- Open Access
Ecological and genetic relationships of the Forest-M form among chromosomal and molecular forms of the malaria vector Anopheles gambiae sensu stricto
© Lee et al; licensee BioMed Central Ltd. 2009
Received: 30 December 2008
Accepted: 21 April 2009
Published: 21 April 2009
Anopheles gambiae sensu stricto, one of the principal vectors of malaria, has been divided into two subspecific groups, known as the M and S molecular forms. Recent studies suggest that the M form found in Cameroon is genetically distinct from the M form found in Mali and elsewhere in West Africa, suggesting further subdivision within that form.
Chromosomal, microsatellite and geographic/ecological evidence are synthesized to identify sources of genetic polymorphism among chromosomal and molecular forms of the malaria vector Anopheles gambiae s.s.
Cytogenetically the Forest M form is characterized as carrying the standard chromosome arrangement for six major chromosomal inversions, namely 2La, 2Rj, 2Rb, 2Rc, 2Rd, and 2Ru. Bayesian clustering analysis based on molecular form and chromosome inversion polymorphisms as well as microsatellites describe the Forest M form as a distinct population relative to the West African M form (Mopti-M form) and the S form. The Forest-M form was the most highly diverged of the An. gambiae s.s. groups based on microsatellite markers. The prevalence of the Forest M form was highly correlated with precipitation, suggesting that this form prefers much wetter environments than the Mopti-M form.
Chromosome inversions, microsatellite allele frequencies and habitat preference all indicate that the Forest M form of An. gambiae is genetically distinct from the other recognized forms within the taxon Anopheles gambiae sensu stricto. Since this study covers limited regions of Cameroon, the possibility of gene flow between the Forest-M form and Mopti-M form cannot be rejected. However, association studies of important phenotypes, such as insecticide resistance and refractoriness against malaria parasites, should take into consideration this complex population structure.
Anopheles gambiae sensu stricto is one of the major vectors responsible for malaria transmission in Africa. Genetic polymorphism in An. gambiae s.s. is a major factor contributing to the widespread occurrence of this disease vector both in time and space. Population genetic analysis of An. gambiae s.s. populations across Africa has revealed at least three genetically distinct groups within this single species. However, an extensive literature describes a highly complex population genetic structure on a much smaller spatial scale in West and Central Africa [2–8].
Touré and his colleagues  examined the distribution of five paracentric chromosome inversions on the right arm of chromosome 2 (2R j, b, c, d and u). They established a convention for describing the karyotype of an individual mosquito, wherein a dash ('-') is used to designate the standard homozygote, '1' for a heterozygote and '2' for the inverted homozygote for each chromosome inversion. Each karyotype, thus, includes five characters, each character denoting the "genotype" for each of the five 2R chromosome regions that include the j, b, c, d and u inversions. Analysis of karyotype frequencies among An. gambiae s.s. collected from a number of villages led to the identification of discrete subpopulations within this species which can be distinguished by their karyotype. These subpopulations were given non-Linnean designations and are collectively known as "chromosomal forms" [2, 5]. Five chromosomal forms have been identified and named Mopti, Bamako, Bissau, Forest and Savanna according to the regions from which they were first collected, underscoring the association of each with a particular type of habitat. Various field-based studies clearly demonstrate that these forms show distinct patterns of seasonal and geographic distributions [2, 5, 6, 9]. Detailed analysis of populations where multiple forms exist sympatrically revealed a high degree of reproductive isolation between the forms, with a strong preference for mating within rather than between forms .
Attempts to develop molecular diagnostics for the chromosomal forms culminated in the recognition of two distinct sequences in the intergenic spacer region of the ribosomal DNA locus [10, 11]. A PCR-RFLP technique is now widely used to distinguish between individuals carrying one or the other of the ribosomal "alleles". These have been termed "molecular forms" of An. gambiae. There are two molecular forms, M and S, which in some places (e.g. Mali) nearly always correspond with the chromosomal forms (S = Savanna or Bamako Form, M = Mopti Form). In many places, however, the association between chromosomal form and molecular form appears to break down. For example in Senegal the Savanna chromosomal form is frequently of the M molecular form [12, 13] and in Cameroon populations of the Forest chromosomal form may be either M or S .
Analysis of gene flow between molecular forms has revealed that, as with the chromosomal forms, there is very strong positive assortative mating within molecular forms with little or no mating between forms [8, 14]. Wondji et al.  found significant genetic differentiation between the M and S forms in Cameroon. The high levels of differentiation (FST) they observed were uniformly distributed over ten microsatellite loci covering the entire genome, leading them to conclude that differentiation is the consequence of complete reproductive isolation between the M and S forms in Cameroon. Likewise Slotman et al.  reported significant genetic differentiation between the M and S forms in Cameroon. They also compared M form populations in Cameroon with those in Mali and surprisingly found even higher levels of divergence than those reported between the M and S forms in both countries. These findings led them to conclude that there are two distinct M forms, which they termed Mopti-M (described from Mali) and Forest-M (described from Cameroon). These findings led to the hypothesis that both reproductive isolation, as determined by molecular form, and ecological factors affect the genetic makeup of An. gambiae s.s. and other markers such as chromosomal inversions should reflect such genetic divergence.
On the contrary, Yawson et al.  report a lack of differentiation between M and S forms in Ghana and Burkina Faso. They used both Bayesian clustering methods and FST-based analysis of allele frequencies at seven microsatellite loci. Although they did not find M/S hybrids, an admixture analysis suggests introgression between the two forms. They did, however, find strong differentiation between populations occupying different ecological zones, irrespective of M or S form, leading them to conclude that ecological barriers are more significant in restricting gene flow among populations than is reproductive isolation between the two forms.
In this paper, the results of a study investigating associations between chromosome inversions and molecular forms as well as the association between chromosome inversions and levels of precipitation are presented.
Average precipitation in millimeters recorded near collection sites.
Major IGBP land cover type within 10 km radius
Mean annual precipitation (mm/year)
Evergreen broadleaf forests
2789 ± 1207
Evergreen broadleaf forests
2800 ± 1240
1774 ± 280.3
1749 ± 271.0
Mosaic of grasslands, permanent wetlands and croplands
826.8 ± 256.6
Mosaic of woody savannas and savannas
1004 ± 133.2
958.5 ± 105.0
830.9 ± 157.5
380.1 ± 98.16
141.5 ± 69.82
170.8 ± 72.77
Species and molecular form diagnostic
The PCR assay described by Scott et al.  was used to identify members of the An. gambiae species complex. Molecular forms within An. gambiae s.s. were identified based on published methods [10, 19].
Polytene chromosomes were extracted from ovarian nurse cells using the protocol described by Hunt . Chromosome banding patterns were examined using an Olympus BX-50 phase contrast microscope. Species identification and karyotype scoring were accomplished using the polytene chromosome map for the An. gambiae complex developed by Coluzzi et al.  (maps available as supplement material). The chromosomal forms of An. gambiae s.s. were distinguished by scoring paracentric inversions on the right arm of chromosome 2, as described by Coluzzi et al. . The genotypes of six chromosome inversions --- 2La, 2Rj, 2Rb, 2Rc, 2Rd, and 2Ru --- were scored for each individual mosquito. Photographic images of chromosomes for the majority of individual mosquitoes used in this study are available on request.
Twenty microsatellite loci located on the second and the third chromosome were screened, namely AG2H57, AG2H85, AG2H125, AG2H135, AG2H164, AG2H175, AG2H197, AG2H675, AG3H119, AG3H127, AG3H242, AG3H249, AG3H312, AG3H555, AG3H577, AG3H59, AG3H746, AG3H812, AG3H817, AG3H93 . The same set of 12 markers on the third chromosome was used in the previous study by Slotman et al. . Some specimens used in Slotman et al.  that had associated karyotype data are also included in this study. Microsatellite markers were amplified using fluorescent primers and a PTC-225 thermolcycler (MJ Research, Watertown, MA). PCR products were mixed with a DS-30 Matrix Standard Kit (Dye Set D) (Applied Biosystems, Foster City, CA) and run on an ABI PRISM® 3100 Genetic Analyzer. Electrophoretograms were analysed using ABI PRISM® GeneScan analysis software and Genotyper 3.7 NT.
Average annual precipitation for each collection site listed in Table 1 were computed using daily precipitation data, over multiple years, from weather stations within 15 km radius of each sample collection site. Daily precipitation recorded in Cameroon stations near mosquito collection sites was acquired by personal communication with Dr. Yongkang Xue in the Department of Geography at the University of California – Los Angeles. Precipitation data for Mali stations were acquired by personal communication with Dr. Charles E. Taylor in the Department of Ecology and Evolutionary Biology at the University of California – Los Angeles. These data are provided in Additional file 1.
The International Geosphere-Biosphere Programme (IGBP) global vegetation classification scheme in 1 km resolution collected in 2004 was acquired in the form of the Land Cover Yearly L3 Global 1 km (MOD12Q1) data set produced by the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) project [22, 23]. The data is in Hierarchical Data Format (HDF), which is the standard data format for all NASA Earth Observing System (EOS) data products. The HEG version 2.9 (HDF-EOS to Geotiff) conversion tool was used to convert the acquired HDF file to Geotiff . ArcMap version 9.1 was used to draw maps illustrating the habitat types surrounding each collection site.
STRUCTURE software v2.2 [25–27] was used for Bayesian clustering analysis of seven markers which include genotypes of molecular form and of 2La, 2Rj, 2Rb, 2Rc, 2Rd, and 2Ru chromosome inversions. Six consecutive symbols are used to describe karyotypes representing the genotype of inversions 2La, 2Rj, 2Rb, 2Rc, 2Rd, and 2Ru. '-' represents the standard (uninverted) homozygous arrangement, '1' heterozygotes, '2' inverted homozygotes. So, for example an individual that is homozygous for the inverted arrangement at 2La (2), homozygous standard for 2Rj (-), heterozygous for 2Rb (1), heterozygous for 2Rc (1), homozygous standard for 2Rd (-) and heterozygous for 2Ru (1) would be represented as: 2-11-1. Scoring of karyotypes that include inversions in the heterozygous condition is often ambiguous. For example, it is not possible to distinguish the 2Rbcu/+ from 2Rbc/u karyotype (i.e. gametic phase is uncertain). Regardless of gametic phase or haplotype, the input for this genotype is (- 11-1, -----) or (-1---, --1-1). For this type of Bayesian analysis, either input represents exactly the same genotype so will have the same effect on the outcome of the clustering analysis. The Bayesian model does not distinguish the two but considers the fact that the corresponding individual is heterozygous for 2Rb, 2Rc, and 2Ru. Linkage between loci is something the model explores when it tries to find the most likely solution (grouping).
The same analyses were applied to microsatellite genotypes. Individuals were clustered into a hypothetical number of discrete populations, K = [1, 8] and the probability of the data given K, Prob(X|K), was computed for each run. Various combinations of burn-in period and sampling period were tested. In this case, a burn-in period of 100,000 and sampling period of 100,000 were sufficient. The probability of each individual belonging to each of the K populations was plotted using distruct software . Raw data is provided in Table S1.
The ELB algorithm  as implemented in the Arlequin software package  was used to identify the gametic phase of karyotypes. A burn-in period of 10,000 and sampling period of 1,000 were used for estimating gametic phase. Estimated gametic phase information was subsequently used for a Hardy- Weinberg equilibrium (HWE) test. The Guo's Exact HWE Test  was performed both on the locus and gamete level for each collection, which consists of mosquitoes collected at the same site, same day, and of the same molecular form. A burn-in period of 100,000 and sampling period of 100,000 was used to calculate the probability of HWE. Arlequin was also used to calculate pair-wise FST values based on microsatellite genotypes among the six groups identified in Bayesian clustering analysis. The neighbourjoining algorithm implemented in the neighbor program, a feature of Phylip v.3.67 [32, 33] was used to illustrate relationships among groups. The drawtree program, also included in Phylip v.3.67, was used to generate a phylogenetic tree based on a pair-wise FST distance matrix. R software  was used for drawing boxplots, computing Wilcoxon rank sum tests, and performing linear regression.
Distribution of An. gambiae s.s. forms and inversions
The distribution of molecular forms and 2La inversion genotypes (karyotypes) are illustrated in Figure 1. In Cameroon, S forms were found in all five sample locations, while M forms were localized in the evergreen broadleaf forest region below latitude 4.5°N. In Mali, S forms were dominant below latitude 12°N in mostly savanna regions, while M forms were predominant above 15°N where land cover types are mostly grasslands or open shrublands (Table 1, Figure 1).
Chromosome inversion polymorphism was examined on the second chromosome, specifically 2La, 2Rj, 2Rb, 2Rc, 2Rd and 2Ru. Karyotypes, as well as molecular forms were distributed in a non-random fashion. For instance, only 25% of samples collected in Cameroon were 2La inversion homozytoes (a/a), mostly concentrated in a village called Godola located in northern Cameroon. The 2La inversion was completely absent in Tiko and Mutengene. In contrast, about 87% of specimens collected in Mali were 2La inversion homozygotes (Table 1, Figure 1) and only three specimens were 2La standard homozygotes (+a/+a).
The M forms found in Mali were polymorphic for all six inversions (96% of the samples from Mali carried at least one copy of the 2Rb and/or c inversions), but were predominantly 2La inversion homozygotes. M form populations in Cameroon were predominantly fixed for the standard karyotype (M------) for all six inversions, including 2La. In the savanna region of northern Cameroon the M form occurred, but was rare (e.g. 1 M form from Maroua, 10.60 N 14.33E, 3 M forms from Ourodoukoudje, 9.083 N 13.683E and 4 M forms from Raio, 9.117 N 13.717E). In these cases all had the M2-22-- (2La a/a, 2R bc/bc) karyotype. Due to very small sample size, these sites were not included in this study.
The S forms collected in Cameroon were polymorphic for the 2La and 2Rb inversions, although inversion homozygotes for 2La and 2Rb were relatively rare compared to Mali. In Cameroon, 30% (43/143) of S forms were 2Rb homozygotes, but 93% of these were from a single site (Godola). The standard arrangement for 2Rb (+b/+b) was also common, occurring at a frequency of 45% (64/143) in Cameroon. Fewer +b/+b S form individuals were observed in Mali (18%, 48/273) than in Cameroon. S form 2Rb homozygotes occurred with equivalent frequencies in the villages of Banambani, Kela and Pemperena, perhaps because these occur in similar land cover types (Table 1). In drier areas of Mali such as grasslands and open shrublands, the S form was rare. The correlation of 2La and 2Rb inversions with an environmental parameter, precipitation, is discussed below.
Identification of discrete populations based on molecular form and karyotype
Sample table site names, locations, sample sizes and molecular form and 2La inversion composition
Molecular form & 2La karyotype
The analyses depicted in Figures 2A and 3A included molecular form and six chromosome inversion genotypes as criteria. A range of values for K was tested, which denotes the hypothetical number of populations. The solutions for K = 7 have the highest likelihood (Figure 3A).
The Forest M form was classified as a discrete population, fixed for the standard arrangement (M------) for all six inversions and carrying the M ribosomal allele (Table 2, shown in green in Figure 2). These are the same individuals identified as 'M 2L+/+' in Figure 1 (marked in green) mostly found in evergreen broadleaf forest regions in Cameroon. The observed lack of inversion polymorphism is consistent with previous observations by Wondji et al. [14, 35]. Although some inversion polymorphism in M forms was observed in their study these were restricted to woody savanna areas (Tibati, Dschang) or to habitats at the border of evergreen forests and woody savannas. Two M form samples from Mali (one from Kela and the other from Banikane) had standard 2R arrangements. However, both were homozygous for 2La (2La a/a), consequently the Bayesian models predicted that these two samples belong to the Mopti-M form group rather than Forest M form (See Additional file 2 for genotype and corresponding membership coefficients).
The S molecular form was divided into four groups, Forest-S (light blue), Savanna-S (blue), Bamako-S (purple), and Godola-S (yellow, Figure 2A). Forest-S forms were polymorphic for the 2La and 2Rb inversions, although inversion homozygotes for 2La or 2Rb were absent (Table 2). In contrast, inversion homozygotes for 2La and 2Rb were very common in the Savanna-S form (Table 2). The amount of gene flow between these two groups was relatively high as indicated by the presence of many individuals with mixed light blue and blue colors. This could be interpreted as active gene flow between the two groups, or less confidence in the clustering due to a lack of more discriminatory markers and/or the groups having relatively similar inversion frequencies.
Individuals homozygous for 2Rj, c, and u inversions were classified as the Bamako-S group (shown in purple in Figure 2A); this group represents the Bamako chromosomal form [5, 9]. The 2Rb inversion was polymorphic within this group. Deviation from HWE were significant among S forms collected in Kela (P = 0.0000 ± 0.0000) and Banambani (P = 0.0000 ± 0.0000) as expected considering the S forms in these locations include both Bamako and Savanna chromosomal forms.
The samples collected in Godola were all of the S molecular form, but included unusual karyotypes that do not fit within the conventional definitions for An. gambiae chromosomal forms [5, 9, 36]. The Savanna form typically has the 2Rb inversion alone or in combination with the 2Rc and u inversions. At Godola, with the exception of a single individual, the 2Ru inversion was absent. The most common arrangement (82% of individuals) was 2Rbcd and 40% of the individuals sampled from this site were homozygous for the 2Rb, c and d inversions (Table 2). Deviation from HWE in the S-Godola population was significant (P = 0.0064 ± 0.0002), mainly due to 1 specimen with the S2-22-2 (bcu/bcu) genotype. Only six individuals (13%) had typical Savanna karyotypes. The 2Rd inversion is a very unusual arrangement both in Mali (freq. = 0.008) and Cameroon (freq. at sites other than Godola = 0.026). In Godola the 2Rd inversion occurred at a frequency of 0.544. Moreover, S form individuals carrying the 2Rbc combination (without 2Ru), typical arrangements for the Mopti chromosomal form (M molecular form), is noteworthy.
Identification of discrete populations based on microsatellite genotypes
Major karyotypes associated with each population identified in Bayesian clustering analyses.
Clustering based on microsatellite genotype (K = 5)
Clustering based on karyotype (K = 7)
Color (Figure 2)
The outcome of the Bayesian clustering analysis was compared based on karyotypes with those based on microsatellite genotypes (Figures 2B, C and 3B). Wilcox two-sample test indicates that K = 4 and K = 5 have similar likelihoods (P = 0.42). K = 5 generated a solution with the highest posterior probability Prob(X|K), where X is genotype data. Boxplots drawn after collecting posterior probabilities from 5 independent runs are presented in Figure 3B and resulting membership coefficients are plotted in Figure 2B (K = 4) and 2C (K = 5).
Under the assumption of K = 5 (Figure 2C), the Bayesian model identified the Forest-M form group (green) in Tiko and Mutengene, a Forest-S form (light blue) group, mostly concentrated in southern and central Cameroon, Bamako-S form group, Savanna-S group, and the Mopti-M form (red) group in Mali. The Savanna-S form group indicated in blue subsumes the Godola-S group identified based on karyotype (Figure 2A). Unlike karyotype clustering, the Mopti-M form remained as a single group based on microsatellite genotypes at both K = 4 and K = 5. This is reflected in the relatively low FST values between the two groups (Table 3, Figure 4).
Chromosome inversions, molecular forms and the environment
Estimation of pair-wise genetic divergence (FST).
Mopti M other
The International Geosphere-Biosphere Programme (IGBP) land cover scheme (Figure 1) identifies Tiko and Mutengene, where the Forest-M form is abundant, as an evergreen broadleaf forest region. On the other hand, the Mopti-M form is collected in the much drier savanna (Kela, Banambani), grassland (Nara), or open shrubland habitats (Dire, Banikane).
A similar trend was observed for the 2Rb inversion. The frequency of 2Rb increased as precipitation decreased, as reported by Coluzzi et al. . Individuals homozygous for 2Rb (b/b) and 2La (a/a) had a high probability of being classified in the Savanna-S group rather than Forest-S group. Because of the inverse correlation between 2Rb and precipitation, division between the Forest-S group and Savanna-S or Godola-S groups is likely due to the skewed 2Rb frequency.
Clustering results based on microsatellite genotypes indicates that the Godola-S forms are relatively close to the Savanna-S and Bamako-S forms (Figure 2B and 2C, and Figure 4). The frequency of 2La and 2Rb in Godola were in line with the expectation based on precipitation. Genetic differentiation, based on microsatellite markers (Figure 2B and 2C), were also somewhat consistent with the association of land cover types and inversion polymorphism.
An unusual concentration of the 2Rd inversion in Godola resulted in the Bayesian analysis distinguishing it as a separate population (Figure 2A). Godola is located in an area surrounded by a mosaic of grasslands, permanent wetlands and croplands (Table 4). Permanent wetlands surround 19% of Godola, highest among our collection sites. A more detailed analysis of the effect of environmental parameters on the distribution of the 2Rd inversion will be necessary to gain an understanding of the relationship between the Godola-S form and other S forms.
The correlation between genetic markers and standard deviation of precipitation was also investigated. Areas with higher precipitation had greater variation in rainfall as well, as illustrated in Table 4. Thus the trend illustrated in Figures 5 and 6 remained the same between genetic markers and rainfall variation.
Classification of populations of An. gambiae s.s. into chromosomal forms using four paracentric chromosome inversions (2Rb, c, j and u) has been useful in identifying discrete sub-populations in Mali. Expanding this interpretation to include other areas in Africa has proven difficult because a significant proportion of karyotypes are ambiguous with respect to the classification scheme developed and used in Mali. Additional information, including molecular form, the 2La inversion and multi-locus microsatellite genotypes was employed in an attempt to obtain higher resolution of population structure in this species.
Seven groups were identified in the Bayesian analysis using molecular form, 2La, 2Rj, 2Rb, 2Rc, 2Rd, and 2Ru inversion genotypes as markers (Figure 2A). These seven groups are Forest-M, Forest-S, Godola-S, Savanna-S, Bamako, Mopti-M bc/bc, and other Mopti-M forms (Figure 2A). Each group showed distinctive inversion polymorphism, and each occupied a different land cover type (Figure 1). Bayesian analysis using microsatellite data for the most part supported the analysis based on chromosome inversions and molecular form, with some notable exceptions (Figures 2B and 2C). The microsatellite analyses at K = 4 and K = 5 resolved groups with roughly equivalent likelihoods (Figure 3B). Microsatellites failed to resolve the Godola-S group within the range of K values (K = [1, 8]). At K = 4 the S form was divided into the Forest-S form and all other S forms. At K = 5 the other S form group was split into the Bamako and Savanna forms.
The M form
The Bayesian analysis based on molecular form and karyotype divided M form populations into three discrete groups. The Forest M form was characterized as having the standard arrangement for all six major chromosomal inversions, namely 2La, 2Rj, 2Rb, 2Rc, 2Rd and 2Ru. The Mopti-M form was further divided into two groups, one that is fixed for the 2Rbc inverted arrangement and the other M forms with the 2La/a genotype. The Forest-M form was the most distinct (Figures 2 and 4). The high degree of genetic divergence between the Forest-M form and all the others was indicated not only by the 2R chromosome arrangement, but also at 20 microsatellite markers on the second and third chromosomes (Table 3, Figure 4). Moreover, the Forest-M form showed a strong association with very wet environments, distinguishing this form from the Mopti-M form, which is most abundant in much drier habitats (Figures 1 and 6, Table 4).
Despite occupying the same ecological zone and being identical with respect to karyotype (homosequential), the level of genetic differentiation (FST = 0.04263, P = 0.0000) between the Forest-M and Forest-S forms was higher than among any of the other subspecific forms of An. gambiae s.s. (Table 3, Figure 4). The relationship between the Forest-M and Forest-S forms is clearly different from the relationship between the Mopti-M and Savanna-S forms in Mali. Hybridization between the Mopti-M and Savanna-S forms, although rare, has been frequently reported [8, 39–41], whereas reproductive isolation between sympatric Forest-M and S forms appears to be complete [14, 15, 35]. These results demonstrate that reproductive isolation, and not ecological barriers, plays the major role in limiting gene flow between the Forest M and S forms in Cameroon and between the Mopti-M and Savanna-S forms in Mali.
The M forms were further sub-divided into two groups based on the Bayesian analysis of karyotype data (Mopti-M and "Other"-M, Figure 2A), while microsatellite data structured them as a single group (Figure 2B and 2C). The Mopti-M form is characterized as being fixed for the double homozygous b/c karyotype, whereas the "Other"-M form group carried the 2Rb and c inversions in other combinations or were standard karyotype. Both were fixed for the 2La inversion. Although genetic differentiation between the two groups as measured by pair-wise FST is significant, the degree of divergence is relatively small and insufficient to classify them as two separate groups, that is as two distinct gene pools.
The S form
The Bayesian clustering analysis based on molecular form and karyotype grouped S form populations into four group: Forest-S, Godola-S, Savanna-S and Bamako-S (Figure 2A). The Forest-S group includes individuals with the standard (non-inverted) karyotype and individuals carrying the 2La and/or 2Rb inversions. Forest-S 2Rb heterozygotes were located in woody savanna habitats (Ndop and Foumbot), while only Forest-S form with standard arrangement were collected from evergreen forest habitats (Mutengene and Tiko). This distribution may reflect selection for the 2Rb inversion in drier habitats. S form 2Rb homozygotes were classified as Godola-S form if the 2Rd inversion is also present, Savanna-S form if no other inversions occur on chromosome 2R, or Bamako-S if the 2Rj, 2Rc and 2Ru inversions occur together.
The Bamako-S form was not resolved by the analysis based on microsatellites with K = 4, but was resolved at K = 5. The Godola-S form however, was not resolved by the microsatellite data at either K = 4 or K = 5. The Godola-S group represents a distinctive group with respect to inversion polymorphism, notably a high frequency of the 2Rd inversion and 2Rb and c combinations not typically found in individuals within the S molecular form. However, the distinctive karyotypic polymorphism was not captured by the Bayesian analysis of microsatellite polymorphism.
Analysis of levels of genetic divergence, described with microsatellite-based FST values (Table 3, Figure 4) suggest several relationships among the An. gambiae populations studied here. The major division distinguishes the Forest-M form from all others (Figure 4). S form populations form four groups.
Interestingly, the Bamako-S and Savanna-S forms are the least diverged of the four, although it is the Bamako chromosomal form that has received the most attention, including the suggestion that it represents a distinct species [37, 42].
Forces affecting the distribution of forms
Recently Yawson et al.  reported higher levels of genetic divergence between within-form populations from different environments relative to between-form populations from the same environments in Ghana and Burkina Faso. This led them to conclude that barriers to gene flow among populations are due more to ecological barriers than reproductive isolation between molecular forms. The two sites in Cameroon from which we described the Forest-M form occur within a region that is more or less contiguous with the mangrove strand sites in southern Ghana from which Yawson et al.  collected M forms for their study . Furthermore, the 1 km resolution land-cover map of Africa by Mayaux et al.  indicates that land-cover in northern Ghana is similar to southern Mali. These results suggest that the M form collected by Yawson et al.  in northern Ghana represent the Mopti- M form while the M form they collected in the mangrove strand of southern Ghana are of the Forest-M form. It is, therefore, likely that what Yawson et al.  describe as intra-form comparisons of M form populations are in fact inter-form comparisons between Forest-M and Mopti-M forms. Overall, the results suggest that reproductive isolation among forms plays an important role in restricting gene flow among populations. Evidence laid out here does, however, support the idea that ecological barriers also play a significant role.
The correlations between 2La and 2Rb inversion frequencies and precipitation (Figure 6) were significant. In this study, precipitation is used as a proxy for different habitat type, rather than the causal factor for the distribution of 2La and 2Rb. Inversion polymorphisms in natural populations of An. gambiae s.s. are very complex and simple summary statistics, such as annual precipitation, are not sufficient to capture the diverse nature of the forces driving their distribution in nature. For example, the Godola collection has an unusual concentration of the 2Rd inversion which is difficult to explain by precipitation alone. Variation in rainfall was an obvious first environmental factor to consider, however describing this variable is not as trivial as it may seem. Rainfall is measured from 0 (meaning no rain) to some rain, and most of the regions included in this study have both rainy and dry seasons. Regions with high precipitation show greater variation in rainfall. Therefore, the correlation between genetic markers and the standard deviation of precipitation resulted in the same trend as the correlation with precipitation. Moreover, the standard deviation in rainfall is far greater than the mean (Table 4), illustrating the volatile nature of precipitation data. Examining the relationship between the distribution of genetic markers and rainfall in places like Tiko and Mutengene is further complicated because they have two rainy and two dry seasons. Other measures of rainfall variation, such as number of consecutive dry days or length of dry season can be explored. Development of summary statistics that may better reflect various rainfall patterns, as well as investigation of other environmental parameters are needed.
Microsatellites did detect population groups according to ecology and molecular forms. Division of Mopti-M, Savanna-S and Bamako-S groups are consistent with previous studies. Subdivision of the M form into Mopti-M and Forest-M forms has strong support in all of the Bayesian analyses presented here, as was subdivision of the S form into Savanna-S and Forest-S forms, although examination of Figures 2B and 2C suggest that the latter division is less clear.
Overall this study confirms the Forest-M form as a distinct group, that lacks chromosome inversion polymorphism, is the most highly diverged of all the recognized subspecific forms of An. gambiae s.s. and is distinguished from other M molecular form populations by having a strong preference for environments with relatively high precipitation. The Forest-M form occurs in sympatry with the Forest- S form and reproductive isolation between the two appears to be complete, that is, no hybrids were observed. The relationship between the Forest-M form and Mopti-M form is less clear. It is clear that the two are relatively highly diverged, FST = 0.02487 compared for example to the level of divergence between the Bamako and Savanna chromosomal forms in Mali, FST = 0.00559. However, we lack good information as to whether the Forest-M and Mopti-M forms are reproductively isolated since we have not identified sites at which the two are sympatric.
When investigating associations between genetic markers and important phenotypes such as insecticide resistance and malaria parasite refractory features, disregard of existing population structure may lead to identification of spurious associations or may obfuscate true associations. The level of genetic differentiation and geographic distribution of the Forest-M form illustrates the potential danger of relying solely on molecular form for genotype/phenotype association studies.
We thank Dr. Yongkang Xue and Dr. Charles E. Taylor for the precipitation data. The research described in this paper was supported by a grant from the NIH to GCL (R01AI40308).
- Lehmann T, Licht M, Elissa N, Maega BT, Chimumbwa JM, Watsenga FT, Wondji CS, Simard F, Hawley WA: Population structure of Anopheles gambiae in Africa. J Hered. 2003, 94: 133-147. 10.1093/jhered/esg024.View ArticlePubMedGoogle Scholar
- Coluzzi M, Sabatini A, Petrarca V, Di Deco MA: Chromosomal differentiation and adaptation to human environments in the Anopheles gambiae complex. Trans R Soc Trop Med Hyg. 1979, 73: 483-497. 10.1016/0035-9203(79)90036-1.View ArticlePubMedGoogle Scholar
- Touré YT, Petrarca V, Traoré SF, Coulibaly A, Maïga HM, Sankaré O, Sow M, Di Deco MA, Coluzzi M: Ecological genetic studies in the chromosomal form Mopti of Anopheles gambiae s.str. in Mali, west Africa. Genetica. 1994, 94: 213-223. 10.1007/BF01443435.View ArticlePubMedGoogle Scholar
- Lanzaro GC, Touré YT, Carnahan J, Zheng L, Dolo G, Traoré S, Petrarca V, Vernick KD, Taylor CE: Complexities in the genetic structure of Anopheles gambiae populations in west Africa as revealed by microsatellite DNA analysis. Proc Natl Acad Sci USA. 1998, 95: 14260-14265. 10.1073/pnas.95.24.14260.PubMed CentralView ArticlePubMedGoogle Scholar
- Touré YT, Petrarca V, Traoré SF, Coulibaly A, Maïga HM, Sankaré O, Sow M, Di Deco MA, Coluzzi M: The distribution and inversion polymorphism of chromosomally recognized taxa of the Anopheles gambiae complex in Mali, West Africa. Parassitologia. 1998, 40: 477-511.PubMedGoogle Scholar
- Bayoh MN, Thomas CJ, Lindsay SW: Mapping distributions of chromosomal forms of Anopheles gambiae in West Africa using climate data. Med Vet Entomol. 2001, 15: 267-274. 10.1046/j.0269-283x.2001.00298.x.View ArticlePubMedGoogle Scholar
- Wang-Sattler R, Blandin S, Ning Y, Blass C, Dolo G, Touré YT, della Torre A, Lanzaro GC, Steinmetz LM, Kafatos FC, Zheng L: Mosaic genome architecture of the Anopheles gambiae species complex. PLoS One. 2007, 2: e1249-10.1371/journal.pone.0001249.PubMed CentralView ArticlePubMedGoogle Scholar
- Tripet F, Touré YT, Taylor CE, Norris DE, Dolo G, Lanzaro GC: DNA analysis of transferred sperm reveals significant levels of gene flow between molecular forms of Anopheles gambiae. Mol Ecol. 2001, 10: 1725-1732. 10.1046/j.0962-1083.2001.01301.x.View ArticlePubMedGoogle Scholar
- Coluzzi M, Petrarca V, Di Deco MA: Chromosomal inversion intergradation and incipient speciation in Anopheles gambiae. Bollettino di Zoologia. 1985, 52: 45-63.View ArticleGoogle Scholar
- Fanello C, Santolamazza F, della Torre A: Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Med Vet Entomol. 2002, 16: 461-464. 10.1046/j.1365-2915.2002.00393.x.View ArticlePubMedGoogle Scholar
- Favia G, della Torre A, Bagayoko M, Lanfrancotti A, Sagnon N'F, Touré YT, Coluzzi M: Molecular identification of sympatric chromosomal forms of Anopheles gambiae and further evidence of their reproductive isolation. Insect Mol Biol. 1997, 6: 377-383. 10.1046/j.1365-2583.1997.00189.x.View ArticlePubMedGoogle Scholar
- della Torre A, Fanello C, Akogbeto M, Dossou-yovo J, Favia G, Petrarca V, Coluzzi M: Molecular evidence of incipient speciation within Anopheles gambiae s.s. in West Africa. Insect Mol Biol. 2001, 10: 9-18. 10.1046/j.1365-2583.2001.00235.x.View ArticlePubMedGoogle Scholar
- Gentile G, Slotman M, Ketmaier V, Powell JR, Caccone A: Attempts to molecularly distinguish cryptic taxa in Anopheles gambiae s.s. Insect Mol Biol. 2001, 10: 25-32. 10.1046/j.1365-2583.2001.00237.x.View ArticlePubMedGoogle Scholar
- Wondji C, Simard F, Fontenille D: Evidence for genetic differentiation between the molecular forms M and S within the Forest chromosomal form of Anopheles gambiae in an area of sympatry. Insect Mol Biol. 2002, 11: 11-19. 10.1046/j.0962-1075.2001.00306.x.View ArticlePubMedGoogle Scholar
- Slotman MA, Tripet F, Cornel AJ, Meneses CR, Lee Y, Reimer LJ, Thiemann TC, Fondjo E, Fofana A, Traoré SF, Lanzaro GC: Evidence for subdivision within the M molecular form of Anopheles gambiae. Mol Ecol. 2007, 16: 639-649. 10.1111/j.1365-294X.2006.03172.x.View ArticlePubMedGoogle Scholar
- Yawson AE, Weetman D, Wilson MD, Donnelly MJ: Ecological Zones Rather Than Molecular Forms Predict Genetic Differentiation in the Malaria Vector Anopheles gambiae s.s. in Ghana. Genetics. 2007, 175: 751-761. 10.1534/genetics.106.065888.PubMed CentralView ArticlePubMedGoogle Scholar
- Hunt RH: A cytological technique for the study of Anopheles gambiae complex. Parassitologia. 1973, 15: 137-139.PubMedGoogle Scholar
- Scott JA, Brogdon WG, Collins FH: Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am J Trop Med Hyg. 1993, 49: 520-529.PubMedGoogle Scholar
- Favia G, Lanfrancotti A, Spanos A, Sidén-Kiamos I, Louis C: Molecular characterization of ribosomal DNA polymorphisms discriminating among chromosomal forms of Anopheles gambiae s.s. Insect Mol Biol. 2001, 10: 19-23. 10.1046/j.1365-2583.2001.00236.x.View ArticlePubMedGoogle Scholar
- Coluzzi M, Sabatini A, della Torre A, Di Deco MA, Petrarca V: A polytene chromosome analysis of the Anopheles gambiae species complex. Science. 2002, 298: 1415-1418. 10.1126/science.1077769.View ArticlePubMedGoogle Scholar
- Zheng L, Benedict MQ, Cornel AJ, Collins FH, Kafatos FC: An integrated genetic map of the African human malaria vector mosquito, Anopheles gambiae. Genetics. 1996, 143: 941-952.PubMed CentralPubMedGoogle Scholar
- Moderate Resolution Imaging Spectroradiometer. [http://modis.gsfc.nasa.gov]
- The Warehouse Inventory Search Tool. [https://wist.echo.nasa.gov/~wist/api/imswelcome/]
- HEG (HDF-EOS to GeoTIFF convertersion Tool). [http://newsroom.gsfc.nasa.gov/sdptoolkit/HEG/HEGHome.html]
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 949-959.Google Scholar
- Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.PubMed CentralPubMedGoogle Scholar
- Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes. 2007, 7: 574-578. 10.1111/j.1471-8286.2007.01758.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Rosenberg NA: DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2003, 4: 137-138. 10.1046/j.1471-8286.2003.00566.x.View ArticleGoogle Scholar
- Excoffier L, Laval G, Balding D: Gametic phase estimation over large genomic regions using an adaptive window approach. Human Genomics. 2003, 1: 7-19.PubMed CentralView ArticlePubMedGoogle Scholar
- Excoffier L, Laval G, Schneider S: Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online. 2005, 1: 47-50.PubMed CentralGoogle Scholar
- Guo SW, Thompson EA: Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 1992, 48: 361-372. 10.2307/2532296.View ArticlePubMedGoogle Scholar
- Felsenstein J: PHYLIP – Phylogeny Inference Package (Version 3.2). Cladistics. 1989, 5: 164-166.Google Scholar
- Felsenstein J: PHYLIP (Phylogeny Inference Package) version 3.2. 2005, Department of Genome Sciences, University of WashingtonGoogle Scholar
- The R Project for Statistical Computing. [http://www.r-project.org/index.html]
- Wondji C, Simard F, Petrarca V, Etang J, Santolamazza F, della Torre A, Fontenille D: Species and Populations of the Anopheles gambiae complex in Cameroon with special emphasis on chromosomal and molecular forms of Anopheles gambiae s.s. J Med Entomol. 2005, 42: 998-1005. 10.1603/0022-2585(2005)042[0998:SAPOTA]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Touré Y, Petrarca V, Coluzzi M: Nuove entitá del complesso Anopheles gambiae in Mali. Parassitologia. 1983, 25: 367-370.Google Scholar
- Manoukis NC, Powell JR, Touré MB, Sacko A, Edillo FE, Coulibaly MB, Traoré SF, Taylor CE, Besansky NJ: A test of the chromosomal theory of ecotypic speciation in Anopheles gambiae. Proc Natl Acad Sci USA. 2008, 105: 2940-2945. 10.1073/pnas.0709806105.PubMed CentralView ArticlePubMedGoogle Scholar
- Coluzzi M: Malaria Vector Analysis and Control. Parasitology Today. 1992, 8: 113-118. 10.1016/0169-4758(92)90277-9.View ArticlePubMedGoogle Scholar
- Taylor C, Touré YT, Carnahan J, Norris DE, Dolo G, Traoré SF, Edillo FE, Lanzaro GC: Gene flow among populations of the malaria vector, Anopheles gambiae, in Mali, West Africa. Genetics. 2001, 157: 743-750.PubMed CentralPubMedGoogle Scholar
- Tripet F, Dolo G, Lanzaro GC: Multilevel analyses of genetic differentiation in Anopheles gambiae s.s. reveal patterns of gene flow important for malaria-fighting mosquito projects. Genetics. 2005, 169: 313-324. 10.1534/genetics.104.026534.PubMed CentralView ArticlePubMedGoogle Scholar
- Weill M, Chandre F, Brengues C, Manguin S, Akogbeto M, Pasteur N, Guillet P, Raymond M: The kdr mutation occurs in the Mopti form of Anopheles gambiae s.s. through introgression. Insect Mol Biol. 2000, 9: 451-455. 10.1046/j.1365-2583.2000.00206.x.View ArticlePubMedGoogle Scholar
- Powell JR, Petrarca V, della Torre A, Coccone A, Coluzzi M: Population Structure, Speciation, and Introgression in the Anopheles gambiae Complex. Parassitologia. 1999, 41: 101-113.PubMedGoogle Scholar
- Mayaux P, Bartholomé E, Fritz S, Belward A: A new land-cover map of Africa for the year 2000. J Biogeography. 2004, 31: 861-877. 10.1111/j.1365-2699.2004.01073.x.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.