# Targeted recombination to pinpoint responsible regions within large susceptibility loci in mice

> **NIH NIH R21** · HENRY M. JACKSON FDN FOR THE ADV MIL/MED · 2020 · $152,496

## Abstract

ABSTRACT
Positional cloning to identify the genes responsible for disease or particular traits involves
mapping of the susceptibility locus through genome-wide association studies in humans and
phenotypic analysis of recombinant strains and back-crossing in mice. Then responsible genes
within such loci are identified based on their expression patterns, biochemical activities of the
gene products and phenotypes of the corresponding knockout animal models. Unfortunately,
the large size of susceptibility loci (usually >5 Mb) makes the downstream investigative steps
rather challenging. The only way to narrow down the responsible region so far was to back-
cross the carrier mice in order to trim the locus by homologous recombination. However, the
chance of spontaneous recombination is very low, and trimming the locus to a manageable size
is usually not feasible. We propose to develop the approach to artificially induce recombination
at defined locations within the susceptibility locus (targeted recombination). This will provide the
reliable route to pinpoint the causative mutations and ultimately, the mechanism of the disease
in question.

## Key facts

- **NIH application ID:** 10021676
- **Project number:** 5R21GM135494-02
- **Recipient organization:** HENRY M. JACKSON FDN FOR THE ADV MIL/MED
- **Principal Investigator:** Galina Petukhova
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $152,496
- **Award type:** 5
- **Project period:** 2019-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021676

## Citation

> US National Institutes of Health, RePORTER application 10021676, Targeted recombination to pinpoint responsible regions within large susceptibility loci in mice (5R21GM135494-02). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10021676. Licensed CC0.

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