Biology subjects

Schalkwyk, L. C.

Publications and source records attributed to Schalkwyk, L. C..

15 recordsLinked to original sources

bioRxiv2026

Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency

Assay for transposase accessible chromatin using sequencing (ATAC-seq) and related techniques provide critical insight into the epigenetic mechanisms that regulate gene expression in health and disease. Accurate identification of biologically meaningful chromatin accessibility signals remains an underexplored analytical challenge. Advances in sequencing technologies have increased both the magnitude and complexity of ATAC-seq datasets, leading to demand for scalable, reproducible and robust computational analysis frameworks. Peak calling, which identifies regions of chromatin accessibility, is a critical step that underpins all downstream quantitative analyses. However, there is a lack of consensus regarding the optimal peak calling strategy. Here, we present a novel bioinformatics pipeline PIPPA (Pipeline for Integrative Peak calling in Population-scale ATAC-seq), for quality control and peak set curation of large-scale ATAC-seq datasets. Using two independent cell-type sorted ATAC-seq datasets, we show that peak calling performance is dataset-dependent and that no single significance threshold performs optimally across all scenarios. We provide practical recommendations for parameters to generate high-confidence chromatin accessibility profiles. Our peak calling approach is applicable for other genome wide epigenomic assays such as ChIP-seq and CUT&Tag. By standardising this process, the PIPPA pipeline enhances epidemiological studies focused in identifying epigenetic dysregulation associated with development and disease.

bioinformatics↗

bioRxiv2026

Schizophrenia-associated DNA methylation differences in the cortex are neuron-specific

Schizophrenia is a complex neuropsychiatric disorder in which genetic risk is thought to converge on cell type-specific regulatory mechanisms in the brain. We performed a cell type-resolved epigenome-wide association study (EWAS) of schizophrenia using fluorescence-activated nuclei sorting (FANS) to isolate neuron-enriched (NeuN+), oligodendrocyte-enriched (SOX10+) and other glial-enriched (NeuN-/SOX10-) nuclei populations alongside total prefrontal cortex nuclei fractions from 216 donors (104 schizophrenia cases and 112 controls). We identified 16 differentially methylated positions (DMPs) in neuron-enriched nuclei at experiment-wide significance and more than 400 additional neuronal DMPs at a discovery threshold. In contrast, no significant associations were identified in oligodendrocyte-enriched, glial-enriched or total nuclei fractions, demonstrating that schizophrenia-associated cortical methylomic variation is highly neuron-specific and largely masked in bulk tissue analyses. Neuronal DMPs exhibited a significant bias towards hypomethylation in schizophrenia and were enriched at loci implicated by genetic studies, including CACNA1C, CACNA1G and TRIO. Pathway analyses implicated genes involved in neurodevelopment, cell adhesion, synapse organisation, neurotransmission and synaptic plasticity. Schizophrenia-associated DNA methylation signatures identified in prefrontal cortex neurons showed correlated effects in neuronal nuclei isolated from the hippocampus and striatum, indicating partial conservation of disease-associated epigenetic alterations across brain regions. Together, these findings provide strong evidence for widespread neuron-specific epigenetic dysregulation in schizophrenia and highlight the importance of cell type-resolved approaches for elucidating the molecular mechanisms underlying psychiatric disease.

genomics↗

bioRxiv2025

Reliable repurposing of antibody interactome inside the cell

In biology proximity is paramount and eighty-five percent of the human proteome has at least one documented interacting monoclonal antibody. These molecules penetrate the cytoplasm poorly and are very often non-functional within the cell. Sequence analysis of 106 antibody variable domains alongside the cytoplasmic human proteome shows charge and isoelectric point are characteristics ill adapted to intracellular monodispersity. Characterisation of forty-five single-chain variable fragment (scFv) intrabodies expressed in human cells confirmed charge to have the greatest impact on solubility. We created new interdomain linkers, optimised scFv domain orientation and found variable heavy domain framework sites to be generally positively charged, and promote insolubility, but be amenable to optimisation. This is applied in combination to reduce the search space and refine the products of AI-led inverse folding to create highly soluble, abundant and thermally stable intrabodies that maintain parent antibody epitope recognition. Over six hundred intrabody sequences are described targeting sixty cytoplasmic proteins with linear, conformational, post-translational modification or oligomeric state specificity. Interactions were validated for p53, -synuclein, SOD1, polyQ, FUS/TLS, UCHL1 and GFP. This approach removes obstacles hindering intracellular repurposing of the vast sequenced antibody interactome with applications relevant to many human disease states.

molecular biology↗

bioRxiv2024

Guidance for the design and analysis of cell-type specific epigenetic epidemiology studies.

Recent studies on the role of epigenetics in disease have focused on DNA methylation profiled in bulk tissues limiting the detection of the cell-type affected by disease related changes. Advances in isolating homogeneous populations of cells now make it possible to identify DNA methylation differences associated with disease in specific cell-types. Critically, these datasets will require a bespoke analytical framework that can characterise whether the difference affects multiple or is specific to a particular cell-type. We take advantage of a large set of DNA methylation profiles (n = 751) obtained from five different purified cell populations isolated from human prefrontal cortex samples and evaluate the effects on study design, data preprocessing and statistical analysis for cell-specific studies, particularly for scenarios where multiple cell types are included. We describe novel quality control metrics that confirm successful isolation of purified cell populations, which when included in standard preprocessing pipelines provide confidence in the dataset. Our power calculations show substantial gains in detecting differentially methylated positions for some purified cell populations compared to bulk tissue analyses, countering concerns regarding the feasibility of generating large enough sample sizes for informative epidemiological studies. In a simulation study, we evaluated different regression models finding that this choice impacts on the robustness of the results. These findings informed our proposed two-stage framework for association analyses. Overall, our results provide guidance for cell-specific EWAS, establishing standards for study design and analysis, while showcasing the potential of cell-specific DNA methylation analyses to reveal links between epigenetic dysregulation and disease.

bioinformatics↗

bioRxiv2024

Systematic investigation of interindividual variation of DNA methylation in human whole blood

Interindividual genetic variability is well characterised, but we still lack a complete catalogue of loci displaying variable and stable epigenetic patterns. Here, we report a catalogue of stable and variable interindividual DNA methylation in human whole blood by analysing the DNA methylation patterns in 3642 individuals using the IlluminaEPIC array. Our results showed that 41,216 CpGs display stable methylation (SMPs) and 34,972 CpGs display variable methylation levels (VMPs). This catalogue will be a useful resource for interpretation of results when associating epigenetic signals to phenotypes. We observed that SMPs are highly enriched in CpG islands, depleted at CpG shelves and open sea regions of the genome. In addition, we found that the VMPs were under higher genetic control than the SMPs and that trans mQTL pairs are often located in the same TAD or connected by chromatin loops. A subset of these VMPs (784) were classified as putative epialleles and our results demonstrate that these loci located in regulatory regions exhibit a link with gene expression.

genomics↗

bioRxiv2023

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BackgroundDue to inter-individual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell-types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal- and three glial-cell subtypes for quantifying the cellular composition of the human cortex. ResultsWe tested eight reference panels containing different combinations of neuronal- and glial-cell types and characterized their performance in deconvoluting cell proportions from computationally reconstructed or empirically-derived human cortex DNA methylation data. Our analyses demonstrate that these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimers disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. ConclusionsOur novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

genomics↗

bioRxiv2022

Recalibrating the cerebellum DNA methylation clock: implications for ageing rates comparison

BackgroundDNA methylation (DNAm) based age clocks have been studied extensively as a biomarker of human ageing and risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly, however, this claim was drawn from a single clock using a small sample size and so warrants further investigation. ResultsWe collected the largest cerebellum DNAm dataset (N=752). We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N=404), of which only 201 CpGs are both age-associated in the two tissue types. We built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Furthermore, based on the 201 age-associated CpGs, we built two other clocks CerebellumClockcommon and CortexClockcommon for the cerebellum and non-cerebellar brain cortex tissues separately, they both support that the cerebellum has a relative lower DNAm ageing rate. ConclusionsThe large underestimation for the cerebellum by previous clocks mainly reflects the improper usage of the age clocks. There exist strong and consistent ageing effects on the cerebellar methylome despite the cerebellum having unique age-dependent methylome changes. The DNAm clock based ageing rates comparisons are valid only upon models constructed on a small group of CpGs, therefore, more evidence is required to support the idea that different DNAm ageing rates represent different biological ageing rates.

bioinformatics↗

bioRxiv2022

Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionise the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1,779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the aetiology of complex disease.

neuroscience↗

bioRxiv2021

interpolatedXY: a two-step strategy to normalise DNA methylation microarray data avoiding sex bias

MotivationData normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes, thus it poses a significant challenge to normalise sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalise sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. ResultsHere, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalisation methods. By this new strategy, the autosomal CpGs are first normalised independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome linked CpGs are estimated as the weighted average of their nearest neighbours on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalisation methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalisation effect. AvailabilityThe proposed methods are available by calling the function adjustedDasen or adjustedFunnorm in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. Contactxzhai@essex.ac.uk; lschal@essex.ac.uk Supplementary informationSupplementary data are available at...

bioinformatics↗

bioRxiv2021

Characterising sex differences of autosomal DNA methylation in whole blood using the Illumina EPIC array

Sex differences are known to play a role in disease etiology, progression and outcome. Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood (n=1171). We identified 554 sex-associated differentially methylated CpG sites (saDMPs) with the majority found to be hypermethylated in females (70%). These saDMPs are enriched in CpG islands and CpG shores and located preferentially at 5UTRs, 3UTRs and enhancers. Additionally, we identified 311 significant sex associated differentially methylated regions (saDMRs). Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and SOX9. Our study reports a reliable catalogue of sex associated CpG sites and elucidates several characteristics of these sites.

genomics↗

bioRxiv2020

DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy

Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. In this paper, we presented a novel method to predict sex using only DNA methylation density signals, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only density signals) uploaded to GEO. We identified 4345 significantly (p < 0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first components of PCAs from the two sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. The proposed method has been integrated into the wateRmelon Bioconductor package.

bioinformatics↗

bioRxiv2020

DNA methylation covariation in human whole blood and sperm: implications for studies of intergenerational epigenetic effects

BackgroundEpidemiological studies suggest that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals suggest that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in matched human blood and sperm from lean (discovery n=47; replication n=21) and obese (n=22) males to analyse tissue covariation of DNA methylation, and identify whether this covariation is influenced by obesity. ResultsDNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites ([~]1%). While at the majority of these sites, DNA methylation appears to be influenced by genetic variation, obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity did not influence covariation patterns. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P=8.95 x 10-8; beta=0.02) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n=5,917), spermatozoa displayed differential DNA methylation in pathways enriched in transcriptional regulation. ConclusionsHuman sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. Obesity only nominally influences sperm DNA methylation, making it an unlikely mediator of intergenerational effects of metabolic traits.

genomics↗

bioRxiv2020

Meta-analysis of epigenome-wide association studies in Alzheime's disease highlights 220 differentially methylated loci across cortex

Epigenome-wide association studies of Alzheimers disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimers disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N=1,408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further > 600 unique donors. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).

genomics↗

bioRxiv2018

Properties of the epigenetic clock and age acceleration

BackgroundThe methylation status of numerous CpG sites in the human genome varies with age. The Horvath epigenetic clock used a wide variety of published DNA methylation data to produce an age prediction that has been widely used to, predict age in unknown samples, and draw conclusions about speed of ageing in various tissues, environments, and diseases. Despite its utility, there are a number of assumptions in the model that require examination. We explore the characteristics of the model in whole blood and multiple brain regions from older people, who are not well represented in the original training data, and in blood from a cross-sectional population study.\n\nResultsWe find that the model systematically underestimates age in tissues from older people. A decrease in slope of the predicted ages were observed at approximately 60 years, indicating that some loci in the model may change differently with age, and that age acceleration measures will themselves be age-dependent. This is seen most strongly in the cerebellum but is also present in other examined tissues, and is consistently observed in multiple datasets. An apparent association of Alzheimers disease with age acceleration disappears when age is used as a covariate. Association tests in the literature use a variety of methods for calculating age acceleration and often do not use age as a covariate. This is a potential cause of misleading findings.\n\nConclusionsAssociations of phenotypes with age acceleration should be evaluated cautiously, and chronological age should be included as a covariate in all analyses.

genomics↗

bioRxiv2018

Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits.

Characterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data-based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

genetics↗