# Functional Genetics Approaches to Investigate Human Evolution

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $44,117

## Abstract

Analyses of human and chimpanzee genomes have catalogued the single-nucleotide and structural variants that
have emerged since humans diverged from nonhuman primates. Nearly all of these genetic variants remain
functionally uncharacterized. Contained within these genetic variants are alterations to cis-regulatory elements,
protein-coding genes, noncoding RNAs, gene copy number, repetitive elements, and other genomic features
that underlie phenotypic differences between humans and nonhuman primates. Because it is difficult to predict
how genomic differences within the hominid lineage contribute to phenotypic differences, there is a critical need
for high-throughput, systematic approaches to interrogate functional genetic variation. Therefore, I seek to
develop a quantitative genome-scale platform for identifying the phenotypic consequences of human-specific
evolutionary mutations at a cellular and molecular level. To accomplish this objective, I will combine advances
in chimpanzee pluripotent stem cell-derived models with CRISPR interference (CRISPRi), single-cell RNAsequencing (scRNA-seq), and single-cell ATAC-sequencing (scATAC-seq). This project is of great

## Key facts

- **NIH application ID:** 10521247
- **Project number:** 5F31HG011569-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Tyler Fair
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $44,117
- **Award type:** 5
- **Project period:** 2021-08-11 → 2025-08-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10521247

## Citation

> US National Institutes of Health, RePORTER application 10521247, Functional Genetics Approaches to Investigate Human Evolution (5F31HG011569-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10521247. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*