Eli A. Stahl

mTADA jointly analyzes de novo mutation data from multiple neuropsychiatric disorders to identify shared and specific risk genes.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper validates EHR-based BD phenotyping algorithms by showing their genetic correlation with traditional BD cases, suggesting they identify genetically similar cases.

The paper identifies shared expression patterns of genetic variants across tissues and cell types, enabling fine mapping of disease-associated variants and revealing key cell types involved in disease pathogenesis.

The paper identifies concordant transcriptional signatures of schizophrenia in hiPSC-derived neurons and post mortem brains, suggesting neurons better model genetic risk and recommends increasing cohort size in hiPSC studies.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.

The paper identifies a common collagen disease in Puerto Ricans using identity-by-descent mapping and genomic data from a health system.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.

The paper introduces BUHMBOX, a statistical method to distinguish pleiotropy from heterogeneity in genetic data, finding shared genetic bases in autoimmune diseases and between MDD and schizophrenia.

This paper introduces LDpred, a method that improves polygenic risk score accuracy by modeling linkage disequilibrium, showing increased R2 values in schizophrenia and multiple sclerosis datasets.

This paper partitions heritability across functional genomic elements using GWAS summary statistics to identify trait-specific enrichments.

The paper shows that haplotypes of common SNPs explain more heritability of complex diseases than individual SNPs alone.

The paper shows regulatory variants explain significantly more heritability than coding variants for 11 diseases, using imputed data across 218 cell types.