# EDGE CMT: deleterious recessive variation - from experimental data to predictive models

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2024 · $373,797

## Abstract

In population samples, segregating recessive variants at low frequency are largely inaccessible to study. 
This project uses an experimental system in which rare alleles have been made common, recessive 
alleles can be homozygosed on demand, and cumulative effects of large numbers of small-effect variants 
can be measured in an unbiased way. The project centers on the nematode Caenorhabditis becei, which 
has all the experimental virtues of C. elegans but with a mating system and population biology that make 
it better suited to questions about genetic diversity in general and recessive variation in particular. This 
project will measure sex-specific competitive fitnesses and sex- and allele-specific transcript abundances 
in a specially designed panel of C. becei to reveal the architecture of segregating recessive variation and 
its molecular characteristics. The data will address basic questions in evolutionary biology while 
generating generalizable gene- and variant-scale models that predict whether variants are likely to have 
recessive effects.

## Key facts

- **NIH application ID:** 10789970
- **Project number:** 5R01HG013015-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Matthew Rockman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $373,797
- **Award type:** 5
- **Project period:** 2023-02-17 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10789970

## Citation

> US National Institutes of Health, RePORTER application 10789970, EDGE CMT: deleterious recessive variation - from experimental data to predictive models (5R01HG013015-02). Retrieved via AI Analytics 2026-06-13 from https://api.ai-analytics.org/grant/nih/10789970. Licensed CC0.

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