Annalisa Buniello

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.

This study shows that plasma protein quantitative trait loci (pQTL) support therapeutic hypotheses more effectively than other genetic evidence, increasing the likelihood of drug development success.

The paper introduces a framework for extracting biological evidence from literature to aid drug discovery, identifying millions of associations between targets, diseases, and drugs.

The paper introduces 'Associations on the Fly' (AOTF), a new feature in the Open Targets Platform that allows users to dynamically adjust evidence weights for target-disease associations.

The paper defines a standard format for GWAS summary statistics to improve data sharing and analysis.

PGS Catalog standardizes 192 curated polygenic scores and metadata (86 traits); reapplied in UK Biobank, eight of nine colorectal cancer scores predicted disease in European-ancestry participants.

The paper presents an open resource for fine-mapping and gene prioritization at GWAS loci, identifying causal variants and genes and improving drug target identification.

The paper introduces a standardized framework for ancestry data representation in genomics studies and applies it to the NHGRI-EBI GWAS Catalog to improve data integration and analysis.