Molecular safeguarding of CRISPR gene drive experiments.

CRISPR-based homing gene drives have sparked both enthusiasm and deep concerns due to their potential for genetically altering entire species. This raises the question about our ability to prevent the unintended spread of such drives from the laboratory into a natural population. Here, we experimentally demonstrate the suitability of synthetic target site drives as well as split drives as flexible safeguarding strategies for gene drive experiments by showing that their performance closely resembles that of standard homing drives in Drosophila melanogaster. Using our split drive system, we further find that maternal deposition of both Cas9 and gRNA is required to form resistance alleles in the early embryo and that maternally-deposited Cas9 alone can power germline drive conversion in individuals that lack a genomic source of Cas9.

CRISPR Gene Drive Efficiency and Resistance Rate Is Highly Heritable with No Common Genetic Loci of Large Effect.

Gene drives could allow for control of vector-borne diseases by directly suppressing vector populations or spreading genetic payloads designed to reduce pathogen transmission. Clustered regularly interspaced short palindromic repeat (CRISPR) homing gene drives work by cleaving wild-type alleles, which are then converted to drive alleles by homology-directed repair, increasing the frequency of the drive in a population over time. However, resistance alleles can form when end-joining repair takes place in lieu of homology-directed repair. Such alleles cannot be converted to drive alleles, which would eventually halt the spread of a drive through a population. To investigate the effects of natural genetic variation on resistance formation, we developed a CRISPR homing gene drive in Drosophila melanogaster and crossed it into the genetically diverse Drosophila Genetic Reference Panel (DGRP) lines, measuring several performance parameters. Most strikingly, resistance allele formation postfertilization in the early embryo ranged from 7 to 79% among lines and averaged 42 ± 18%. We performed a genome-wide association study using our results in the DGRP lines, and found that the resistance and conversion rates were not explained by common alleles of large effect, but instead there were several genetic polymorphisms showing weak association. RNA interference knockdown of several genes containing these polymorphisms confirmed their effect, but the small effect sizes imply that their manipulation would likely yield only modest improvements to the efficacy of gene drives.

Maximum Likelihood Estimation of Fitness Components in Experimental Evolution.

Estimating fitness differences between allelic variants is a central goal of experimental evolution. Current methods for inferring such differences from allele frequency time series typically assume that the effects of selection can be described by a fixed selection coefficient. However, fitness is an aggregate of several components including mating success, fecundity, and viability. Distinguishing between these components could be critical in many scenarios. Here, we develop a flexible maximum likelihood framework that can disentangle different components of fitness from genotype frequency data, and estimate them individually in males and females. As a proof-of-principle, we apply our method to experimentally evolved cage populations of Drosophila melanogaster, in which we tracked the relative frequencies of a loss-of-function and wild-type allele of yellow This X-linked gene produces a recessive yellow phenotype when disrupted and is involved in male courtship ability. We find that the fitness costs of the yellow phenotype take the form of substantially reduced mating preference of wild-type females for yellow males, together with a modest reduction in the viability of yellow males and females. Our framework should be generally applicable to situations where it is important to quantify fitness components of specific genetic variants, including quantitative characterization of the population dynamics of CRISPR gene drives.