Genetics-Driven Novel Target Discovery
Integrative multi-omics pipeline for identifying and validating drug targets at Pfizer.
When I joined Pfizer’s Internal Medicine Research Unit, genetics-informed target discovery relied on ad-hoc analyses run by individual scientists on legacy HPC infrastructure. There was no scalable, reusable pipeline connecting GWAS evidence to functional genomics to target nomination. Over 4+ years I built that connective layer: an integrative approach combining human genetic evidence (GWAS, exome-wide association studies, colocalization, and Mendelian randomization) with functional genomics layers including single-cell chromatin accessibility, eQTL and pQTL datasets, and deep learning-based functional predictions to nominate and prioritize novel targets with genetic support for efficacy and selectivity. Several targets identified through this pipeline advanced into the Pfizer portfolio.
Beyond individual target programs, I led the development of cloud-native genomics infrastructure on AWS, enabling large-scale analyses across the organization: scalable GWAS and fine-mapping pipelines, standardized summary statistics harmonization, and integration of emerging multi-omics datasets. This was a cross-organizational collaboration spanning Internal Medicine, Inflammation & Immunology, Statistics, and Machine Learning & Computational Sciences.
Related: UK Biobank Pharma Proteomics Project — the pQTL resource that informed target prioritization analyses described above.