# Population genetics of rapid evolutionary processes

> **NIH NIH R35** · CORNELL UNIVERSITY · 2024 · $435,571

## Abstract

Project Summary. Rapid evolution lies at the core of some of the greatest challenges humanity faces today,
ranging from the evolution of drug and antibiotic resistance to the rapid emergence of new Covid variants.
Researchers are now envisioning even faster evolutionary dynamics brought about by CRISPR gene drives.
This fascinating new technology could be used to directly suppress wild populations, or to rapidly spread an
engineered allele through a population, for example a gene that reduces pathogen transmission in mosquitoes.
Unfortunately, current population genetic models are not well-suited to describing such rapid processes,
because they are often still grounded in simplistic assumptions such as a homogeneous, randomly mating
population. Research in my lab centers on the development of new population genetic models and
computational tools for studying rapid evolutionary processes such as CRISPR gene drives that allow us to
better predict their expected outcomes. Over the past five years, my lab has developed a comprehensive
modeling framework for gene drive dynamics. Here, we propose to incorporate increasing levels of biological
realism into this framework to address three broad questions: (1) How can we systematically identify the
features and parameters that are most critical for determining the outcome of a drive release in our
simulations? (2) Is it possible to reliably confine a gene drive to an intended target population, and how could
this be achieved? (3) Could a suppression drive in a mosquito population eradicate diseases such as malaria
or dengue even when it does not achieve complete suppression of the mosquito vector? As gene drive
technology comes ever closer to field experimentation, answers to these questions will be essential for a
realistic evaluation of the expected outcomes of a drive release into a wild population. Motivated by insights
from our modeling work on gene drives, we propose a second line of research focusing on the question of how
continuous space can affect the dynamics of other rapid evolutionary processes, such as strong selective
sweeps, which conceptually resemble the spread of a gene drive in many ways. We hypothesize that similar to
what we found for gene drives, continuous spatial structure could also have a profound impact on the
population dynamics of strong selective sweeps, and thus the signatures they leave in population genomic
data. We plan to study this question using forward genetic simulations together with recently developed
methods for inferring sweep parameters based on supervised machine learning. Finally, we plan to implement
critical improvements in our SLiM evolutionary simulation framework, enabling forward simulation of
populations of billions of individuals, so that we can predict the outcomes of the release of a CRISPR gene
drive into a mosquito population with sufficient accuracy and robustness to facilitate a well-informed discussion
about the feasibility, reliability, and ri...

## Key facts

- **NIH application ID:** 10765372
- **Project number:** 1R35GM152242-01
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Philipp W Messer
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $435,571
- **Award type:** 1
- **Project period:** 2024-01-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765372

## Citation

> US National Institutes of Health, RePORTER application 10765372, Population genetics of rapid evolutionary processes (1R35GM152242-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10765372. Licensed CC0.

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