Maciej Trzaskowski

The paper uses AI-derived retinal thickness phenotypes from 2D images to identify novel genetic loci for neurodegenerative diseases.

STimage predicts gene expression and classifies cell types from histopathological images, improving robustness and interpretability for cancer and disease analysis.

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

The paper uses AI to label optic nerve head images, enabling cross-ancestry genetic studies and identifying over 200 new loci associated with glaucoma risk.

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.

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.

A meta-analysis of 807,553 people from 23andMe, UK Biobank and PGC finds 102 depression variants. 87 of them replicate in 1,306,354 individuals, and heritability is enriched in prefrontal brain regions.

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

The paper quantifies assortative mating on complex traits by analyzing SNP data from 400,000 individuals, finding significant evidence for height and educational attainment.

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

This paper conducts a GWAS on suicide attempt across psychiatric disorders, identifying genetic loci and showing polygenic risk for major depression predicts suicide attempt in multiple disorders.

The paper finds a genetic association between schizophrenia risk and urban living, suggesting genetic factors influence migration to densely populated areas.

This study supports Cheveruds conjecture by showing genetic correlations in humans predict phenotypic correlations across independent samples.

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 uses genetic data to identify causal relationships between risk factors and common diseases, finding protective and bidirectional effects.

This study introduces metrics and tools to assess quality control in genome-wide association meta-analyses using summary statistics from multiple cohorts.