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

> **NIH NIH U01** · STANFORD UNIVERSITY · 2022 · $727,363

## Abstract

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.

## Key facts

- **NIH application ID:** 10474459
- **Project number:** 5U01HG012069-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Anshul Kundaje
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $727,363
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10474459

## Citation

> US National Institutes of Health, RePORTER application 10474459, Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code (5U01HG012069-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10474459. Licensed CC0.

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