Daniel L. McCartney

The paper identifies age-stable and age-variable DNA co-methylation modules in whole-blood data from over 7,500 individuals, showing most modules remain stable with aging while a small subset undergoes reorganisation.

The paper performs genome-wide association studies on 439 high-abundance serum proteins in 15,035 Scottish individuals, identifying 1,553 SNP signals and 13 putative causal protein-outcome associations.

This study identifies genetic variants associated with healthcare costs across multiple systems, finding polygenic influences and large effects from specific loss-of-function variants.

This study identifies aging genes in human blood by integrating epigenetic and transcriptomic data, finding genes enriched for immune functions that better replicate across populations and link to aging outcomes.

The paper compares genetic effects on disease susceptibility and progression, finding limited overlap and suggesting larger samples or different progression measures are needed.

This study examines how polygenic scores and socioeconomic status jointly influence risk for 19 diseases, finding distinct and partly interacting effects across populations.

The paper describes a large DNA methylation dataset from the Generation Scotland cohort, providing methylation profiles and associated phenotypes for research purposes.

The paper conducts a Bayesian EWAS and GWAS of smoking using DNA methylation data from blood and brain tissues, identifying smoking-related loci and developing an epigenetic biomarker.

This paper introduces a framework to build saliva-based DNA methylation biomarkers, demonstrating a new epigenetic clock (InflammAge) that quantifies systemic chronic inflammation and outperforms CRP in several health outcomes.

MethylGPT is a transformer-based model trained on human DNA methylation data that accurately predicts methylation values and improves age and disease prediction.

The paper develops epigenetic scores for GDF15 and NT-proBNP levels, showing associations with disease risks and brain health traits in two cohorts.

The paper shows that imputing DNA methylation loci from epigenome-wide data provides stronger associations with smoking and age than directly measured CpGs.

The paper introduces MAJA, a method for joint association testing in multi-omics data, finding shared epigenetic pathways across multiple traits in methylation data.

This paper develops an epigenetic score to objectively measure alcohol consumption using blood-based DNA methylation and validates it across multiple cohorts, finding associations with brain health metrics.

This paper introduces RTFS, a method for pre-selecting CpGs for disease prediction models, and shows it performs well for predicting type 2 diabetes onset risk.

The paper calculates DNA methylation correlations across body fat traits using genome-wide data to identify shared associations and sex-specific differences.

The paper evaluates normalization methods for DNA methylation data to improve BMI prediction across cohorts when projecting new data onto a reference panel.

The paper refines epigenetic predictors of chronological and biological age using large-scale EWAS and external validation, improving cAge prediction and outperforming existing bAge predictors.

The paper conducts blood-based epigenome-wide association studies on 19 disease states, identifying novel CpG-disease associations and highlighting poor replication in existing literature.

The paper identifies DNA methylation and proteomic markers associated with brain health through integrated methylome and phenome analyses.

This study identifies epigenetic markers associated with night shift work, showing links to BMI, education, smoking, and accelerated biological aging.

This study compares DNA methylation signatures of ageing and inflammation in blood and brain tissues, finding divergent correlations and a link between epigenetic age acceleration and microglial burdens in the hippocampus.

This study evaluates how biological and statistical properties of CpGs affect epigenetic predictions for eighteen traits, showing that CpG methylation QTL status and beta-value distribution influence prediction accuracy.

This study identifies DNA methylation predictors of cognitive function using blood samples, showing they explain variance in cognitive ability and associate with biological markers.

The paper develops epigenetic scores (EpiScores) for plasma proteins to predict disease risk, finding 137 associations with incident morbidities independent of known factors.

The paper identifies genetic and epigenetic factors influencing plasma protein levels linked to Alzheimer's disease, finding novel associations with TREM2 and TBCA.

The paper applies blood-derived epigenetic predictors of lifestyle traits to brain samples, finding variable correlations across brain regions and a strong link between smoking and the dorsolateral prefrontal cortex.

The paper develops a DNA methylation-based proxy for interleukin-6 and finds it correlates with age and cognitive functioning better than serum IL-6.

This study shows that DNAm GrimAge predicts disease incidence and prevalence better than other epigenetic clocks in a large cohort.