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Location: WE Technologiepark Zwijnaarde. View on Google Maps. View library. You are free to copy, distribute and use the database; to produce works from the database; to modify, transform and build upon the database. As long as you attribute the data sets to the source, publish your adapted database with ODbL license, and keep the dataset open don't use technical measures such as DRM to restrict access to the database.

The datasets are also available as weekly exports. NL EN. More about Epigenesis Plant genetic regulation. Plant Biotechnology and Bioinformatics Open print view. Mon 23 Sep closed deels verhuisd naar S5 ; mail naar webib ugent. Publisher: New York N. Description: X, p. Moreover, CpG sites with higher DNAm variability tend to be more correlated between matched tissues [ 29 , 30 , 31 , 34 ]. Although these results provide important insights into the comparability of DNAm measures across matched tissues, the analyses to date have been conducted in adult tissues, thereby limiting their relevance to DNAm profiles from pediatric samples.

As previous studies have demonstrated that developmental changes in blood DNAm patterns tend to be more pronounced and occur more rapidly in childhood, the examination of DNAm concordance and variability in pediatric tissues represents an important and currently missing step in our understanding of EWAS associations from pediatric peripheral tissues [ 21 , 22 ]. Methylation quantitative trait loci mQTL , sites at which DNAm is associated with genetic variation, are present across the genome and are often consistent across tissues, ancestral populations and developmental stage [ 41 , 42 , 43 , 44 ].

Notably, genetically influenced sites of inter-individual DNAm variation, which can co-occur across tissues, may be biologically informative.

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For example, allele-specific DNAm of the FK binding protein 5 FKBP5 gene, which has been associated with risk of developing stress-related psychiatric disorders, responds to glucocorticoid stimulation in a similar way in peripheral blood cells and neuronal progenitor cells [ 45 ]. Within a particular tissue, such as blood, mQTL often are stable across development [ 43 , 46 ]. As such, delineating the contribution of genetic influences to tissue-specific DNAm may help clarify the interpretation of EWAS associations.

Given that early life development brings about sizable changes to DNAm patterns, it is important to examine DNAm variability and concordance between peripheral tissues, as well as genetic influences on early life DNAm patterns, in childhood [ 21 , 22 ]. To this end, we used matched PBMC and BEC samples, two commonly used peripheral tissues in EWAS, from two independent early life cohorts in order to identify a differences in inter-individual variability and concordance of DNAm between these tissues and b genetic contributions to these patterns at the site-specific level.

Moreover, we found that highly variable CpGs were more likely to be positively correlated between matched tissues and enriched for DNAm sites under genetic influence. Collectively, these findings highlighted a number of potential insights and considerations for the appropriate design and interpretation of EWAS analyses performed in commonly used peripheral tissues of pediatric samples.

Each K dataset was normalized to remove probe type differences and adjusted for cell type heterogeneity in each tissue using established bioinformatic correction methods [ 34 , 48 , 49 , 50 , 51 ]. Genetic variants were measured genome-wide using the Illumina Infinium PsychChip. As inter-individual DNAm variability within a tissue likely relates to the potential effect sizes that are detectable in EWAS analyses, we were interested in assessing tissue-specific DNAm variability.

To this end, we first interrogated the global differences in inter-individual DNAm variability between PBMC and BEC samples, following in silico correction for cell type differences in each tissue. We used reference range as a measure of DNAm variability as opposed to absolute range in order to minimize potential skewing by outlier values and non-normal DNAm values at individual CpGs, as previously described [ 31 , 52 ].

Similarly, in C3ARE, the median reference range was 1. In addition, tissue-specific differences in DNAm variability were observed at individual CpGs, as determined by a Fligner—Killeen test, a nonparametric test measuring homogeneity of variances between two groups. Apart from tissue-specific differences in reference range, we also observed a cohort-specific difference in DNAm variability.

In BECs, the median reference range was 1. Taking advantage of the matched tissue design of our cohorts, we evaluated whether DNAm variation in one tissue reflected DNAm variation in the other. Using multiple reference range thresholds to capture increasingly variable CpGs, as previously described, we observed progressively greater enrichment of highly positively correlated CpGs, irrespective of sample size Fig.

This suggested that, broadly speaking, CpGs with greater variability were more likely to be correlated between these tissues than less variable CpGs. Variable CpGs were more highly correlated between tissues. Reference range thresholds were set along a sliding scale with cut-offs at 0, 0. Top-ranking correlated informative sites shown in the left two columns exhibited continuous distributions. In contrast, top-ranking variable informative sites shown in the right two columns exhibited discrete distributions, suggesting that these Cps may be under genetic influence.

To be classified as informative, i. Overlapping CpGs that met these criteria in both cohorts resulted in a set of informative sites. Visualization of our six most correlated informative sites revealed continuous distributions of positively correlated DNAm values between the tissues, as expected Fig. However, the most variable informative sites exhibited discrete distributions with 2—3 distinct clusters, rather than a typical continuous distribution, suggesting that these CpGs may be enriched for CpGs which are likely under genetic influence Fig.

Independently validated cis -mQTL were more likely to be shared across tissues than expected by chance. This suggested that genetic influences contributed to covariation between tissues. We next sought to characterize our validated cis -mQTL by their genomic localization and functional features.

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In particular, both the CpGs associated with tissue-specific cis -mQTL and the CpGs associated with shared-tissue cis -mQTL were significantly enriched at intergenic and intragenic regions and showed significant depletion at promoters and CpG islands, where DNAm levels tend to be low and there is limited inter-individual variation Additional file 4 : Fig. Tissue-specific differential DNA methylation was consistent across cohorts. CpGs in dark purple met the effect size and significance cut-offs independently in all three datasets , CpGs.

To provide a granular categorization of CpGs measured on the K array, we overlapped CpGs that were identified as a informative i. Scatterplots display three representative CpGs from the pairwise intersections between categories. We selected five published studies that used the K array in pediatric BECs or peripheral blood to assess DNAm variation associated with puberty, aging in early life, childhood psychotic symptoms, fetal alcohol spectrum disorder and autism spectrum disorder [ 61 , 62 , 63 , 64 , 65 ]. Differentially methylated CpGs comprised the most represented type of CpG across all five studies with only one study demonstrating an overlap of Finally, we tabulated our CpGs classifications across all , DNAm probes assessed in our study in order to serve as a resource for researchers wishing to compare their own EWAS results Additional file 7.

Collectively, these findings reveal the importance of considering DNAm variability and concordance between tissues, as well as genetic influences on these patterns, when interrogating and interpreting EWAS findings from pediatric peripheral tissues. Moreover, we leveraged the strength of paired DNAm and genotyping profiles to define cis -mQTL across the genome and assess the influence of local genetic variation on DNAm variability and tissue concordance.

Our findings showed that at the genomic and site-specific level, BECs had greater inter-individual DNAm variability over PBMCs, with highly variable CpGs more likely to be positively correlated between the matched tissues. In our subsequent cis -mQTL analyses, we observed distinct genetic influences on tissue-specific DNAm and confirmed that a sizeable proportion of shared DNAm patterns between tissues resulted from allelic variation.

Our findings highlighted extensive differences in DNAm patterns between tissues and thus the importance of tissue selection when designing an EWAS. To a large extent, EWAS tissue selection in early life cohorts is guided by two factors. Firstly, ease of collection is particularly important in this age range and may restrict tissue availability. Buccal swabs are less invasive than intravenous puncture, and the latter contributes to participation refusal in pediatric cohorts [ 66 ]. Secondly, the relevance of the tissue to the phenotype or exposure being tested represents an important consideration for all EWAS analyses, irrespective of age.

As peripheral blood represents a circulating tissue with broad immune and inflammatory functions, it might be more relevant to a wider range of health phenotypes than BECs.

Transient Stability of Epigenetic Population Differentiation in a Clonal Invader

However, another hypothesis posits that tissues that arise from the same germ layer are more epigenetically similar and thus might be a preferred choice for surrogate tissue selection [ 67 ]. Having a higher proportion of variable CpGs might be desirable for EWAS analyses as testing any tissue with little inter-individual DNAm variation would naturally limit effect sizes. From this perspective, BECs might represent a more appropriate choice of peripheral tissue for population-based epigenetic studies over PBMCs. However, it is worth noting that while we did correct for cellular heterogeneity in both tissues using bioinformatic deconvolution approaches, the higher proportion of variable CpGs in BECs may, to some extent, be attributed to the increased diversity of cell types or residual cellular heterogeneity in BECs over PBMCs i.


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CpGs with greater variability were more likely to be correlated between matched tissues, as best exemplified by the informative sites we identified. In the latter case, cross-tissue replication typically involves the generation of candidate gene lists in accessible tissues for validation in less available tissues, such as postmortem samples, an approach which can boost confidence in identified associations [ 69 , 70 , 71 ].

A guide to the newest techniques for examining epigenetics in single cells

However, we found only 1. These quantitative differences might be due to a number of reasons, with the most likely being that the blood—brain informative sites were identified using a single cohort, while our blood—buccal informative sites were filtered down to sites that were common across both GECKO and C3ARE cohorts; other explanations may be methodological i.

An in-depth analysis of such cross-tissue comparisons between pediatric and adult samples, ideally by means of longitudinal sampling of DNAm, may help elucidate such sources of tissue variation across the lifespan. Integration of genetic and epigenetic information may further clarify the relative contribution of genetic and environmental factors on inter-individual DNAm variability. We found that genetic variation contributed to both inter-individual DNAm variation within a tissue, as well as common DNAm variation between tissues.

This is in general agreement with previous findings that show that many—but not all—mQTLs have consistent effects across tissues and human populations and are generally depleted in genomic regions which tend to have low DNAm variability such as promoters and CpG island but enriched in more variable intergenic and intragenic regions [ 41 , 43 , 44 , 46 , 72 ].