René Pool

This study conducts a GWAS meta-analysis of stuttering, identifying genetic links with speech disorders and highlighting shared genetic architecture across different ascertainment methods.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

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 identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

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 investigates the link between blood metabolites and wellbeing in adults using a large sample from the Netherlands Twin Register, finding no significant associations but suggesting a potential role for lipid metabolism.

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.

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 paper conducts a genome-wide association meta-analysis of age at onset of walking, identifying 11 loci and showing genetic correlations with ADHD, BMI, intelligence, and brain structure.

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.

This study uses machine learning to predict 2-year MDD chronicity by integrating proteomics with clinical data, showing improved accuracy over psychiatrists' predictions.

This paper conducts a GWAS of subclinical obsessive-compulsive symptoms (OCS) in 33,943 individuals, finding polygenic risk and genetic overlap with OCD.

This study identifies 400+ genetic loci associated with circulating metabolic traits and highlights pleiotropy and novel disease pathways.

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

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

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

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.

The paper identifies context-dependent eQTLs in peripheral blood RNA-seq data from Dutch individuals using a hypothesis-free approach, revealing gene expression influenced by cell type and signaling pathways.

The paper evaluates the performance of likelihood ratio and score tests under weight misspecification in rare variant association studies and finds LRT more robust and powerful.