# Precision mapping of regulatory causal variants by expression CROPseq

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2024 · $682,624

## Abstract

ABSTRACT
Genetic differences among individuals ultimately trace to single nucleotide polymorphisms, the majority of
which affect traits, including disease susceptibility, by influencing the expression of nearby genes. Over the
past decade, tens of thousands of loci of this type have been mapped to what are called credible intervals,
namely sets of anywhere from a handful to over one hundred polymorphisms that have similar statistical
signals. The next step is to resolve the identities of the causal variant(s) within these credible intervals.
Similarly, there is a parallel pressing need to validate that de novo and ultra-rare variants in the promoter of
a gene thought to contribute to a congenital abnormality actually disrupt expression of the gene. This project
proposes a systematic effort to fine map causal variants for hundreds of genes that influence autoimmune
and other immune conditions. It utilizes a combination of genome engineering and single cell genomics, in
a recently developed assay called expression CROP-seq, or eCROPseq. The idea is to knock out each of
the variants in a credible interval with a pool of CRISPR-Cas9 guide RNAs, each targeting one SNP for
microdeletion of mutation, and taken up by about 100 cells. The expression of the gene in those cells is then
compared with the expression in all other cells that receive different guide RNAs in the same experiment.
Aim 1 is to use eCROPseq to identify causal variants in up to 600 credible intervals associated with 350
immune loci, measured in both a myeloid (HL60) and lymphoid (Jurkat) cell line. Aim 2 is to perform a similar
analysis of up to 500 rare variants in the promoter regions of these genes to evaluate whether new mutations
can cause extreme levels of aberrant gene expression. With these results in hand, Aim 3 utilizes a more
precise genome editing tool, search-and-replace CRISPR (also known as prime editing) to substitute one
allele for another instead of just evaluating mutations. Then Aim 4 asks whether the effects observed in the
cell lines are also seen in primary T cells from eight different people, and whether the magnitude of effect
differs among people. Collectively the proposed experiments will systematically map the causal regulatory
variants for hundreds of autoimmune genes, and establish a tool that should be readily adapted by even
small labs to test the function of new genome-wide associations.

## Key facts

- **NIH application ID:** 10809580
- **Project number:** 5R01HG011459-04
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Gang Bao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $682,624
- **Award type:** 5
- **Project period:** 2021-02-04 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10809580

## Citation

> US National Institutes of Health, RePORTER application 10809580, Precision mapping of regulatory causal variants by expression CROPseq (5R01HG011459-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10809580. Licensed CC0.

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