Till F. M. Andlauer

The paper presents a visual analytics tool for identifying target genes in spatial transcriptomics data by analyzing brain circuits.

The paper identifies novel CNV loci and dose-dependent gene effects across six psychiatric disorders using genome-wide analysis.

The paper identifies shared and divergent genetic mechanisms across psychiatric disorders using gene-set burden analysis of copy number variants.

The paper develops pathway-specific polygenic scores to predict lithium treatment response in bipolar disorder and finds associations with specific biological pathways.

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

This study identifies lifestyle, early-life, and genetic factors associated with brain age gaps in individuals with major depressive disorder compared to controls.

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

The paper imputes structural variants using a multi-ancestry long-read sequencing panel and identifies disease associations in large-scale datasets.

This study uses GWAS and Mendelian randomization to show that lower sclerostin levels increase risk of hypertension, type 2 diabetes, myocardial infarction, and atherogenic lipid profiles.

This study identifies genetic variants associated with antisocial behavior through a genome-wide association meta-analysis, finding significant links to FOXP2 and correlations with multiple health and behavioral traits.

The paper investigates genetic correlations between psychiatric disorders, personality traits, and SARS-CoV-2 susceptibility using GWAS data and individual-level polygenic risk scores.

The paper investigates genetic factors influencing a neurobiological substrate for psychiatric disorders and finds that a common genetic variant is associated with the substrate but not directly with disease risk.

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

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 paper shows that GWAS using minimal phenotyping for depression identifies non-specific genetic factors shared with other psychiatric conditions, not specific to major depressive disorder.

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

The paper reports a detailed genetic and genomic map of multiple sclerosis, identifying susceptibility genes in peripheral immune cells and brain-resident microglia.

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 investigates genetic associations between major depression and cardio-metabolic traits, finding shared genetic aetiology regardless of depression onset age.

The paper conducts a GWAS to identify genetic variants associated with a cognitive predictor of dyslexia, finding significant associations with variants near MIR924HG and NKAIN3.

The paper identifies cis-epistasis at the LPA locus involving two SNPs that interact to modulate coronary artery disease risk and related traits.

The paper identifies low-frequency coding variants contributing to multiple sclerosis risk using a large case-control study.

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.