Eske M. Derks

This study identifies genetic pleiotropy between major depressive disorder and four physical disease clusters, finding strong associations with gastrointestinal diseases and uncovering novel biological pathways.

The paper uses computational drug repurposing to identify N-acetylglucosamine as a potential therapy for CLN3 Batten disease, showing it reduces lipofuscin burden in cell models.

This study identifies 1,257 genetic variants associated with human personality traits using large-scale GWAS and finds widespread genetic correlations with behaviors and life outcomes.

This paper introduces GNA, a framework that uses network modeling on GWAS data to identify genetic architecture and trait-specific biology.

This study identifies genetic subtypes of major depressive symptoms by analyzing data from clinical and community cohorts, finding distinct factors for appetite/weight symptoms and missing data patterns.

The paper develops TWAS models using eQTL summary statistics to predict gene expression and identifies novel schizophrenia associations.

The paper identifies novel genetic risk loci for depression and anxiety using factor analysis and genomic SEM, revealing shared and trait-specific genetic influences.

The paper investigates how genetic correlates of socio-economic status affect shared heritability across mental health traits using genome-wide association summary statistics.

eMAGMA converts SNP-level GWAS summary statistics into gene-level associations using tissue-specific eQTL data, outperforming existing methods in identifying causal genes for complex diseases.

This study identifies a genome-wide significant variant for OC traits and shows shared genetic risk between OC traits and OCD.

The paper identifies 1,645 genes associated with 16 mental health phenotypes using transcriptome-wide association studies and finds higher transcriptomic correlations than genetic correlations for some phenotype pairs.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

This study integrates genetic and gene expression data to identify functional gene networks and drug targets in Alzheimer's disease.

This study identifies 73 genome-wide significant loci for mood disorders, revealing shared and distinct genetic factors between major depressive disorder and bipolar disorder.

This study performs item-level genetic analyses of the PHQ-9 to explore genetic heterogeneity in major depression, identifying novel loci and revealing distinct genetic correlations across symptoms.

This study investigates whether genetic variation in the Major Histocompatibility Complex (MHC) is associated with depression risk, finding no significant associations at a conservative threshold.

The paper identifies 99 risk genes and gene co-expression modules associated with major depression using a multi-tissue eQTL-based approach, highlighting tissue-specific biological pathways.

The paper examines how genetic and environmental factors influence DNA methylation at variably methylated regions in neonatal blood, finding that combined effects best explain methylation variability.

This study uses Mendelian randomization to find a causal link between major depression and alcohol dependence using large genetic datasets.

This paper uses GWAS data from MDD to identify potential drug targets and repurposing opportunities by analyzing drug-target networks and gene expression effects.

This study investigates sex differences in the genetic architecture of OCD using large-scale genomic data and finds evidence for sex-specific regulatory variant enrichment without genome-wide associations.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

Genome-wide association meta-analysis of 130,664 major depression cases and 330,470 controls identifies 44 independent risk loci enriched in cortical neurons, conserved mammalian regions, splicing networks, and antidepressant targets.

This study identifies 4 new risk loci for cannabis use through GWAS, finds genetic overlap with mental health, and shows a causal link between schizophrenia and cannabis use.