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Inferring selection in the Anopheles gambiae species complex: an example from immune-related serine protease inhibitors

Malaria Journal20098:117

https://doi.org/10.1186/1475-2875-8-117

©  Obbard et al; licensee BioMed Central Ltd. 2009

  • Received: 19 February 2009
  • Accepted: 04 June 2009
  • Published:

Abstract

Background

Mosquitoes of the Anopheles gambiae species complex are the primary vectors of human malaria in sub-Saharan Africa. Many host genes have been shown to affect Plasmodium development in the mosquito, and so are expected to engage in an evolutionary arms race with the pathogen. However, there is little conclusive evidence that any of these mosquito genes evolve rapidly, or show other signatures of adaptive evolution.

Methods

Three serine protease inhibitors have previously been identified as candidate immune system genes mediating mosquito-Plasmodium interaction, and serine protease inhibitors have been identified as hot-spots of adaptive evolution in other taxa. Population-genetic tests for selection, including a recent multi-gene extension of the McDonald-Kreitman test, were applied to 16 serine protease inhibitors and 16 other genes sampled from the An. gambiae species complex in both East and West Africa.

Results

Serine protease inhibitors were found to show a marginally significant trend towards higher levels of amino acid diversity than other genes, and display extensive genetic structuring associated with the 2La chromosomal inversion. However, although serpins are candidate targets for strong parasite-mediated selection, no evidence was found for rapid adaptive evolution in these genes.

Conclusion

It is well known that phylogenetic and population history in the An. gambiae complex can present special problems for the application of standard population-genetic tests for selection, and this may explain the failure of this study to detect selection acting on serine protease inhibitors. The pitfalls of uncritically applying these tests in this species complex are highlighted, and the future prospects for detecting selection acting on the An. gambiae genome are discussed.

Keywords

  • Effective Population Size
  • Adaptive Evolution
  • Serine Protease Inhibitor
  • Anopheles Mosquito
  • Chromosomal Inversion

Background

By vectoring Plasmodium parasites, Anopheles mosquitoes are a central component of the Malaria crisis. Consequently, there has been a substantial effort to identify the genes involved in the mosquito immune response against Plasmodium, including studies to identify genes associated with variation in vector competence [1–4]. It has been widely hypothesized that these immune response genes may be subject to strong parasite-mediated selection, such as that which occurs in a coevolutionary 'arms-race' [5, 6]. Such arms-races involve strong reciprocally-antagonistic selection, leading to the frequent and rapid fixation of new alleles. This reduces within-species diversity, while driving between-species protein divergence, and leaves a genomic signature of past selection that can be identified through DNA sequence analysis [7, 8]. Thus, DNA sequence analysis and the tools of population genetics can augment understanding of immune gene function in host-parasite interaction by identifying genes that are the target of parasite adaptation, and even distinguish between forms of parasite-mediated selection [5, 6, 9].

Population genetic methods have previously shed light on the nature and intensity of selection in both mammalian and Drosophila immune systems. For example, Drosophila studies have suggested that pathogens which manipulate signal transduction pathways or the antiviral RNAi pathway have been a major selective force [10, 11]. In Anopheles mosquitoes, the potential for immune-related genes to determine vector competence provides a clear incentive to elucidate the selective forces that drive evolution. Serine protease inhibitors (serpins, or SRPNs) are prime candidates for such parasite-mediated selection in Anopheles mosquitoes. Serpins comprise a large and rapidly evolving super-family of proteins (reviewed in [12, 13]) with key roles in the immune systems of vertebrates [14] and invertebrates [15]. In particular, Drosophila serpins, such as Nec and SRPN27A, modulate two of the most important defense pathways: the Toll-pathway [16, 17], and the melanization cascade [18, 19], and many are up-regulated on septic injury (Spn28D, SRPN27A, Spn5, CG6687 and Spn4, see [20]). Moreover, some Drosophila serpins display very high rates of amino acid substitution, and/or other signatures of adaptive evolution, e.g. [21–23].

Three Anopheles serpins have been experimentally associated with Plasmodium-interaction phenotypes (see Table 1). In Anopheles gambiae and Anopheles stephensi SRPN10 is expressed in the mosquito midgut and in haemocytes [24], and during Plasmodium berghei (a rodent parasite) invasion of the midgut epithelium SRPN10 moves from the nucleus to the cytoplasm, and its expression is strongly induced [25]. SRPN6 is also expressed in infected midgut epithelial cells and in haemocytes, and again its expression is strongly induced by P. berghei invasion in both An. gambiae and An. stephensi. The expression of SRPN6 is also induced by the human parasite Plasmodium falciparum [26]. RNAi knockdown of SRPN6 in An. stephensi resulted in a significant increase in the number of developing P. berghei oocysts, and although knockdown had no effect on oocyst numbers in susceptible strains of An. gambiae, in a resistant strain, the number of melanized P. berghei ookinetes was significantly increased [26]. More recently it has also been shown that SRPN6 is induced in the salivary glands of An. gambiae in response to P. berghei sporozoite invasion, and knock-down of SRPN6 by RNAi significantly increases the number of sprozoites reaching the salivary glands [27]. Finally, knockdown of SRPN2 in P. berghei-susceptible An. gambiae has a broadly opposite effect, resulting in a 97% decrease in oocyst formation through increased lysis and melanization, following midgut invasion [28].
Table 1

Locus Details and location

NAME

Identifier

Putative function

Genomic location

SRPN1

AGAP006909

Inhibitory Serine Protease inhibitor

2L:39892128-39893864

SRPN2

AGAP006911

Plasmodium-related Inhibitory Serine Protease inhibitor

2L:39897002-39899744

SRPN3

AGAP006910

Inhibitory Serine Protease inhibitor

2L:39895229-39896338

SRPN4C

AGAP009670

Inhibitory Serine Protease inhibitor

3R:38145527-38154288

SRPN5

AGAP009221

Inhibitory Serine Protease inhibitor

3R:28858000-28859778

SRPN6

AGAP009212

Plasmodium-related Inhibitory Serine Protease inhibitor

3R:28811997-28818217

SRPN7

AGAP007693

Inhibitory Serine Protease inhibitor

2L:49090665-49091915

SRPN8

AGAP003194

Inhibitory Serine Protease inhibitor

2R:33744972-33746720

SRPN9

AGAP003139

Inhibitory Serine Protease inhibitor

2R:33148444-33154607

SRPN10

AGAP005246

Plasmodium-related Inhibitory Serine Protease inhibitor

2L:12996143-13001508

SRPN11

AGAP001377

Non-inhibitory Serine Protease inhibitor

2R:4017728-4019706

SRPN12

AGAP001375

Non-inhibitory Serine Protease inhibitor

2R:4010431-4012512

SRPN14

AGAP007692

Non-inhibitory Serine Protease inhibitor

2L:49084812-49086463

SRPN16

AGAP009213

Inhibitory Serine Protease inhibitor

3R:28824548-28826209

SRPN17

AGAP001376

Inhibitory Serine Protease inhibitor

2R:4015617-4016537

SRPN18

AGAP007691

Non-inhibitory Serine Protease inhibitor

2L:49086842-49088278

Control1

AGAP006906

Adenosine deaminase-related growth factor

2L:39852471-39854636

Control2

AGAP006904

Matrix metalloproteinase

2L:39831595-39836700

Control3

AGAP006918

Putative NADH:ubiquinone dehydrogenase

2L:39995907-39997095

Control4

AGAP009673

glutaminyl-peptide cyclotransferase

3R:38248845-38249780

Control5

ENSANGG8091

(retrotransposon)

3R:28965847-28968579

Control6

AGAP009207

Mitogen-activated protein kinase ERK

3R:28697030-28708787

Control7

AGAP007712

Putative RHO guanyl-nucleotide exchange factor

2L:49181235-49190516

Control8

AGAP003205

Similar to Drosophila CG8468

2R:33825401-33827998

Control9

AGAP003143

Similar to Drosophila CG9904

2R:33211906-33213476

Control10

AGAP005247

no annotation

2L:13062962-13067750

Control11

AGAP001384

cAMP-dependent protein kinase, beta-catalytic subunit

2R:4098545-4103634

Control12

AGAP001371

Similar to Drosophila CG18643

2R:3885127-3885956

Control14

AGAP007713

Similar to human solute carrier family 39

2L:49196817-49198177

Control16

AGAP900209

DNA-directed RNA polymerase II subunit J

3R:28746535-28747425

Control17

AGAP001388

Similar to human mab-3-related transcription factor 3

2R:4120810-4122586

Control18

AGAP007717

Similar to Drosophila CAP CG18408-PE

2L:49212258-49224889

Here, population-genetic approaches are used to search for evidence of natural selection acting on 16 serpin genes in the An. gambiae species complex, including those implicated in immune function. First, by comparing serpins to other nearby genes, patterns of genetic diversity within and between populations of An. gambiae, Anopheles arabiensis, and Anopheles melas are used to identify loci that deviate strongly from neutral predictions. Second, a recent extension of the McDonald-Kreitman test is used to test for evidence of adaptive substitution between species [29, 30]. The data are then discussed in terms of on-going population processes in the An. gambiae complex, many of which have important implications for the robust inference of selection.

Serpins were found to have slightly higher levels of amino acid diversity than other genes, consistent with either reduced constraint, or potentially with balancing selection. In common with previous analyses. [31], considerable structuring of genetic diversity in the SRPN1-2-3 cluster was found in association with the 2La chromosomal inversion. However, although serpins are good a priori candidates as targets for strong 'arms-race' selection, as with similar studies on other Anopheles gambiae immune-related genes (e.g. [6, 32–34]), the tests for adaptive evolution presented here are largely inconclusive. The results show how standard population-genetic tests for selection may be difficult to apply in the An. gambiae species complex; this is due to for both demographic and phylogenetic factors that are already widely known, and further supported by the present data.

Methods

Samples

Anopheles gambiae individuals were collected from West Africa ('BK': Burkina Faso, Koubri village, 12°11'54 N; 1°23'43W) and East Africa ('KY': Kenya, Mbita, Suba District). Anopheles arabiensis individuals were also collected from West Africa ('BK', in the same collections as An. gambiae, above) and East Africa ('TZ', Tanzania, Ifakara). All An. gambiae and An. arabiensis used in this study were provided by H. M. Ferguson (University of Glasgow, UK). Anopheles melas individuals were collected from Coastal Ghana (Ghana, Essiama, 4°57.4 N; 2°24.1W) by N. Tuno (Institute of Tropical Medicine, Nagasaki University, Japan). Anopheles merus and Anopheles quadriannulatus species A (hereafter An. quadriannulatus) were both obtained from laboratory colonies, maintained by the Medical Research Council of South Africa (provided by R. Maharaj; MRC, Durban, South Africa) and the University of Wageningen (Strain 'Sangqua', Zimbabwe, provided by W. Takken), respectively.

The species identity of all gambiae complex members was verified by diagnostic PCR [35], and the M and S molecular forms of An. gambiae were distinguished by PCR-RFLP [e.g. [36]]. As expected from their known geographic distributions [37], all KY An. gambiae individuals were S form, and all but two of the BK An. gambiae sample were M-form. All An. gambiae individuals were also surveyed for 2La/+ chromosomal inversion status, using the PCR assay of White et al [38], derived from the sequenced breakpoints [39]. As reported previously, in addition to the expected diagnostic 207 bp and 492 bp fragment lengths, these primers were found to amplify fragments of lengths ca. 687 bp, 672 bp, 760 bp and 1020 bp in some individuals [32]. Direct sequencing of these fragments from a subset of individuals suggest they are insertion/deletion derivatives of expected assay products [32, 40], and within the polymorphic KY population the 2La/2L+a amplification fragments were in Hardy-Weinberg equilibrium (51 individuals, χ2 = 0.44, 1df., p = 0.51), allowing us to tentatively assign 2La/2L+a inversion status to all individuals [32].

Loci

Thirty-two loci were selected for sequencing and analysis, including 16 of the 18 serpins currently identified in the An. gambiae genome (M. Kanost and K. Michel, pers. comm.), and 16 other protein-coding loci chosen to match the genomic position of the serpins without regard to function. SRPN19 (a non-inhibitory serpin with 1:1:1 orthologs in Drosophila melanogaster, Aedes aegypti and An. gambiae) and SRPN13, which does not appear in the current An. gambiae assembly, were not sequenced. The total sequenced length was ~19 Kbp of coding sequence per individual (i.e. approximately 600 bp from each locus; range 240 bp–800 bp). Not all loci were sequenced from the same individuals within populations, and not all loci were amplified from An. melas. A full summary of gene names, locations, and classification is presented in Table 1.

The 'control' loci should represent an unbiased sample of Anopheles genes, to which serpins can be compared. Because these control genes are position-matched, each lying ~90 Kbp (range 40–125 Kbp) from a 'partner' serpin, they should control for the effects of large-scale position-based variation in recombination and mutation rates. Note that improvements to the An. gambiae annotation have subsequently identified control locus 5 (previously annotated as ENSANGG000008091) as deriving from a retrotransposon (AgamP3.4, July, 2007).

PCR and sequencing

Genomic DNA was extracted from single mosquitoes using DNeasy kits (QIAgen). PCR primers were designed from the published An. gambiae genome sequence [41], and the final primer sequences selected after trouble-shooting (sequences are given in Additional file 1). Only one PCR amplicon was used per locus, thus sequences do not represent entire genes. Following PCR, unincorporated primers and dNTPs were removed using exonuclease I (New England BioLabs) and shrimp alkaline phosphatase (Amersham). PCR products were sequenced in both directions using BigDye™ reagents (v3.1, Applied BioSystems) and an ABI capillary sequencer. In some amplicons, indel polymorphism required the use of additional sequencing primers. The sequence chromatograms were assembled using SeqManII (DNAstar Inc., Madison USA) then inspected by eye to confirm the validity of all differences within and between species and all heterozygous base-calls. The heterozygous sequence from each diploid individual was decomposed into two pseudohaplotypes for analysis using PHASE [42, 43]. However, the presented analyses should be highly robust to any errors in phase assignment, as only explicitly tree-based results, such as Hudson's nearest neighbour statistic (Snn), are affected by allelic phase. All unphased sequences have been submitted to GenBank as population sets, using ambiguity codes to indicate heterozygous sites. Sequence accession numbers span the range GQ146469–GQ148534.

Divergence, diversity and differentiation

The number of synonymous and non-synonymous polymorphisms and substitutions, and the average pairwise genetic diversity (π) were calculated using DnaSP [version 4.50.3, ref [44]]. Diversity was calculated separately for synonymous (π s ) and non-synonymous (π a ) sites, and used a Jukes-Cantor correction for multiple substitutions, as implemented in DnaSP. Departures from the allele frequency spectrum expected under the standard neutral model were quantified using Tajima's D statistic [45], also calculated using DnaSP. Tajima's D (which measures departures from the expected allele frequency distribution under a standard neutral model) was calculated using synonymous sites only, and was calculated separately for both populations of An. gambiae and An. arabiensis (but not for An. melas, where sample sizes were too small to give meaningful results). The significance of departures from the expected allele frequency distribution were tested using 10,000 rounds of coalescent simulation (as implemented in DnaSP) conditional on the number of segregating sites and conservatively assuming no recombination within loci.

Genetic differentiation between populations was quantified in DnaSP using Hudson's K ST statistic [equations 7 to 9 in reference 46] which is calculated from the average number of pairwise differences between sequences taken within populations and between all populations, and is identical to Nei's γ ST [47] except for the population-size weighting scheme [see [46]]. Significant departures from zero population differentiation were inferred by permuting sequences between populations to create a null distribution of K ST values. All non-parametric statistical tests on diversity and differentiation were performed using the R statistical language (R Development Core Team, 2008 http://www.R-project.org). Although non-parametric tests (Spearman's rank correlation, paired Wilcoxon tests) are presented below, except where noted explicitly, parametric equivalents (Pearson's correlation, paired t-tests) gave qualitatively identical results.

The proportion of adaptive substitutions

If it is assumed that synonymous mutations are effectively neutral, and that the fixation or loss of selected amino-acid variants is so rapid that the vast majority of non-synonymous polymorphisms are also effectively neutral, then the relative numbers of polymorphisms (P, within species) and fixed differences (D, between species) at synonymous and non-synonymous sites can be used to identify the action of selection [see [7] for an introduction]. This forms the basis of the McDonald-Kreitman test [MK test, [29]], which seeks to detect a departure from independence in a simple 2 × 2 contingency table of polymorphisms (P N and P S ) and fixed differences (D N and D S ). For a single gene, the departure from neutrality can easily be quantified by summary statistic such as the neutrality index (N.I.= (P N /P S )/(D N /D S ) [48]), or by the estimated proportion of adaptive substitutions (α = 1 - (D S P N )/(D N P S ) = 1 - N.I., [49]). This approach can be extended to multiple genes using D S , P N , D N and P S averaged across genes [49], or using a more sophisticated maximum-likelihood estimator of α, such as that of Bierne and Eyre-Walker [50].

Here, an extension of the maximum-likelihood method of Welch [30] was used. This method is very closely related to that of Bierne and Eyre-Walker [50], but additionally allows for the possibility that some apparent fixed differences may actually be polymorphisms that only appear fixed due to small sample size [51]. The method was extended to include polymorphism values from two species simultaneously e.g. [29, 51]. Using this approach, models were fitted in which expected neutral divergence, λ = μt, took a single value at all loci; expected neutral diversity, θ = 4N e μ, was also shared between all loci, but free to vary between species; and selective constraint, f, was free to vary between loci [see [30] for other details of the model]. Three nested models were fitted, in which (1) α was constrained to zero at all loci, i.e. no adaptive evolution, (2) a single α was shared by all genes, and (3) α was free to differ between serpins and 'control' genes. In this way, it was possible to test both for evidence of adaptive evolution, and whether serpins have a different rate of adaptive evolution to other genes. Model fit was tested using both likelihood ratio tests and Akaike weighting (derived from the Akaike information criterion) [52]. Confidence intervals on α were obtained by adjusting α away from its maximum likelihood value, and allowing the other parameters to take their maximum likelihood value, conditional on that α, until log likelihood decreased by 2 units [50]. C code to fit these models is available on request from the authors, or from [53].

Results

Synonymous site diversity

Across all 32 loci, average pairwise genetic diversity at synonymous sites (π s ) was highest in An. gambiae (π s = 2.82%, 95% bootstrap interval 2.25–3.41%), lowest in An. melas (π s = 0.86%, 0.53–1.19%), and intermediate in An. arabiensis (π s = 1.97%, [1.50, 2.46]; Figure 1a). The difference in π s between An. gambiae and An. arabiensis was highly significant (Paired Wilcox test using 32 loci, V = 444, p = 0.0004), and π s correlated strongly between these species (Spearman's rho = 0.72, S = 1504, p < 6 × 10-6, Additional file 2). Diversity in An. gambiae and An. arabiensis did not correlate with diversity in An. melas (p > 0.5 in both cases, 26 loci). For a full summary of synonymous diversity, and all other summary stats that follow, see Additional file 3.
Figure 1
Figure 1

Genetic diversity at synonymous and non-synonymous sites. Genetic diversity (the percentage of sites that differ on average between haplotypes) at synonymous (π s ) and nonsynonymous (π a ) sites measured at 32 loci in populations of An. gambiae, An. arabiensis and An. melas. Diversity is shown separately for control loci (dark bars) and serpins (pale bars), and is shown for the species as a whole, and for each population separately. Note that although only two individuals (4 haplotypes) were sampled for S-form An. gambiae in population BK, ~19 Kbp of sequence will provide a good estimate of π if mating within the population is random. Diversity was significantly higher in An. gambiae than in An. arabiensis, and significantly lower in An. melas. For non-synonymous sites, serpins had significantly higher diversity than control loci, but this trend was non-significant at synonymous sites. See main text for details, and Additional File 1 for the raw data.

For An. gambiae, genetic diversity was slightly higher in West Africa (π s = 2.74%; BK M-form) than in East Africa (π s = 2.17%; KY S-form), and although the effect was small, it was statistically significant (Paired Wilcox V = 380, p = 0.03, Figure 1a). Diversity was also highly correlated between East- and West-African populations (Spearman's rho = 0.72, S = 1520, p = 3 × 10-6). Although only two S-form individuals were sampled from West Africa, they displayed higher diversity than the either of the other two An. gambiae populations (BK S-form; π s = 2.98%). For An. arabiensis, diversity correlated even more strongly between East and West Africa (rho = 0.87, S = 729, p < 2 × 10-10) and did not differ significantly between the populations (π s = 1.96% in BK vs. π s = 1.79% in TZ, Paired Wilcoxon test V = 316, p = 0.19).

Synonymous site diversity did not differ significantly between serpins and other genes in either An. gambiae (3.10% vs. 2.53%, Paired Wilcoxon V = 48, p = 0.32, Figure 1a) or An. arabiensis (2.00% vs. 1.93%, Paired Wilcox V = 60, p = 0.71). Although position-matched, no correlation in diversity could be detected between serpins and their corresponding control genes (p > 0.1 in both cases), suggesting that the effect of genomic location on neutral diversity was relatively weak.

Non-synonymous site diversity

Non-synonymous diversity (π a ) was very similar between An. gambiae (π a = 0.22%; 95% bootstrap interval 0.13–0.31%) and An. arabiensis (π a = 0.18%; bootstrap interval: 0.12–0.24%, Figure 1b), and did not differ significantly between the species (Paired Wilcoxon test V = 285, p = 0.29). Although π a did not correlate significantly with π s in either An. gambiae or An. arabiensis (correlation coefficients were 0.17 and 0.07 respectively, p > 0.3 in both cases), there was a strong correlation in π a between the two species (rho = 0.81, S = 1037, p = 2 × 10-8, Additional file 2). East and West African populations did not differ significantly in π a for either An. gambiae or An. arabiensis (p > 0.1 in both cases), but π a did correlate very highly between East and West African populations (rho = 0.80 p = 6. × 10-8, and rho = 0.88, p = 2 × 10-11, respectively). Interestingly, π a was higher for serpins than for other genes in both An. gambiae and An. arabiensis (π a = 0.30% vs. 0.13%, and π a = 0.25% vs. 0.11%), although statistical significance was marginal for An. gambiae (Paired Wilcoxon V = 30 p = 0.051, and V = 24, p = 0.024 for An. gambiae and An. arabiensis respectively, Figure 1b). Despite fewer loci being sequenced, this effect could also be detected in An. melas (πa = 0.15% vs. 0.06%, unpaired Wilcoxon test W = 25, p = 0.034, Figure 1b).

Allele frequency spectra

Tajima's D statistic for synonymous sites was negative in both populations of An. arabiensis (D = -0.29 and D = -0.38, averages across loci in TZ and BK respectively) and in An. gambiae BK (D = -0.71, M-form individuals only), and did not differ between serpins and other genes (Wilcoxon tests, p > 0.5 in all cases). Tajima's D did not differ significantly between the two populations of An. arabiensis (Paired Wilcoxon V = 193, p = 0.42), but did correlate between the populations (rho = 0.43 S = 2543.131, p = 0.017). In population BK, Tajima's D was correlated between An. gambiae and An. arabiensis (rho = 0.490 S = 2293, p = 0.006), but was significantly more negative in An. gambiae (Paired Wilcoxon V = 115, p = 0.015). Strikingly, Tajima's D was generally positive in An. gambiae population KY (mean across loci 0.77, 22 out of the 28 genes with non-zero diversity had D > 0, with overall 95% bootstrap interval [0.40, 1.15]). This was significantly higher than in BK (Paired Wilcoxon test V = 15, p = 1 × 10-6).

In An. arabiensis, five genes had individually significantly negative Tajima's D statistics (p < 0.05 in all cases, no correction for multiple tests): control loci 1 (BK and TZ) and 5 (BK), and serpins 10 (BK), 6 and 7 (TZ). In An. gambiae population BK (M-form only) 7 genes had significantly negative D values: serpins 7, 9 and 14, and control loci 5, 6, 10 and 11 (p < 0.05 in all cases).

Genetic differentiation between populations

In An. arabiensis, differentiation between East and West Africa was very low (K ST = 0.08) and not significantly different from zero at 15 of the 32 loci examined. In An. gambiae, differentiation between East and West Africa was much higher (K ST = 0.14; all loci except control locus 9 were individually significantly differentiated) and this difference between the species was statistically significant (paired Wilcoxon test V = 392, p = 0.016). Note that this differentiation reflects not only geographic separation, but also differentiation between M and S molecular forms of An. gambiae. In West Africa (BK) differentiation between M- and S- form was very low (K ST = 0.016), and significantly lower than differentiation between S-form sampled from East Africa and S-form sampled from West Africa (K ST = 0.073, paired Wilcoxon test V = 442, p = 0.0005). Only two S-form An. gambiae individuals were sampled from BK, making estimates of differentiation potentially poor and reducing the power of the test. However, assuming random mating, the estimates should not be biased by the small number of individuals sampled, and the large number of loci (32, ~19 Kbp of sequence) will reduce sampling error. For an overview of genetic differentiation see Figure 2.
Figure 2
Figure 2

Genetic differentiation between populations. Arrows indicate approximate sampling locations within Africa, and letters identify species (A- An. arabiensis, G- An. gambiae, M-form and S-form). Dashed lines indicate pairs of populations for which genetic differentiation was calculated, and numbers are K ST statistics, averaged across all 32 loci.

The 2La chromosomal inversion

Anopheles gambiae population KY was highly polymorphic for the 2La/+ chromosomal inversion, allowing us to test for differentiation between the two inversion states. Dividing population KY into two groups on the basis of on inversion status (2La homozygotes versus 2L+a homozygotes, heterozygotes excluded) identified very strong population structure associated with the inversion. At the six loci sampled from within the inversion (serpins 1–3 and control loci 1–3) mean differentiation between inversion-groups was K ST = 0.25 (all six loci were significantly differentiated: permutation p < 0.01 for each locus). Across the six other polymorphic loci sequenced from chromosome arm 2L (serpins 7, 10, 14 and 18, control loci 10 and 18), differentiation between these groupings was K ST = 0.03, and was not significantly different from zero in any locus (p > 0.14 by permutation, in each locus).

Divergence and differentiation between species

Genetic divergence (substitutions per site) between An. gambiae and An. arabiensis was very low: K S = 3.5% (averaged across loci), and when corrected for diversity (i.e. , page 220 in [54]), K S = 1.2%. No synonymous fixed differences were identified between these species, and only three non-synonymous fixed differences (all in control locus 5, derived from a transposable element). Divergence from An. melas was higher for both An. gambiae (K S = 6.4%, corrected K S = 4.6%) and An. arabiensis (K S = 6.3%, corrected K S = 4.9%), as was divergence from An. merus (uncorrected K S = 5.3% and K S = 4.8%, respectively); divergence between these species and An. quadriannulatus was intermediate (uncorrected): K S = 4.01% and K S = 4.30% respectively. For an overview of interspecies divergence and an illustration of gene trees versus species trees, see Figure 3.
Figure 3
Figure 3

Genetic divergence within the gambiae species complex. (a) An un-rooted neighbour-joining tree, calculated from pairwise K S between species averaged across loci. Branch lengths are to scale. The filled triangles illustrate the relative scale of diversity and divergence within the complex, such that the length of the triangle is half the divergence between haplotypes within species (i.e π s /2) and net divergence (K S -π s ) corresponds to branch-lengths that are not part of the triangle. (Note that population samples and thus π s were not available for An. quadriannulatus A and An. merus). (b)-(d) Neighbour-joining cladograms (i.e. topology only, branch-lengths uninformative) showing the unique alleles sequenced from three loci. Note that in all cases An. gambiae and An. arabiensis alleles are intermixed. (b) to (d) are control locus 14, SRPN7 and SRPN11, selected to illustrate a wide range of K ST values between An. gambiae and An. arabiensis (K ST = 0.51, 0.10 and 0.09, respectively).

Genetic differentiation (the proportion of total diversity attributable to between-species differences) between An. gambiae and An. arabiensis species was K ST = 0.19 (95% bootstrap interval across loci [0.16, 0.24]). Differentiation between An. gambiae and An. melas was much higher (K ST = 0.33 [0.27, 0.40]), as was differentiation between An. arabiensis and An. melas (K ST = 0.50 [0.44, 0.57]). Differentiation between An. gambiae and An. arabiensis was lower for serpins than for control loci (K ST = 0.14 vs. K ST = 0.25, paired Wilcox test V = 113, p = 0.02). However, although the trend was in the same direction in East and West Africa, this effect was only significant in West Africa (V = 108, p = 0.04, as compared to V = 99, p = 0.12).

Inference of adaptive substitutions

Due to the lack of fixed differences between An. gambiae, An. arabiensis and An. quadriannulatus, McDonald-Kreitman based approaches were not applied to these data (see Discussion). However, it was possible to apply the method of Welch [30] using An. gambiae/An. arabiensis, and their divergence from An. melas and/or An. merus. According to the likelihood ratio test, neither analysis using An. melas provided any evidence supporting adaptive substitutions between the lineages (there was no significant improvement in model fit between fixing α = 0 and allowing α to take its maximum likelihood value; p > 0.19 in all cases: Table 2). This was also true for the test using An. gambiae and An. merus (p > 0.2: Table 2). For the test using An. arabiensis and An. merus there was some evidence that α was significantly greater than zero, (α = 0.34 [0.08, 0.53]; 2ΔlnL = 6.16, 1d.f., p = 0.013, significance lost if a correction is made for multiple tests), but no significant improvement in model fit was obtained by allowing α to differ between serpins and control loci, or between Plasmodium-related serpins and all other loci. This suggests that approximately 8–53% of amino-acid substitutions between An. arabiensis and An. merus were adaptive, but this value did not differ significantly between serpins and the control loci. Analysis of Akaike weights gives a qualitatively identical result: in each species-pair no model is strongly preferred, but in the merus-arabiensis comparison, α = 0 receives relatively little weight. Full results of the McDonald-Kreitman analysis are presented in table 2, and raw data are given in Additional file 4.
Table 2

Estimates of the proportion of adaptive substitutions

α model

Par

log(L)

2Δlog(L)

χ2 p-value

AIC

Akaike weight

αa

αb

An. gambiae vs. An. melas

α = 0

29

-336.49

  

730.98

0.424

[0]

[0]

α ~ (all loci)

30

-335.76

1.46

0.23

731.52

0.323

0.23

[0.23]

α ~ (control, serpin)

31

-335.66

0.19

0.91

733.33

0.131

0.18

0.25

α ~ (other, immune)

31

-335.73

0.06

0.97

733.46

0.123

0.24

0.07

An. arabiensis vs. An. melas

α = 0

29

-329.36

  

716.73

0.495

[0]

[0]

α ~ (all loci)

30

-329.31

0.10

0.75

718.63

0.191

-0.05

[-0.05]

α ~ (control, serpin)

31

-328.07

2.48

0.29

718.15

0.243

-0.54

0.10

α ~ (other, immune)

31

-329.30

0.02

0.99

720.61

0.071

-0.03

-0.15

An. gambiae vs. An. merus

α = 0

34

-333.36

  

734.72

0.349

[0]

[0]

α ~ (all loci)

35

-332.60

1.52

0.22

735.21

0.274

0.15

[0.15]

α ~ (control, serpin)

36

-331.59

2.02

0.36

735.18

0.277

0.34

0.03

α ~ (other, immune)

36

-332.60

0.00

1.00

737.21

0.101

0.15

0.17

An. arabiensis vs. An. merus

α = 0

34

-311.04

  

690.07

0.053

[0]

[0]

α ~ (all loci)

35

-307.96

6.15

0.01

685.92

0.422

0.34

[0.34]

α ~ (control, serpin)

36

-307.22

1.48

0.48

686.44

0.325

0.47

0.25

α ~ (other, immune)

36

-307.71

0.50

0.78

687.42

0.200

0.36

0.10

αa and αb are estimates of the proportion of adaptive substitutions in each of the two classes of gene (control/serpin or non-immune/immune, respectively), Par is the number of parameters in the model. Where the value of α is constrained by the model it is marked in square brackets. Negative values arise from an 'excess' of non-synonymous polymorphism, and could represent sampling error, or mildly deleterious polymorphisms [7]. AIC is the Akaike Information Criterion. The Akaike weighting can be interpreted as the weight of evidence in favour of the corresponding model, given the relative support for all the available models [52].

Discussion

Population history and speciation in Anopheles

The An. gambiae complex falls within the Pyretophorus series of the subgenus Cellia, and comprises a closely-related group of approximately eight species (An. gambiae s.s., An. arabiensis, Anopheles bwambae, An. quadriannulatus A and B, An. merus, An. melas and Anopheles comorensis) [55–57], plus at least one case of incipient speciation (M and S molecular forms of An. gambiae s.s. [37, 58–60]). Because lineages within the complex differ in their importance as Plasmodium vectors [e.g., [61]], in their ecological preferences [62, 63], and in their resistance to pesticides [e.g. [63]], there is considerable value in understanding both species relationships and how populations are structured. This may be of particular consequence if any attempt is ever made to genetically modify wild Anopheles populations to block or reduce Plasmodium transmission [64, 65]. However, in addition to having important implications for vector control, as discussed below, understanding phylogeny and population history are also essential to the robust inference of selection.

Previous analyses suggest that An. gambiae and An. arabiensis are sister taxa, and the data presented here from five of the eight species are in strong agreement, placing An. gambiae and An. arabiensis as the most closely related species pair, with greater divergence to An. merus and An. melas (Figure 3a; note that the tree is unrooted). However, in common with previous studies [e.g. [33, 66, 67]], these data suggest extensive shared polymorphism (Figure 3) and very low differentiation between An. gambiae and An. arabiensis (K ST = 0.19). The inter-species differentiation between An. gambiae and An. arabiensis is approximately the same as inter-population differentiation between African and European D. melanogaster (K ST = 0.16 to 0.24, depending on population; pers. comm. P. R. Haddrill, data from [68]), and is lower than inter-population differentiation in the predominantly selfing nematode Caenorhabditis elegans (K ST = 0.38, pers. com. A. D. Cutter, data from [69]). Thus, although An. gambiae and An. arabiensis are largely reproductively isolated and significantly differentiated [70, 71], these data confirm that either they share extensive ancestral polymorphism, or that there is considerable introgression between them [see also [67, 72, 73]]. This is further supported by the very high correlation in neutral diversity across genes, between these two species (Additional file 2).

Given a particular divergence time, effective population size is the primary determinant of the amount of shared ancestral polymorphism between taxa, because drift (and therefore lineage-sorting) is faster in small populations [e.g. [74]]. Thus, although it is likely that An. gambiae and An. arabiensis share a more recent common ancestor that either does with An. melas, the lower differentiation between An. gambiae and An. arabiensis could also be explained (at least in part) by differences in effective population size within the complex. Since π s is an estimator of 4N e μ, differences in neutral diversity imply that An. gambiae and An. arabiensis have larger effective population sizes than An. merus, consistent both with their wider geographic range, and with potentially higher levels of shared ancestral polymorphism (see above: π s ~2.8%, 2.0%, and 0.9% for An. gambiae, An. arabiensis and An. melas, respectively). Diversity in An. gambiae and An. arabiensis is similar to that seen in African populations of Drosophila simulans and D. melanogaster (π s = 3.2% and π s = 1.7%, respectively) [e.g. [75]], and assuming the mutation rate (μ) is similar between mosquitoes and Drosophila, this also suggests a long-term effective population size for An. gambiae that is about 70% larger than D. melanogaster [76], i.e. well in excess of 1 million. This is broadly consistent with previous estimates from mitochondrial sequence, but is much larger than estimates based on microsatellites and allozyme variants [reviewed in [77]].

Incipient speciation between the M and S molecular forms of An. gambiae is a major focus of ongoing research [37, 58–60], culminating in the recently completed sequencing of the M and S molecular-form genomes [78]. Although differentiation between the M and S form of An. gambiae in West Africa is extremely low (K ST = 0.02; Figure 2), it is consistently non-zero at some loci, even where the lineages are sympatric (see e.g. [6]). This unambiguously identifies M and S form An. gambiae as being (at least partly) reproductively isolated [58], and it has been argued that different M and S-form niches may be distinguishable [37]. Moreover, differentiation is variable around the genome, being higher at so-called 'islands of speciation', potentially associated with adaptive differences [58, 59]. However, other studies have shown that in some geographic regions microclimate is a better predictor of population divergence than is molecular form [79], and it is clear that the S-form of An. gambiae is not a single homogenous lineage [66, 80]. Indeed, it is well-established that there is extensive genetic differentiation associated with the Great Rift Valley [80], and consistent with this, the data presented here not only identify considerable differentiation between East and West Africa (KY S-form versus BK M-form, K ST = 0.12), but also show that West African M-form and S-form are less differentiated from each other than either is from East African S-form (Figure 2). This suggests either that the West African M and S lineages share a more recent common ancestor, or alternatively that they have experienced considerable recent gene flow [[37], e.g. [80], but see also [81]]. For An. arabiensis, differentiation across the width of the continent is only K ST = 0.08, which is very similar to that between Eastern and Western S-form An. gambiae (K ST = 0.07; Figure 2), and approximately twice that between D. melanogaster populations sampled across a similar geographic range (Gabon vs. Kenya or Zimbabwe, K ST = 0.04) [P. R. Haddrill pers. comm., data from [68]].

Most populations showed a slight skew in the allele-frequency spectrum toward low-frequency variants (i.e., average Tajima's D was negative), consistent either with population growth, or with weak selection against some synonymous variants. In contrast, however, the data presented here also identify a strong skew toward intermediate frequency alleles in An. gambiae population KY (i.e., a positive Tajima's D, 0.77 averaged across loci, 95% bootstrap interval 0.40–1.15). One potential explanation for this is that population KY is admixed or contains cryptic population structure, for example as would be the case if two divergent lineages of S-form are coexisting there. Alternatively, a positive Tajima's D could also result from a recent decrease in effective population size.

The phylogenetic and phylogeographic complexity of the An. gambiae species group means that inferences drawn from single individuals should be treated with caution, as the low differentiation between species and high diversity within species means that any one individual is not necessarily typical or representative [e.g. [66]]. For example, the recently sequenced M and S-form genomes [78] were both obtained from mosquitoes sampled in Mali, but S-form divergence between East and West Africa is considerably greater than M-S divergence within West Africa (see Figure 2 above, and [66, 80], cf. [81]). It is therefore not clear that any conclusions regarding M - S genome divergence will generalize to S-form individuals from the east African coast. The same issue arises with inter-species comparisons. For example, the divergence between two randomly sampled An. gambiae genomes is K S ~3%, and that between one randomly selected An. gambiae genome and one randomly selected An. quadriannulatus genome is only K S ~4% (above, and Figure 3). This means that a single inbred strain of An. quadriannulatus is only marginally more informative about An. quadriannulatus than it is about An. gambiae, and without more extensive sampling it cannot reliably be used to identify genetic differences between the species [cf. [61]]. These issues will be of paramount importance in analysing the recently approved complete genomes of An. arabiensis, An. quadriannulatus and An. merus [82].

Evidence for adaptive evolution in Anopheles serpins

There is considerable evidence from other taxa that serpins are an evolutionarily dynamic gene family, with high turnover between lineages and occasional lineage-specific expansions (e.g. [23], see [13] for a review). For example, although most Anopheles and Aedes serpins have 1:1 orthologs, there are 29 serpins in D. melanogaster but only 18 in An. gambiae, and very few mosquito serpins have 1:1 orthologs in Drosophila [83]. Some serpins also show very high rates of adaptive evolution, such as Drosophila Spn28D [CG7219 in ref. [21]], and it has been suggested that, in general, rapid turnover and strong selection in serpins may be associated with serpin immune function, and could be driven by an evolutionary 'arms-race' [13]. In An. gambiae, three serpins are known to have immune-related function in response to Plasmodium infection (SRPN2, SRPN6 and SRPN10 in [24, 26, 28]).

Strong selection can affect patterns of genetic diversity, both between populations, and between chromosomal inversions. In An. gambiae population KY, considerable differentiation was identified between 2La and 2L+a homozygotes around the SRPN1, 2 and 3 cluster (and control loci 1, 2 and 3). For these loci K ST = 0.25 between inversion states, which is actually higher than the overall differentiation between An. gambiae and An. arabiensis, and twice as high as the differentiation between M-form and S-form An. gambiae. No such differentiation was seen for other loci on chromosome arm 2L, indicating that this is strongly associated with the inversion. Although chromosomal inversions are in general expected to suppress recombination, especially near breakpoints, this is unlikely to lead to extreme or long-term differentiation unless maintained by selection [e.g. [84, 85]]. In particular, despite recombination being suppressed in heterozygous individuals, genetic exchange (including recombination and gene-conversion) within the region of the 2La/+ inversion is not zero [86], and this should allow such differentiation to break down rapidly if it is not selectively maintained. The finding of elevated differentiation around these loci agrees with previous analyses, which found the SRPN1-3 cluster to be close to the region of highest differentiation between 2La and 2L+a. [31]. While this doesn't provide strong evidence that any of these serpins are being directly selected, it is interesting to note that SRPN2 is required for successful infection of An. gambiae by P. berghei [28], and that 2La inversion-status was identified with vector-competence in some early studies [87, 88]. Thus it is possible that genetic structuring in serpins 1–3, introduced and maintained by the 2La inversion, may affect variation in vector competence, even if the underlying cause of 2La/+ differentiation is elsewhere.

In contrast, using a McDonald-Kreitman based approach to detecting adaptive substitutions [29, 30], no strong or consistent evidence of selection could be detected, nor could differences in the rate of adaptive evolution between serpins and other genes (or between immune serpins and other genes; Table 2). Specifically, in three of the four species pairings that were analysed, the rate of adaptive evolution could not be distinguished from zero (Table 2). This may indicate that neither Anopheles immune-related serpins, nor Anopheles serpins as a family, are subject to selection for rapid change, and consequently that all selection acting on these genes is purifying. However, it may also be an artefactual result arising either from limitations of the McDonald-Kreitman framework, or from issues specific to the gambiae species complex (see next section). In particular, the McDonald-Kreitman approach assumes that non-synonymous substitutions can be divided into three classes: strongly advantageous mutations that fix rapidly, strongly deleterious mutations that are rapidly lost, and effectively neutral mutations that drift in frequency [50]. If there is also a large class of weakly deleterious mutations that remain polymorphic for an extended period, but are lost by selection in the long term, then this will reduce estimates of α [50].

In agreement with this, a trend toward higher amino-acid diversity was found in serpins, as compared to other genes, in all three species (An. gambiae, An. arabiensis and An. melas; Figure 1b). This could suggest that purifying selection on serpins is weak or intermittent (as compared to purifying selection on other genes), or that there is selection favouring diversity in serpins, such as balancing selection [89]. However, if the latter were the case, then one might also expect to find an increase in diversity at linked synonymous sites, and although slightly higher, synonymous diversity in serpins did not differ significantly from other genes (Figure 1a), suggesting that they neither experience long-term selection for increased polymorphism, nor undergo more frequent selective sweeps [e.g. [8]]. Moreover, in no population or species did a serpin display the highest or lowest neutral genetic diversity, and there was no clear pattern in the allele frequency spectrum (as measured by Tajima's D statistic) or inter-population differentiation (measured by K ST ) that supports the notion of strong selection acting on Anopheles serpins. These data therefore fail to identify any serpins as candidates for recent strong selection within the gambiae complex.

Prospects and pitfalls in inferring adaptive evolution in Anopheles

A null result in tests for selection may also result from low power or model violations associated with the phylogenetic and population history in the An. gambiae complex. Motivated by an interest in identifying targets of pathogen-mediated selection, several studies have now attempted to identify adaptive evolution in immune-related genes from the Anopheles gambiae species complex [5, 6, 32–34, 90–92]. However, in stark contrast to almost identical studies in taxa such as Drosophila [for example [10, 11, 21, 93–95]], to date there is very little clear evidence supporting adaptive evolution in the An. gambiae complex [but see [90, 92]].

One likely reason for the difference between Anopheles and Drosophila studies is the dearth of suitable outgroups for An. gambiae [e.g. [32]]. First, well-studied Anopheles species outside of the gambiae complex appear to be too distantly related to reliably infer the divergence between them. For example, the An. stephensi SRPN6 cDNA sequence [26] suggests that K S between with An. stephensi and An. gambiae is ~1.33 (± 0.157) substitutions per site when estimated by maximum likelihood [96], or 0.92 (± 0.117) by the method of Li [97]. Second, low divergence within the complex means there is little power to infer the substitution rate between them [6, 32, 33]. For example, across all 8 serpins appearing in the extensive survey of Cohuet et al. [6], there are only two fixed amino acid differences between An. arabiensis and An. gambiae. Similarly, the present study found no fixed differences at all in the same genes, probably because wider geographic sampling provided greater power to distinguish between polymorphisms and fixed differences. The stochastic errors involved here mean that estimates of substitution rate are likely to be wildly variable, reducing the power to estimate the fraction of adaptive substitutions using a McDonald-Kreitman framework (Figure 4).
Figure 4
Figure 4

The power to estimate α using An. gambiae and An. arabiensis. The relative log-likelihood of α (the proportion of amino-acid substitutions that are adaptive) estimated using the modified McDonald-Kreitman approach [30]. The grey curve is calculated from all 102 genes for which both An. arabiensis and An. gambiae population samples were available in the dataset of Cohuet et al. [6]. The black curve shows an equivalent dataset of 102 genes from Drosophila melanogaster and D. simulans, with genes selected to be the same average length as those in the Cohuet dataset (D. J. Obbard, J. J. Welch and F. M. Jiggins, unpublished data). Despite both pairs of species having similar levels of diversity (π s from 1.6% to 2.9%), for the Anopheles dataset the bounds (2 units of log Likelihood) stretch from -0.33 to 0.32 (and include zero) while for Drosophila the bounds only stretch from 0.26 to 0.44, and the maximum-likelihood estimate of α is 35%. The low precision in the second estimate reflects the very low power available due to the low divergence in An. gambiae-An. arabiensis comparisons

In principle, other species from within the gambiae complex might be informative outgroups for An. gambiae and An. arabiensis: although divergence is small (1 to 5%; Figure 3a), this may be sufficient in other taxa, such as the human-chimp comparison [e.g. [98]]. However, unlike the human-chimp case, diversity in Anopheles is very high compared to humans (πs~3% in An. gambiae, ~2% in An. arabiensis, Figure 1, c.f. ~0.1% in humans, e.g. [99]), and this leads to two potential problems. First, a large proportion of apparent substitutions will in reality be polymorphisms [30, 51], and although this effect is small enough to be negligible for pairs of species in which K S >> π S , it becomes a concern when comparing An. gambiae to An. arabiensis, for which K S <π S (Figure 3). However, as here, this effect can be accounted for using models which include the sample size and diversity, and thereby infer the 'true' number of substitutions [30, 51]. Second, and potentially more serious, is the opportunity for extensive shared polymorphism. The McDonald-Kreitman framework uses information from current diversity (i.e. πs and πa) to infer whether some proportion of historic substitutions (K A ) cannot be explained by purely neutral processes. This model implicitly assumes a time period when the two lineages were diverging from their common ancestor, during which selection (that is to be detected) was able to act. If there is extensive shared polymorphism between the species, for example if gene trees are rarely reciprocally monophyletic (as is the case with An. gambiae and An. arabiensis; see Figure 3b–d and e.g. [33]), then it is hard to see how the McDonald-Kreitman approach can ever be usefully applied. In other words, unlike the straight-forward cases where divergence is too high (the branch is too long) or divergence is too low (the branch is too short), for An. gambiae and An. arabiensis, in effect there is no branch at all (Figure 3b–d).

Unfortunately, for similar reasons, there are also serious concerns about the application of other selection-inference methods to the gambiae complex, such as the phylogenetic methods implemented in PAML [96] and HyPhy [100]. These methods use multiple sequences related by a gene (or species) tree to infer relative rates of synonymous and non-synonymous substitution, allowing variable rates at different sites or in different parts of the gene. Most phylogenetic methods assume there is no recombination within loci [but see OmegaMap, [101]], and simulation suggests false positives can reach >50% when 2N e r > 0.01 [102]. Because 2N e r (i.e: 2 × effective population size × recombination rate per codon per generation) in An. gambiae is likely to be of the order 0.01 – 0.1 or higher – primarily due to the large effective population size – such phylogenetic approaches cannot be applied reliably to within-species Anopheles data.

Additionally, where the McDonald-Kreitman framework assumes that between-species K A results from the joint action of selection and drift, and within-species π a results only from drift, the phylogenetic approaches (as they are most commonly applied) assume either that all amino acid variants have the same cause (i.e. K A and π a do not provide independent information about selection and drift) or that most differences are fixed between species, (i.e. K A >> π a , such that π a is negligible). Thus the phylogenetic and McDonald-Kreitman approaches constitute very different models that lend themselves to different datasets, and it is not clear that any single dataset can reasonably be analysed using both. Instead, to analyse a dataset that includes substantial within-species sampling using a phylogenetic approach, it may be more rational to fit a model which allows the relative rates of synonymous and non-synonymous substitution to differ between within- and between-species branches [e.g. [103]]. However, in the case of the gambiae complex, the low power associated with the low inter-species divergence will then be encountered again.

If McDonald-Kreitman tests have relatively low power within the gambiae complex, and phylogenetic methods cannot easily be applied to within-species data, how can selection be inferred from Anopheles population genetic data? One possibility is to use approaches based solely on within-population diversity to identify recent selective sweeps or regions of elevated polymorphism, and this will work for some loci [e.g. TEP1, [92]], though the possibility of introgression, and/or chromosomal inversions that might affect the distribution of diversity, should then be taken into account. Nevertheless, at present it seems the best options for outgroup-based analyses are An. merus and/or An. melas, and both the data presented here (Figure 3) and previous studies [e.g. [33, 66]] suggest that their divergence from the An. gambiae/An. arabiensis clade should be sufficient in the case of genes evolving under very strong selection. However, the recently approved genome sequences from 13 more species of Anopheles mosquitoes [82] may hold the solution, and particularly the complete genome sequence of Anopheles sundaicus (also subgenus Cellia, Pyretophorous series).

Declarations

Acknowledgements

We thank H. M. Ferguson and N. Tuno for providing field samples of Anopheles mosquitoes, R. Maharaj and W. Takken for providing preserved mosquitoes from laboratory culture, and G. Yan for ongoing logistical support with fieldwork. We thank Abraham Eappen and Marcelo Jacobs-Lorena for providing the cDNA sequence of An. Stephensi SRPN6, Asher Cutter and Penny Haddrill for unpublished K ST statistics, and Mike Kanost and Kristin Michel for sharing pre-publication data on Anopheles serpin classification and function. We thank an anonymous reviewer for helpful comments on the manuscript, and Marcel Hommel for careful copyediting. This work was funded by Wellcome Trust Grant 073210 to TJL, and DJO also received Wellcome Trust funding in the form of 'Value In People' bridging salary and Wellcome Trust Research Career Development Fellowship 085064/Z/08/Z. JJW is funded by BBSRC grant DO17750 awarded to Andrew Rambaut.

Authors’ Affiliations

(1)
Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh, UK

References

  1. Riehle M, Markianos K, Lambrechts L, Xia A, Sharakhov I, Koella J, Vernick K: A major genetic locus controlling natural Plasmodium falciparum infection is shared by East and West African Anopheles gambiae. Malar J. 2007, 6: 87-10.1186/1475-2875-6-87.PubMed CentralPubMedGoogle Scholar
  2. Dong Y, Aguilar R, Xi Z, Warr E, Mongin E, Dimopoulos G: Anopheles gambiae immune responses to human and rodent Plasmodium parasite species. PLoS Pathog. 2006, 2: e52-10.1371/journal.ppat.0020052.PubMed CentralPubMedGoogle Scholar
  3. Michel K, Kafatos FC: Mosquito immunity against Plasmodium . Insect Bioch Mol Biol. 2005, 35: 677-689. 10.1016/j.ibmb.2005.02.009.Google Scholar
  4. Osta MA, Christophides GK, Kafatos FC: Effects of mosquito genes on Plasmodium development. Science. 2004, 303: 2030-2032. 10.1126/science.1091789.PubMedGoogle Scholar
  5. Little TJ, Cobbe N: The evolution of immune-related genes from disease carrying mosquitoes: diversity in a peptidoglycan- and a thioester-recognizing protein. Insect Mol Biol. 2005, 14: 599-605. 10.1111/j.1365-2583.2005.00588.x.PubMedGoogle Scholar
  6. Cohuet A, Krishnakumar S, Simard F, Morlais I, Koutsos A, Fontenille D, Mindrinos M, Kafatos F: SNP discovery and molecular evolution in Anopheles gambiae, with special emphasis on innate immune system. BMC Genomics. 2008, 9: 227-10.1186/1471-2164-9-227.PubMed CentralPubMedGoogle Scholar
  7. Eyre-Walker A: The genomic rate of adaptive evolution. Trends Ecol Evolution Trends Ecol Evol. 2006 Oct;21(10):569-75. 2006, 21 (10): 569-575. 10.1016/j.tree.2006.06.015.Google Scholar
  8. Nielsen R: Molecular signatures of natural selection. Ann Rev Genetics. 2005, 39: 197-218. 10.1146/annurev.genet.39.073003.112420.Google Scholar
  9. Boëte C: Anopheles mosquitoes: not just flying malaria vectors especially in the field. Trends Parasitol. 2009, 25: 53-55. 10.1016/j.pt.2008.10.005.PubMedGoogle Scholar
  10. Obbard DJ, Jiggins FM, Halligan DL, Little TJ: Natural selection drives extremely rapid evolution in antiviral RNAi genes. Current Biol. 2006, 16: 580-585. 10.1016/j.cub.2006.01.065.Google Scholar
  11. Sackton TB, Lazzaro BP, Schlenke TA, Evans JD, Hultmark D, Clark AG: Dynamic evolution of the innate immune system in Drosophila . Nature Genetics. 2007, 39: 1461-1468. 10.1038/ng.2007.60.PubMedGoogle Scholar
  12. Gettins PGW: Serpin structure, mechanism, and function. Chem Rev. 2002, 102: 4751-4803. 10.1021/cr010170+.PubMedGoogle Scholar
  13. Christeller JT: Evolutionary mechanisms acting on proteinase inhibitor variability. FEBS J. 2005, 272: 5710-5722. 10.1111/j.1742-4658.2005.04975.x.PubMedGoogle Scholar
  14. Mangan MSJ, Kaiserman D, Bird PI: The role of serpins in vertebrate immunity. Tissue Antigens. 2008, 72: 1-10. 10.1111/j.1399-0039.2008.01059.x.PubMedGoogle Scholar
  15. Jiravanichpaisal P, Lee BL, Söderhäll K: Cell-mediated immunity in arthropods: Hematopoiesis, coagulation, melanization and opsonization. Immunobiology. 2006, 211: 213-236. 10.1016/j.imbio.2005.10.015.PubMedGoogle Scholar
  16. Levashina EA, Langley E, Green C, Gubb D, Ashburner M, Hoffmann JA, Reichhart JM: Constitutive activation of toll-mediated antifungal defense in serpin-deficient Drosophila . Science. 1999, 285: 1917-1919. 10.1126/science.285.5435.1917.PubMedGoogle Scholar
  17. Ligoxygakis P, Pelte N, Hoffmann JA, Reichhart JM: Activation of Drosophila Toll during fungal infection by a blood serine protease. Science. 2002, 297: 114-116. 10.1126/science.1072391.PubMedGoogle Scholar
  18. De Gregorio E, Han SJ, Lee WJ, Baek MJ, Osaki T, Kawabata SI, Lee BL, Iwanaga S, Lemaitre B, Brey PT: An immune-responsive serpin regulates the melanization cascade in Drosophila . Developmental Cell. 2002, 3: 581-592. 10.1016/S1534-5807(02)00267-8.PubMedGoogle Scholar
  19. Ligoxygakis P, Pelte N, Ji CY, Leclerc V, Duvic B, Belvin M, Jiang HB, Hoffmann JA, Reichhart JM: A serpin mutant links Toll activation to melanization in the host defence of Drosophila . EMBO J. 2002, 21: 6330-6337. 10.1093/emboj/cdf661.PubMed CentralPubMedGoogle Scholar
  20. De Gregorio E, Spellman PT, Rubin GM, Lemaitre B: Genome-wide analysis of the Drosophila immune response by using oligonucleotide microarrays. Proc Natl Acad Sci USA. 2001, 98: 12590-12595. 10.1073/pnas.221458698.PubMed CentralPubMedGoogle Scholar
  21. Jiggins FM, Kim KW: A screen for immunity genes evolving under positive selection in Drosophila . J Evolut Biol. 2007, 20: 965-970. 10.1111/j.1420-9101.2007.01305.x.Google Scholar
  22. Heger A, Ponting CP: Evolutionary rate analyses of orthologs and paralogs from 12 Drosophila genomes. Genome Res. 2007, gr.6249707.Google Scholar
  23. Borner S, Ragg H: Functional diversification of a protease inhibitor gene in the genus Drosophila and its molecular basis. Gene. 2008, 415: 23-31. 10.1016/j.gene.2008.02.004.PubMedGoogle Scholar
  24. Danielli A, Kafatos FC, Loukeris TG: Cloning and characterization of four Anopheles gambiae serpin isoforms, differentially induced in the midgut by Plasmodium berghei invasion. J Biol Chem. 2003, 278: 4184-4193. 10.1074/jbc.M208187200.PubMedGoogle Scholar
  25. Danielli A, Barillas-Mury C, Kumar S, Kafatos FC, Loukeris TG: Overexpression and altered nucleocytoplasmic distribution of Anopheles ovalbumin-like SRPN10 serpins in Plasmodium-infected midgut cells. Cell Microbiol. 2005, 7: 181-190.PubMedGoogle Scholar
  26. Abraham EG, Pinto SB, Ghosh A, Vanlandingham DL, Budd A, Higgs S, Kafatos FC, Jacobs-Lorena M, Michel K: An immune-responsive serpin, SRPN6, mediates mosquito defense against malaria parasites. Proc Natl Acad Sci USA. 2005, 102: 16327-16332. 10.1073/pnas.0508335102.PubMed CentralPubMedGoogle Scholar
  27. Pinto SB, Kafatos FC, Michel K: The parasite invasion marker SRPN6 reduces sporozoite numbers in salivary glands of Anopheles gambiae . Cell Microbiol. 2008, 10: 891-898. 10.1111/j.1462-5822.2007.01091.x.PubMedGoogle Scholar
  28. Michel K, Budd A, Pinto S, Gibson TJ, Kafatos FC: Anopheles gambiae SRPN2 facilitates midgut invasion by the malaria parasite Plasmodium berghei. EMBO Reports. 2005, 6: 891-897. 10.1038/sj.embor.7400478.PubMed CentralPubMedGoogle Scholar
  29. McDonald JH, Kreitman M: Adaptive protein evolution at the adh locus in Drosophila . Nature. 1991, 351: 652-654. 10.1038/351652a0.PubMedGoogle Scholar
  30. Welch JJ: Estimating the genomewide rate of adaptive protein evolution in Drosophila . Genetics. 2006, 173: 821-837. 10.1534/genetics.106.056911.PubMed CentralPubMedGoogle Scholar
  31. White BJ, Hahn MW, Pombi M, Cassone BJ, Lobo NF, Simard F, Besansky NJ: Localization of candidate regions maintaining a common polymorphic inversion (2La) in Anopheles gambiae . Plos Genetics. 2007, 3: 2404-2414. 10.1371/journal.pgen.0030217.Google Scholar
  32. Obbard DJ, Linton YM, Jiggins FM, Yan G, Little TJ: Population genetics of Plasmodium resistance genes in Anopheles gambiae: no evidence for strong selection. Molecular Ecology. 2007, 16: 3497-3510. 10.1111/j.1365-294X.2007.03395.x.PubMedGoogle Scholar
  33. Parmakelis A, Slotman M, Marshall J, Awono-Ambene P, Antonio-Nkondjio C, Simard F, Caccone A, Powell J: The molecular evolution of four anti-malarial immune genes in the Anopheles gambiae species complex. BMC Evolutionary Biology. 2008, 8: 79-10.1186/1471-2148-8-79.PubMed CentralPubMedGoogle Scholar
  34. Lehmann T, Hume JCC, Licht M, Burns CS, Wollenberg K, Simard F, Ribeiro JMC: Molecular Evolution of Immune Genes in the Malaria Mosquito Anopheles gambiae . PLoS ONE. 2009, 4: e4549-10.1371/journal.pone.0004549.PubMed CentralPubMedGoogle Scholar
  35. 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
  36. Favia G, della Torre A, Bagayoko M, Lanfrancotti A, Sagnon N, Toure 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.PubMedGoogle Scholar
  37. Lehmann T, Diabate A: The molecular forms of Anopheles gambiae: A phenotypic perspective. Infection, Genetics and Evolution. 2008, 8: 737-746. 10.1016/j.meegid.2008.06.003.PubMed CentralPubMedGoogle Scholar
  38. White BJ, Santolamazza F, Kamau L, Pombi M, Grushko O, Mouline K, Brengues C, Guelbeogo W, Coulibaly M, Kayondo JK, Sharakhov I, Simard F, Petrarca V, Della Torre A, Besansky NJ: Molecular karyotyping of the 2La inversion in Anopheles gambiae . Am J Trop Med Hyg. 2007, 76: 334-339.PubMedGoogle Scholar
  39. Sharakhov IV, White BJ, Sharakhova MV, Kayondo J, Lobo NF, Santolamazza F, della Torre A, Simard F, Collins FH, Besansky NJ: Breakpoint structure reveals the unique origin of an interspecific chromosomal inversion (2La) in the Anopheles gambiae complex. Proc Natl Acad Sci USA. 2006, 103: 6258-6262. 10.1073/pnas.0509683103.PubMed CentralPubMedGoogle Scholar
  40. Ng'habi K, Meneses C, Cornel A, Slotman M, Knols B, Ferguson H, Lanzaro G: Clarification of anomalies in the application of a 2La molecular karyotyping method for the malaria vector Anopheles gambiae . Parasites & Vectors. 2008, 1: 45-10.1186/1756-3305-1-45.Google Scholar
  41. Holt RA, Subramanian GM, Halpern A, Sutton GG, Charlab R, Nusskern DR, Wincker P, Clark AG, Ribeiro JM, Wides R, Salzberg SL, Loftus B, Yandell M, Majoros WH, Rusch DB, Lai ZW, Kraft CL, Abril JF, Anthouard V, Arensburger P, Atkinson PW, Baden H, de Berardinis V, Baldwin D, Benes V, Biedler J, Blass C, Bolanos R, Boscus D, Barnstead M, Cai S, Center A, Chatuverdi K, Christophides GK, Chrystal MA, Clamp M, Cravchik A, Curwen V, Dana A, Delcher A, Dew I, Evans CA, Flanigan M, Grundschober-Freimoser A, Friedli L, Gu ZP, Guan P, Guigo R, Hillenmeyer ME, Hladun SL, Hogan JR, Hong YS, Hoover J, Jaillon O, Ke ZX, Kodira C, Kokoza E, Koutsos A, Letunic I, Levitsky A, Liang Y, Lin JJ, Lobo NF, Lopez JR, Malek JA, McIntosh TC, Meister S, Miller J, Mobarry C, Mongin E, Murphy SD, O'Brochta DA, Pfannkoch C, Qi R, Regier MA, Remington K, Shao HG, Sharakhova MV, Sitter CD, Shetty J, Smith TJ, Strong R, Sun JT, Thomasova D, Ton LQ, Topalis P, Tu ZJ, Unger MF, Walenz B, Wang AH, Wang J, Wang M, Wang XL, Woodford KJ, Wortman JR, Wu M, Yao A, Zdobnov EM, Zhang HY, Zhao Q, Zhao SY, Zhu SC, Zhimulev I, Coluzzi M, della Torre A, Roth CW, Louis C, Kalush F, Mural RJ, Myers EW, Adams MD, Smith HO, Broder S, Gardner MJ, Fraser CM, Birney E, Bork P, Brey PT, Venter JC, Weissenbach J, Kafatos FC, Collins FH, Hoffman SL: The genome sequence of the malaria mosquito Anopheles gambiae . Science. 2002, 298: 129-10.1126/science.1076181.PubMedGoogle Scholar
  42. Stephens M, Donnelly P: A comparison of Bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003, 73 (5): 1162-1169. 10.1086/379378.PubMed CentralPubMedGoogle Scholar
  43. Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001, 68 (4): 978-989. 10.1086/319501.PubMed CentralPubMedGoogle Scholar
  44. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R: DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics. 2003, 19: 2496-2497. 10.1093/bioinformatics/btg359.PubMedGoogle Scholar
  45. Tajima F: Statistical-Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics. 1989, 123: 585-595.PubMed CentralPubMedGoogle Scholar
  46. Hudson RR, Boos DD, Kaplan NL: A statistical test for detecting geographic subdivision. Mol Biol Evol. 1992, 9: 138-151.PubMedGoogle Scholar
  47. Nei M: Evolution of human races at the gene level. Human genetics, part A: The unfolding genome. Edited by: Bonne-Tamir B, Cohen T, Goodman RM. 1982, New York.: Alan R. LissGoogle Scholar
  48. Rand DM, Kann LM: Excess amino acid polymorphism in mitochondrial DNA: contrasts among genes from Drosophila, mice, and humans. Mol Biol Evol. 1996, 13: 735-748.PubMedGoogle Scholar
  49. Smith NGC, Eyre-Walker A: Adaptive protein evolution in Drosophila . Nature. 2002, 415: 1022-1024. 10.1038/4151022a.PubMedGoogle Scholar
  50. Bierne N, Eyre-Walker A: The Genomic Rate of Adaptive Amino Acid Substitution in Drosophila . Mol Biol Evol. 2004, 21: 1350-1360. 10.1093/molbev/msh134.PubMedGoogle Scholar
  51. Sawyer SA, Hartl DL: Population-Genetics Of Polymorphism And Divergence. Genetics. 1992, 132: 1161-1176.PubMed CentralPubMedGoogle Scholar
  52. Burnham KP, Anderson DR: Model Selection and Multi-model Inference: A Practical Information-theoretic Approach. 2002, Springer, 2Google Scholar
  53. MKtest. [http://tree.bio.ed.ac.uk/software/mktest/]
  54. Nei M: Molecular Evolutionary Genetics. 1987, New York: Columbia University PressGoogle Scholar
  55. Anthony TG, Harbach RE, Kitching IJ: Phylogeny of the Pyretophorus Series of Anopheles subgenus Cellia (Diptera: Culicidae). Systematic Entomol. 1999, 24: 193-205. 10.1046/j.1365-3113.1999.00092.x.Google Scholar
  56. Harbach RE: Review of the internal classification of the genus Anopheles (Diptera, Culicidae) – The foundation for comparative systematics and phylogenetic research. Bull Entomol Res. 1994, 84: 331-342.Google Scholar
  57. Foley DH, Bryan JH, Yeates D, Saul A: Evolution and Systematics of Anopheles: Insights from a Molecular Phylogeny of Australasian Mosquitoes. Mol Phylogenet Evol. 1998, 9: 262-10.1006/mpev.1997.0457.PubMedGoogle Scholar
  58. Turner TL, Hahn MW: Locus- and Population-Specific Selection and Differentiation between Incipient Species of Anopheles gambiae. Mol Biol Evol. 2007, 24: 2132-2138. 10.1093/molbev/msm143.PubMedGoogle Scholar
  59. Turner TL, Hahn MW, Nuzhdin SV: Genomic islands of speciation in Anopheles gambiae. PLoS Biology. 2005, 3: e285-10.1371/journal.pbio.0030285.PubMed CentralPubMedGoogle Scholar
  60. della Torre A, Costantini C, Besansky NJ, Caccone A, Petrarca V, Powell JR, Coluzzi M: Speciation within Anopheles gambiae – the glass is half full. Science. 2002, 298: 115-117. 10.1126/science.1078170.PubMedGoogle Scholar
  61. Habtewold T, Povelones M, Blagborough AM, Christophides GK: Transmission blocking immunity in the malaria non-vector mosquito Anopheles quadriannulatus species A. PLoS Pathog. 2008, 4: 8-10.1371/journal.ppat.1000070.Google Scholar
  62. Lindsay SW, Parson L, Thomas CJ: Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proc R Soc London Series B-Biol Sci. 1998, 265: 847-854. 10.1098/rspb.1998.0369.Google Scholar
  63. Kerah-Hinzoumbe C, Peka M, Nwane P, Donan-Gouni I, Etang J, Same-Ekobo A, Simard F: Insecticide resistance in Anopheles gambiae from south-western Chad, Central Africa. Malar J. 2008, 7: 192-10.1186/1475-2875-7-192.PubMed CentralPubMedGoogle Scholar
  64. 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 CentralPubMedGoogle Scholar
  65. Little TJ: Immune system polymorphism: Implications for genetic engineering. Genetically modified mosquitoes for malaria control. Edited by: Boete C. 2006, Georgetown, Texas: Landes Bioscience, 36-59.Google Scholar
  66. Wang-Sattler R, Blandin S, Ning Y, Blass C, Dolo G, Toure YT, Torre Ad, 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 CentralPubMedGoogle Scholar
  67. Donnelly MJ, Pinto J, Girod R, Besansky NJ, Lehmann T: Revisiting the role of introgression vs shared ancestral polymorphisms as key processes shaping genetic diversity in the recently separated sibling species of the Anopheles gambiae complex. Heredity. 2004, 92: 61-68. 10.1038/sj.hdy.6800377.PubMedGoogle Scholar
  68. Haddrill PR, Thornton KR, Charlesworth B, Andolfatto P: Multilocus patterns of nucleotide variability and the demographic and selection history of Drosophila melanogaster populations. Genome Research. 2005, 15: 790-799. 10.1101/gr.3541005.PubMed CentralPubMedGoogle Scholar
  69. Cutter AD: Nucleotide polymorphism and linkage disequilibrium in wild populations of the partial selfer Caenorhabditis elegans. Genetics. 2006, 172: 171-184. 10.1534/genetics.105.048207.PubMed CentralPubMedGoogle Scholar
  70. Slotman MA, Della Torre A, Calzetta M, Powell JR: Differential introgression of chromsomal regions between Anopheles gambiae and An. arabiensis. Am J Trop Med Hyg. 2005, 73: 326-335.PubMedGoogle Scholar
  71. Slotman M, Della Torre A, Powell JR: Female sterility in hybrids between Anopheles gambiae and An. arabiensis, and the causes of Haldane's rule. Evolution. 2005, 59: 1016-1026.PubMedGoogle Scholar
  72. Besansky NJ, Krzywinski J, Lehmann T, Simard F, Kern M, Mukabayire O, Fontenille D, Toure Y, Sagnon NF: Semipermeable species boundaries between Anopheles gambiae and Anopheles arabiensis: Evidence from multilocus DNA sequence variation. Proc Natl Acad Sci USA. 2003, 100: 10818-10823. 10.1073/pnas.1434337100.PubMed CentralPubMedGoogle Scholar
  73. Black WC, Lanzaro GC: Distribution of genetic variation among chromosomal forms of Anopheles gambiae s.s.: introgressive hybridization, adaptive inversions, or recent reproductive isolation?. Insect Molecular Biology. 2001, 10: 3-7. 10.1046/j.1365-2583.2001.00234.x.PubMedGoogle Scholar
  74. Rosenberg NA: The Probability of Topological Concordance of Gene Trees and Species Trees. Theoretical Population Biology. 2002, 61: 225-247. 10.1006/tpbi.2001.1568.PubMedGoogle Scholar
  75. Andolfatto P: Contrasting patterns of X-linked and autosomal nucleotide variation in Drosophila melanogaster and Drosophila simulans. Mol Biol Evol. 2001, 18: 279-290.PubMedGoogle Scholar
  76. Andolfatto P, Przeworski M: A Genome-wide departure from the standard neutral model in natural populations of Drosophila. Genetics. 2000, 156: 257-172.PubMed CentralPubMedGoogle Scholar
  77. Lehmann T, Hawley WA, Grebert H, Collins FH: The effective population size of Anopheles gambiae in Kenya: implications for population structure. Mol Biol Evol. 1998, 15: 264-276.PubMedGoogle Scholar
  78. Besansky NJ: White Paper: Proposal for the Eight Genomes Cluster for Genus Anopheles. 2005, NIH, National Human Genome Research InstituteGoogle Scholar
  79. 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 CentralPubMedGoogle Scholar
  80. 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 (2): 133-147. 10.1093/jhered/esg024.PubMedGoogle Scholar
  81. Esnault C, Boulesteix M, Duchemin JB, Koffi AA, Chandre F, Dabiré R, Robert V, Simard F, Tripet F, Donnelly MJ, Fontenille D, Biémont C: High Genetic Differentiation between the M and S Molecular Forms of Anopheles gambiae in Africa. PLoS ONE. 2008, 3: e1968-10.1371/journal.pone.0001968.PubMed CentralPubMedGoogle Scholar
  82. Besansky NJ: White Paper: Genome analysis of vectorial capacity in major Anopheles vectors of malaria parasites. 2008, NIH, National Human Genome Research InstituteGoogle Scholar
  83. Waterhouse RM, Kriventseva EV, Meister S, Xi Z, Alvarez KS, Bartholomay LC, Barillas-Mury C, Bian G, Blandin S, Christensen BM, Dong Y, Jiang H, Kanost MR, Koutsos AC, Levashina EA, Li J, Ligoxygakis P, MacCallum RM, Mayhew GF, Mendes A, Michel K, Osta MA, Paskewitz S, Shin SW, Vlachou D, Wang L, Wei W, Zheng L, Zou Z, Severson DW, Raikhel AS, Kafatos FC, Dimopoulos G, Zdobnov EM, Christophides GK: Evolutionary dynamics of immune-related genes and pathways in disease-vector mosquitoes. Science. 2007, 316: 1738-1743. 10.1126/science.1139862.PubMed CentralPubMedGoogle Scholar
  84. Andolfatto P, Depaulis F, Navarro A: Inversion polymorphisms and nucleotide variability in Drosophila. Genetical Research. 2001, 77: 1-8. 10.1017/S0016672301004955.PubMedGoogle Scholar
  85. Schaeffer SW, Anderson WW: Mechanisms of genetic exchange within the chromosomal inversions of Drosophila pseudoobscura. Genetics. 2005, 171: 1729-1739. 10.1534/genetics.105.041947.PubMed CentralPubMedGoogle Scholar
  86. Stump AD, Pombi M, Goeddel L, Ribeiro JMC, Wilder JA, Torre AD, Besansky NJ: Genetic exchange in 2La inversion heterokaryotypes of Anopheles gambiae. Insect Mol Biol. 2007, 16: 703-709.PubMedGoogle Scholar
  87. Vernick KD, Collins FH: Association of a Plasmodium refractory phenotype with an esterase locus in Anopheles gambiae. Am J Trop Med Hyg. 1989, 40: 593-597.PubMedGoogle Scholar
  88. Petrarca V, Beier JC: Intraspecific chromosomal polymorphism in the Anopheles gambiae complex as a factor affecting malaria transmission in the Kisumu area of Kenya. Am J Trop Med Hyg. 1992, 46: 229-237.PubMedGoogle Scholar
  89. Charlesworth D: Balancing selection and its effects on sequences in nearby genome regions. PloS Genetics. 2006, 2 (4): e64-10.1371/journal.pgen.0020064.PubMed CentralPubMedGoogle Scholar
  90. Slotman MA, Parmakelis A, Marshall JC, Awono-Ambene PH, Antonio-Nkondjo C, Simard F, Caccone A, Powell JR: Patterns of selection in anti-malarial immune genes in malaria vectors: evidence for adaptive evolution in LRIM1 in Anopheles arabiensis. PLoS ONE. 2007, 2: e793-10.1371/journal.pone.0000793.PubMed CentralPubMedGoogle Scholar
  91. Simard F, Licht M, Besansky NJ, Lehmann T: Polymorphism at the defensin gene in the Anopheles gambiae complex: Testing different selection hypotheses. Infect Genet Evol. 2007, 7 (2): 285-292. 10.1016/j.meegid.2006.11.004.PubMed CentralPubMedGoogle Scholar
  92. Obbard DJ, Callister D, Jiggins FM, Soares D, Yan G, Little TJ: The evolution of TEP1, an exceptionally polymorphic immunity gene in Anopheles gambiae. BMC Evolutionary Biology. 2008, 8: 274-10.1186/1471-2148-8-274.PubMed CentralPubMedGoogle Scholar
  93. Begun DJ, Whitley P: Adaptive eolution of relish, a Drosophila NF-{kappa}B/I{kappa}B Protein. Genetics. 2000, 154: 1231-1238.PubMed CentralPubMedGoogle Scholar
  94. Schlenke TA, Begun DJ: Natural selection drives Drosophila immune system evolution. Genetics. 2003, 164: 1471-1480.PubMed CentralPubMedGoogle Scholar
  95. Lazzaro BP: Elevated polymorphism and divergence in the class C scavenger receptors of Drosophila melanogaster and D. simulans. Genetics. 2005, 169: 2023-2034. 10.1534/genetics.104.034249.PubMed CentralPubMedGoogle Scholar
  96. Yang ZH: PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007, 24: 1586-1591. 10.1093/molbev/msm088.PubMedGoogle Scholar
  97. Li WH: Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J Mol Evol. 1993, 36: 96-99. 10.1007/BF02407308.PubMedGoogle Scholar
  98. Ramensky VE, Nurtdinov RN, Neverov AD, Mironov AA, Gelfand MS: Positive selection in alternatively spliced exons of human genes. Am J Human Genetics. 2008, 83: 94-98. 10.1016/j.ajhg.2008.05.017.Google Scholar
  99. Li WH, Sadler LA: Low nucleotide diversity in man. Genetics. 1991, 129: 513-523.PubMed CentralPubMedGoogle Scholar
  100. Kosakovsky Pond SL, Frost SDW, Muse SV: HyPhy: hypothesis testing using phylogenies. Bioinformatics. 2005, 21: 676-679. 10.1093/bioinformatics/bti079.Google Scholar
  101. Wilson DJ, McVean G: Estimating diversifying selection and functional constraint in the presence of recombination. Genetics. 2006, 172: 1411-1425. 10.1534/genetics.105.044917.PubMed CentralPubMedGoogle Scholar
  102. Anisimova M, Nielsen R, Yang ZH: Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics. 2003, 164: 1229-1236.PubMed CentralPubMedGoogle Scholar
  103. Hasegawa M, Cao Y, Yang ZH: Preponderance of slightly deleterious polymorphism in mitochondrial DNA: Nonsynonymous/synonymous rate ratio is much higher within species than between species. Mol Biol Evol. 1998, 15: 1499-1505.PubMedGoogle Scholar

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