Harm-Jan Westra

The paper integrates genotype, clinical, and neuropathological data from 2,553 donors to show genetic risk overlaps across brain disorders and neuropsychiatric symptoms.

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

The paper identifies highly cell-type-specific eQTLs using snRNA-seq data from brain and blood, showing that bulk analyses overestimate eQTL agreement.

The paper identifies co-eQTLs that alter gene co-expression relationships in specific cell types, linking these variants to immune disease associations.

This study uses sequence-based deep learning models to identify functional promoter mutations in cancer, finding enrichment in known and novel cancer genes.

This paper links common and rare disease genetics by identifying key genes affected by common variants through gene regulatory networks, finding many key genes are outside GWAS loci.

The paper links genes in cancer-susceptibility loci to known somatic driver genes through tissue-specific co-expression networks derived from non-cancer samples.

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

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.

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 identifies a non-coding variant that increases ICOS expression, leading to more T peripheral helper cells and contributing to autoimmune diseases.

This paper introduces PICALO, a method to identify unknown contexts influencing eQTLs, finding 31 and 21 PICs in blood and brain datasets, respectively, associated with cell type, RNA quality, and environmental factors.

The paper identifies co-expression QTLs (co-eQTLs) using single-cell RNA-seq data, revealing how genetic variants affect gene co-expression patterns and regulatory networks in specific cell types.

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

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 uses single-cell RNA-sequencing to show that gene expression in immune cells is influenced by genetic variation and pathogen exposure, revealing context-specific regulatory mechanisms.

The paper compares two models for correcting cell type composition in blood gene expression data, finding that accounting for more cell types reduces the number of age-associated genes and highlights platelet-related pathways.

The paper identifies 31 host genetic loci influencing gut microbiome composition through GWAS and suggests causal effects in inflammatory diseases.

This paper introduces MR-link, a Mendelian randomization method that identifies causal relationships between gene expression and LDL-C levels, finding 19 causally linked genes including PVRL2.

The paper identifies pQTLs for cardiovascular proteins and uses them to evaluate drug targets and suggest new candidates for precision medicine.

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.

IMPACT improves regulatory element annotation by integrating TF binding profiles with epigenomic data, showing better performance in predicting eQTLs and capturing RA heritability.

The paper identifies in vivo eQTL interactions with cytokine levels in a lupus trial, finding novel regulatory mechanisms and potential therapeutic targets.

This study fine-maps RA and T1D loci outside the MHC, identifying causal variants and confirming their functional impact through allele-specific binding and enhancer activity.

This paper introduces SAPPHO methods to detect pleiotropy in GWAS by modeling shared patterns of genetic associations, finding 16 additional and 124 novel loci in meta-analysis.

This study identifies 47 novel genetic loci associated with PR interval through genome-wide association meta-analysis across multiple populations.

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 develops a statistical method to distinguish causal non-coding variants from colocalizing annotations in complex trait loci, identifying independent effects in disease-associated regions.