Daniëlle Posthuma

ALLSPICE identifies heterogeneous rare variant effects across traits in gene-trait associations using summary statistics from the UK Biobank.

The paper identifies sex differences in genetic and biological factors contributing to ischaemic heart disease-depression comorbidity, finding higher genetic correlation in females and distinct risk factors by sex.

This study identifies shared genetic factors across eight cardiovascular diseases using large-scale genetic data, revealing clusters and pathways related to vascular biology and systemic risk factors.

The paper identifies genetic variants associated with functional brain connectivity in a large cohort, linking them to neurodevelopment and health-related traits.

This study identifies 1,294 genomic regions and 961 effector genes associated with externalizing disorders, revealing neural mechanisms and polygenic predictors of variance in these conditions.

This study examines how genetic risk scores for alcohol use behaviors change over time across multiple cohorts, finding varying associations with drinking patterns and trajectories.

The paper links GWAS-associated genes to single-cell RNA-seq data to identify cell types associated with 14 brain phenotypes, confirming and discovering new cell-type-disease associations.

This study identifies shared genetic factors between insomnia, depression, and anxiety, highlighting synaptic processes as potential drug targets.

This study benchmarks the impact of non-additive genetic effects on polygenic prediction and machine learning approaches using simulated and real data.

The paper introduces the BPC approach to improve polygenic score calibration for binary disorders, achieving better calibration than existing methods in simulated and empirical data.

This paper prioritizes Parkinson's disease risk genes in GWAS loci using a polygenic priority score, identifying 46 genes with potential for therapeutic development.

This study identifies 142 genes associated with both schizophrenia and height, finding enrichment in immune response genes and partial genetic overlap with white blood cell count.

This study aggregates rare variants in genes associated with brain volume variation in the UK Biobank and finds links to rare brain disorders.

This study uses multimodal brain imaging data from UK Biobank participants to identify shared and distinct brain properties associated with insomnia, depression, and anxiety symptoms.

The paper uses GWEIS and in silico methods to study gene-environment interactions in alcohol misuse, identifying SES-modulated genes and highlighting challenges in detecting molecular GxE effects.

This paper introduces CALDERA, a gene prioritization tool for GWAS loci that outperforms existing methods in identifying causal genes.

This paper introduces FLAMES, a framework that improves gene prioritization in GWAS loci by integrating SNP-based and convergence-based evidence, outperforming single-modality approaches.

The paper investigates the association between autozygosity and common diseases in multiple cohorts, finding significant links to twelve diseases and estimating autozygosity's contribution to type 2 diabetes cases.

This study shows that interpreting TWAS results as evidence of genetic relationships between gene expression and phenotypes leads to inflated type 1 errors and unreliable conclusions.

The paper constructs a single-cell chromatin accessibility atlas of the human first trimester brain, identifies regulatory elements linked to gene expression, and links disease SNPs to potential pathogenic mechanisms.

The paper identifies local genetic sex differences in quantitative traits using UK Biobank data, showing that sex-dimorphic effects are common and that the scale of genetic effects impacts heritability interpretations.

This paper shows that using the same genetic variants to create a polygenic risk score (PRS) and then to perform a GWAS on the PRS-derived phenotype leads to inflated false positive rates.

This study identifies shared genetic loci and biological factors across four neurodegenerative diseases: Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease.

This study uses local genetic correlation analysis to link depression with molecular and brain imaging endophenotypes, identifying potential genes and mechanisms involved in major depressive disorder.

The paper identifies the temporal emergence of genetic variants linked to human brain and cognitive traits over the past five million years.

The paper identifies abundant genetic pleiotropy across neuroimaging modalities using a multivariate genome-wide association study in two cohorts.

DRUGSETS identifies therapeutic mechanisms by analyzing GWAS associations with drug and mechanism-of-action gene sets, revealing both established and novel targets across CNS and non-CNS phenotypes.

This study identifies genetic resilience factors for Alzheimer's disease by constructing polygenic resilience scores that reduce genetic risk penetrance and correlate with polygenic risk scores in unaffected individuals.

This study identifies 29 genome-wide significant loci and 85 genes associated with cerebellar volume using UK Biobank data, revealing shared genetic links with neurodevelopmental and neurodegenerative disorders.

This study identifies molecular signatures and cellular diversity during mouse habenula development using single-cell transcriptomics and links developmental subtypes to psychiatric disease.