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Discussion

Malaria remains a major health problem in Central India despite of 50 years of

control measures. Malaria is complex in Central India due to vast tracts of forest with

tribal settlement (Singh et al., 2004c). The nomadism practiced by tribal people and

developmental activities has posed a sizeable problem with an increase in malaria cases

(Singh et al., 2003). Anopheles stephensi contributes about 12% of the total malaria

cases in India (Adak et al., 2005). Determining genetic structure can help understand

the heterogeneities of disease transmission due to genetically distinct vector

populations and to predict the spread of genes of interest, such as those involved in

insecticidal resistance or refractoriness (Ndo et al., 2010). It was hypothesized that

“Since Central India has a complex topography with vast forest tract, large river basins

and several different hill ranges, there should be significant genetic structuring in An.

stephensi in this region”.

5.1. GENOMIC DNA AND PCR AMPLIFICATION

Genomic DNA was isolated from the legs and wings of the individual field

collected mosquitoes to avoid any contamination of unwanted foreign DNA. The

isolated DNA was found to be of good quality as the O.D. 260/280 ratio for all

individuals varied from 1.8-2.0. The quality of DNA was considered good at this O.D.

(Sambrook et al., 1989). The DNA yield was also good ranging from 20 ng/µl to 210

ng/µl. Microsatellite markers are informative, easy to score, polymorphic and co-

dominant, therefore, they are considered today‟s method of choice for DNA

polymorphic studies or microsatellite characterization (Crawford and Littlejohn, 1998).

Sixteen microsatellite markers, 15 developed by Veradi et al. (2002) and one by Djadid

et al. (2003; unpublished), for An. stephensi were used in this study. Amplification of

specific microsatellite loci by multiplex PCR resulted in a clear band of similar size in

all the studied population of An. stephensi (Figure 3.4). Out of 16 microsatellite

markers i.e. F10, H2II, E7T, E7, E12, B2N, G1, H1, A7, D7, G11, B1, D8T, A10, B2

and MSH1, only 13 could be analyzed. Loci E7T, D7 and G11 did not result in a

visible PCR product in all the population.

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5.2. MICROSATELLITE MARKERS

The microsatellite markers used in the population studies are expected to be

neutral and it is assumed that no selection is acting on the locus. The Ewens Watterson

test for neutrality was performed to estimate the selection on each locus. It revealed that

the microsatellite markers E7, H2II, E12 and H1 in nine populations of An. stephensi

were not neutral because the observed F values for these markers out lied the lower and

upper limits of 95 % confidence region of expected F values. This indicated that these

markers may be associated with some coding regions of the genome. In the present

study, it was also observed that all of the expected homozygosity values were higher in

comparison to observed homozygosity values, thereby revealing that natural selection

is working against homozygotes and heterozygotes are being favored. Since

heterozygotes, have a better chances for adaptation and survival under harsh climatic

conditions, so they were favored most as indicated by high level of heterozygosity in all

the nine studied populations of An. stephensi from Central India.

All the 13 microsatellite loci used in the present study were highly polymorphic

with Polymorphism Information Content (PIC) values higher than 0.5 and thus useful

for exploring the population genetic structure (Mukesh et al., 2013). Previous studies

on An. stephensi in Rajasthan using these microsatellite loci also revealed similar

results and all the loci were found to be polymorphic (Vipin et al., 2010a, b

). No other

information is available about the population genetic studies of An. stephensi in any

other part of India using microsatellites.

5.3. GENETIC DIVERSITY

Genetic diversity refers to the total number of genetic characteristics in the

genetic makeup of a species. It is distinguished from genetic variability, which

describes the tendency of genetic characteristics to vary. Genetic diversity serves as a

way for populations to adapt the changing environments. With more variation, it is

more likely that some individuals in a population will possess variations of alleles that

are suited for the environment. Those individuals are more likely to survive to produce

offspring bearing that allele. The population will continue for more generations because

of the success of these individuals. The genetic diversity can be measured by number of

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alleles (Na) and expected heterozygosity (He) or the allelic richness (Ar) and expected

heterozygosity (He).

The genetic diversity for each population as assessed by allelic richness (Ar) and

expected heterozygosity (He) was found to be low Bhopal (BPL) while the maximum

genetic diversity was observed in the Gwalior (GWL). The average observed and

expected heterozygosities were found to be more than 0.3 and 0.5, respectively in all

the studied populations, which indicated the presence of large number of polymorphic

loci and sufficient variability in all the populations of An. stephensi from Central India.

The average numbers of alleles (average Na) were found to be ≥ 5.000 in all the

studied populations of An. stephensi which further supported the presence of large

variability in the studied microsatellite loci. The high mutation rate seems to account

for the large variability observed in the present study. As explained by Arranz et al.

(2001), unique recombination exchange between homologous chromosomes during

meiosis and polymerase slippage mechanism that tend to produce non-identical copies

of repeated DNA sequences can contribute to high microsatellite mutation rate.

The effective number of alleles (Ne) is the number of equally frequent alleles it

would take to achieve a given level of gene diversity. It allows us to compare

populations where the number and distributions of alleles differ drastically. The

effective numbers of alleles (Ne) were found to be more than half of the observed

number of alleles (Na) in the present study populations. Effective number of alleles are

more useful component in population genetic studies than the observed number of

alleles (Crow and Kimura, 1970), since most of the alleles may be represented only

once or twice in a population and contribute very little to the genetic variance in the

population. Genetic drift and mutations lead to divergence of allelic frequencies among

subpopulations. The effective number of alleles will be same as that of the observed

number of alleles in a population when population is showing maximum gene diversity

and it will be minimum when one allele (the only real contributor to the effective allele

number) dominates the allele frequencies and all the others are very rare. The locus E12

was the most polymorphic locus in all the populations showing the highest number of

alleles (Na) and effective number of alleles (Ne).

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The overall high genetic diversity suggests that An. stephensi is able to maintain

its effective population size in spite of seasonal variation, insecticide spray and other

environmental factors (Ma et al., 2011). The previous studies have also indicated high

genetic diversity (Vipin et al., 2010a) in the populations of An. stephensi in Rajasthan

(Vipin et al., 2010a). Nine microsatellite loci developed for An. stephensi were used in

the study and seven loci were common to current study. In India, another recent

population study on An. culicifacies using eight microsatellite loci indicated similar

results with number of alleles (Na) in same range as in the current study (Sunil et al.,

2013).

5.4. HARDY-WEINBERG EQUILIBRIUM AND LINKAGE DISEQUILIBRIUM

The Hardy-Weinberg principle states that under the condition of large

population size, diploid organisms with non-overlapping generations and random

mating, the genotype frequencies at a locus are determined by the allele frequencies,

and both the genotype and the allele frequencies will stay constant in future generations

with the conditions of no mutation, no migration and no selection hold. The deviation

from the Hardy-Weinberg equilibrium strongly suggests that at least one of these

assumptions is violated (Chen, 2010).

Significant deviation from the Hardy-Weinberg Equilibrium was found in the

11.11% of the total tests conducted for the conformance of the Hardy-Weinberg

Equilibrium. Almost all the deviations were associated with the positive Inbreeding

coefficient (FIS) suggesting heterozygote deficiency. These could be attributed to the

wahlund effect, inbreeding, selection or null alleles i.e. mutation at the binding site

which prevents annealing of primers to the binding site (Vipin et al., 2010a, b

). Null

alleles are locus specific whereas Wahlund effect and inbreeding affect the entire

genome (Lehmann et al., 1999). Wahlund effect and the presence of subpopulation can

be ruled out as the collection was done from the indoor sites (Human dwelling and

cattle sheds) and samples were collected from a single area or two- three more sites

within 5-10 km range (Sunil et al., 2013). If Wahlund effect or inbreeding were the

cause of heterozygote deficits, it should cause linkage disequilibrium between loci

because members of the different subpopulation have different probabilities to carry

certain combinations of alleles. But if null alleles caused the heterozygote deficits,

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linkage equilibrium is not expected, because all individuals are equally likely to carry a

null allele and the association between alleles from different loci is not disturbed

(Lehmann et al., 1999).

Linkage disequilibrium tests were carried out to know the exact reasons of

observed heterozygote deficiencies. Only 1.70% significant deviations from linkage

disequilibrium were detected out of the 720 tests ruling out the possibility of inbreeding

in the samples and indicating the statistical independence of the loci. Inbreeding often

arises in small and genetically isolated or closed populations which tend to lose genetic

variability over time and thus may in turn increase the probability of extinction or

reduces the opportunity for future adaptive change (Mukesh et al., 2013). The results

strongly indicated that heterozygote deficits may have been caused by null alleles

instead of Wahlund effect and inbreeding. Further, the DNA samples of some

individuals could not be amplified by PCR which suggested the presence of null allele

homozygotes in the studied populations of An. stephensi. Therefore, the null alleles

were considered to be solely responsible for heterozygote deficiency as confirmed by

their presence across all populations at 11 out of 13 microsatellite loci. The null alleles

can results in either total failure of the amplification or mostly one strand will be

amplified and the sample will be scored as a homozygote, which in fact is a

heterozygote (Lehmann et al., 1996). Sequencing studies indicated that changes in

flanking region sequences occur at a non-negligible rate (Angers and Bernatchez,

1997; Grimaldi and Crouau-Roy, 1997). Such variation in the nucleotide sequences of

flanking regions may prevent the primer annealing to template DNA during

amplification of the microsatellite locus by PCR, resulting in a null allele. Other

possible causes of microsatellite null alleles include the preferential amplification of

short alleles (due to inconsistent DNA template quality or quantity) or slippage during

PCR amplification (Gagneux et al., 1997; Shinde et al., 2003).

Null alleles can bias parametric population structure inference because number

of homozygous individuals is increased due to their presence (Putman and Carbone,

2014). Null alleles affect the estimation of population differentiation by reducing the

genetic diversity within populations (Paetkau and Strobeck, 1995). Markedly, FST and

genetic distances values generally increase with decreasing within-population genetic

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diversity (Slatkin, 1995; Paetkau et al., 1997). The presence of microsatellite null

alleles has been reported frequently in PCR primer characterization and in population

genetics studies (Dakin and Avise, 2004). Although microsatellite null alleles have

been found in a wide range of taxa, species with large effective population sizes have a

particularly high frequency of null alleles; examples include insects (Lepidoptera,

Meglecz et al., 2004; Diptera, Lehmann et al., 1997; and Orthoptera, Chapuis et al.,

2005) and mollusks (Li et al., 2003; Astanei et al., 2005). However, comparing the null

allele corrected data and original data, minimal non-significant differences were found

between them. Similarly, null alleles were also found to be responsible for heterozygote

deficit observed in previous studies on An. stephensi (Vipin et al., 2010a; 2010b) in

Rajasthan.

5.5. BOTTLENECK

Certain events such as selection and genetic bottleneck can accelerate the rate of

inbreeding due to the limited number of individuals that contribute to future

generations. Bottlenecks can increase demographic stochasticity, rates of inbreeding,

loss of genetic variation, and fixation of mildly deleterious alleles, thereby reducing

evolutionary potential and increasing the probability of population extinction

(Brakefield and Saccheri, 1994; Frankel and Soule, 1981; Frankham ,1995a; Hedrick

and Miller, 1992; Jimenez et al., 1994; Lande, 1988; 1995; Mills and Smouse, 1994;

Newman, 1996; Ralls et al., 1988; Vrijenhoek, 1994). The non-bottlenecked

populations are likely to be near mutation-drift equilibrium. For selectively neutral loci,

allele number and frequency distribution in a natural population results from a dynamic

equilibrium between mutation and genetic drift. This „„mutation-drift‟‟ equilibrium will

be approximately reached if the effective population size (Ne) remains stationary for

(4–10 multiplied by Ne) generations (Nei and Li, 1976).

In a population experiencing bottleneck expected heterozygosity under

equilibrium (Heq) based on number of alleles and sample size is expected to be lower

than its expected heterozygosity (He) based on allele frequencies because allele number

is reduced faster by bottleneck than expected heterozygosity (Cornuet and Luikart,

1996). This forms the basis of detection of bottleneck signature. These parameters are

expected to be the same in populations at equilibrium, provided that the correct

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mutation model is used. The two-phase mutation model (TPM) is considered as the

most suitable model for microsatellites (Cornuet and Luikart, 1996; Di Rienzo et al.,

1994 and Jarne and Lagoda, 1996). No significant deviations were found in all the

studied populations of An. stephensi under Two Phase mutation model (TPM) with

fraction of mutations set to 10%, 20% and 30%, however, under stepwise mutation

model (SMM) significant deviations associated with He < Heq and strong heterozygote

deficiency were found in the population of Anuppur (ANP), Ujjain (UJN), Indore

(IND) and Umaria (UMR), suggesting a demographic expansion in these populations

and that SMM is an inadequate model for these loci. The results showed no obvious

violation of equilibrium and therefore suggested no recent bottleneck. The absence of

bottleneck was further confirmed by M-ratio test in which the Garza Williamson ratio

for all the studied populations was found to be greater than bottleneck threshold ratio.

Allele frequency distributions contain information about both quantitative diversity, the

frequency of alleles and the total number of alleles, k ; and spatial diversity, the

distance between alleles in number of repeats and the overall range in allele size, r.

When a population is reduced in size, genetic drift is enhanced and alleles will

eventually be lost. However, because the loss of any allele will contribute to a reduction

in k, but only a loss of the largest or smallest allele will contribute to a reduction in r, k

is expected to be reduced more quickly than r. Thus, we expect the ratio M = k / r to be

smaller in recently reduced populations than in equilibrium populations (Garza and

Williamson, 2001). No bottleneck was found in the populations of An. stephensi from

Rajasthan in the earlier study (Vipin et al., 2010a), however, in a similar study among

the three ecological variants „type’, „mysorensis’ and „intermediate’, population

bottleneck was found in the field collected samples of „type’ variant (Vipin et al.,

2010b). The absence of bottleneck in most An. stephensi populations from Central India

suggests that it is able to maintain large population size and thrive well, without being

affected by climatic conditions and various vector control measures implemented for

malaria control.

5.6. POPULATION STRUCTURING

Due to the complex topography and vast number of geographic factors that

might affect the population genetic structure of An. stephensi in the Central India, it

was hypothesized that there would be significant genetic structuring in the population

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of An. stephensi from Central India. The bayesian clustering analysis, however,

revealed only two overlapping genetic pools of the An. stephensi in the Central India.

The individuals from all the populations were confined to two overlapping genetic

clusters. No substantial genetic structuring was found in populations studied.

Out of the total 248 individuals from all the populations, 230 were assigned to

either of the clusters while 18 individuals were found to be hybrid individuals, who

could not be assigned to any the genetic cluster based on their q values. The cluster I

was made up of most of the populations of An. stephensi in Central India (Figure 5.1).

A unidirectional sharing of the genetic identity was seen because in two clusters, the

maximum individuals from cluster I were pure with very less mixing, whereas, cluster

II shared more genetic identity with cluster I (Figure 4.11). The weak genetic

structuring in the populations of An. stephensi from Madhya Pradesh was further

confirmed by the three-dimensional Factorial correspondence analysis (FCA).

Similar less genetic structuring were found in many previous studies on other

malaria vectors around the world using microsatellites (Table 5.1).

Table 5.1: Summary of studies on the genetic diversity of malaria vectors (Loaiza

et al., 2012).

Species Markers Likely cause of genetic structure References

An. gambiae ND5,

msats

High genetic variation and large Ne due to high

dispersal capabilities

Lehmann et al.,

1997

An. arabiensis Msats Extensive gene flow across 250 km Simard et al., 2000

An. arabiensis Msats No IBD and little population structure Nyanjom et al., 2003

An. arabiensis Msats Species occurs as a single, continuous panmictic

population

Muturi et al., 2010

An. nilli msats,

ITS2, D3,

COII, ND4

Extensive gene flow due to recent demographic

expansion

Ndo et al., 2010

No such study has been reported in India on An. stephensi. However, in a study

on An. culicifacies in India, significantly high population structuring was found due to

high differentiation values (Sunil et al., 2013).

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Figure 5.1: Populations of An. stephensi from Central India showing two

overlapping genetic pools.

Figure 5.2: UPGMA dendrogram based on Nei`s (1978) genetic distance values

between the populations of An. stephensi from Central India.

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5.7. GENETIC DIFFERENTIATION AND GENE FLOW

The genetic differentiation studies further supported the weak structuring as

very less genetic differentiation was found between different populations of An.

stephensi from Central India. The population of Bhopal (BPL) seems to be isolated

from all other populations because comparatively high genetic differentiation was

observed around the Bhopal (BPL). This was also evident from the UPGMA

denodogram constructed on the basis of Nei`s (1978) genetic distance, which showed

population of Bhopal genetically different from all the other studied populations

(Figure 5.2).

As compared to FST, RST is expected to be a more sensitive measure of

differentiation for microsatellites (Kimmel et al., 1996 and Slatkin, 1995). However, in

maximum of the pairwise comparisons, the RST was found to be less than the FST.

Lower sensitivity of RST in comparison to FST was earlier reported in humans by Perez-

Lezaun et al. (1997), in bears by Paetkau et al. (1997), in An. gambiae by Lehmann et

al. (1998) and in An. stephensi (Vipin et al., 2010a, b

). RST was especially designed to

capture the effect of high mutation rate under the stepwise mutation model, which is a

function of time and degree of isolation (Slatkin, 1995). The lower sensitivity of RST

suggested that differentiation between regions was primarily generated by drift and not

by mutation. However the presence of thirty unique alleles and the absence of

bottleneck in all nine populations indicate that differentiation was caused by mutations.

Why then is mutation effect not detected? The reasons may by the same as quoted by

the Lehman et al. (1999) in their hypothesis (1) mutation rate in the loci studied is too

low compared with drift, and (2) constraints on allele size arrest allele distributions.

The upper bound mutation rate in dinucleotide repeats of An. gambiae was estimated as

3.4X 10-5

(Lehmann et al., 1998 and Zheng et al., 1996). This is not an unusually low

rate since a lower mutation rate of 6.3 X 10-6

was measured in Drosophila melanogaster

(Schug et al., 1997). There are no other estimates available for other dipteran insects

thus it is reasonable to assume that the average mutation rate in An. stephensi

microsatellites is around 105 but not much higher. Temporal variation in allele

frequency due to drift is expected to be 1/ (2Ne) per generation (Waples, 1991). Under

these conditions, drift effect would be nearly 10 times stronger than mutations. Drift

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would also have greater effect than mutation if populations became separated only

recently or a population has passed a recent bottleneck. Alternatively, constraints on

allele size that bound the range of allele size would also minimize the role of mutations

(Lehmann et al.1999). However, the effect of constraints depends on high mutation rate

(Nauata and Weissing, 1996) which is not supported by the available data.

The values of gene flow were calculated based on both FST and RST. Overall

high gene flow was observed in the populations of An. stephensi in Central India. The

less genetic differentiation and high gene flow between the studied populations reject

the assumed hypothesis of significant genetic structuring in the Central India due to its

complex topography. Analysis of molecular variance (AMOVA) further confirmed this

as very less variation was explained by the different population and among individuals

in populations while 76 % of the total variation confers to within individuals.

5.8. ISOLATION BY DISTANCE

The differentiation studies indicated absence of role of geographic distance in

the genetic differentiation, as even populations distant apart was having high gene flow

and less differentiation. This was confirmed by Mantle`s test indicating no significant

correlation between the geographic distance and genetic differentiation thereby

rejecting the assumption of isolation by distance. In many past studies on other

Anopheles species using different molecular markers, differentiation pattern was found

to be influenced by the geographic distance (de Merida et al., 1999; Lehmann et al.,

2003; Scarpassa and Conn, 2007; Mirabello et al., 2008; Conn et al., 2006; Antonio-

Nkondjio et al., 2008). Isolation by distance was also found in the population of An.

stephensi in Rajasthan (Vipin et al., 2010a); however, in differentiation study of three

ecological variants of An. stephensi, no isolation by distance was found in the studied

variants (Vipin et al., 2010b). Geographic distance was also found to be playing no role

in differentiation in An. culicifacies populations in India (Sunil et al., 2013). The high

gene flow observed suggests more passive migration (i.e. through bus, trains or

airplanes) of the mosquitoes than the active, as An. stephensi is known to have flight

range of 3-5 Km (Quareshi et al., 1966). The demographic expansion linked with

human influence have already been reported in Anopheles (Donnelly et al., 2001;

Hasan et al., 2008a, b

)

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5.9. EFFECTIVE POPULATION SIZE

In addition to the various geographical barriers, the use of various insecticides

(DDT, malathion and deltamethrin) in Central India for vector control might also affect

the differentiation between different populations, however, we assumed that

differentiation is not being affected by the insecticides. Pyrethroids are extensively used

in malaria control programmes for indoor residual spray and for impregnation of bed

nets (Mishra et al., 2012). Though insecticide resistance has not been reported in An.

stephensi from Central India till now, the development of resistance in An. culicifacies

mosquito to pyrethroid has already been reported in Madhya Pradesh (Central India).

The An. stephensi genome might also be stable for the effect of insecticides in Central

India. Also, high effective population size was observed considering each population

separately, all the effective population size (Ne) values in the present study were high

under LD and HE model. Effective population size (Ne) helps us quantify how a

particular population will be affected by drift or inbreeding. Effective size takes into

account not only the current census size of a population, but also the history of the

population. Effective population size is the size of an “ideal population” of organisms

(ideal refers to a hypothetical population in the Hardy- Weinberg sense with a constant

population size, equal sex ratio, and no immigration, emigration, mutation, or selection)

that would experience the effects of drift or inbreeding to the same degree as the

population we are studying. Ne was ∞ for population of Sagar under both models

suggesting a very high density of An. stephensi locally.

The high values of the Ne suggest that despite frequent application of

insecticides, large populations of An. stephensi are being maintained. This implies that

insufficient levels of insecticide are currently being used or exclusively local

interventions may result in the influx of mosquitoes from untreated neighborhood

areas, resulting in operational failures and large population size again a short time after

application of insecticides (Scarpassa and Conn, 2007). The large populations of An.

stephensi reflected in the high Ne values may be contributing to an increase in gene

flow among subpopulations, resulting in little genetic structure for An. stephensi from

Central India.

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5.10. GEOGRAPHICAL BARRIERS

Along with the vast forest tracts and river basins, various mountain ranges exist

in Central India which might be acting as geographical barrier in the gene flow of An.

stephensi. Previous population genetic studies have indicated that mountain ranges can

act as barrier to gene flow of mosquitoes affecting genetic structure in their populations

(Molina-Cruz et al., 2004; Fairley et al., 2000; Rongnoparut et al., 2006; Jung et al.,

2007; Lehmann et al., 1999; 2000; Temu and Yan, 2005). In some studies, forest cover

(Antonio-Nkondjio et al., 2008) and rivers (Pedro and Sallum, 2009; Conn et al., 2006;

Reiff et al., 2007; Kayondo et al., 2005; Moreno et al., 2007;) has also been reported to

act as barrier restricting gene flow in mosquito populations.

The longest of theses ranges is Vindhya mountain range ,therefore, the

population structure of An. stephensi mosquitoes around the Vindhya mountain range

in Central India, which geographically separates the Indian subcontinent into northern

India (the Indo-Gangetic plain) and southern India was assessed to know the effect of

Vindhya mountain range on the population structuring of An. stephensi.

From Gujarat state on the west, it extends about 675 miles (1,086 km) across Madhya

Pradesh state to about Uttar Pradesh near Varanasi on the Ganges (Ganga) River valley.

The Vindhya Range has an elevation from 457 to 1066 m.

Five populations out of the nine were selected to measure the effect of Vindhya

mountain range in gene flow of An. stephensi. The five population were grouped

according to their geography around Vindhya mountain range as north and south

population The populations from north of Vindhya mountain range included Indore

(IND), Bhopal (BPL) and Sagar (SGR). Similarly, south populations included

Khandwa (KNW) and Jabalpur (JBL).

The genetic differentiation across the Vindhya mountain range was found to be

less and high gene flow was observed suggesting that Vindhya mountain range is not

acting as geographical barrier in the gene flow of the An. stephensi in Central India

(Appendix V). This was further confirmed by the analysis of molecular variance

(AMOVA) and Principal coordinate analysis (PCoA). The AMOVA revealed that 0%

variation in the dataset is explained by differences between two groups (north and

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south) indicating that the mountain range have no effect on differentiation (Appendix

VI). In agreement with the AMOVA, the PCoA showed the considerable overlapping

between the five populations. No significant structuring was found between north and

south populations by the PCoA analysis (Appendix VII). Although Vindhya mountain

range extend upto 675 miles separating Indian sub-continent into two parts, the

discontinuous nature of Vindhya mountain range can easily allow the movement of An.

stephensi mosquitoes through the gaps in the mountains. Similar results were reported

in previous study on An. stephensi across the Aravalli mountain range in India (Vipin et

al., 2010a) where the Aravalli range was found to be an inefficient geographical barrier.

In Central India, the malaria is complex because of the vast tracts of forest with

the tribal settlement (Singh et al., 2004c). The presence of various malarial parasites

and vector species, climatic diversity favoring growth and proliferation of parasite and

vector as well as a highly susceptible human population have resulted in high malaria

transmission in forested area of Central India (Singh et al., 2013). Large scale

development activities in the tribal belt of Madhya Pradesh due to several multipurpose

projects resulted in movement of people from one area to another. This coupled with

nomadism practiced by the tribal population of the state has posed a sizable problem

(Singh et al., 2004a) as P. falciparum is on the increase in tribal districts (Singh et al.,

2003; 2004a, b

). All these factors along with development of insecticide resistance in the

vector species might be responsible for increase in malaria incidence in Central India.

The Vindhya mountain range does not have any role in confining the malaria to

north and south of Madhya Pradesh.

The results of current genetic analysis of An. stephensi in Central India have

important implication for development of more effective and preventive strategies for

vector control using transgenic mosquitoes in near future and for predicting population

response to climate changes. We hope that these results may add a further step in

planning effective malaria control activities based on population genetic structure.