. We evaluate and compare our proposed approach of filtering observations with conventional ones by assessing the detection of Y-chromosome probes among males and females in 2755 samples from 17 studies on the 450K microarray and masking of large outliers between technical replicates and their impact downstream via an EWAS reanalysis., Results: In contrast to conventional approaches, ours marks most Y-chromosome probes in females as undetected while removing a median of only 0.14% of the data per sample, catches more (30% vs. 6%) of large outliers (more than 20 percentage point difference between technical replicates), and helps to identify strong associations previously obfuscated by outliers between whole blood DNA methylation and chronological age in a well-powered EWAS (n = 729)., Conclusions: We provide guidance for filtering both 450K and EPIC microarrays as an essential preprocessing step to reduce spurious values. An implementation (including a function compatible with objects from the popular minfi package) was added to ewastools, an R package for comprehensive quality control of DNA methylation microarrays. Scripts to reproduce all analyses are available at doi.org/10.5281/zenodo.1443561 .

Background: Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets., Methods: Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting sample donors and a measure of sample contamination based on probes querying high-frequency SNPs. These checks were tested on 80 datasets comprising 8327 samples run on the 450K microarray from the GEO repository., Results: Nine hundred forty samples were flagged by at least one control metric and 133 samples from 20 datasets were assigned the wrong sex. In a dataset in which a subset of samples appear contaminated with a single source of DNA, we demonstrate that our measure based on outliers among SNP probes was strongly correlated (> 0.95) with another independent measure of contamination., Conclusions: A more complete examination of samples that may be mislabeled, contaminated, or have poor performance due to technical problems will improve downstream analyses and replication of findings. We demonstrate that quality control problems are prevalent in a public repository of DNA methylation data. We advocate for a more thorough quality control workflow in epigenome-wide association studies and provide a software package to perform the checks described in this work. Reproducible code and supplementary material are available at 10.5281/zenodo.1172730., Competing Interests: Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aim: One concern in epigenome-wide studies investigating leukocyte DNA methylation is that observed associations may at least partly reflect differences in leukocyte composition (LC) rather than changes in methylation. We estimated the magnitude of confounding by LC for common risk factors and diseases., Materials & Methods: Variation of LC according to sex, race, age, smoking, alcohol consumption, BMI, cardiovascular fitness, hypertension, coronary heart disease and diabetes was analyzed using blood differentials from 4117 participants of NHANES. Furthermore, leukocyte DNA methylation levels of biomarkers of smoking, BMI, diabetes, age and sex were regressed on these outcomes in a sample of 989 participants of ESTHER, and regression coefficients with and without adjustment for estimated LC were compared., Results: Aside from race and ages below 25 years, none of the investigated factors had substantial impact on LC. Adjusted and unadjusted coefficients were virtually identical., Conclusion: Confounding by LC might often be a minor issue.

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. If detected at an early stage, prognosis is good. Despite increasing evidence for the benefits of implemented screening programs, such as screening colonoscopy, compliance is rather low. Hence there is demand for non-invasive tests for the early detection of CRC with high acceptance in population-wide screening. The objective of this study was to identify and evaluate leukocyte DNA methylation patterns as a potential biomarker for early detection of CRC., Methods: Blood samples of patients scheduled for a screening colonoscopy were collected before the procedure. Additionally, blood samples from CRC cases recruited in a clinical setting were collected. DNA was extracted from leukocytes, and DNA methylation was measured with the Infinium 450K BeadChip. In total, 46 CRC cases and 140 controls from the screening setting and 93 CRC cases from the clinical setting were measured., Results: An epigenome-wide discovery revealed two CpG sites in the promoter region of KIAA1549L that were significantly differentially methylated between cases and controls. A third marker in the body region of BCL2 was discovered in a candidate approach testing biomarkers reported in the literature. Logistic regression models built on these three markers yielded an optimism-corrected c -statistic of 0.69 in the screening setting and 0.73 in the clinical setting., Conclusions: Although diagnostic performance of the DNA methylation signature identified in this first epigenome-wide association study of leukocyte DNA methylation with CRC in a screening setting is not competitive with established screening tests, the identified markers may contribute to multimarker panels for early detection of CRC.

Aim: Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification., Materials & Methods: Using whole-blood DNA methylation and corresponding five-part cell counts from 2445 participants from the London Life Sciences Prospective Population Study, a model was trained on a subset of 175 subjects and evaluated on the remaining., Results: Correlations between cell counts and estimated cell proportions were high (neutrophils 0.85, eosinophils 0.88, basophils 0.02, lymphocytes 0.84, monocytes 0.55) and estimated proportions explained more variance in whole-blood DNA methylation levels than counts., Conclusion: Our model provided precise estimates for the common cell types.

Background: Timely diagnosis of cancer represents a challenging task; in particular, there is a need for reliable non-invasive screening tools that could achieve high levels of adherence at virtually no risk in population-based screening. In this review, we summarize the current evidence of exhaled breath analysis for cancer detection using standard analysis techniques and electronic nose., Methods: Relevant studies were identified searching Pubmed and Web of Science databases until April 30, 2015. Information on breath test performance, such as sensitivity and specificity, was extracted together with volatile compounds that were used to discriminate cancer patients from controls. Performance of different breath analysis techniques is provided for various cancers together with information on methodological issues, such as breath sampling protocol and validation of the results., Results: Overall, 73 studies were included, where two-thirds of the studies were conducted on lung cancer. Good discrimination usually required a combination of multiple biomarkers, and area under the receiver operating characteristic curve or accuracy reached levels of 0.9 or higher in multiple studies. In 25% of the reported studies, classification models were built and validated on the same datasets. Huge variability was seen in different aspects among the studies., Conclusions: Analyses of exhaled breath yielded promising results, although standardization of breath collection, sample storage and data handling remain critical issues. In order to foster breath analysis implementation into practice, larger studies should be implemented in true screening settings, paying particular attention to standardization in breath collection, consideration of covariates, and validation in independent population samples.

The Illumina Infinium HumanMethylation450 BeadChip is frequently used in epigenetic research. Besides quantile normalization there is currently no standard method to normalize the data between arrays. We describe some properties of the data generated by this platform and present a normalization method based on local regression. We compare the performance of this method with other commonly used approaches in three benchmarks (correlation between 21 pairs of technical replicates, detection of differential methylation and correlation of methylation levels for smoking-associated CpG sites with smoking behavior of 655 participants of an epidemiological study). Results indicate that the proposed method improves reproducibility, whereas some commonly used methods can have adverse effects.