Daniel Considine

VIDRA models genetic dose-response relationships to predict drug mechanisms, therapeutic potential, and adverse events using phenotypically associated variants.

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper identifies co-eQTLs that alter gene co-expression relationships in specific cell types, linking these variants to immune disease associations.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.