Stephen J. Glatt

Ultra-rare damaging coding variants in 87,959 schizophrenia cases and 150,587 controls implicate 40 genes (16 exome-wide, 24 at 5% FDR), half overlapping autism, developmental delay or bipolar genes.

The paper introduces an adversarial neural network to identify genetic resilience markers in Alzheimer's disease, finding five SNPs associated with resilience independent of polygenic risk scores.

This paper develops a framework using genomic context and genotype data in convolutional neural networks to predict type 2 diabetes risk, showing improved performance over polygenic risk scores.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

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 estimates genetic and environmental contributions to module eigengenes (MEs) derived from blood lymphocytes in older male twins, finding significant heritability in 56% of detected MEs.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

The paper develops a method to infer compound heterozygosity from exome sequencing data, achieving high accuracy in phasing rare variants for recessive disease diagnosis.

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 paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

Exomes of 24,248 schizophrenia cases and 97,322 controls implicate ultra-rare damaging variants in 10 genes, including GRIN2A and GRIA3, with odds ratios of 3 to 50.

The paper identifies shared disruptive coding mutations between schizophrenia, autism spectrum disorders, and other developmental disorders, suggesting overlapping molecular aetiology.

This study identifies genetic loci associated with suicide attempts and shows shared genetic architecture with psychiatric disorders and non-psychiatric risk factors.

BrainGENIE predicts brain-tissue gene expression from peripheral blood transcriptomes, outperforming existing methods in accuracy and biological relevance.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

This study identifies schizophrenia genetic associations in East Asian and European populations, finding similar genetic effects across ancestries but highlighting challenges in polygenic risk prediction across groups.

The paper investigates selective brain region vulnerability in ADHD by analyzing gene expression, cell abundance, and brain size changes using imaging and postmortem data.

The paper identifies SNRNP35 as a trauma type-specific PTSD gene through analysis of genetically regulated gene expression in PTSD cases and controls.

This study examines the contribution of protein-coding de novo mutations to schizophrenia risk using whole-exome sequencing data from parent-offspring trios, finding a modest but polygenic effect.

This study identifies sex-dependent and -independent biomarkers in ASD using post-mortem brain RNA-Seq and blood microarray data, highlighting sex-specific gene expression differences and pathway dysregulation.

The paper investigates genetic correlations between psychiatric and immune-related phenotypes using GWAS summary data, finding significant shared genetic variants.

This study identifies shared and sex-specific inflammatory pathways in PTSD through a meta-analysis of blood transcriptomes across different trauma types and biological sexes.