Riccardo Emilio Marioni

The paper introduces vampW, a Bayesian method for joint variable selection in omic biomarkers and time-to-event data, showing reduced false discoveries and improved prediction.

This study shows that early-life stress accelerates epigenetic aging in zebra finches by altering DNA methylation and metabolic pathways linked to aging.

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.

The paper improves epigenetic age estimation by developing a TFMethyl Clock model that incorporates transcription factor binding sites, revealing DNA methylation changes linked to aging.

The paper introduces JODIE, a model to estimate direct, indirect, and parent-of-origin genetic effects on height, BMI, and educational attainment using family trios from two biobanks.

This study identifies 58 genomic regions associated with meiotic recombination, 24 of which are sex-specific, and finds a moderate genetic correlation between male and female recombination rates.

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 develops Biolearn, an open-source framework for curating and evaluating aging biomarkers across datasets, finding that harmonization and standardization improve their performance and generalizability.

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 introduces a weighted 2D-kernel density estimation method for epigenetic age prediction, achieving similar accuracy to existing clocks and providing variation scores linked to mortality risk.

This paper develops an epigenetic clock for wild wood mice to estimate age and ageing rates, showing slower ageing in supplemented and parasite-treated individuals.

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.

The paper investigates how mutation timing, fitness, and clonal structure influence the clinical progression of clonal hematopoiesis using longitudinal cohort data.

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.

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

The paper identifies genetic regulators of 184 neuro-related proteins in human plasma and links them to disease traits and drug targets.

This study identifies gene-environment interactions affecting plasma proteins in UK Biobank, discovering 683 vQTLs and over 1,400 GEIs across 571 proteins.

MOGDx integrates multi-omic data using a graph convolutional network to classify heterogeneous diseases, achieving state-of-the-art performance and identifying relevant genomic markers.

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.

isGWAS enables fast and scalable genetic association analysis using cohort-level summary data, achieving speeds up to 1500 times faster than existing methods.