Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code

ABSTRACT A central challenge in human genomics is to interpret the regulatory functions of the noncoding genome, and to identify and interpret variants with regulatory functions. In this project we plan to leverage recent advances in experimental functional genomics (including single cell methods and high throughput perturbation methods) alongside recent progress in deep learning models of gene regulation, to make fundamental progress on these problems. We have assembled a team of investigators with diverse and complementary expertise – in deep learning, single-cell genomics, cellular QTLs and GWAS, and high throughput validations – to build, test, and implement predictive models for interpreting disease associations. Specifically, we aim to (1) Develop interpretable base-resolution deep-learning models for regulatory sequences; (2) Predict and validate cell type- specific effects of regulatory variants on molecular phenotypes and disease; (3) Collaborate with the IGVF Consortium to build nucleotide-level regulatory maps. Our ultimate goal in this project will be to create a nucleotide-resolution cis-regulatory map of the human genome to connect disease variants to functions and phenotypes, in diverse cell types, states, and spatial contexts.