Leonard Cornelis Schalkwyk

Neuropsychiatric polygenic indices (NPGIs) are used as genetic predictors of poor mental health. However, NPGIs are also associated with environmental factors which could affect mental health in adulthood, including the rearing environment. Hence, their “genetic” effects are both direct and environmentally mediated. There is a need to identify alternative genetic predictors without environmental signal. Endophenotype-based polygenic indices (EPGIs) trained on brain structure and function are under-studied alternatives which, due to their relative biological proximity, may exhibit associations with mental health outcomes which are less environmentally mediated than those of NPGIs. Using four representative UK samples ( Understanding Society ; UKHLS, NCDS, BCS70 and MCS) we employ sex-stratified path models to estimate the direct and environmentally mediated effects of eleven NPGIs and 30 EPGIs on adult mental health, focussing on the rearing environment. The depression NPGI is consistently associated with mental health symptoms across most sex-stratified sub-samples (best meta-analysis β = 0.091, p 0.001) but demonstrates 1.6–24.5% environmental mediation. Seven other NPGIs and three EPGIs show sample- and sex-specific associations with mental health symptoms. NPGIs for attention deficit hyperactivity disorder, depression and substance use disorder are robustly associated with measures of the rearing environment, which in turn are frequently associated with mental health symptoms. Sensitivity analyses find that NPGI associations vary substantially depending on who is included in the sample. In conclusion, the rearing environment likely mediates a substantial portion of NPGIs’ so-called “genetic” effects on mental health symptoms, but EPGIs are not currently powerful enough to replace them.

Neuropsychiatric polygenic indices (NPGIs) are used as genetic predictors of poor mental health. However, NPGIs are also associated with environmental factors which could affect mental health in adulthood, including the rearing environment. Hence, their “genetic” effects are both direct and environmentally mediated. There is a need to identify alternative genetic predictors without environmental signal. Endophenotype-based polygenic indices (EPGIs) trained on brain structure and function are under-studied alternatives which, due to their relative biological proximity, may exhibit associations with mental health outcomes which are less environmentally mediated than those of NPGIs. Using four representative UK samples ( Understanding Society ; UKHLS, NCDS, BCS70 and MCS) we employ sex-stratified path models to estimate the direct and environmentally mediated effects of eleven NPGIs and 30 EPGIs on adult mental health, focussing on the rearing environment. The depression NPGI is consistently associated with mental health symptoms across most sex-stratified sub-samples (best meta-analysis β = 0.091, p 0.001) but demonstrates 1.6 - 24.5% environmental mediation. Seven other NPGIs and three EPGIs show sample- and sex-specific associations with mental health symptoms. NPGIs for attention deficit hyperactivity disorder, depression and substance use disorder are robustly associated with measures of the rearing environment, which in turn are frequently associated with mental health symptoms. Sensitivity analyses find that NPGI associations vary substantially depending on who is included in the sample. In conclusion, the rearing environment likely mediates a substantial portion of NPGIs’ so-called “genetic” effects on mental health symptoms, but EPGIs are not currently powerful enough to replace them.

Background: Interindividual genetic variability is well characterised, but we still lack a complete catalogue of loci displaying variable and stable epigenetic patterns. Results: Here, we report a catalogue of stable and variable interindividual DNA methylation sites in human whole blood by analysing the DNA methylation patterns in 3642 individuals from a representative cohort for the British population using the IlluminaEPIC array. Our results show that 34,972 CpGs display variable methylation levels (VMPs) and 41,216 CpGs display stable methylation. Human whole blood is a widely used tissue in epigenetic research, particularly in Epigenome-Wide Association Studies, due to its accessibility and its ability to provide insights into systemic biological processes and disease mechanisms. This catalogue is a useful resource for interpretation of results when associating epigenetic signals to phenotypes. VMPs are highly enriched in CpG shores, enhancers and intergenic regions and approximately half of the VMPs are under genetic control. Our results also showed that trans mQTL-mCpG pairs (that is a SNP and CpG located > 500bp apart) are often located in the same TAD or connected by chromatin loops. A subset of these VMPs (784) are classified as putative epialleles and there is a link between some of these epialleles located in regulatory regions and gene expression. Conclusions: Our study provides of a comprehensive and reliable catalogue of CpG sites displaying variable interindividual DNA methylation across the human epigenome.

Eighty-five percent of the human proteome has at least one interacting monoclonal antibody. These molecules penetrate the cytoplasm poorly and are very often non-functional within the cell. Analysis of antibody variable domains and characterisation of forty-five single-chain variable fragment (scFv) intrabodies expressed in human cells indicated charge to have the greatest impact on solubility. We created new interdomain linkers, optimised scFv domain orientation and found an optimisable charge discrepancy between variable heavy framework and CDR sites. When applied to reduce the search space and rank the products of AI-led inverse folding this creates a single highly soluble, abundant and stable intrabody with parent antibody epitope recognition. Over six hundred intrabody sequences are presented targeting sixty cytoplasmic proteins with linear, conformational, post-translational modification or oligomer specificity. Interactions were validated for p53, α-synuclein, SOD1, polyQ, FUS/TLS, UCHL1 and GFP. Here we show reliable repurposing of the sequenced antibody interactome inside the cell.

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.

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a 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 relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. 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). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClock_specific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

Data 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 normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. Here, 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 normalization methods. By this new strategy, the autosomal CpGs are first normalized 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 neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization 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 normalization effect. The 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. Supplementary data are available at Bioinformatics online.

Background: DNA 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. Results: We 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 CerebellumClock _specific (Pearson correlation=0.941, MAD=3.18 years). Furthermore, based on the 201 age-associated CpGs, we built two other clocks CerebellumClock _common and CortexClock _common for the cerebellum and non-cerebellar brain cortex tissues separately, they both support that the cerebellum has a relative lower DNAm ageing rate. Conclusions: The 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.

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 revolutionize 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 1779 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 etiology of complex disease.