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Nominating candidate risk variants, genes and cellular programs underlying disease-critical processes is of utmost importance for developing drug targets and informing CRISPR screening. The Dey Lab focuses on developing machine learning and statistical methods integrating epigenomic and single-cell transcriptomic data from RNA-seq, ChiP-seq, Perturb-seq experiments with genetic association studies (GWAS, WES/WGS) to enhance our understanding of the functional architecture of all heritable complex diseases, like Alzheimers’, Type 2 Diabetes, Lupus, and several heritable cancers like Breast and Prostate cancers. Some of the research directions of interest include developing (i) Prioritizing variants, genes and cell states for disease using a combination of genetic, genomic and perturbation data. (ii) Knowledge graph-based models to identify causal regulatory mechanisms underlying a disease. (iii) Building better monogenic and polygenic risk score models informed by functional and genetic data.