CRISPR gene drives can efficiently convert heterozygous cells with one copy of the drive allele into homozygotes, thereby enabling super-Mendelian inheritance. Such a mechanism could be used, for example, to rapidly disseminate a genetic payload through a disease-vector population that reduces pathogen transmission or directly suppresses the vector, promising novel strategies for the control of vector-borne diseases. However, our current understanding of how such an approach would perform in a natural population is at best rudimentary. CRISPR gene drive is a complex evolutionary process that depends on various factors such as the likelihood that resistance evolves against the drive, the spatial structure and migration patterns of the target population, and genetic variation among individuals. The overarching goal of this proposal is to gain a better understanding of the evolutionary dynamics of CRISPR gene drive strategies in realistic models of target populations that take these complexities into account.
In Specific Aim 1 we will develop a comprehensive modeling framework for CRISPR gene drives that will be informed by our recent experimental findings on drive mechanisms, resistance allele formation, and variation in resistance rates in genetically diverse populations. This framework will allow us to explore the performance of different drive strategies aimed at reducing resistance potential, such as the use of multiple gRNAs and the targeting of haploinsufficient genes.
In Specific Aim 2 we will utilize cutting-edge simulation approaches developed in our lab to study how CRISPR gene drives will perform in spatially explicit population models, in which individuals move across a continuous landscape and can experience complex interactions with each other and their local environment. We hypothesize that these spatial models will give rise to new phenomena that are not present in panmictic population models, such as the elimination of a drive that has already spread into a large fraction of the population when populations collapse locally due to the fitness cost of the drive.
In Specific Aim 3 we will use the modeling framework developed in the first two aims to study whether recently proposed safety measures can reliably confine and control a drive after it has been released into a target population, focusing on the complex interplay between drive genetics, the evolution of resistance, and the migration dynamics of individuals over realistic landscapes. Our framework will allow us to probe and predict the population dynamics of CRISPR gene drive approaches under specific empirical conditions, which will be integral to any informed discussion about the feasibility, robustness, and risks of such approaches.
CRISPR gene drives promise novel strategies for the control of vector-borne diseases by enabling the rapid dissemination of transgenes that suppress vector populations or reduce pathogen transmission. However, important questions still loom large about the practicality of these approaches in real-world applications. The goal of the proposed research is to develop a comprehensive modeling framework for CRISPR gene drives that will help us gain a better understanding of their evolutionary dynamics and evaluate the feasibility of proposed safety measures for local confinement and reversal of a drive.
| Haller, Benjamin C; Galloway, Jared; Kelleher, Jerome et al. (2018) Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. Mol Ecol Resour : |
