Lude Franke

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 integrates single-cell gene expression and circulatory protein data to identify genetic regulators of inflammation in response to Candida albicans.

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 identifies host genetic loci associated with gut microbiome composition and function in a large cohort, finding significant associations near LCT and ABO genes.

This study identifies sex-dependent genetic risk loci shared and specific to mood and psychotic disorders using genome-wide interaction analysis.

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

Idefix identifies sample mix-ups in biobanks using polygenic scores, achieving an AUC of 0.90 compared to 0.75 with sex alone.

The paper uses symptom-based case predictions to conduct a GWAS on COVID-19, finding new genetic susceptibility insights and suggesting different genetic architectures for severity and susceptibility.

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.

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 compares ten polygenic score methods for psychiatric disorders across multiple cohorts, finding that some methods significantly improve prediction statistics over the baseline PC+T method.

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 TRAFD1 as a master regulator of IFNγ signalling in celiac disease by systematically prioritizing candidate genes in disease loci using multiple computational methods.

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

This study identifies barriers and facilitators to implementing pharmacogenomic screening in Dutch outpatient care through stakeholder perspectives.

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

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 paper identifies DNA methylation sites associated with aggression through a meta-analysis of epigenome-wide studies across different age groups and tissues.

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.

This study investigates genetic variants in ACE2 and TMPRSS2 genes for associations with quantitative phenotypes and medication use in a population cohort, finding suggestive links with hypertension and inflammation-related drugs.

The paper investigates sex-specific cis-eQTLs in whole blood RNA-seq data and finds they do not systematically translate into detectable sex-specific trait associations due to limited power and compensatory effects.

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.

This paper introduces GADO, a tool that uses gene expression data to predict gene-phenotype associations and improve diagnostic yield in exome sequencing by prioritizing variants in patients with unknown genetic diagnoses.

The paper identifies over 30,000 SNPs affecting regulatory element activity using SuRE reporter technology, linking them to gene regulation and potential human traits.

The paper uses RNA-Seq data from 296 family trios to identify genomic imprinting by analyzing allelic expression and parental haplotypes, finding 45 imprinted genes and one novel imprinted transcript.

This paper introduces StructLMM, a linear mixed model for analyzing multivariate gene-environment interactions, demonstrating its effectiveness in detecting GxE signals in BMI and eQTL data.

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

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.