Niek de Klein

The paper constructs a multi-ancestry single-cell eQTL map for SLE, identifying colocalized eQTLs with GWAS loci and highlighting cell-type-specific regulatory mechanisms.

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper identifies misexpression of inactive genes in whole blood and links it to nearby rare structural variants using RNA and genome sequencing data.

The paper integrates brain RNA-seq and genotype data to identify eQTLs and their downstream effects on brain-related diseases.

KidneyNetwork predicts kidney-specific gene functions and disease phenotypes using co-expression networks, identifying ALG6 as a candidate gene for kidney and liver cysts in undiagnosed patients.

The paper integrates DNA methylation and gene expression networks to improve prediction of functional gene-gene associations using eQTMs and a novel integration pipeline.

This study identifies DNA methylation sites associated with coffee and tea consumption using EWAS, finding potential links to fatty liver disease through PHGDH expression.

The paper uses allele-specific expression analysis in a large cohort to identify high-impact, autosomal-dominant variants that are compensated by imbalanced expression.

The paper introduces Decon2, a method to deconvolute bulk blood eQTL effects into immune cell subpopulations, showing high concordance with experimental and single-cell studies.