René Luijk

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data

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.

Genomic and phenomic insights from an atlas of genetic effects on DNA methylation

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

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

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.

Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

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.

Genome-wide identification of directed gene networks using large-scale population genomics data

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

Disease variants alter transcription factor levels and methylation of their binding sites

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.