Wednesday, April 2, 2025
4/2/2025
Population genetics of rapid evolutionary processes - 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 risks of such approaches.
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