David Burstein

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

This paper models diagnostic code dropout in EHR data to improve phenotyping for schizophrenia, enhancing polygenic score transferability across ancestries.

This study uses EHR data to estimate TRD probability and identifies two genome-wide significant loci linked to metabolic traits, suggesting shared biology with related conditions.

This paper introduces PheMED, a tool to quantify effect size dilution in GWAS due to phenotypic misclassification, and demonstrates its applications in improving meta-analysis.

The paper validates polygenic risk scores for schizophrenia, bipolar disorder, and depression using VA EHR data and shows their association with disease risk and hospitalization.