# Mapping genetic interactions between growth-promoting mutations in yeast

> **NIH NIH R01** · LEHIGH UNIVERSITY · 2021 · $330,629

## Abstract

A global understanding of genetic interaction networks, and how network perturbations affect
cellular function, is crucial to preventing and treating human disease. Currently there is a fundamental
gap in our understanding of these networks. Most of our knowledge of genetic interactions comes from
the systematic analysis of double deletion (or knockdown) mutants, primarily in the yeast
Saccharomyces cerevisiae. However, the reality is that loss-of-function mutations are rarely beneficial
and account for less than 5% of the known natural genetic variation. Continued existence of this gap is a
significant problem because many biomedically-important interactions are likely missed by current
methods. The proposed research will identify genetic interactions involving alteration-of-function variants,
variants of essential genes, and higher-order interactions using a novel “Evolve-and-Map” approach,
which combines experimental evolution and quantitative-trait locus mapping. The rationale for this
approach is that experimental evolution efficiently selects for perturbations to the genetic interaction
network that promote rapid growth, and that the genetic variants isolated in this way will be comparable
to the natural genetic variants underlying complex traits in other organisms, including humans. AIM 1 will
leverage the power of evolutionary “replay” experiments to identify a local network of genetic interactions
between cell polarity genes and cell cycle genes. These interactions are strongly supported by
preliminary laboratory evolution experiments, but are largely absent from the double-deletion genetic
interaction network. AIM 2 will extend this analysis genome-wide, producing the largest data set to date
on the genetic interactions between variants that arose in the context of experimental evolution.
Thousands of double-barcoded segregants will be generated from crosses between evolved lines and
their ancestor or between pairs of evolved lines. Each segregant will be genotyped by low-coverage
sequencing and its fitness will be measured using a highly-multiplexed barcode-sequencing-based assay
that is capable of measuring the fitness of thousands of segregants en masse. These data will be used
to detect additive effects as well as pairwise and three-way genetic interactions. Since these mapping
populations contain far fewer variants than is typical in a genome-wide scan, the power of this method to
detect genetic interactions is very high. AIM 3 will determine the extent to which these genetic
interactions persist across environments, including different carbon and nitrogen sources, inhibitory
concentrations of antifungals, and non-optimal temperatures. This will add an important new dimension
to genetic interaction networks. Overall the results obtained from this work will test the ability of the
double-deletion genetic interaction network to predict interactions between growth-promoting variants,
and will advance our understanding of genetic inter...

## Key facts

- **NIH application ID:** 10151635
- **Project number:** 5R01GM127420-04
- **Recipient organization:** LEHIGH UNIVERSITY
- **Principal Investigator:** Gregory I Lang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $330,629
- **Award type:** 5
- **Project period:** 2018-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10151635

## Citation

> US National Institutes of Health, RePORTER application 10151635, Mapping genetic interactions between growth-promoting mutations in yeast (5R01GM127420-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10151635. Licensed CC0.

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