Biology subjects

McCartney, D. L.

Publications and source records attributed to McCartney, D. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Age-related dynamics of DNA co-methylation modules in humans

Age-related changes in the mean or variance of DNA methylation (DNAm) at individual CpG sites are well established. However, CpGs that share biological functions often exhibit coordinated regulation. Here, we analysed whole-blood DNAm data from over 7,500 individuals and identified modules of co-methylated CpGs that either maintain consistent correlations with age (age-stable modules) or undergo age-related remodelling (age-variable modules). We show that co-methylation modules are largely preserved during healthy aging, with only a small subset (~15%) showing substantial age-related reorganisation. Age-stable modules are enriched for CpG loci within promoters, transcription start sites, and CpG islands, whereas age-variable modules harbour loci that are depleted in these genomic domains and enriched for repressive chromatin marks. Age-stable co-methylation modules are also associated with long-range genetic regulation (trans-meQTLs), promoter-associated chromatin states, and higher-order chromatin interactions. While both age-stable and age-variable modules are enriched for developmental pathways, immune-related processes are distinctly associated with age-stable modules. Overall, our findings suggest that epigenetic aging is characterised by a spectrum of co-methylation network trajectories, ranging from stability to disruption, shaped by underlying genomic regulatory architecture.

systems biology↗

bioRxiv2024

MethylGPT: a foundation model for the DNA methylome

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the models attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

systems biology↗

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗

bioRxiv2024

Integrative epigenetics and transcriptomics identify aging genes in human blood

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these "multi-omic aging genes" in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

systems biology↗

bioRxiv2024

Discovery of shared epigenetic pathways across human phenotypes

Omics-based association studies typically consider the marginal effects of a feature, such as CpG DNA methylation, on a trait (e.g, independent models for each feature). Although some methods can assess all features together in joint and conditional estimation, this is currently done on a trait-by-trait basis. Here, we introduce MAJA, a method to learn shared and outcome-specific effects for multiple traits in multi-omics data. MAJA determines the unique contribution of individual loci, genes, or molecular pathways, to variation in one or more traits, conditional on all other measured "omics" data genome-wide. Simulations show MAJA accurately finds shared and distinct associations between omics-data and multiple traits and estimates omics-specific (co)variances, allowing for sparsity and correlations within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation data (n=18,264), we find novel shared epigenetic pathways among cholesterol metabolism, osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we find only 10 CpG probes with significant effects above the genome-wide background. This highlights the need for joint association testing in highly correlated methylation data from whole blood and for studies of increased sample size in order to refine epigenomic associations in observational data.

genetics↗

bioRxiv2023

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. In this study, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm data to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We then apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve our understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

bioinformatics↗

bioRxiv2023

Blood-based genome-wide DNA methylation correlations across body fat and adiposity-related biochemical traits

The recent increase in obesity levels across many countries is likely to be driven by nongenetic factors. The epigenetic modification DNA methylation (DNAm) may help to explore this as it is sensitive to both genetic and environmental exposures. While the relationship between DNAm and body fat traits has been extensively studied [1-9], there is limited literature on the shared associations of DNAm variation across such traits. Akin to genetic correlation estimates, which measure the degree of common genetic control between two traits, here we introduce an approach to evaluate the similarities in DNAm associations between traits, DNAm correlations. As DNAm can be both a cause and consequence of complex traits [5, 10, 11], DNAm correlations have the potential to provide novel insights into trait relationships above that currently obtained from genetic and phenotypic correlations. Utilising 7,519 unrelated individuals from Generation Scotland (GS), we calculated DNAm correlations using the bivariate OREML framework in the OSCA software [12] to investigate the shared associations of DNAm variation between traits. For each trait we also estimated the shared contribution of DNAm between sexes. We identified strong, positive DNAm correlations between each of the body fat traits (BMI, body fat % and waist to hip ratio; ranging from 0.96 to 1.00), finding larger associations than those identified by genetic and phenotypic correlations. We identified a significant deviation from 1 in the rDNAm for BMI between males and females, with sex-specific DNAm changes associated with BMI identified at eight DNAm probes. Employing genome-wide DNAm correlations to evaluate the similarities in the associations of DNAm with complex traits has provided novel insight into obesity related traits beyond that provided by genetic correlations.

genomics↗

bioRxiv2022

Refining epigenetic prediction of chronological and biological age

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to refine predictors and improve understanding of the epigenomic architecture of cAge and bAge. First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to improve cAge prediction, we use methylation data from 24,673 participants from the Generation Scotland (GS) study, the Lothian Birth Cohorts (LBC) of 1921 and 1936 and 8 publicly available datasets. Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection/dimensionality reduction in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross validation framework, we arrive at an improved cAge predictor (median absolute error = 2.3 years across 10 cohorts). In addition, we train a predictor of bAge on 1,214 all-cause mortality events in GS, based on epigenetic surrogates for 109 plasma proteins and the 8 component parts of GrimAge, the current best epigenetic predictor of all-cause mortality. We test this predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Womens Health Initiative study) where it outperforms GrimAge in its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 x 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 x 10-60). Finally, we introduce MethylBrowsR, an online tool to visualize epigenome-wide CpG-age associations.

genomics↗

bioRxiv2022

Epigenetic markers of adverse lifestyle identified among night shift workers

BackgroundEpigenetic changes in the form of DNA methylation (DNAm) may act as biological markers of risk factors or adverse health states. We investigated associations between night shift work and established DNAm predictors of lifestyle, and compared them with those observed between night shift work and self-reported or conventionally-measured phenotypes. MethodsIn two cohort studies, Generation Scotland (GS) (n=7,028) and Understanding Society (UKHLS) (n=1,175), we evaluated associations between night shift work and four lifestyle factors (body mass index, smoking, alcohol, education) using both conventionally-measured phenotypes and DNA methylation-based scores proxying the phenotypes. DNA methylation-based measures of biological ageing were also generated using six established "epigenetic clocks". Meta-analysis of GS and UKHLS results was conducted using inverse-variance weighted fixed effects. ResultsNight shift work was associated with higher BMI (0.79; 95%CI 0.02, 1.56; p=0.04) and lower education (-0.18; -0.30, -0.07; p=0.002). There was weak evidence of association between night shift work and DNAm scores for smoking (0.06, -0.03, 0.15; p=0.18) and education (-0.24; -0.49, 0.01; p=0.06) in fully adjusted models. Two of the epigenetic age measures demonstrated higher age acceleration among night shift workers (0.80; 0.42, 1.18; p<0.001 for GrimAge and 0.46; 0.00, 0.92; p=0.05 for PhenoAge). ConclusionsNight shift work is associated with phenotypic and DNAm-based measures of lower education. Night shift work was also related to DNAm predictors of smoking and ageing.

genetics↗

bioRxiv2022

The influence of biological and statistical properties of CpGs on epigenetic predictions of eighteen traits

BackgroundCpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying CpG subsets based on biological and statistical properties can maximise predictions while minimising array content. MethodsVariance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of CpGs considered, (ii) mean CpG methylation variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test sets comprised [≤]4,450 and [≤]2,578 unrelated individuals from Generation Scotland, respectively. ResultsAs the number of CpG sites under consideration decreased, so too did the estimates from the variance components and prediction analyses. Methylation QTL status and mean CpG variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on CpGs with methylation QTLs compared to sites without methylation QTLs. Relative effect sizes were 45% larger for predictors based on CpGs with mean beta-values between 10%-90% compared to those using hypo- or hypermethylated CpGs (beta-value [≤]10% or [≥]90%). ConclusionArrays with fewer CpGs could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpGs in array content selection.

genomics↗

bioRxiv2020

DNA methylation proxies for 16 plasma proteins predict the incidence of 7 leading causes of morbidity

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore - disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

genomics↗

bioRxiv2020

A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens

Inflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain, and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post-mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers ( epigenetic clocks), and two inflammatory biomarkers (DNA methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent correlations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus ({beta} range=0.83-1.14, p[≤]0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus ({beta}=1.32, p=5x10-4); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions ({beta}=0.40, p=0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

neuroscience↗

bioRxiv2020

Variation in VKORC1 is associated with vascular dementia

Genetic variation in VKORC1 is associated with differences in the coagulation of blood and consequentially with sensitivity to the drug warfarin. Variation in VKORC1 has also been linked to parental dementia. However, it is unclear whether the relationship persists for the diagnosis in patients themselves, whether the association holds only for certain forms of dementia, and if those taking warfarin are at greater risk. Here, we use data from 211,423 participants from UK Biobank to examine the relationship between VKORC1, risk of dementia, and the interplay with warfarin use. We find that the T-allele in rs9923231 confers a greater risk for vascular dementia (OR=1.28, p=0.0069), but not for general dementia (OR=1.04, p=0.21) or Alzheimer dementia (OR=1.05, p=0.35), and that the risk of vascular dementia is not affected by warfarin use in carriers of the T-allele. Our study reports for the first time an association between rs9923231 and vascular dementia, but further research is warranted to explore potential mechanisms and specify the relationship between rs9923231 and features of vascular dementia.

genetics↗

bioRxiv2020

Genome-wide association studies identify 137 loci for DNA methylation biomarkers of ageing

Biological ageing estimators derived from DNA methylation (DNAm) data are heritable and correlate with morbidity and mortality. Leveraging DNAm and SNP data from >41,000 individuals, we identify 137 genome-wide significant loci (113 novel) from meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We report strong genetic correlations with longevity and lifestyle factors such as smoking, education, and obesity. Significant associations are observed in polygenic risk score analysis and to a lesser extent in Mendelian randomization analyses. This study illuminates the genetic architecture underlying epigenetic ageing and its shared genetic contributions with lifestyle factors and longevity.

genetics↗

bioRxiv2020

Integrative omics approach to identify the molecular architecture of inflammatory protein levels in healthy older adults

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. Therefore, in this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink(R) inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified three CpG sites spread across three proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to one polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease, and between IL12B and Crohns disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

genomics↗

bioRxiv2020

Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden

Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These include methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). In this study, we test the association between these epigenetic markers of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries. Furthermore, we test the clocks relationships with phenotypic measures associated with these conditions, including spirometric and biochemical traits. We carry out these analyses in 9,537 individuals from the Generation Scotland: Scottish Family Health Study. We find that DNAm GrimAge outperforms other epigenetic clocks in its associations with self-report disease prevalence and related clinical traits. DNAm GrimAge associates with chronic obstructive pulmonary disease (COPD) prevalence (Odds Ratio = 3.29, P = 3.0 x 10-4) and pulmonary spirometry tests ({beta} = [-0.10 to -0.15], P = [1.4 x 10-4 to 1.4 x 10-6]) at study baseline after adjusting for possibly confounding risk factors including alcohol, body mass index, deprivation, education and smoking. After adjusting for these confounding risk factors, DNAm GrimAge, DNAm PhenoAge and DNAm Telomere Length, measured at study baseline, predict incidence of ICD-10-coded disease states including COPD, type 2 diabetes and cardiovascular disease after thirteen years of follow-up (Hazard Ratios = [0.80 (telomere length) to 2.19 (GrimAge)], P = [9.9 x 10-4, 1.9 x 10-14]). Our data show that despite accounting for several possible confounding variables, epigenetic markers of ageing predict incidence of common disease. This may have significant implications for their potential utility in clinical settings to complement gold-standard methods of clinical assessment and management.

genomics↗

bioRxiv2019

DNA methylation in the APOE gene: its link with Alzheimer’s and cardiovascular health

Genetic variation in the apolipoprotein E (APOE) gene is associated with Alzheimers disease (AD) and risk factors for cardiovascular disease (CVD). DNA methylation at APOE has been linked to altered cognition and AD. It is unclear if epigenetic marks could be used for predicting future disease. We assessed blood-based DNA methylation at 13 CpGs in the APOE gene in 5828 participants from the Generation Scotland (GS) cohort. Using linear regression, we examined the relationship between APOE methylation, cognition, cholesterol, and the risks for AD and CVD. DNA methylation at two CpGs was associated with the ratio of total-to-HDL cholesterol, but not with cognition, or the risks of AD or CVD. APOE methylation could be involved in the levels of blood cholesterol, but there is no evidence for the utility of APOE methylation as a biomarker for predicting AD or CVD.

neuroscience↗

bioRxiv2019

Characterisation of an inflammation-related epigenetic score and its association with cognitive ability

Results from large cohort studies investigating the association between inflammation and cognition have been mixed, possibly due to methodological disparities. However, a key issue in research utilising inflammatory biomarkers is their typically phasic responses. C-reactive protein (CRP) is widely used to investigate the association between chronic inflammation and cognition, but its plasma concentrations can markedly deviate in response to acute infection. Recently a large-scale epigenome-wide association study identified DNA methylation correlates of CRP. DNA methylation is thought to be relatively stable in the short term, marking it as a potentially useful signature of exposure. Here, we generate an epigenetic CRP score and investigate its trajectories with age, and associations with cognitive ability, in comparison to serum CRP in two cohorts: a longitudinal study of older adults (the Lothian Birth Cohort 1936, n=889) and a large, cross-sectional cohort (Generation Scotland, n=7,028).\n\nWe identified differing trajectories of serum CRP across the cohorts, with no homogeneous trends seen with age. Conversely, the epigenetic score was consistently found to increase with age, and to do so more rapidly in males compared to females. Higher levels of serum CRP were found to associate with poorer cognition in Lothian Birth Cohort 1936, but not in Generation Scotland. However, a consistent negative association was identified between cognition and the epigenetic score in both cohorts. Furthermore, the epigenetic score accounted for a greater proportion of variance in cognitive ability.\n\nOur results suggest that epigenetic signatures of acute inflammatory markers may provide an enhanced signature of chronic inflammation, allowing for more reliable stratification of individuals, and thus clearer inference of associations with incident health outcomes.

genomics↗