# Single-Cell Analysis of the Noncoding Genome in Human Diseases and Evolution

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $403,750

## Abstract

Project Summary/Abstract
 Noncoding regulatory mutations had driven human evolution since the split from chimpanzees and more than
90% of disease-associated loci reside in noncoding regions. Because gene regulation is dynamic and context
dependent, functions of noncoding regulatory mutations should be defined in specific cell types and at particular
developmental stages. However, such a fine-resolution mapping of noncoding mutations in human diseases and
evolution has been lacking in the literature, and this proposal aims to develop a comprehensive research program
to close the knowledge gap by pushing our genome analysis to a single-cell resolution.
 My recent work has developed innovative approaches for disease genome analysis and has identified key
elements driving recent human evolution. Given the prime importance of the noncoding genome in human
diseases and evolution, the long-term goal of my research is to identify causal noncoding mutations that affect
human phenotypes by perturbing gene regulation. Built on our recent success in capturing pathogenic noncoding
somatic mutations that are predictive of prostate tumor characteristics, in the next five years, my research will
push our genome analysis to a single-cell resolution. By developing a series of machine learning frameworks,
we will be able to directly determine the allelic effects on altering cell-type-specific epigenomic architecture,
revealing the cellular contribution to human diseases or to any evolutionary traits. Towards this goal, my
laboratory is actively generating single-cell epigenome data, have obtained access to large-scale genomes for
different disease categories, and have developed an innovative deep learning model achieving substantially
enhanced precision for capturing pathogenic noncoding mutations.
 To demonstrate the general applicability of our research framework, we will investigate rare germline
mutations in prostate cancer, common variants in preterm birth, and the Neanderthal introgressed alleles in the
modern human genome for their regulatory effects on affecting human brain development. These conditions
have wide population prevalence, and our preliminary analyses have clearly demonstrated the effectiveness of
our machine learning model on identifying consequential noncoding mutations in specific cell types. We will also
build an open-access genome browser which will allow users to visualize and analyze regulatory effects of any
mutations in a given cell type. Overall, the proposed program will uncover new disease mechanisms from the
noncoding genome, will reveal the Neanderthal impact on brain development of modern humans, and will provide
a generally applicable tool for genome analysis at a single-cell resolution. Different from conventional
approaches, this proposed research by introducing single-cell analysis will directly reveal the most affected cell
populations in diseases, thereby guiding the future development of therapeutic strategies targeting th...

## Key facts

- **NIH application ID:** 10276412
- **Project number:** 1R35GM142983-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jingjing Li
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $403,750
- **Award type:** 1
- **Project period:** 2021-08-13 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276412

## Citation

> US National Institutes of Health, RePORTER application 10276412, Single-Cell Analysis of the Noncoding Genome in Human Diseases and Evolution (1R35GM142983-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10276412. Licensed CC0.

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