Jenny van Dongen

This study conducts an epigenome-wide association study (EWAS) meta-analysis of wellbeing using DNA methylation data, finding no significant methylation sites but suggesting the need for larger studies.

The paper identifies vmeQTLs for blood DNA methylation variance in MZ twins, showing gene-environment interactions and cell-type effects relevant to immune disease.

This study uses multi-omics data from 2,320 participants to identify latent factors capturing shared variation across genomic, epigenomic, and transcriptomic layers, finding one factor associated with wellbeing, though its significance diminishes after covariate correction.

The paper develops a polygenic score for dizygotic twinning, showing it explains 1.6% of liability variance and is linked to fertility outcomes and genetic correlations with related conditions.

The paper investigates how parental polygenic scores for complex traits are associated with offspring DNA methylation, finding significant links for schizophrenia, educational attainment, BMI, and height.

This study uses twin-based Mendelian randomisation to find bidirectional causation between smoking and blood DNA methylation at multiple genomic sites.

The paper compares transcriptomic and metabolomic data from monozygotic and dizygotic twins to identify molecular biomarkers of twinning, finding no replicable transcript-level differences but highlighting potential pathways.

This study examines the impact of negative life events on epigenetic aging in twins, finding associations between life stressors and accelerated aging markers, which are reduced in monozygotic twins.

The paper develops interpretable neural networks for multi-omics data to predict smoking status, age, and LDL levels, showing improved performance and generalizability across cohorts.

This study identifies two CpGs associated with the gut microbiome genus Eggerthella and shows microbiome mediates environmental effects on DNA methylation.

The paper identifies smoking-reactive DNA methylation changes in monozygotic twins and shows reversibility upon smoking cessation.

This study integrates genomics, epigenomics, and metabolomics data to identify ADHD biomarkers and explore omics-level connections, finding associations between PGSs, CpGs, and metabolites.

This study identifies proteins and genes associated with BMI and its changes in twins, showing shared genetic and environmental factors across proteome, transcriptome, and metabolome layers.

The paper develops a multi-omics data analysis workflow packaged as a FAIR Digital Object to improve reproducibility and reuse in computational research.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

This study examines heritability of urinary metabolites in children, finding substantial genetic contributions across amines, organic acids, and steroids.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

The paper identifies genetic variants influencing DNA methylation in blood and shows their associations with complex traits and diseases, but finds limited direct causality.

The paper identifies 818 genes influencing DNA methylation in blood using genetic anchors and causal inference, revealing both transcription factors and non-DNA-binding proteins with regulatory roles.

This paper identifies DNA methylation sites associated with aggression through a meta-analysis of epigenome-wide studies across different age groups and tissues.

This study identifies 137 genome-wide significant loci associated with DNA methylation biomarkers of ageing using DNAm and SNP data from over 41,000 individuals.

The paper reports epigenome-wide meta-analyses of blood DNA methylation with subcortical brain volumes, identifying significant associations with hippocampal volume and potential biological mechanisms.

This study estimates heritability and genetic contributions to metabolite levels using genome-wide data and twin-family cohorts, revealing class-specific genetic influences.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

This study identifies nine novel DNA methylation sites associated with fasting glucose and insulin levels by integrating cross-omics data from multiple cohorts.

The paper identifies 49 gene drivers of downstream transcriptional changes using population genomics data and RNA-sequencing from 3,072 individuals.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

The paper identifies 63 genetic signals associated with well-being traits using genome-wide analyses and links them to gene expression and methylation in brain tissues.

The paper identifies trans-meQTLs linking disease variants to methylation changes at transcription factor binding sites, revealing eight-fold more downstream effects than trans-eQTLs.