DOI: 10.5061/dryad.70dn712
title: Data from: Tethered homing gene drives: a new design for spatially restricted population replacement and suppression

description: Optimism regarding potential epidemiological and conservation applications
 of modern gene drives is tempered by concern about the possibility of
 unintended spread of engineered organisms beyond the target population. In
 response, several novel gene drive approaches have been proposed that can,
 under certain conditions, locally alter characteristics of a population.
 One challenge for these gene drives is the difficulty of achieving high
 levels of localized population suppression without very large releases in
 the face of gene flow. We present a new gene drive system, Tethered Homing
 (TH), with improved capacity for both localization and population
 suppression. The TH drive is based on driving a payload gene using a
 homing construct that is anchored to a spatially restricted gene drive. We
 use a proof-of-concept mathematical model to show the dynamics of a TH
 drive that uses engineered underdominance as an anchor. This system is
 composed of a split homing drive and a two-locus engineered underdominance
 drive linked to one part of the split drive (the Cas endonuclease). We use
 simple population genetic simulations to show that the tethered homing
 technique can offer improved localized spread of costly transgenic payload
 genes. Additionally, the TH system offers the ability to gradually adjust
 the genetic load in a population after the initial alteration, with
 minimal additional release effort. We discuss potential solutions for
 improving localization and the feasibility of creating TH drive systems.
 Further research with models that include additional biological details
 will be needed to better understand how TH drives would behave in natural
 populations, but the preliminary results shown here suggest that tethered
 homing drives can be a useful addition to the repertoire of localized gene
 drives.
description: Figure 2
 codeThis file includes the
 code used to create Figure 2. It includes the life cycle loop iterated
 over multiple generations.Figure 3
 codeThis file includes the
 code used to create Figure 3. It includes data (in form of matrices) that
 was generated using separate files. One such data generating file is
 provided separately here. See "Note" at the top of the code in
 this file.Figure 3 data generation
 file Sc=0.05This file includes the
 code used to create one of the panels in Figure 3. It includes the life
 cycle loop iterated over multiple generations across a large number of
 combinations of parameter values.Figure 3 data generation file
 ScFigure 4
 codeThis file includes the
 code used to create Figure 4. It includes data (in form of matrices) that
 was generated using separate files. One such data generating file is
 provided separately here. See "Note" at the top of the code in
 this file.Figure 4 data generation
 file Sc=0.05This file includes the
 code used to create one of the panels in Figure 4. It includes the life
 cycle loop iterated over multiple generations across a large number of
 combinations of parameter values.Figure 4 data generation file
 ScFigure 5
 codeThis file includes the
 code used to create Figure 5. It includes the life cycle loop iterated
 over multiple generations.Figure 6
 codeThis file includes the
 code used to create Figure 6. It includes data (in form of matrices) that
 was generated using separate files. One such data generating file is
 provided separately here. See "Note" at the top of the code in
 this file.Figure 6 data generation
 file Sp=0.05This file includes the
 code used to create one of the panels in Figure 6. It includes the life
 cycle loop iterated over multiple generations across a large number of
 combinations of parameter values.Figure 6 data generation file
 SpFigure 7
 codeThis file includes the
 code used to create one of the panels in Figure 7. It includes the life
 cycle loop iterated over multiple generations, for different parameter
 values.