BackgroundMeasurement of genome-wide DNA methylation (DNAm) has become an important avenue for investigating potential physiologically-relevant epigenetic changes. Illumina Infinium (Illumina, San Diego, CA, USA) is a commercially available microarray suite used to measure DNAm at many sites throughout the genome. However, it has been suggested that a subset of array probes may give misleading results due to issues related to probe design. To facilitate biologically significant data interpretation, we set out to enhance probe annotation of the newest Infinium array, the HumanMethylation450 BeadChip (450 k), with >485,000 probes covering 99% of Reference Sequence (RefSeq) genes (National Center for Biotechnology Information (NCBI), Bethesda, MD, USA). Annotation that was added or expanded on includes: 1) documented SNPs in the probe target, 2) probe binding specificity, 3) CpG classification of target sites and 4) gene feature classification of target sites.ResultsProbes with documented SNPs at the target CpG (4.3% of probes) were associated with increased within-tissue variation in DNAm. An example of a probe with a SNP at the target CpG demonstrated how sample genotype can confound the measurement of DNAm. Additionally, 8.6% of probes mapped to multiple locations in silico. Measurements from these non-specific probes likely represent a combination of DNAm from multiple genomic sites. The expanded biological annotation demonstrated that based on DNAm, grouping probes by an alternative high-density and intermediate-density CpG island classification provided a distinctive pattern of DNAm. Finally, variable enrichment for differentially methylated probes was noted across CpG classes and gene feature groups, dependant on the tissues that were compared.ConclusionDNAm arrays offer a high-throughput approach for which careful consideration of probe content should be utilized to better understand the biological processes affected. Probes containing SNPs and non-specific probes may affect the assessment of DNAm using the 450 k array. Additionally, probe classification by CpG enrichment classes and to a lesser extent gene feature groups resulted in distinct patterns of DNAm. Thus, we recommend that compromised probes be removed from analyses and that the genomic context of DNAm is considered in studies deciphering the biological meaning of Illumina 450 k array data.

BackgroundDNA methylation is an epigenetic mark that balances plasticity with stability. While DNA methylation exhibits tissue specificity, it can also vary with age and potentially environmental exposures. In studies of DNA methylation, samples from specific tissues, especially brain, are frequently limited and so surrogate tissues are often used. As yet, we do not fully understand how DNA methylation profiles of these surrogate tissues relate to the profiles of the central tissue of interest.ResultsWe have adapted principal component analysis to analyze data from the Illumina 450K Human Methylation array using a set of 17 individuals with 3 brain regions and whole blood. All of the top five principal components in our analysis were associated with a variable of interest: principal component 1 (PC1) differentiated brain from blood, PCs 2 and 3 were representative of tissue composition within brain and blood, respectively, and PCs 4 and 5 were associated with age of the individual (PC4 in brain and PC5 in both brain and blood). We validated our age-related PCs in four independent sample sets, including additional brain and blood samples and liver and buccal cells. Gene ontology analysis of all five PCs showed enrichment for processes that inform on the functions of each PC.ConclusionsPrincipal component analysis (PCA) allows simultaneous and independent analysis of tissue composition and other phenotypes of interest. We discovered an epigenetic signature of age that is not associated with cell type composition and required no correction for cellular heterogeneity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-015-0011-y) contains supplementary material, which is available to authorized users.

BackgroundThe presence of an extra whole or part of chromosome 21 in people with Down syndrome (DS) is associated with multiple neurological changes, including pathological aging that often meets the criteria for Alzheimer’s Disease (AD). In addition, trisomies have been shown to disrupt normal epigenetic marks across the genome, perhaps in response to changes in gene dosage. We hypothesized that trisomy 21 would result in global epigenetic changes across all participants, and that DS patients with cognitive impairment would show an additional epigenetic signature.MethodsWe therefore examined whole-genome DNA methylation in buccal epithelial cells of 10 adults with DS and 10 controls to determine whether patterns of DNA methylation were correlated with DS and/or cognitive impairment. In addition we examined DNA methylation at the APP gene itself, to see whether there were changes in DNA methylation in this population. Using the Illumina Infinium 450 K Human Methylation Array, we examined more than 485,000 CpG sites distributed across the genome in buccal epithelial cells.ResultsWe found 3300 CpGs to be differentially methylated between the groups, including 495 CpGs that overlap with clusters of differentially methylated probes. In addition, we found 5 probes that were correlated with cognitive function including two probes in the TSC2 gene that has previously been associated with Alzheimer’s disease pathology. We found no enrichment on chromosome 21 in either case, and targeted analysis of the APP gene revealed weak evidence for epigenetic impacts related to the AD phenotype.ConclusionsOverall, our results indicated that both Trisomy 21 and cognitive impairment were associated with distinct patterns of DNA methylation.

The hypothalamic–pituitary–adrenal axis (HPAA) plays a critical role in the functioning of all other biological systems. Thus, studying how the environment may influence its ontogeny is paramount to understanding developmental origins of health and disease. The early post-conceptional (EPC) period could be particularly important for the HPAA as the effects of exposures on organisms’ first cells can be transmitted through all cell lineages. We evaluate putative relationships between EPC maternal cortisol levels, a marker of physiologic stress, and their children’s pre-pubertal HPAA activity (n=22 dyads). Maternal first-morning urinary (FMU) cortisol, collected every-other-day during the first 8 weeks post-conception, was associated with children’s FMU cortisol collected daily around the start of the school year, a non-experimental challenge, as well as salivary cortisol responses to an experimental challenge (all Ps<0.05), with some sex-related differences. We investigated whether epigenetic mechanisms statistically mediated these links and, therefore, could provide cues as to possible biological pathways involved. EPC cortisol was associated with >5% change in children’s buccal epithelial cells’ DNA methylation for 867 sites, while children’s HPAA activity was associated with five CpG sites. Yet, no CpG sites were related to both, EPC cortisol and children’s HPAA activity. Thus, these epigenetic modifications did not statistically mediate the observed physiological links. Larger, prospective peri-conceptional cohort studies including frequent bio-specimen collection from mothers and children will be required to replicate our analyses and, if our results are confirmed, identify biological mechanisms mediating the statistical links observed between maternal EPC cortisol and children’s HPAA activity.

BackgroundDNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking.ResultsHere we train a dense neural network to solve for the local folding of chromatin, connecting structure, represented as a contact map, to a sequence of bound chromatin factors. The network includes a convolutional filter that compresses the large number of bound chromatin factors into a single 1D sequence representation that is optimized for predicting structure. We also train a network to solve the inverse problem, namely given only structural information in the form of a contact map, predict the likely sequence of chromatin states that generated it.ConclusionsBy carrying out sensitivity analysis on both networks, we are able to highlight the importance of chromatin contexts and neighborhoods for regulating long-range contacts, along with critical alterations that affect contact formation. Our analysis shows that the networks have learned physical insights that are informative and intuitive about this complex polymer problem.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2286-z) contains supplementary material, which is available to authorized users.