Josef Frank

This study shows estrogen receptor alpha in the amygdala influences alcohol craving in females through mouse models and human genetics.

This study identifies DNA methylation changes over time during antidepressant treatment in MDD, linking them to neuroplasticity and inflammation, and shows differences between responders and non-responders.

This study identifies genetic risk loci and factors underlying clinical subtypes of bipolar disorder using GWAS and meta-analysis.

This study characterizes molecular changes in the ventral striatum of individuals with cocaine use disorder using multi-omics and single-cell approaches, identifying perturbations in metabolic pathways and cell-type-specific alterations.

The paper identifies genetically distinct patient subgroups across diseases by analyzing polygenic risk and pathway convergence.

The paper uses brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia and identifies enriched networks and genes associated with the disease.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This study examines DNA methylation at birth and its association with childhood cognitive skills, finding weak single CpG site associations and one region near DUSP22 linked to non-verbal cognition.

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

This study identifies genetic variants associated with AUD treatment outcomes using pharmacogenomic analyses of naltrexone and acamprosate responses.

Meta-analysis of 41,917 European-ancestry bipolar disorder cases versus 371,549 controls yields 64 risk loci (33 novel), neuronal and synaptic gene enrichment, and 15 eQTL-prioritized genes.

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 identifies genetic associations with five personality dimensions and links them to psychiatric symptoms over time.

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

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.

DeepWAS integrates regulatory effect predictions of variants into GWAS to identify regulatory SNPs associated with diseases and traits.

The paper examines common genetic variants' contribution to bipolar disorder risk in multiplex families and finds higher polygenic risk scores in affected individuals compared to controls and unaffected family members.

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 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 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 identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.

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

This study conducts a trans-ancestral GWAS of alcohol dependence, identifying genetic variants and correlations with psychiatric disorders.

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

The paper finds a genetic association between schizophrenia risk and age at first birth in women, with different genetic correlations in younger and older age groups.

This study compares LDSC and GREML methods for estimating genetic correlation, showing GREML provides higher accuracy, especially in the presence of genetic heterogeneity.

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.

The paper applies association rule mining to GWAS data of bipolar disorder to identify novel genotype-phenotype relationships not detected by traditional GWAS methods.