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1.Malaria, dengue, Zika, and other mosquito‐borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide‐based tools and antimalarial drugs. The advent of CRISPR/Cas9‐based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has enabled a wide range of gene drive architectures to be realized, creating a need for their population‐level and spatial dynamics to be explored. 2.We present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially‐explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying userdefined inheritance patterns, b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pests, and c) a landscape module generates the metapopulation model by which insect populations are connected via migration over space. 3.Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9‐based homing gene drive for: a) driving a disease‐refractory gene into a population (i.e. population replacement), and b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing‐resistant alleles in both cases. Further documentation and use examples are provided at the project's Github repository. 4.MGDrivE is an open‐source R package freely available on CRAN. We intend the package to provide a flexible tool capable of modeling novel inheritance‐modifying constructs as they are proposed and become available. The field of gene drive is moving very quickly, and we welcome suggestions for future development.
Methods Ecol Evol. 2019;00:1–11.    
DOI : 10.1111/20 41-210X .1331 8
MGDrivE: A modular simulation framework for the spread of
gene drives through spatially explicit mosquito populations
Héctor M. Sánchez C.1| Sean L. Wu1| Jared B. Bennett2| John M. Marshall1,3
Thisisanop enaccessarti cleundertheter msoftheCreativeCommonsAttribution‐NonCommercialLicen se,whichpermi tsuse,distri butionandrepr oduction
inanymedium,providedtheoriginalworkisproperl ycitedan disnotuse dforcomm ercialpurposes.
©2019TheAuth ors.Me thods in Ecology and EvolutionpublishedbyJohnWiley&So nsLtdonbehalfofBritishEcol ogicalSociet y.
SeanL .WuandJare dB.Ben nettco ntrib utedeq uallytot hiswork .
1Divisio nofEpidem iolog yand
Biostatisti cs,SchoolofPublic
Health ,Univer sityofC alifornia,Berkeley,
2BiophysicsGraduateG roup,Divisionof
Biologi calSci ences,CollegeofLettersand
Science ,Univer sityofC alifornia,Berkeley,
3InnovativeGeno micsIn stitute,Berkeley,
Funding information
InnovativeGeno micsIn stitute;UCIr vine
MalariaInitiat ive;Defe nseAdvanced
ResearchProjec tsA gency,Gr ant/Award
Number :HR0011‐17‐2‐00 47
1. Malaria, dengue, Zika and other mosquito‐borne diseases continue to pose a
major globalhealth burden through muchofthe world, despitethe widespread
distribution of insecticide‐based tools and antimalarial drugs. The advent of
CRISPR/Cas9‐basedgene editingand itsdemonstrated ability to streamline the
development of ge ne drive systems has re ignited interest in th e application of
tobe realized,creating aneed fortheir population‐level and spatialdynamics to
2. WepresentMGDr ivE (Mosquito Gene Drive Explorer): asimulation framework
tecturesandtheir spread throughspatiallyexplicit mosquito populations. A key
streng th of the MGDrivE f ramework is its m odularity : (a)a gen etic inherita nce
module accommodates the dynamics of gene drivesystems displaying user‐de-
fined inheritancepatterns, (b) a population dynamic module accommodates the
3. Example MGDrivE simulations arepresented to demonstrate the application of
ing‐resistantalleles in both cases. Furtherdocumentation and useexamples are
4. MGDrivE is an open‐source r packagefreely available on CRAN. Weintend the
Methods in Ecology and Evoluon
Theadvent ofCRISPR/Cas9‐based gene editingtechnology and its
application to the engineeringof genedrive systemshasled to re-
mosquito ve ctors of human d iseases and ins ect agricult ural pests
(Champe r, Buchman , & Akbari, 2016; Es velt, Smidler, Cat teruccia,
& Church, 2 014).A pplicatio ns to control mos quito‐bor ne diseases
have gained the most at tention due to the major global health
burden they pose through much of the world and the difficult y of
controlling them using currently available tools ( Walker, Griffin,
Th eeas eof gen e edi t ing affo r d edb yCR I SPR has a lso ledt os i gni f-
icantversatilityintermsoft hegenedrivesystemsthatarenowreal-
izab le(C ham peretal ., 20 16;M ar sha ll &A kba ri,2018).Th es eincl ude:
drivesys temandarethencopie dtothissitebyser vingasatemplate
forhomology‐directedrepair(HDR)(Burt,20 03;Windbichleretal.,
2011),( b)r em ed ia ti onsystemsthatco ul dbeusedtoremoveeffector
ofunwantedconsequences (Gantz& Bier,2014;Marshall&Akbari,
2018), and (c) thre shold‐depen dent systems th at may permit con-
fineable and reversiblereleases (Akbari etal., 2013; Buchman, Ivy,
ecosystemsrequiresafl exiblemodellingframeworkthatcanaccom -
landscape details where a construct may be released. To this end,
wep re sentMG DrivE(MosquitoGeneDriveExplorer):aflexiblesim-
ulationframeworkdesignedtoinvestigatethepopulation dynamics
MGDrivEi suniq ueinitsabi li t yt os imu la tediver s e, us er‐s pec if ied
simulationsandappliedtogene drive studies( Table1).For example,
Eckh off(2011)usedtheEMODmalariamodeltosimul atethespread
ofhoming‐basedgenedrivesystemsthrough spatialpopulations of
Anopheles gambiae.EMODisopensourceand apowerfulmodelling
etal.(20 09)createdSkeeterBusterbyextendingtheCIMSiMmodel
(Focks, Da niels, Hail e, & Keesling , 1995)to in corporate ge netic in-
herita nce and spatia l struct ure. Skeeter B uster capt ures most pe r-
tinent mosquitoecology considerations,butisnot opensourceand
can only simulate a handful of geneticcontrolstrategies (Legroset
systems inametapopulation;however,itdoesnotcurrentlyaccom-
modatelife‐histor yecologyandoverlappinggenerations.
Inthispaper,we describe the keycomponentsoftheMGDrivE
framework – genetic inheritance, mosquito life histor y and land-
scape. We th en demonst rate the appl ication of the f ramework to
refractory gene intoapopulation (i.e. population replacement), and
(b)d isrupti ngagenereq uiredforfe ma lefer tili ty(i.e.po pulat io nsu p-
Aedesaeg ypti,Anophelesgambiae,inheritancepattern,landscape,lifehistory,mathematical
TABLE 1 Comparisonofspatiallyexplicitgenedrivemodels
Inheritance patterns Life‐history ecology Spatial and landscape details Software
MGDrivE Veryflexible,canbe
responsivetoenvironment al
(Eck hof f,
couldbeex tendedwith
responsivetoenvironment al
releaseofinsect scarrying
responsivetoenvironment al
Execut ablefile,notopen
histor yatpresent
interf ace,opensource
Methods in Ecology and Evoluon
The MGD rivE frame work is a geneti c and spatial e xtension of t he
lumped age‐class model of mosquitoecolog y(Hancock&Godfray,
Sánchez C., and A kbari (2017) to population‐suppressing homing
systems in the presence of resistant alleles. The framework incor-
porates the egg,larval, pupaland adultlife stages, withegg geno-
typesdeterminedbymaternalandpaternalgenot ypesandtheallelic
inheritance pattern.In MGDrivE, bytreating the lumped age‐class
model equationsinavariable‐dimensiontensoralgebraicform, the
asrequired by the number of genotypes associated with the drive
system.Spatialdynamics aresimulatedbyametapopulationstruc-
ture in which migrants are exchanged between populations with
definedprobabilities. Full detailsofthisframeworkareprovided in
The core framework is developed in r (https://www.r‐projewithcer tainroutinesinRcppforcomputationalspeed.By
combiningthetensormodelling framework with object‐oriented
programming, the genetic, life history and spatial components
ofthemodel are ableto be separated into‘modules’ to facilitate
2.1 | Modules
2.1.1 | Genetic inheritance
Thefundament almoduleforgenedrivedynamicsisthatdescribing
sional tensor contained in a drive‐specific R fileand referred to as
an‘inheritancecube’(Figure 1).Thefirst andseconddimensions of
theinheritancecube refer tothematernalandpaternal genotypes,
respec tively, and the t hird refers to t he offspr ing genotyp e. Cube
entries f or each combin ation of parent al genotyp es represen t the
propor tion of off spring that a re expecte d to have each genot ype,
and should sum to one, as fitness and viability are accommodated
The R func tion that build s the inherita nce cube may receive a
based drive system, the list of input parameters should include the
homi ngeff i ci enc y,t her at eo fi n‐fr a me re sis tan ta lle le ge ner ation ,an d
therateofout‐of‐frameorotherwisecostly resistantallelegenera-
resistant alleles are those forwhich the codingframe ofthe target
site is not alte red, leading t o minimal fitne ss effect s, while out‐of‐
fitnesseffects. Inputparameters also include those associated with
organis ms having each ge notype – fo r example, ge notype‐ specific :
FIGURE 1 Inheritancemodule.Geneticinheritanceisembodiedbyathree‐dimensionaltensorreferredtoasaninheritancecube(left),
heredepictedforaCRISPR‐basedhomingconstruc t.Maternalandpaternalgenotypesaredepictedonthex and y‐axesandoffspring
genotypesonthez‐axis,withslicesofthecubeper tainingtoeachoffspringgenotypeshowntotheright.Theinheritancepatternshown
deviatesfromstandardMendelianinheritancesuchthat,inthegermlineofHhparent s,themajorityofwild‐type(h)allelesareconverted
examplepictured,thefrequenc yofaccuratehominggivencleavageinHhheterozygotesis98%,withtheremaining2%ofwild‐typealleles
beingconver tedtoeitherin‐frame(1%),orout‐of‐frame(1%)resistantalleles
Methods in Ecology and Evoluon
adultsur vivalrates,and(e)femaleandmalepupatorysuccess.These
describ ed next . Finally, a ‘viab ility mas k’ is applie d to the offs pring
At the time of p ublication, t he MGDrivE pac kage includes in-
herita nce cubes for: (a) st andard Mendel ian inheritanc e, (b) hom-
(Gantzetal.,2015;Hammond etal.,2016;Marshalletal.,2017),(c)
Medea (a maternal tox in linked to a zygoti c antidote) (Chen et al .,
asUDMEL (Akbari et al.,2013;Marshall, Pittman, Buchman, & Hay,
2011), (e) reciproca l chromosomal tr anslocations (B uchman et al.,
2018), (f) Wolbachia (Hancock, Sinkins, & Godfray, 2011), and (g)
the RIDL a nd pgSIT sys tems (Kandul et a l., 2019; Wise de Valdez
precision‐guidedsterileinsecttechnique).Detailsof each of these
systems are providedintheonlinedocumentation athttps://marshvE/docs/reference/.
2.1.2| Mosquito life history
Th em os quitoli fe ‐h istor ymo dulefol lo wsfro mt he lumpe da ge‐cl as s
model of Ha ncock and Go dfray (2007 ) adapted by De redec et al.
(2011).Inthismodel(depictedinFigure2),theinsec tlifecycleisdi-
vid edintofours tages–egg(E),la rva(L),pupa(P)anda dult(Fforfe -
maleandMformale).InMGDrivE,eachlifes tageisassociatedwit h
agenot ype. Adultfemalesmateonceandproducebatchesofeggs
fromthespermofthesamemale,sotheyobt ainacompositegeno-
type uponmating(theirownandthatof the maletheymatewith).
inheritance pattern as provided in theinheritance cube.The adult
equilibrium population size, N, in a given h abitat patch is u sed to
determine the carrying capacity ofthat patchfor lar vae, K, which
thelarvalstage in thatpatch. FollowingDeredecetal.(2011),this
isdescribed byan equationofthe form:
isthe numberoflarvaeinthepatch,TListhedurationofthelarval
stage, and αisa parameterdescribingthe strength ofdensityde-
pendence.Further detailsonthe mathematical formulationof the
lumped‐ageclass modeland its generalizationtoanarbitrar ynum-
species canbe achievedbyalteringparametervalues within this
mod ule:(a)th enu mb erofeg gsprodu cedpe radultfe malep erday,
(b)thedurationsofthe egg,larvalandpupal juvenile life stages,
(c)thedailymortalit yriskfortheadultlifestage,and(d)thedaily
population growth rate (in the absence of density‐dependent
juvenile s tages are assume d to be identical an d are chosen for
consisten cy with the dai ly population gr owth rate. Def ault life‐
interest:(a)A. gambiae,themainAfricanmalariavector,(b)Aedes
capitata, a wo rldwide ag ricultura l crop pest . In some cas es, life‐
genedriveconstruc t.Acurrentlimitationoftheframeworkisthat
equilibriumpopulationsize remainsconstantovertime. This will
beaddressedinthenextreleasedversionofMGDriv E.
FIGURE 2 Mosquitolifehistor ymodule.Lifehistoryismodelledaccordingtoanegg(E)‐larva(L)‐pupa(P)‐adult(Fforfemale,Mformale)
i∈{1, ..., g}
.Forexample,Mirepresentsthenumberofadultmaleshavingtheithgenot ype.
Egggenot ypesaredeterminedbytheadultfemale'scompositegenotypeandtheinheritancepattern,whichisspecifictothegenedrive
Methods in Ecology and Evoluon
2.1.3 | Landscape
Thelan ds cap em od uledesc ri besth ed is tribu ti onofmo sq uitopop-
ulations inspace, with movementthroughthe resulting network
determinedby adispersalkernel. Discretepopulationsin there-
the equatio nsoft helum pe dage‐cl assmo de lapply.T heresolut io n
ofthe individualpopulations (in terms of size)should be chosen
according tothe dispersal properties of the insect species of in-
terest and the research question being investigated. A. aegypti
mosquito es, for insta nce, are thought to b e relatively loc al dis-
persers,often remainingin thesamehousehold forthe duration
cies, populations onthescale ofhouseholds maybeappropriate.
A. gambiaemosquitoes,ontheotherhand,arethoughttodisplay
movement (Taylor etal., 2001). Thiswouldsuggest villagesasan
appropriatepopulationunit; however otherlevelsofaggregation
are also possible, inboth cases, dependingon the resolutionre-
Daily per‐capita movement probabilities between populations
(nodes in thenetwork)for theseexampleswere calculated froma
cess,wherebya mosquitofirstdecideswhethertoleavethecur-
of movement , select s the destin ation node fro m the full set wi th
tothe mean dispersal distancein a largelandscape). As the simu-
lation only requiresamatrix ofinter‐nodemovement probabilities,
arbitrarily complexkernels that accountforbarriers,suchasroads
(Sc hmidtetal.,2018),mayb eusedwithoutal teringt hemodelar chi-
tec ture.Thematri xofmoveme ntp robab ilitiesi sin corpo ratedint he
Finally, any t ype of releas e can be simul ated by increas ing the
number of in sects having a gi ven sex and genoty pe at a specific
populat ion and time. A s demons trated in the f ollowing use ex am-
individuals, (b) numberofreleases,(c)timeoffirst release, (d) time
2.2 | Deterministic versus stochastic simulations
Simulations can be run either in deterministic or stochastic form.
ofchance events that occurat low population sizes and genot ype
frequencies. In the stochastic implementation of MGDrivE, daily
egg prod uction foll ows a Poisson dist ribution, of fspring gen otype
follows a multinomial distribution informed by parental genotypes
andthe inheritance cube,mate choice followsamultinomial distri-
butiondeterminedbyadultgenot ypefrequencies,and survivaland
death event s follow bino mial distri butions at th e populatio n level.
When interpreting stochastic models, many simulations should be
runtounderstandtherange of outputs possible for a given model
To demonstrate how the MGDriv E framework can be used to
initialize a nd run a simulat ion of a gene drive sy stem through a
metapo pulation, we have provided vignettes with the package,
available via instillation from CRAN at https://CRAN.R‐projege=MGDrivE, and additional examples andinforma-
tion on Github atht tps://allLab/MGDrivE and
thepackageweb site,ht tp s://marshallla b.git hu GDrivE/.The
vignettesprovideexamplesofsimple simulations and landscape
TABLE 2 Life‐historymoduleparametervaluesforthreespeciesofinterest(atatemperatureof25°)
Parameter Symbol Aedes aegypti Anopheles gambiae Ceratitis capitata
female(day−1 )
20(Diamantidis,C arey,Nakas ,&
TE5(Christophers,1960) 1(Depinayetal.,
TL6(Christophers,1960) 13(Depinayetal.,
TP4(Christophers,1960) 1(Depinayetal.,
Dailymor talityriskof
adultst age(day−1 )
Methods in Ecology and Evoluon
to Hardy‐Weinber g equilibrium, before violating some of these
assumptions. Next, they explore the effects of genotype‐spe-
cific fi tness costs o n genotype tr ajectories a nd population s ize.
He re ,wed e sc rib et h ea ppl ic ati onofthe pa cka getot wo homin g
gene drive s trategies : (a)d riving a dise ase‐refra ctory ge ne into a
population(Gantz et al.,2015),and(b)disruptinga generequired
etal.,2016). Inbothcases,we considerapopulation of A. aegypti
dis tr ibute dthroug hthen et workl an dscaped ep ic tedinFigur e3.To
demonstratethefunctionalityoftheMGDr ivEpackage,wemodel
bothstrategies using the deterministic andstochastic implemen-
tations.In both cases, we include the generation of in‐frame and
out‐of‐frameorother wise costly resistant alleles (Champer et al.,
2017)and parameterize the genedrive model based on recently
engineeredconstruct s(Gantzetal.,2015;Hammondetal.,2016).
3.1 | Population replacement
that engineeredby Gant z et al. (2015). This was the first CRISPR‐
based hom ing constru ct demons trated in a mos quito diseas e vec-
tor – namely, Anopheles stephensi, the main urban malaria vector
in India. Fo r this constr uct, ho ming and resi stant all ele generat ion
were shown to o ccur at diffe rent rates in mal es and female s, and
50% in females – i.e. 90% of wild‐type (h) alleles areconvertedto
homing(H)allelesinthegermlineofHhmales,and 50%ofhalleles
areconverted toHallelesinthegermline ofHhfemales. Athirdof
the remaining h alleles in Hh individualsareconverted to in‐frame
orotherwise costlyresistant alleles(B)due to error‐pronecopying
The general workflow for the simulation is shown in
Figure 4, with thefullcodeavailableat https://
MGDrivE package in randsettingtheworkingandoutputdirec-
tories.Wethenchoose between the deterministicandstochastic
sion.Next,wespecifythebionomicparametersofthe specieswe
aremodelling –in this case, A. aegypti, whosedefaultlife‐history
parameters are provided in Table2.Followingthis, we definethe
landsc ape through whic h we will model the sp read of the drive
(longitudeandlatitude)of thepopulationsinFigure 3. A function
ofthe populationsbased onazero‐inflated exponential dispersal
kernel, the parameters for which we provide. Equilibrium adult
FIGURE 3 Landscapemodule.
Insect saredistributedaspopulations,
Methods in Ecology and Evoluon
eve rinthiscase,wea ss um et he se ar eidentic al an dp rovideasi ng le
Wi tho ur lif eh ist oryand lan dsc ape mod ule sd efin ed,we no w
mo del(C ode sam ple 2) .Weu se ap r e‐ spe cif ied inh eri tan cecub e ,
homing‐basedgenedrive system.Wespecifysex‐specifichom-
ingrates,resistanta llelegenerationr atesa ndgenot ype‐sp ec if ic
fi t nes sef f ect s bas e do nth eco nstr u cte ngi n eer edb yG a ntze ta l .
conta ining:(a)t hereleasesize,(b)nu mb erofr el eases,(c)t im eof
firs trelease,and(d)timebet weenreleases .Thisisincorporated
into a vec tor also specifying the inheritance cube and the sex
andgenot ypeofthereleasedinsects.Finally,thepopulationsin
FIGURE 4 WorkflowofanMGDrivE
Code sam ple 1:Loadingthep ackagea ndsett ingup th elifehistor yand
Code sample 2:Settinguptheinheritance/genedrivemoduleandde-
fining thereleasescheme.Here, codeis shown forboth: A) homing‐
Methods in Ecology and Evoluon
3.2 | Population suppression
As a second example, we demonstrate the application of the
MGDrivE package to a population suppression homing construct
similar tothat engineered by(Hammondetal.,2016).For this con-
struct,thehomingsystemtarget sagenerequiredforfemalefertil-
ity, causin g females lac king the gene (th ose having the g enotype s
HH,HBandBB)tobeinfert ile,andindu cin galargefe cundityreduc-
(those having thegenotypesHh,HR, hB and RB). The homingeffi-
first C RISPR‐based ho ming constr uct demo nstrated in A . gambiae,
althoug h with a higher homin g efficiency t hat could be achieved
that differ for this systemare shownin Code sample 2.While the
sameinheritancecubeapplies,specificparametersdiffer– namely,
homingandresistantallelegenerationrates, andgenotype‐specific
Inth ec ur ren tv er sio nofM GDr ivE,completesimulationresultsare
out putasC SVfiles,twobasi cplottingfunctionsa reprovi de dinR ,
FIGURE 5 ExampleMGDrivEsimulationsforCRISPR‐basedhomingconstructs.Inbothcases,anAedes aegyptipopulationissimulated
thatdrivesadisease‐refrac torygeneintothepopulationissimulatedhavingahomingefficiencyof90%inmalesand50%infemales.Wild‐
type(h)allelesthatarenotconvertedtohoming(H)allelesinthegermlineofHhheteroz ygotesarecleavedandconvertedtoeitherin‐frame
homingconstruc tthatinterfereswithagenerequiredforfemalefertilityissimulatedhavingahomingefficiencyof99.9%inbothfemales
andmales.Wild‐typeallelesthatarenotconvertedtohomingallelesinthegermlineofHhheterozygotesarecleavedandconver tedto
resistantallelesareomit tedduetotheirlowfrequenciesinbothsimulations.Dashedverticallinesrepresentthebeginningandendofthe
Code sample 3:Preparingoutputfoldersandrunningthemodel.Itisrec-
ommendedtostor esimul ationfil esfore achru ninitsownsepar atefolde r.
Methods in Ecology and Evoluon
andseveral functionsareprovided for aggregatingthedata – by
In F igur e 5,w e dis p l aya p oten t i alv i s ual i z atio n s chem e p rodu c e d
in Mathematica for the simulations described above (additional
videos are providedin the Supplementary Information: S1Video
(Figure 5a andc),wesee the gene drive allele (H) spread through
smallextent.Thisoccursbecausethe Rallele hasneither afitness
cost norbenefit relative to the H allele once it has saturated the
selectivedisadvantage.Stochasticity slowsthese dynamics, onav-
Forpopulation suppression(Figure 5band d), we see the gene
wesee an in‐frame resistant allele(R) emerge and spread into the
populat ion due to its s electi ve advantage ove r both the wil d‐type
introducing variabilit y around the mean. Mathematica and Python
nity reg arding futur e directions . The field of gene d rive has been
geneediting,andweintendt heM GD rivEp ackagetop rov ideafl exi-
‘shadow drive’,inwhich the Cas9 enzyme is passed on tothe off-
spring evenif the gene expressing it isnot (Champer et al., 2017),
ease vec tors and agricult ural pests, a nd (c) populations tha t vary
in size seaso nally or in resp onse to environ mental dri vers such as
temperatureandrainfall.Incorporationofenvironment aldriverswill
allowbothsea sonaltre ndsandshor t‐ter mfluc tuati onstob eac com -
Additionally, we are developing a corresponding individual‐
based model that iscapable of modelling multi‐locussystems for
which the number of possible genotypes exceeds the number
of individ uals in the pop ulation. T his will enabl e us to efficie ntly
model confineable systems suchas daisy‐drive involvingseveral
tore du cethe rateofres ist antal lel ef or mat ion(P ro wseetal .,2017 ).
logical c onsideratio ns, and Dr. Gregor y Lanzaro, D r.Yoosook L ee,
Dr. Gordana Ra šić and Partow I mani for discussio ns on mosquito
ecolog y, life histor y and dispersa l behaviour. This work was sup-
portedbyaDARPASafeGenesProgramGrant(HR0 011‐17‐2‐0 047)
awarded to J.M.M. andfunds fromtheUC Irvine MalariaInitiative
H.M.S.C. and J.M.M. conceivedtheproject.H.M.S.C. led MGDrivE
de ve lop m ent .H . M .S.C .an dJ . M.M .wr oteth efi r std raf tof thema nu-
Héctor M. Sánchez C.‐0001‐7378‐8853
John M. Marshall‐0003‐0603‐7341
MGDrivE version 1.1.0 is available on CRAN at https://CRAN.R‐
projege=MGDrivE. Additional examples and plotting
scripts are available on Github at https://allLab/
MGDrivE, and the package website, https://marsh
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Additional supporting information may be found online in the
How to cite this article:SánchezC.HM,WuSL,BennettJB,
populations.Methods Ecol Evol. 2019;00:1–11. h t t p s : / / d o i .
org /10.1111/2 041‐210X.13318
... Larvae The mosquito GD explorer (MGDrivE v 1.6.0) was used to model the inheritance of the 443 AeaNosC109 GD and AeaZpgC109 GD lines (Sánchez et al. 2020). A stable population was set to 444 10000 mosquitoes at a 1:1 sex ratio with no migration out of, or into, the main patch. ...
... Based on the experimentally derived data regarding GD inheritance including frequency of GDBI 715 accrual, testing for maternal effect of the CRISPR/Cas9 RNP, and analysis of fitness parameters 716 of the GD harboring mosquito lines (Table S3), we modeled the performance of AaeNosC109 GD 717 or AeaZpgC109 GD in a hypothetical field release scenario for the purpose of population 718 replacement (Figure 7; Table S4, Table S5). To model the lines, we used the MGDrivE mosquito 719 population modeling package in R (Sánchez et al. 2020) and modeled for a static population of 720 ...
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The yellow fever mosquito Aedes aegypti is a major vector of arthropod-borne viruses, including dengue, chikungunya, and Zika. A novel approach to mitigate arboviral infections is to generate mosquitoes refractory to infection by overexpressing antiviral effector molecules. Such an approach requires a mechanism to spread these antiviral effectors through a population, for example, by using CRISPR/Cas9-based gene drive (GD) systems. Critical to the design of a single-locus autonomous GD is that the selected genomic locus is amenable to both GD and appropriate expression of the antiviral effector. In our study, we used reverse engineering to target two intergenic genomic loci, which had previously shown to be highly permissive for antiviral effector gene expression, and we further investigated the use of three promoters (nanos, β2-tubulin, or zpg) for Cas9 expression. We then quantified the accrual of insertions or deletions (indels) after single generation crossings, measured maternal effects, and assessed fitness costs associated with the various transgenic lines to model the rate of GD fixation. Overall, MGDrivE modeling suggested that when an autonomous GD is placed into an intergenic locus, the GD system will eventually be blocked by the accrual of GD blocking resistance alleles and ultimately be lost in the population. Moreover, while genomic locus and promoter selection were critically important for the initial establishment of the autonomous GD, it was the fitness of the GD line that most strongly influenced the persistence of the GD in the simulated population. As such, we propose that when autonomous CRISPR/Cas9 based GD systems are anchored in an intergenic locus, they temporarily result in a strong population replacement effect, but as GD-blocking indels accrue, the GD becomes exhausted due to the fixation of CRISPR resistance alleles.
... To determine whether iterative releases of fertile Ifegenia males could facilitate population suppression and elimination, we incorporated the above data on Ifegenia performance into a mathematical model (26) and simulated releases into a population of 10,000 A. gambiae adults (Figure 2). Weekly releases of up to 500 Ifegenia eggs (female and male) per wild-type adult (female and male) were simulated over 1-48 weeks. ...
... To model the expected performance of Ifegenia and pgSIT at suppressing and eliminating local A. gambiae populations, we used the MGDrivE simulation framework (26). This framework models the egg, larval, pupal, and adult mosquito life stages with overlapping generations, larval mortality increasing with larval density, and a mating structure in which females retain the genetic material of the adult male with whom they mate (sperm) for the duration of their adult lifespan. ...
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Malaria is among the world's deadliest diseases, predominantly affecting sub-Saharan Africa, and killing over half a million people annually. Controlling the principal vector, the mosquito Anopheles gambiae, as well as other anophelines, is among the most effective methods to control disease spread. Here we develop an innovative genetic population suppression system termed Ifegenia (Inherited Female Elimination by Genetically Encoded Nucleases to Interrupt Alleles) in this deadly vector. In this bicomponent CRISPR-based approach, we disrupt a female-essential gene, femaleless (fle), demonstrating complete genetic sexing via heritable daughter gynecide. Moreover, we show that Ifegenia males remain reproductively viable, and can load both fle mutations and CRISPR machinery to induce fle mutations in subsequent generations, resulting in sustained population suppression. Through modeling, we demonstrate that iterative releases of non-biting Ifegenia males can act as an effective, confinable, controllable, and safe population suppression and elimination system.
... 或者选 择能加入特定物种的生活史参数的软件, 以进行更有 针对性的驱动模拟. 如Marshall团队 [8,11,21] 利用融入了 蚊虫生活史生态学的R语言程序包MGDrivE(Mosquito Gene Drive Explorer) [64] , 预测了分离驱动、携带重编 Figure 3 The simulation study of gene drive, take the split drive system as an example. A: Using the experimental data of the few generations in the cage as parameters to simulate the driving effect of the split drive system in multi-generation and large populations. ...
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Gene drive refers to the phenomenon that specific genes or genetic elements are passed from parent to offspring in the form of super-Mendelian inheritance. In recent years, based on the genetic characteristics of gene drive and the theoretical basis of their molecular mechanisms, and supported by CRISPR/Cas9 gene-editing system, gene-driven genetic control technology has become an advanced research hotspot in the field of basic mosquito biology and genetic control. And there have emerged some practical and effective achievements that take into account ecological stability. This paper reviews the basic principles of gene drive, CRISPR/Cas9-mediated and HDR-type drive technology strategies, improved strategies to reduce gene-driven resistance and potential risks, and simulation analysis of gene drive. It is hoped to provide a reference for the development of a gene-driven mosquito genetic control technology system that takes both high efficiency and safety into consideration.
... Malaria Journal (2022) 21:226 replacement gene drive mosquitoes (classic and integral), as well as with and without other forms of vector control are quantified. Previous modelling work has focused on understanding changes in vector populations with release of GM mosquitoes without considering other types of vector control and without also examining the downstream effects on malaria transmission within corresponding human populations [31,[33][34][35][36][37]. An advantage of this model [32,38] is that it can simulate the effects of gene drive-induced vector population changes on malaria transmission within a realistic human population directly making it possible to quantify the gene drive system and other logistical release parameters needed to reach full malaria elimination. ...
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Background Gene drives are a genetic engineering method where a suite of genes is inherited at higher than Mendelian rates and has been proposed as a promising new vector control strategy to reinvigorate the fight against malaria in sub-Saharan Africa. Methods Using an agent-based model of malaria transmission with vector genetics, the impacts of releasing population-replacement gene drive mosquitoes on malaria transmission are examined and the population replacement gene drive system parameters required to achieve local elimination within a spatially-resolved, seasonal Sahelian setting are quantified. The performance of two different gene drive systems—“classic” and “integral”—are evaluated. Various transmission regimes (low, moderate, and high—corresponding to annual entomological inoculation rates of 10, 30, and 80 infectious bites per person) and other simultaneous interventions, including deployment of insecticide-treated nets (ITNs) and passive healthcare-seeking, are also simulated. Results Local elimination probabilities decreased with pre-existing population target site resistance frequency, increased with transmission-blocking effectiveness of the introduced antiparasitic gene and drive efficiency, and were context dependent with respect to fitness costs associated with the introduced gene. Of the four parameters, transmission-blocking effectiveness may be the most important to focus on for improvements to future gene drive strains because a single release of classic gene drive mosquitoes is likely to locally eliminate malaria in low to moderate transmission settings only when transmission-blocking effectiveness is very high (above ~ 80–90%). However, simultaneously deploying ITNs and releasing integral rather than classic gene drive mosquitoes significantly boosts elimination probabilities, such that elimination remains highly likely in low to moderate transmission regimes down to transmission-blocking effectiveness values as low as ~ 50% and in high transmission regimes with transmission-blocking effectiveness values above ~ 80–90%. Conclusion A single release of currently achievable population replacement gene drive mosquitoes, in combination with traditional forms of vector control, can likely locally eliminate malaria in low to moderate transmission regimes within the Sahel. In a high transmission regime, higher levels of transmission-blocking effectiveness than are currently available may be required.
... Characterizing mosquito movement is important to understanding the 23 spatial transmission of vector-borne diseases [10], and to designing optimal biocontrol 24 strategies, such as those involving Wolbachia, for vector-borne disease control. By 25 analyzing close-kin pairs, these two studies estimated mean dispersal distances in 26 agreement with previous mark-recapture studies [7,11], and isolated a radius of 27 dispersal specific to Ae. aegypti oviposition behavior [6]. 28 In this paper, we extend the CKMR formalism described by Bravington et al. [1] to 29 mosquitoes, using Ae. ...
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Close-kin mark-recapture (CKMR) methods have recently been used to infer demographic parameters such as census population size and survival for fish of interest to fisheries and conservation. These methods have advantages over traditional mark-recapture methods as the mark is genetic, removing the need for physical marking and recapturing that may interfere with parameter estimation. For mosquitoes, the spatial distribution of close-kin pairs has been used to estimate mean dispersal distance, of relevance to vector-borne disease transmission and novel biocontrol strategies. Here, we extend CKMR methods to the life history of mosquitoes and comparable insects. We derive kinship probabilities for mother-offspring, father-offspring, full-sibling and half-sibling pairs, where an individual in each pair may be a larva, pupa or adult. A pseudo-likelihood approach is used to combine the marginal probabilities of all kinship pairs. To test the effectiveness of this approach at estimating mosquito demographic parameters, we develop an individual-based model of mosquito life history incorporating egg, larva, pupa and adult life stages. The simulation labels each individual with a unique identification number, enabling close-kin relationships to be inferred for sampled individuals. Using the dengue vector Aedes aegypti as a case study, we find the CKMR approach provides unbiased estimates of adult census population size, adult and larval mortality rates, and larval life stage duration for logistically feasible sampling schemes. Considering a simulated population of 3,000 adult mosquitoes, estimation of adult parameters is accurate when a total of 1,000 adult females are sampled biweekly-to-fortnightly over a three month period. Estimation of larval parameters is accurate when adult sampling is supplemented with a total of 4,000 larvae sampled biweekly over the same period. As the cost of genome sequencing declines, these methods hold great promise for characterizing the demography of mosquitoes and comparable insects of epidemiological and agricultural significance. Author summary Close-kin mark-recapture (CKMR) methods are a genetic analogue of traditional mark-recapture methods in which the frequency of marked individuals in a sample is used to infer demographic parameters such as census population size and mean dispersal distance. In CKMR, the mark is a close-kin relationship between individuals (parents and offspring, siblings, etc.). While CKMR methods have mostly been applied to aquatic species to date, opportunities exist to apply them to insects and other terrestrial species. Here, we explore the application of CKMR to mosquitoes, with Aedes aegypti , a primary vector of dengue, chikungunya and yellow fever, as a case study. By analyzing simulated Ae. aegypti populations, we find the CKMR approach provides unbiased estimates of adult census population size, adult and larval mortality rates, and larval life stage duration. Optimal sampling schemes are consistent with Ae. aegypti ecology and field studies, requiring only minor adjustments to current mosquito surveillance programs. This study represents the first theoretical exploration of the application of CKMR to an insect species, and demonstrates its potential for characterizing the demography of insects of epidemiological and agricultural importance.
... Modeling analysis. Model fitting was carried out using a discrete-generation adaptation of the Mosquito Gene Drive Explorer (MGDrivE) 62 . A likelihood-based Markov chain Monte Carlo (MCMC) procedure was used to estimate gene drive efficacy and genotype-specific fitness costs, employing initial parameter projections from the single-pair test crosses providing Maximum a Posteriori estimates and 95% credible intervals for each parameter 63 . ...
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A recurring target-site mutation identified in various pests and disease vectors alters the voltage gated sodium channel (vgsc) gene (often referred to as knockdown resistance or kdr) to confer resistance to commonly used insecticides, pyrethroids and DDT. The ubiquity of kdr mutations poses a major global threat to the continued use of insecticides as a means for vector control. In this study, we generate common kdr mutations in isogenic laboratory Drosophila strains using CRISPR/Cas9 editing. We identify differential sensitivities to permethrin and DDT versus deltamethrin among these mutants as well as contrasting physiological consequences of two different kdr mutations. Importantly, we apply a CRISPR-based allelic-drive to replace a resistant kdr mutation with a susceptible wild-type counterpart in population cages. This successful proof-of-principle opens-up numerous possibilities including targeted reversion of insecticide-resistant populations to a native susceptible state or replacement of malaria transmitting mosquitoes with those bearing naturally occurring parasite resistant alleles.
Gene drive technology has been proposed to control invasive rodent populations as an alternative to rodenticides. However, this approach has not undergone risk assessment that meets criteria established by Gene Drives on the Horizon, a 2016 report by the National Academies of Sciences, Engineering, and Medicine. To conduct a risk assessment of gene drives, we employed the Bayesian network–relative risk model to calculate the risk of mouse eradication on Southeast Farallon Island using a CRISPR‐Cas9 homing gene drive construct. We modified and implemented the R‐based model “MGDrivE” to simulate and compare 60 management strategies for gene drive rodent management. These scenarios spanned four gene drive mouse release schemes, three gene drive homing rates, three levels of supplemental rodenticide dose, and two timings of rodenticide application relative to gene drive release. Simulation results showed that applying a supplemental rodenticide simultaneously with gene drive mouse deployment resulted in faster eradication of the island mouse population. Gene drive homing rate had the highest influence on the overall probability of successful eradication, as increased gene drive accuracy reduces the likelihood of mice developing resistance to the CRISPR‐Cas9 homing mechanism.
The emergence of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein (CRISPR/Cas) and its reengineering into a potent genome editing system has revolutionized life sciences. It has brought much excitement and hope in medical and agricultural research for unprecedented control over the redesigning of genomes. Based on CRISPR, many genome engineering tools have been developed and extensively used for the identification of new genes and therapeutic targets, functional genomics, gene therapies, and the development of transgenic animals and plants. The successful applications of CRISPR/Cas depend on the safe and efficient transportation of CRISPR/Cas reagents into the cell nucleus. In this chapter we discuss the merits and demerits of different cargo reagents used for genome editing through CRISPR/Cas. In addition, we detail several delivery methods reported for CRISPR/Cas, including physical, viral, and non-viral delivery methods. We also highlight different emerging delivery methods not currently reported for delivery of CRISPR/Cas reagents. Finally, we discuss available delivering methods of CRISPR/Cas components for plant genome editing.
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Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse, and could be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit, allows for overlapping generations and a fluctuating population size, and includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.
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The sterile insect technique (SIT) is an environmentally safe and proven technology to suppress wild populations. To further advance its utility, a novel CRISPR-based technology termed precision guided SIT (pgSIT) is described. PgSIT mechanistically relies on a dominant genetic technology that enables simultaneous sexing and sterilization, facilitating the release of eggs into the environment ensuring only sterile adult males emerge. Importantly, for field applications, the release of eggs will eliminate burdens of manually sexing and sterilizing males, thereby reducing overall effort and increasing scalability. Here, to demonstrate efficacy, we systematically engineer multiple pgSIT systems in Drosophila which consistently give rise to 100% sterile males. Importantly, we demonstrate that pgSIT-generated sterile males are fit and competitive. Using mathematical models, we predict pgSIT will induce substantially greater population suppression than can be achieved by currently-available self-limiting suppression technologies. Taken together, pgSIT offers to potentially transform our ability to control insect agricultural pests and disease vectors.
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A functioning gene drive system could fundamentally change our strategies for the control of vector-borne diseases by facilitating rapid dissemination of transgenes that prevent pathogen transmission or reduce vector capacity. CRISPR/Cas9 gene drive promises such a mechanism, which works by converting cells that are heterozygous for the drive construct into homozygotes, thereby enabling super-Mendelian inheritance. Although CRISPR gene drive activity has already been demonstrated, a key obstacle for current systems is their propensity to generate resistance alleles, which cannot be converted to drive alleles. In this study, we developed two CRISPR gene drive constructs based on the nanos and vasa promoters that allowed us to illuminate the different mechanisms by which resistance alleles are formed in the model organism Drosophila melanogaster. We observed resistance allele formation at high rates both prior to fertilization in the germline and post-fertilization in the embryo due to maternally deposited Cas9. Assessment of drive activity in genetically diverse backgrounds further revealed substantial differences in conversion efficiency and resistance rates. Our results demonstrate that the evolution of resistance will likely impose a severe limitation to the effectiveness of current CRISPR gene drive approaches, especially when applied to diverse natural populations.
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The recent development of a CRISPR-Cas9-based homing system for the suppression of Anopheles gambiae is encouraging; however, with current designs, the slow emergence of homing-resistant alleles is expected to result in suppressed populations rapidly rebounding, as homing-resistant alleles have a significant fitness advantage over functional, population-suppressing homing alleles. To explore this concern, we develop a mathematical model to estimate tolerable rates of homing-resistant allele generation to suppress a wild population of a given size. Our results suggest that, to achieve meaningful population suppression, tolerable rates of resistance allele generation are orders of magnitude smaller than those observed for current designs for CRISPR-Cas9-based homing systems. To remedy this, we theoretically explore a homing system architecture in which guide RNAs (gRNAs) are multiplexed, increasing the effective homing rate and decreasing the effective resistant allele generation rate. Modeling results suggest that the size of the population that can be suppressed increases exponentially with the number of multiplexed gRNAs and that, with four multiplexed gRNAs, a mosquito species could potentially be suppressed on a continental scale. We also demonstrate successful proof-of-principle use of multiplexed ribozyme flanked gRNAs to induce mutations in vivo in Drosophila melanogaster – a strategy that could readily be adapted to engineer stable, homing-based drives in relevant organisms.
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The alteration of wild populations has been discussed as a solution to a number of humanity’s most pressing ecological and public health concerns. Enabled by the recent revolution in genome editing, clustered regularly interspaced short palindromic repeats (CRISPR) gene drives—selfish genetic elements that can spread through populations even if they confer no advantage to their host organism—are rapidly emerging as the most promising approach. However, before real-world applications are considered, it is imperative to develop a clear understanding of the outcomes of drive release in nature. Toward this aim, we mathematically study the evolutionary dynamics of CRISPR gene drives. We demonstrate that the emergence of drive-resistant alleles presents a major challenge to previously reported constructs, and we show that an alternative design that selects against resistant alleles could greatly improve evolutionary stability. We discuss all results in the context of CRISPR technology and provide insights that inform the engineering of practical gene drive systems.
Replacement of wild insect populations with transgene-bearing individuals unable to transmit disease or survive under specific environmental conditions using gene drive provides a self-perpetuating method of disease prevention. Mechanisms that require the gene drive element and linked cargo to exceed a high threshold frequency in order for spread to occur are attractive because they offer several points of control: they bring about local, but not global population replacement; and transgenes can be eliminated by reintroducing wildtypes into the population so as to drive the frequency of transgenes below the threshold frequency required for drive. Reciprocal chromosome translocations were proposed as a tool for bringing about high threshold population replacement in 1940 and 1968. However, translocations able to achieve this goal have only been reported once, in the spider mite Tetranychus urticae, a haplo-diploid species in which there is strong selection in haploid males for fit homozygotes. We report the creation of engineered translocation-bearing strains of Drosophila melanogaster, generated through targeted chromosomal breakage and homologous recombination. These strains drive high threshold population replacement in laboratory populations. While it remains to be shown that engineered translocations can bring about population replacement in wild populations, these observations suggest that further exploration of engineered translocations as a tool for controlled population replacement is warranted.
The recent discovery of CRISPR and its application as a gene editing tool has enabled a range of gene drive systems to be engineered with greater ease. In order for the benefits of this technology to be realized, in some circumstances drive systems should be developed that are capable of both spreading into populations to achieve their desired impact, and being recalled in the event of unwanted consequences or public disfavor. We review the performance of three broad categories of drive systems at achieving these goals - threshold-dependent drives, homing-based drive and remediation systems, and temporally self-limiting systems such as daisy-chain drives.
The endosymbiotic bacterium Wolbachia suppresses the capacity for arbovirus transmission in the mosquito Aedes aegypti, and can spread spatially through wild mosquito populations following local introductions. Recent introductions in Cairns, Australia have demonstrated slower than expected spatial spread. Potential reasons for this include: (i) barriers to Ae. aegypti dispersal; (ii) higher incidence of long-range dispersal; and (iii) intergenerational loss of Wolbachia. We investigated these three potential factors using genome-wide single-nucleotide polymorphisms (SNPs) and an assay for the Wolbachia infection wMel in 161 Ae. aegypti collected from Cairns in 2015. We detected a small but significant barrier effect of Cairns highways on Ae. aegypti dispersal using distance-based redundancy analysis and patch-based simulation analysis. We detected a pair of putative full-siblings in ovitraps 1312 m apart, indicating long-distance female movement likely mediated by human transport. We also found a pair of full-siblings of different infection status, indicating intergenerational loss of Wolbachia in the field. These three factors are all expected to contribute to the slow spread of Wolbachia through Ae. aegypti populations, though from our results it is unclear whether Wolbachia loss and long-distance movement are sufficiently common to reduce the speed of spatial spread appreciably. Our findings inform the strategic deployment of Wolbachia-infected mosquitoes during releases, and show how parameter estimates from laboratory studies may differ from those estimated using field data. Our landscape genomics approach can be extended to other host/symbiont systems that are being considered for biocontrol.
Self-replicating gene drives that can spread deleterious alleles through animal populations have been promoted as a much needed but controversial ‘silver bullet’ for controlling invasive alien species. Homing-based drives comprise an endonuclease and a guide RNA (gRNA) that are replicated during meiosis via homologous recombination. However, their efficacy for controlling wild populations is threatened by inherent polymorphic resistance and the creation of resistance alleles via non-homologous end-joining (NHEJ)-mediated DNA repair. We used stochastic individual-based models to identify realistic gene-drive strategies capable of eradicating vertebrate pest populations (mice, rats and rabbits) on islands. One popular strategy, a sex-reversing drive that converts heterozygous females into sterile males, failed to spread and required the ongoing deployment of gene-drive carriers to achieve eradication. Under alternative strategies, multiplexed gRNAs could overcome inherent polymorphic resistance and were required for eradication success even when the probability of NHEJ was low. Strategies causing homozygotic embryonic non-viability or homozygotic female sterility produced high probabilities of eradication and were robust to NHEJ-mediated deletion of the DNA sequence between multiplexed endonuclease recognition sites. The latter two strategies also purged the gene drive when eradication failed, therefore posing lower long-term risk should animals escape beyond target islands. Multiplexing gRNAs will be necessary if this technology is to be useful for insular extirpation attempts; however, precise knowledge of homing rates will be required to design low-risk gene drives with high probabilities of eradication success. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.
CRISPR/Cas9 gene drive (CGD) promises a highly adaptable approach for spreading genetically engineered alleles throughout a species, even if those alleles impair reproductive success. CGD has been shown to be effective in laboratory crosses of insects, yet it remains unclear to what extent potential resistance mechanisms will affect the dynamics of this process in large natural populations. Here we develop a comprehensive population genetic framework for modeling CGD dynamics, which incorporates potential resistance mechanisms as well as random genetic drift. Using this framework, we calculate the probability that resistance against CGD evolves from standing genetic variation, de novo mutation of wildtype alleles, or cleavage-repair by nonhomologous end joining (NHEJ) -- a likely byproduct of CGD itself. We show that resistance to standard CGD approaches should evolve almost inevitably in most natural populations, unless repair of CGD-induced cleavage via NHEJ can be effectively suppressed, or resistance costs are on par with those of the driver. The key factor determining the probability that resistance evolves is the overall rate at which resistance alleles arise at the population level by mutation or NHEJ. By contrast, the conversion efficiency of the driver, its fitness cost, and its introduction frequency have only minor impact. Our results shed light on strategies that could facilitate the engineering of drivers with lower resistance potential, and motivate the possibility to embrace resistance as a possible mechanism for controlling a CGD approach. This study highlights the need for careful modeling of the population dynamics of CGD prior to the actual release of a driver construct into the wild.