incorporating population genetics simulators ...shahid et al., the j. anim. plant sci. 28(6): 2018...

Shahid et al., The J. Anim. Plant Sci. 28(6):2018

1868

INCORPORATING POPULATION GENETICS SIMULATORS IN SCIENTIFICPLATFORM FOR CLOUD

Rabia Shahid1, Muhammad Kashif Hanif1*, Wesley Brewer2, Ramzan Talib1, and Omar Hayat1

1Department of Computer Science, Government College University, Faisalabad, Pakistan2Department of Computer Science, Pyongyang University of Science and Technology, Korea

*Corresponding Author e-mail: [email protected]

ABSTRACT

Geneticists employ wide range of simulation programs for the analysis of genomic dataset. Main difficulties are to dealwhich include complex user interface, limited hardware capacity, complex software design, and analyzing diversegenomes. Forward time population genetics is powerful technique to identify gene underlying traits or mutations ofinterest. In this work, population genetics simulators Mendel’s Accountant and Nemo are integrated in a cloudcomputing environment. Wolbachia was used for platform testing. Computational analysis of Wolbachia was performedto highlight the factors that associated with prevention of arboviral diseases.

Key words: Cloud computing, Population genetics, Mendel’s Accountant, Nemo, Wolbachia, Arboviral diseases.

INTRODUCTION

Population is the amount or quantity of all typesof organisms including (human, plant, animal, archaeon,bacterium and fungus) of the identical species or groupthat live in a specific geographical environment.Population genetics is the study of allele frequencydistribution and change under the influence ofevolutionary processes which include natural selection,genetic drift, mutation, and gene flow. Genetic processeswork in environment with an organism as behavioral anddevelopment factors (Hopfet al., 2017)

Many simulation programs have beenextensively used for various analysis in the field ofpopulation genetics. However, most of these simulatorsdo not have broad potential users. The reason isdevelopment of custom systems that address a particularproblem. Most of these simulators are not user friendlyand difficult to export. As a consequence, most of thesimulators fall in disuse in few years. The utilization ofthese simulators can be enhanced if they are easilyaccessed, configured, and operated. Moreover, most ofthe users of these programs are not programmers. Theyrequire virtual environment to run complex scientificsimulations and user friendly interface to interpret resultsof simulations correctly. There is need for middlewareexecution platform which provides different simulationprograms on single platform. Benefit of this type ofplatform to rapidly deploy population geneticssimulations on cloud. This platform also motivatesdevelopers to use standard protocols by usingstandardized input formats. Moreover, this platformshould provide features like real time data modeling,usability, portability, and scalability.

Cloud computing paradigm increasinglybecoming popular. Large number of researchorganizations seek to gain value from its uniquecharacteristic, deployment forms, and service models(Granadoset al., 2017). Genome sequences of differentorganisms are being stored in cloud environment which isaccessible to researchers for various simulation studies.Cloud computing can be defined as a model for enablingubiquitous, on demand, and convenient network used toaccess a shared pool of configurable computingresources. These resources provide provision of nominalconfiguration, minimal interaction with service provider,and less management efforts. Cloud computing providesmany advantages including massive parallel execution ofadvanced data processing, scalability, efficiency, lowcost, and data storage capacity. It also supports full rangeof advance computational analytics and data integration(Sharma and Jha 2015).

In this work, we present an integrated cloudbased scientific platform (Breweret al., 2015), whichhelps to reduce setup procedure and learning curve.Researchers only needs to be familiar with one system. Italso allows users to easily simulate and compare resultsfrom multiple population genetics simulators. Populationgenetics simulation software Mendel’s Accountant andNemo were incorporated in cloud based scientificplatform. Furthermore, computational analysis ofWolbachia was performed to test the environment.

The rest of paper is organized in differentsections. Section II discuss some closely relatedpopulation genetics simulators. Section III outlineframework infrastructure, design, and configuration.Section IV describes detail simulation of Wolbachiapopulation on scientific platform and their role inprevention strategies of arboviral diseases. Section V

The Journal of Animal & Plant Sciences, 28(6): 2018, Page: 1868-1880ISSN: 1018-7081


1869

discusses results. At the end, we conclude the outcomeswith future directions.

Population genetics simulators: Computer basedsimulations have been extensively used to evaluate andvalidate the influence of statistical approaches for theanalysis of population genetics. Due to mountingcomputational influence with diverse proficiencies andcapabilities, simulation programs are becomingprogressively useful and powerful. Large number ofsimulation programs, software packages, frameworkshave been used for population genetics. In this section,widely used forward time population genetics simulatorsare discussed.

Skelesim is population genetic simulator basedon an extensible framework in R language (Parobeket al.,2017). This simulation program support to chooseappropriate models for simulations, setting accurateparameters, organizing all output data, and plot graphsfrom the summary of genetical statistics.Skelesimimplements forward time and coalescent models that areaccessible in the RMetaSim and in FastSimCoal2engines. These engines are helpful to produce nulldistributions under the diversity of many demographicconditions for different statistics of population genetic(Parobeket al., 2017).

SLiM2 is forward time population geneticsimulation software (Haller and Messer 2016). Thissimulator has diversity of modeling techniques forcomposite evolutionary conditions. It delivers advancedcontrol over maximum features of evolutionaryconditions and scenarios by using R alike scriptinglanguage called Eidos. Simulation engine of SLiM2 isvery optimized to qualify modeling technique of wholechromosomes for big population size. This simulationtool has user friendly graphical interface, collaboratingruntime controller, and dynamic visualization features(Haller and Messer 2016).

Fwdpp is a C++ based population geneticsimulation library (Thornton 2014). It supports to developnew forward time simulations under fitness and arbitrarymutation models. This library has a combination oftruncated memory overhead, speed, and flexibility inmodeling that are not currently offered in most offorward time simulators. This library is used for analysisof large populations and rapid implementation ofinnovative models (Thornton 2014).

Mendel’s Accountant is forward time simulationtool that allows modeling of complex biological models(Baumgardneret al., 2008). It is suitable for simulation ofsexual or asexual reproduction of haploid or diploidorganisms. Mendel’s Accountant program can be used tobuild and track a single population of millions ofmutations. It provides analysis of average mutation perindividual, fitness and population size history,distribution of deleterious accumulated mutations,

distribution of beneficial accumulated mutations, nearneutral beneficial effects, near neutral deleterious effects,selection threshold history, selection effect, allelefrequencies, total number of variants vs allelefrequencies, and initial contrasting alleles(Baumgardneret al., 2008).

Nemo is flexible genetically explicit frameworkfor forward time population genetics (Guillaume andRougemont 2006). It is used to study evolution by usinggenetic markers, phenotypic traits, and traits of lifehistory in the meta population. Nemo implementsdifferent lifecycle and evolutionary features. Thissimulation program specifically used for computationalanalysis of species interactions between parasite and theirhosts. Nemo is based on flexible model of metapopulation which allows specific patch carrying capacity,diffusion rate (diffusion matrix), random extinction, anddemographic randomness within the framework. Largenumber of simulations can be run from a single parameterfile with multiple parameter values. Many complex casesof evolution and population can be easily modeled onNemo by providing time varying parameter values(Guillaume and Rougemont 2006).

SCIENTIFIC PLATFORM FOR CLOUD

Fig.1. SPC infrastructure

There exists no platform which integratedifferent population genetics simulators. In this work,we.present a scientific platform for cloud (SPC) whichtakes advantage of cloud computing to simulate differentpopulation genetics programs. The features of the SPCplatform are user friendly interface, job scheduling, jobexecution, job monitoring, robustness, easy migration ofapplications, and plotting of the results.

A. Infrastructure: SPC architecture is mainly based onInput execute Plot style software systems (Fig.1). SPCsupports INI, name list, and XML input formats.Different flags can be specified on command line.Whenever the user submits a task, execution phase readsthe input file and maps all the parameters to commandline inputs. Post processing phase computes the subset ofsimulated data and convert it into Python format. Thedata in Python format can be used as input to plottinglibraries. SPC supports two plotting libraries JQuery andMatplotlib.


1870

SPC provides portability support to simulators.Configuration of new application on SPC requires name,description, input file format, database configuration,default input file, HTML input form template, upload andtest binary file, and setup plot. Nemo configuration steps

for SPC (Fig.2). SPC was designed to allow geneticists toeasily upload anexecutable and sample input file onshared infrastructure and receive a transferable outputfile.

Fig.2. Nemo configuration on SPC.

B. Design: Design goals meets in SPC by using modelview template (MVT) as shown in (Fig. 3).

Fig.3. MVT architecture of SPC Bootstrap (getbootstrap.com) was used forfrontend framework to build responsive projects on theweb. Bottle (bottlepy.org) was used for micro webserver side framework to map URL routes to Pythonmethods with powerful template system. Gluino was used for backend framework thatported from web2py to get support of different databaseslike PostgreSQL, SQLite, MySQL, Oracle and manyothers. It is also used for jobs scheduling. Bottle is not full stack framework but it is easilyextended by using third party plugins to get all featuresthat full stack support. Third party plugins providefeatures of session management, object relationalmapping, and flash messages, etc. Bottle combined withseparate data access layer and job scheduler.

SPC store all information about apps, jobs, users, andplots in database. SPC implemented uni and prioritybased multiprocessor scheduler for spawning newprocesses, which repeatedly polls the database everysecond and execute any job in front of queue. Jobs aresubmitted to a job table in database which maintains jobstates. Jobs states can be C,Q,R and X for completed,waiting in queue, running, and terminated jobs,respectively.

Design goals accomplished in scientific platformare automatic web interface configuration, execution andscheduling of jobs, managing simulations, plottinginterface library, handling multiple users on sharedinfrastructure, facility of easy deployment on Google AppEngine, Computer Engine, Amazon EC2, and onRedHatOpenShift.

C. Incorporation

Fig.4. SPC interface after incorporation.


1871

Fig.5. Incorporation steps of Mendel's Accountant and Nemo on SPC.

SPC is python based open source software. Itcan be easily accessed, and latest version of software canbe downloaded from the give linkhttps://github.com/whbrewer/spc. Fig.4 illustrateincorporation steps of population genetics simulators onSPC. Python 2.7 standard library was used for theestablishment of scientific platform on Linux. It provides

compatibility support for incorporation of additionalmodules or other applications on this platform.

Fig. 5 shows interface of SPC afterincorporation of Mendel’s Accountant and Nemo. Table1displays the important dependencies with version forSPC.

Table1. Dependencies for SPC.

Parameters Version DescriptionBottle 0.12.13 Bottle is simple, fast, and lightweight micro web framework for Python.Beaker 1.9.0 Beaker is caching and web session Python library that includes webserver gateway interface for

web applications.PyYaml 3.12 PyYAML is a YAML emitter and parser for Python.Psutil 5.2.2 Psutil is cross platform library for system and process utilities in Python.pytoml 0.1.13 Parser for Tom’s Obvious Minimal Language in Python.DockerPy 1.1.0 Python library for Docker Engine programming interface.Boto 2.6.0 Python based software development kit for Amazon Web Services.Requests 2.9.1 Hypertext Transfer Protocol library for Python.WebTest 2.0.28 Helper to test web server gateway interface applications.CherryPy 3.2.2 Object oriented web framework.

Wolbachia population simulation: Wolbachia is genusof gram negative bacterial endosymbiont which infectsinvertebrates and some nematodes. Recently, Wolbachiahas gained prominence specially for the prevention ofarboviral diseases (Jiggins 2017). Arboviruses transferthrough arthropod vectors. Arboviruses includeZika(Dutraet al., 2016), Dengue (Yixinet al., 2015),Chikungunya (Van den Hurket al., 2012), etc.

Mosquitoes borne viruses transmission forhuman diseases has been control by release of geneticallymodified mosquitoes which is infected with strain ofWolbachia that originally isolated from Drosophila flies.Naturally, Aedesaegypti and Aedesalbopictus mosquitoesare not infected with its own strain of Wolbachia.Wolbachia strain that extracted from Drosophila simulansis beneficial to block the transmission of arboviruses(Von Seidleinet al., 2017). This controlling strategy iscurrently being tested, implemented, and verified in

definitive trails against Zika in Colombia (Von Seidleinetal., 2017), Brazil, and against dengue in Southeast Asia.Mosquitoes reproduction is controlled by introducingstrain of Wolbachia (extracted from Drosophila simulans)into male mosquitoes and as a result sterilized eggsproduced from mating. This method grasps somepotentials against Aedesaegypti and some other similarvectors with concentrated density of wild populationswith the release of the sterilized males (Benelliet al.,2016). Sterile mosquito technique used in Mexico,Central and North America and proposed to be adoptedfor Aedesemphasisis in Brazil (Reiset al., 2017).

SPC can be used to find factors association forprevention of arboviral diseases. This section discussesWolbachia population analysis using Mendel’sAccountant and Nemo on SPC. Mendel’s Accountant andNemo were used to create virtual population ofWolbachia.


1872

Mendel’s Accountant created possible mutationswith precisely quantified characteristics including:frequency distribution, fraction of beneficial mutations,range of mutation effects, and ratio of dominants torecessives. Mutations were selected randomly from thepool of mutations and assigned erratically to the noveloffspring in respective generations.

Table2. Important parameters used for simulation ofWolbachia in Mendel’s Accountant andNemo.

Parameters ValuesMendel’s AccountantMutation Rate 0.867Fraction Favorable Mutation 0.0001Reproductive Rate 2.0Population Size 1000Number Generations 500Genome Size 98000000Impact Mutation Fraction (High) 0.001Impact Mutation Threshold (High) 0.1Fitness Gain (Max) 0.01Heritability 0.5Haploid Chromosome Number 4Migration Generations 10NemoGeneration 500Patch Number 50Patch Capacity 20Extinction Rate 0.05Mean Fecundity 15Mating Proportion 0.8Dispersal Cost 0.2Neutral Loci 20Neutral Mutation Rate 0.0001Neutral Recombination Rate 0.5Deleterious Loci 100Deleterious Mutation Rate 0.0001Deleterious Recombination Rate 0.5Deleterious Effect Mean 0.05Dispersal Mutation Rate 0.001Dispersal Mutation Mean 0.2

These mutations were constructed on the basisof quantified average mutation rate (PoissonDistribution). Nemo was used to study evolutionaryeffect of phenotypic traits of Wolbachia endosymbiont.Population from generation 0 to generation 500 wasconsidered through successive life cycle iterations. Nemooffers population models (island and lattice), traits(neutral markers, deleterious mutations and more), lifecycle events (dispersal, mating, aging, selection, etc.) andsaving statistics and data as output. Selection ofWolbachia was done on its phenotype or traits values.

Only limited number of Wolbachia traits were selectedusing viability trait selection procedure.

Mendel’s Accountant created possible mutationswith precisely quantified characteristics. Mutations wereselected randomly from the pool of mutations andallocated erratically to the novel offspring in respectivegenerations. These mutations were constructed on thebasis of quantified average mutation rate (PoissonDistribution). Nemo was used to study evolutionaryeffect of phenotypic traits of Wolbachia endosymbiont.Population from generation 0 to generation 500 wasconsidered through successive life cycle iterations.Selection of Wolbachia was done upon its phenotype ortraits values. Wolbachia population dynamically changedduring simulation that allowing patch fission/fusion andpopulation bottlenecks.

Genetic fitness of individuals considered as 1.0.Genetic fitness was adjusted by negative and positiveeffects of mutations. On the basic of phenotypic fitness,Mendel’s Accountant applied different selection schemesi.e., truncation, partial truncation, strict proportionalprobability, and unrestricted probability schemes. Foranalysis, we considered only unrestricted probabilityselection scheme that was applied on mating pool toeliminate a quantified fraction of the individuals.Elimination of fraction factor was based on averageoffspring per female (fertility) of the population.Selection scheme rejects some offspring per female.Selection scheme was applied to keep persistentpopulation size of individuals. Gametes were extractedfrom individuals that subsist form the selectionprocedure. Replicating individuals were paired offrandomly and their were merged to generate thesucceeding generations of individuals.

Some factors that were considered for analysisare deleterious selection trait, direct selection model,monogamy mating system, island model with propagulepool migration, and dispersal model. Table II providesimportant parameters of Mendel’s Accountant and Nemothat were used for simulation of Wolbachia, respectively.

RESULTS AND DISCUSSION

SPC provides powerful capabilities forresearcher to virtually perform migration, mating,offspring creation, and selection procedure in precise anddetailed way on a single platform. Researchers can easilyexplore these procedures how they actually work. Forplatform testing detailed analysis on Wolbachia wasperformed to check the authentication and validity ofplatform. Moreover, this platform is beneficial forresearchers for the detailed analysis of different vectors.Population genetics models highly depends on themutations that are implicit to change fitness. Mutationseffect on organism vary because of genomic informationand types of mutations (beneficial, deleterious, neutral).


1873

Near neutral mutations occur at high frequencies andhave higher impact on fitness. Previous studies haveshown that slight mutations with larger effect, and larger

mutations with smaller effect. Mendel’s Accountant usedWeibull function for tracking fitness distribution effect.

Fig.6. (a) Fitness distribution effect to change in the fraction of high impact mutations. (b) fitness distributioneffect to change in haploid genome size.

Fig. 7. Deleterious and favorable mutation effect (Mendel’s Accountant).


1874

Fig. 8.(a) Fitness effect before selection procedure. (b) Fitness effectafter selection procedure. (c) Number ofindividual’s before and after selectionprocedure.

Fig.6(a) shows the distribution function offitness effect to change in the fraction of high impactmutations. Assumed values for high impact mutations arefollowing: population size 1000, generations 500,frequency range 0.000 to 0.080, genome size= 9.8x109,and fraction of exceeding threshold mutations 0.1 to0.0001. Near neutral mutations always occur at higherfrequencies. We assumed initial fitness value as 1.0.Minimum absolute value of fitness and fraction ofmutations highly influence on the distribution of fitnessfor random mutations. We considered same values fordeleterious and favorable mutations. Inverse of haploidgenome size must be taken for finding minimum absolutevalue of fitness effect. Genome size measured in numbersof nucleotides. For example, for the Wolbachia genomeG=9.8x107, for the computation of the case of deleteriousmutations d(1)=1/G. Therefore, for larger genome size

the least value must nearby to zero, and for smallestgenome size the effect of minimum absolute value mustbe greater. In smaller genome on average, eachnucleotide highly influences on the organism’s fitness.Fig.6(b) represent changes in haploid genome size andtheir response on fitness distribution function. Consideredvalues are population size 1000, generations 500,frequency range 0.000 to 0.080, genome sizes= 9.8x104to 9.8x109, and fraction of exceeding threshold mutations0.001. These graphs only show small portion of effectson fitness distribution. Mostly mutations have showntheir nearly zero effect. Vertical scale is the frequency ofmutations per unit fitness effect. Note that mostly casesdisplayed larger trend of near neutral mutations so,influence of these mutations on fitness must be smaller.

In case of average mutations per individual,favorable mutation rate per offspring is considered 0.01.


1875

We also considered size of population 3000 which isconstant during reproduction, and reproduction ratebefore selection procedure is considered as 6 offspringper female. After selection procedure only 2 offspring arecompulsory to sustain the constant size of population. Ineach generation, 2/3 of the offspring are carefully chosenaway that do not reproduce for next generations. Wechoose hundred new mutation per offspring. The value ofthis favorable mutation is 100 times greater than expectedvalue. Reason of choosing higher favorable rate is toeasily analyzed mutation effect.

Fig. 7 shows the visual representation offavorable and deleterious mutations effect that run for3000 population size and 500 number of generations.This shows that the effect of deleterious mutations perindividual for whole population seems to be constant andthat is nearby the product of elapsed generations andnumeral of new mutations per offspring (100). Thisindicates that the average number of mutations perindividuals that do not reproduce or survive must be closeto the average mutations per individuals that do. Note thatthe increase in deleterious mutations effect is nearlyconstant. By natural selection, these mutations effecthighlighted the fact that majority of large amount ofmutations have smallest influence on fitness. Hence, thefrequencies of vast majority of favorable and deleteriousmutations cannot be altered during natural selectionprocedure phase. Because of insignificant favorablemutations, statistical fluctuations in accumulation rate aremust be obvious. Increase in number of favorablemutations are relatively constant. But due to the smallnumber of mutations the statistical discrepancies aremore prominent. This type of distinction in mutationsclearly represent the consequence that almost allfavorable mutations and vast number of deleteriousmutations are unelectable. Their effects per individual forselection are too small to detect. Selection proceduredirectly acts on environmental conditions and oncumulative phenotypic fitness.

Specifying the selection procedure within thepopulation whose individual’s fitness vary is the criticalaspect of population genetics model. Intensity of fitnessis mainly specified through fertility. Fertility define asmean number of offspring per female. Mostly, during thereproduction the size of population held constant. Duringthe selection phase surplus offspring eliminates thatbeyond the limit of targeted population size. Selectionprocedure basically important because it discriminatesindividuals that will mate and replicates from thoseindividuals that will not. Generally, worst phenotypes donot reproduce while best reproduce. Naturallyenvironmental variations also play a vital role onindividual fitness or survival. For adding environmentalvariation heritability parameter is used. Heritability isused as the ratio of genetics fitness variance to totalfitness variance (environmental variance plus genetic

fitness variance). For simulating Wolbachia weconsidered unrestricted probability selection that does notimpose any restricted limit on the scaling factor. Formaintain population size it selects sufficient offspring tomate and reproduce. By using this method, offspringwhose fitness exceeding with scaled fitness isautomatically selection for reproduction.

Fig. 8(a) illustrate the standard deviation andfitness effect without selection procedure within thespecified population size as function of elapsedgenerations. Note that average fitness of population dropsby 65% over 500 number of generations. Fromgenerations to generations, the inexorable reduction infitness effect is the consequence of the persistentaccumulation of deleterious mutation (unelectable). Thissimulation clearly approves the realism of geneticentropy.Fig.8(b) represents the means deleterious fitnesseffect and mutation rate for 500 generations. By usingunrestricted probability selection procedure, the declinein means fitness is 0.09% per generation. By eliminatingindividuals with the maximum deleterious mutations,selection procedure is able to diminish the decline offitness to only 9% for 500 generations. Selection intensityfor this case is low, four out of six offspring are survivedto mate and reproduce during the selection procedure.Fig. 8(c) shows the number of individual’s before andafter selection procedure. Selection is one of the mostimportant phase during the genetic simulation.

Fig.9(a) displays the distribution effects ofdeleterious mutations that present in 500 generations.This figure represents the relationship of mutationsfrequencies and fitness effect. Red line shows the effectof mutation distribution before the selection procedureapplied on population. Green bars denote the actualeffects of mutations distribution after selection procedureapplied on population. Change in distribution is verysmall that effect lesser in scale than 0.001. Only limitedsize of horizontal and vertical scales displayed. Effects ofmost mutations in the rightmost side. Mutations effecthave been nominated away whose magnitude is largerthan 0.01 lie on left side of the plot. Change in the effectof mutations between 0.0 and 0.002 is small and that canbe negligible due to small influence. This graph showsthat the high numbers of mutations are not being affectedby presence or absence of selection procedure.

Fig.9(b) and 9(c) highlight the distribution effectof accumulated mutations beneficial and deleterious inthe presence of selection procedure. Dominant feature ismore prominent in both cases (beneficial and deleteriousmutations effects). Change in recessive feature is almostzero. In case of deleterious mutation effect the change inthe fraction of mutations that retained in genome size isfrom 0.0 to 1.0 and effect of mutational fitnessdegradation is from 1x10 1 to 1x10 7 but in case ofbeneficial mutation effect the change in the fraction ofmutations that retained in genome size is from 0.0 to 2.0


1876

and effect of mutational fitness degradation is from 1x105 to 1x10 7. Logarithmic scale is used to emphasized thehigh impact effects of distribution of accumulatedmutations. Graph is plotted to retained the effect ofdistribution of mutations under the impact of selectionprocedure comparative to the mutations effect that wouldoccur in the absence of selection procedure. Change inthe height of plot clearly reflect the action of selectionprocedure. Results of these mutation shows that lowinfluence mutations are effectually unelectable. Note thatmutations whose effect is smaller collectively they causesignificant decline in the fitness of population.

Fig.10 display the occurrence frequency ofindividual that carries the same mutations in thepopulation of 500 generations. This figure shows therelationship of number of alleles and alleles frequency.Highest frequency of mutations is only occurred in 1% ofthe population. No mutation of individual occurs formore than 10% of the population. This distribution effectshows the genetic drift. Rate of change of genetic drift isslow in the population of 3000 individuals that willreproduce for 500 generations.

Nemo was used to performed followinganalysis: demography of male or female offspring,dispersal rate, segregation mutations, no of alleles withinwhole population and within demes, deleterious mutationeffect on heterozygosity of alleles, and genetic varianceby using Nei and Chesser model. Demographical analysison Wolbachia was performed by using Nemo simulationtool. Mutations and other environmental factorsinfluenced on the demography of the Wolbachia. Fig.11shows the demographical variations of Wolbachia inmale or female offspring for 1000 generations. Survivalrate of female offspring is more than male offspring thatsurvive after selection procedure. These selected male orfemale offspring are capable of further mating andreproduction into next generations. Plot highlight thehighest density region of female and male offspring. Notethat at 100 generation the density of male offspring ishigher than female offspring but this trend is entirelychanged at 1000 generations. At 1000 generations thedensityof male offspring is less than female offspring.Density offemale offspring is about 0.5% greater thanmale offspring. Result illustrate that the host dynamicsare influenced by the mutational effects, contact patternof individuals, and demographic construction ofpopulation. This result also shows that the male offspringare more influenced by the environmental change andmutations effect relative to female offspring. Dispersalrate of Wolbachia was simulated by using Nemo. Fig.12represents the distribution or spreading of Wolbachia for500 and 1000 generations. Dispersal rate of femaleoffspring and male offspring and their mean is displayedin the plotted region. For 500 generation, at the start thedispersal rate of male offspring is more than the femaleoffspring but after 80 generations the dispersal of rate of

female offspring is more than male offspring. This trendremains constant till the 500 generations. Change in thedispersal rate is due to the demographical changes thatinfluenced on the dispersal rate of the individuals. Notethat the change in the dispersal rate is between 0.50 to0.25 for 500 generations.

Fig.13 shows the segregation mutation effect in500 generations. Segregation mutation refers to as newmutation. When segregation mutation entered intopopulation its divides the whole population intosubpopulation groups one who carry the mutation andone that do not. Segregation mutation only occur in oneindividual and it differentiate that individual from wholepopulation. Segregation mutation may be transfer to morethan one generations. The term demic refers to as thedispersal of population into and across an area that hadbeen priory uninhabited by segregation mutation. Thisplot represents that only 5% of the demic populationcarries the segregation mutation effect in deleteriouslocus and 95% population is without the segregationmutation effect in deleterious locus. In natural selection,elimination of selective alleles is significant for thestabilization of the selection, through the exclusion ofdeleterious discrepancies that arise. Exclusion ofdeleterious alleles can easily be achieved through thesingle point segregation mutation that used as unit ofselection.

Deme is a smaller group or sub populationwithin the whole population that can spontaneouslyinterbreed. Population habitually made of numerousdemes that are isolated(partially) from each other.

We try to estimate the level of gene flowbetween demes for natural population. In this simulation,we considered “Island model” that proposed by SewallWright in 1931. This model specifically used forcalculating gene flow of random migration betweendemes or subpopulation. Within demes, Fig.14 showsthat the amount of gene flow at start is higher thatgradually decrease, but after 100 generations the responseof alleles seems to be stable. Without the selection,genetic variations among demes or subpopulation resultsform an equilibrium between gene flow and genetic drift.This equilibrium effect can easily be seen in figure after120 generations.

Fig.15 highlighted the effect of deleteriousmutation on the heterozygosity of alleles. Heterozygosityis phenomena of having different alleles at single or moreconsistent chromosomal loci for a trait. Terms refers to asho: observed heterozygosity, htnei: expected totalheterozygosity, and Fst: variance between population.Note that the effect of deleterious mutation on theobserved heterozygosity is about 0.76, and effect onexpected heterozygosity is about 0.99 for 100generations. Moreover, effect of deleterious mutation onthe variance of population is about 0.23 for 100generations. Deleterious effect on heterozygosity is used


1877

to measure the genetic variations within the population.This graph displays the influence effect of deleteriousmutation on heterozygosity of alleles. Note that expectedfitness is about 0.77 and observed fitness is about 0.51 for500 generations. Observed fitness is 26% less than the

expected fitness. Fst can easily be find out by: expectedfitness (0.77) observed fitness (0.51). Hence, this resultconcludes that the effect of deleterious mutation is mostimportant factor that highly influenced on the fitness ofthe population.

Fig. 10. Occurrence frequency of individual mutations


1878

Wrights proposed in 1951 whose fixationindices (Fis, Fit, and Fst). According to Wrights theorythese parameters are valuable for the study of geneticvariance of the populations. These parameters weredefined in terms of association of two uniting gametes. Innatural evolutionary processes, there are numerousdifficulties for finding the probability of correlations ofuniting gametes, gene identities in the demes orsubpopulation. In this situation, Nei’s in 1977 redefinedthese parameters in terms of expected or observedheterozygosity and simplify the computational process.Nei’s model applicable on any diploid population, with orwithout selection process, and with or without thepresence of multiple alleles. Nei’s model based on theallele frequencies of the population. Nei’s model iseffective for small population size.

Fig.16 shows the genetic variance analysis byusing Nei and Chesser model. Parameters and theirrespective colors are following, ho(red): observedheterozygosity, hsnei(green): expected

demicheterozygosity, htnei(blue): expected totalheterozygosity, Fis(purple): genetic variance betweenindividuals, Fit(black): genetic variance withinindividuals, and Fst(yellow): genetic variance betweenpopulation. Note that observed heterozygosity andexpected demic heterozygosity is almost same. Also, notethat genetic variance within individuals and geneticvariance between sub populations is nearly similar. Theseplotted lines show the close relationship of geneticvariance within individuals between subpopulations. Aswe see that, observed heterozygosity and expected demicheterozygosity are smaller than the expected totalheterozygosity. These lines reveal the correlation ofvariance in heterozygosity. Moreover, genetic variancebetween individuals is so smaller and it seems to be zero.This plotted graph shows the relationships that existwithin the population by using statistical values and Neiand Chesser model. This graph proves that the Nei andChesser’s model is highly suitable for small populationsize.

Fig. 11.Demography of male and female offspring (Nemo).

Fig. 12.Dispersal rate of male and female offspring (Nemo).


1879

Fig. 13.Segregation mutation (Nemo).

Fig. 14.No of alleles within whole population and within demes (Nemo).

Fig. 15.Deleterious mutation effect on the heterozygosity of alleles (Nemo).

Fig. 16.Genetic variance analysis by using Nei and Chesser model (Nemo).


1880

Conclusion: Target area of this work is development,enrichment, and incorporation of population geneticsimulation tools on cloud based scientific platform. Thisresearch work highlighted different factors of Wolbachiathat directly associate with prevention of arboviraldiseases. Furthermore, this work also improves thefeatures of population genetics simulation tools afterincorporating them on cloud based scientific platform.However, some significant challenges need to beaddressed in future including: development of advancedsimulation tools and platforms for population genetics,efficient analysis of more vectors for human diseases, andadvancement of prevention strategies for congenitalinfections.

REFERENCES

Baumgardner, J.,J. Sanford,W. Brewer,P. Gibson and W.ReMine. (2008). Mendel’s Accountant: A newpopulation genetics simulation tool for studyingmutation and natural selection. Proceedings ofthe sixth international conference oncreationism.8798.

Benelli, G.,C.L. Jeffries and T. Walker. (2016).Biological control of mosquito vectors: past,present, and future. Insects. 7(4):52.

Brewer, W.,W. Scott and J. Sanford. (2015). AnIntegrated Cloud Platform for Rapid InterfaceGeneration, Job Scheduling, Monitoring,Plotting, and Case Management of ScientificApplications. Cloud Computing Research andInnovation (ICCCRI), 2015 InternationalConference on.156165.

Dutra, H.L.C.,M.N. Rocha,F.B.S. Dias,S.B. Mansur,E.P.Caragata and L.A. Moreira. (2016). Wolbachiablocks currently circulating Zika virus isolates inBrazilian Aedes aegypti mosquitoes. Cell host &microbe. 19(6):771774.

Granados, M.P.,Y. Joly and B. Knoppers. (2017).PublicPrivate Partnerships in CloudComputingServices in the Context of Genomic Research.Frontiers in medicine. 4:3.

Guillaume, F. and J. Rougemont. (2006). Nemo: anevolutionary and population genetics

programming framework. Bioinformatics.22(20):25562557.

Haller, B.C. and P.W. Messer. (2016). SLiM 2: flexible,interactive forward genetic simulations.Molecular biology and evolution. 34(1):230240.

Hopf, T.A.,J.B. Ingraham,F.J. Poelwijk,C.P. Schärfe,M.Springer,C. Sander and D.S. Marks. (2017).Mutation effects predicted from sequencecovariation. Nature biotechnology.35(2):128135.

Jiggins, F.M., (2017). The spread of Wolbachia throughmosquito populations. PLoS biology.15(6):e2002780.

Parobek, C.M.,F.I. Archer,M.E. DePrenger Levin,S.M.Hoban,L. Liggins and A.E. Strand. (2017).skelesim: an extensible, general framework forpopulation genetic simulation in R. Molecularecology resources. 17(1):101109.

Reis, N.N.,A.L. da Silva,E.P.G. Reis,F.C. e Silva andI.G.N. Reis. (2017). Viruses vector controlproposal: genus Aedes emphasis. The BrazilianJ. Infectious Diseases.

Sharma, A.K. and R.K. Jha. (2015). Cloud Computing.Expansion, Impact and Challenges of IT & CS.103.

Thornton, K.R., (2014). A C++ template library forefficient forwardtime population geneticsimulation of large populations. Genetics.198(1):157166.

Van den Hurk, A.F.,S. HallMendelin,A.T. Pyke,F.D.Frentiu,K. McElroy,A. Day,S. Higgs and S.L.O'Neill. (2012). Impact of Wolbachia oninfection with chikungunya and yellow feverviruses in the mosquito vector Aedes aegypti.PLoS neglected tropical diseases. 6(11):e1892.

Von Seidlein, L.,A.S. Kekulé and D. Strickman. (2017).Novel Vector Control Approaches: The Futurefor Prevention of Zika Virus Transmission?PLoS medicine. 14(1):e1002219.

Yixin, H.Y.,A.M. Carrasco,F.D. Frentiu,S.F.Chenoweth,N.W. Beebe,A.F. van den Hurk,C.P.Simmons,S.L. O’Neill and E.A. McGraw.(2015). Wolbachia reduces the transmissionpotential of dengueinfectedAedesaegypti. PLoSneglected tropical diseases. 9(6):e0003894.

incorporating population genetics simulators ...shahid et al., the j. anim. plant sci. 28(6): 2018...

Documents