Diana van Heemst

This study identifies genetic variants associated with missing metabolite measurements in two population-based cohorts, suggesting missingness is partly genetic and relevant for clinical and epidemiological research.

This study examines how non-pathogenic CAG repeats in HTT, ATXN3, and CACNA1A genes affect sleep outcomes, finding sex- and depression-specific associations.

This paper identifies age-specific genetic risk factors for cardiometabolic traits using genome-wide interaction analyses across multiple cohorts.

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 43 genomic loci associated with reproductive success in 785,604 individuals, showing ongoing natural selection at the FADS locus.

This paper uses clustered Mendelian Randomization to identify distinct pathways linking IGF-1 to type 2 diabetes, showing opposing causal effects based on genetic instruments.

The paper evaluates methods to handle missing lifestyle data in gene-by-lifestyle interaction meta-analyses and recommends the Safe Approach for accuracy.

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 shows that lifestyle interventions reducing abdominal fat are linked to lower circulating glycerol and higher HDL diameter, independent of BMI or overall weight loss.

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.