Skip to main content
NCBI home page
G3: Genes | Genomes | Genetics logoLink to G3: Genes | Genomes | Genetics
. 2013 Nov 26;4(1):121–131. doi: 10.1534/g3.113.008326

Diversity, Differentiation, and Linkage Disequilibrium: Prospects for Association Mapping in the Malaria Vector Anopheles arabiensis

Clare Diana Marsden *, Yoosook Lee *, Katharina Kreppel †,‡, Allison Weakley *, Anthony Cornel §, Heather M Ferguson ‡, Eleazar Eskin **, Gregory C Lanzaro *,1
PMCID: PMC3887528  PMID: 24281424

Abstract

Association mapping is a widely applied method for elucidating the genetic basis of phenotypic traits. However, factors such as linkage disequilibrium and levels of genetic diversity influence the power and resolution of this approach. Moreover, the presence of population subdivision among samples can result in spurious associations if not accounted for. As such, it is useful to have a detailed understanding of these factors before conducting association mapping experiments. Here we conducted whole-genome sequencing on 24 specimens of the malaria mosquito vector, Anopheles arabiensis, to further understanding of patterns of genetic diversity, population subdivision and linkage disequilibrium in this species. We found high levels of genetic diversity within the An. arabiensis genome, with ~800,000 high-confidence, single- nucleotide polymorphisms detected. However, levels of nucleotide diversity varied significantly both within and between chromosomes. We observed lower diversity on the X chromosome, within some inversions, and near centromeres. Population structure was absent at the local scale (Kilombero Valley, Tanzania) but detected between distant populations (Cameroon vs. Tanzania) where differentiation was largely restricted to certain autosomal chromosomal inversions such as 2Rb. Overall, linkage disequilibrium within An. arabiensis decayed very rapidly (within 200 bp) across all chromosomes. However, elevated linkage disequilibrium was observed within some inversions, suggesting that recombination is reduced in those regions. The overall low levels of linkage disequilibrium suggests that association studies in this taxon will be very challenging for all but variants of large effect, and will require large sample sizes.

Keywords: Anopheles arabiensis, linkage disequilibrium, association mapping, malaria vector, inversion

Genome-wide association studies have become a widely used and successful approach for identifying the genetic basis of traits in humans and model plant and animal species (Hindorff et al. 2013). As the price of genome sequencing decreases and genomic resources become available for a wider range of taxa, it is becoming increasingly possible to apply these approaches to nonmodel species. Association mapping tests for an association between a set of genome-wide single-nucleotide polymorphisms (SNPs) and a specified phenotype, with the expectation that one or more of these SNPs may be linked to the causal variant(s). As such, the level of genetic diversity and linkage disequilibrium (LD) in a species’ genome greatly influences the suitability and power of this approach (Gordon and Finch 2005; Hall et al. 2010).

LD describes the nonrandom association of alleles at different loci within a population and is influenced by many factors, including recombination rates, mutation rates, breeding systems, demography, and selection, all of which vary greatly between species (Ardlie et al. 2002; Gaut and Long 2003; Lewis and Knight 2012). The length of LD in a genome is a critical parameter in association studies because different levels of LD greatly affect the threshold set to account for multiple testing. Specifically, in taxa with long LD [e.g., >1 Mb in dog breeds (Boyko et al. 2010)], fewer markers are assayed and, thus, the threshold is less stringent. However, with long LD, mapping resolution is low, which can make it difficult to identify the precise location of the causal genetic variant(s) (Hall et al. 2010). It is possible to localize causal variants much more precisely (Hall et al. 2010) in taxa with short LD [e.g., LD decays < 1 kb in maize (Gore et al. 2009; Tenaillon et al. 2001)]. However, with rapid linkage decay, many millions of SNPs must be assayed to achieve a high likelihood that one or more of the assayed markers is in linkage with the causal variant(s) (Hall et al. 2010), which would be expensive and difficult to achieve even with whole-genome sequencing. Moreover, the effective number of independent tests in a genome scan is much greater when LD is short, resulting in a more stringent threshold.

As a result of variation in LD between species, it is important to assess LD before conducting an association study. In addition to its variation between species, LD also has been shown to vary greatly across a genome, often as a result of variation in recombination rates. For example, recombination is suppressed, and therefore LD greater, within chromosomal inversions that are segments of the chromosome that have broken off, rotated 180 degrees, and reinserted into the chromosome (White et al. 2007a and b). Such genomic variation in LD must be taken into consideration during association analyses (Ersoz et al. 2009). Finally, it is important to test for evidence of population structure, which results in allele frequency differences between subpopulations. Unless controlled for, such population structure may cause spurious LD between unlinked markers, resulting in false associations and/or inflated true associations (Kang et al. 2008; Lewis and Knight 2012).

Malaria in sub-Saharan Africa is transmitted by many members of the An. gambiae s.l. species complex. Of these, Anopheles gambiae s.s. Giles has been shown to be the most important vector of malaria in many regions, and thus has been the main focus of malaria vector research. However, there is growing evidence that the sister species, An. arabiensis Patton, replaces An. gambiae s.s. as the dominant vector in many areas with high insecticide-treated net coverage (Derua et al. 2012; Russell et al. 2011). As such, there is an urgent need to improve our knowledge of the behavior, ecology, and genetics of this somewhat-understudied vector to prepare for future vector control strategies.

In contrast to An. gambiae s.s. which is an almost entirely anthropophilic, nocturnal and endophagous feeder, An. arabiensis has been shown to exhibit much more variation for these traits (Lyimo and Ferguson 2009; Lyimo et al. 2013; White et al. 1972). Moreover, variation in some of these traits has been shown to have fitness costs [e.g., host choice (Lyimo et al. 2013)], indicating that they may be under genetic control. This has led to considerable interest in applying association mapping to understand the genetic basis of these as well as other traits of medical importance. Although successful in many model taxa, the suitability of such an approach for An. arabiensis is unknown. Genetic studies in An. gambiae s.s. have detected high levels of diversity and estimated LD to decay within <1−3 kb (Harris et al. 2010; Neafsey et al. 2010; Weetman et al. 2010). Moreover, An. gambiae s.s. has been shown to exhibit a complex population structure with two incipient species (Favia et al. 2001; Gentile et al. 2001), five chromosomal forms (Coluzzi et al. 1979, 1985), and a new cryptic subpopulation [Goundry (Riehle et al. 2011)] reported. Less information, however, is available for An. arabiensis. Here we conducted whole-genome sequencing on the vector An. arabiensis to assess genomic patterns of diversity, genetic differentiation, and LD.

Materials and Methods

Samples, DNA extraction, and library preparation

Adult female An. gambiae s.l. mosquitoes were collected by aspiration from the villages of Lupiro (-8.38000, 36.66912), Sagamaganga (-8.06781, 36.80207), and Minepa (-8.25700, 36.68163) in the Kilombero Valley, Tanzania in 2012, and Ourodoukoudje (9.09957, 13.72292) in Cameroon in 2005. Samples were preserved in 80% ethanol. The head and thorax of each specimen was subsequently dissected and used for DNA extraction, which was performed with the QIAGEN Biosprint 96 system using QIAGEN blood and tissue kits (QIAGEN, Valencia, CA). Anopheles arabiensis samples were distinguished from other An. gambiae s.l. species complex members with the Scott polymerase chain reaction assay (Scott et al. 1993) and their DNA content quantified using the Quibit 2.0 Fluorometer (Life technologies, Grand Island, NY). Selecting samples with >80 ng of DNA, we prepared 24 individually barcoded Illumina paired-end sequencing libraries with insert sizes of 320−400 bp by using NEXTflex Sequencing kits and barcodes (Bioo Scientific, Austin, TX); 20 from Tanzania and four from Cameroon. The 24 samples were submitted to the Beijing Genomics Institute at the University of California-Davis for 100-bp paired end sequencing using the Illumina HiSeq2000. All 24 samples were sequenced at low coverage, with 12 samples per lane. In addition, three samples (2012LUPI059; 2012MINE001; 2005OKJ042) were selected to be sequenced together at greater coverage in a single lane. These were used to create a reference of high confidence variant sites for subsequent SNP detection in the lower coverage samples.

Inversions

A number of polymorphic inversions have been identified among the species of An. gambiae s.l. species complex through karyotype analysis of ovarian nurse cells. There was insufficient material to karyotype the samples that were sequenced in this study as the abdomen was dissected for blood meal analysis. Therefore we conducted karyotyping on a sample of 37 indoor resting An. arabiensis collected in 2011 from the village of Lupiro, Tanzania. Ovaries were extracted from half gravid females, fixed in Carnoy’s solution, and banding patterns examined using a phase contrast microscope (Hunt 1973). Inversions were scored following Toure (1998) and are referred to following standard convention; the ‘+’ symbol followed by the letter by which the inversion is known refers to the standard arrangement (e.g., 2R+b), whereas the inverted arrangement is indicated by the letter alone (e.g., 2Rb).

Data processing and variant detection

At the time of analysis (January 2013), the only An. arabiensis genome available was a transcriptome sequence. Therefore, we used the An. gambiae s.s. genome build AgamP3_15 as our reference. The An. gambiae s.s. genome was derived from the PEST strain that was fixed for the standard arrangement for all inversions polymorphic in An. gambiae s.s.

Before the alignment and mapping of our sequences, we removed adaptor sequences using SCYTHE (https://github.com/vsbuffalo/scythe) and conducted quality trimming using SICKLE (quality score > 20; https://github.com/najoshi/sickle). Reads were then aligned to the reference genome using BWA 0.6.1 (Li and Durbin 2009). We ran BWA with default parameters as well as with adjusted parameters (e.g., changing number of permitted gaps and maximum edit distance) but found the default parameters to be the most optimal with our dataset in terms of mapped reads. The An. gambiae s.s. genome includes a number of unmapped haplotype contigs; we excluded data aligned to these contigs. We marked duplicate reads using PICARD (http://picard.sourceforge.net) and realigned reads around indels with realigner target creator and indel realigner from the Genome Analysis Toolkit, GATK 2.4.3 (McKenna et al. 2010).

We detected variants through a two-step process. First, to identify a set of high confidence reference variant sites, we combined the three high coverage samples and called SNPs using Unified Genotyper in GATK. After excluding indels (which are difficult to call reliably in the absence of very high, >100×, coverage), and apparent multiallelic SNPs (which may arise from errors in aligning reads to the genome and must be removed for many analysis programs), we filtered SNPs as per the Broad Institute’s (2012) Genome Analysis Toolkit (i.e., GATK) hard filter recommendations (QD < 2.0; MQ < 40; FS > 60; HaplotypeScore > 13; MQRankSum<-12.5; ReadPosRankSum<-8.0), in addition to a quality score of >30 and combined depth of coverage >30×. The remaining biallelic SNPs constituted our high confidence SNP sites. We then repeated this process on a combined file of the 24 low-coverage samples, with the exception that the quality score threshold was reduced to 4 (Broad Institute 2012), and depth of coverage filter removed. We then excluded any SNP from this low-coverage data set if: (1) the site was not called in all samples; (2) it was not present in the high-confidence reference SNP set; (3) if the minor allele was not detected in at least five individuals; and 4) any multi-allelic SNPs. The resulting SNP set derived from the 24 low-coverage samples was used in all subsequent analyses.

Coverage, LD, and genetic diversity analyses

Coverage across chromosomes was calculated with the depth of coverage tool in GATK. We used PLINK 1.07 (Purcell et al. 2007; Purcell 2009) to calculate pairwise LD as the genotyping correlation coefficient, r2. This analysis was not affected by phasing ambiguities because we calculated r2 directly from the genotypes rather than phased data (e.g., Boyko et al. 2010). However, it is noteworthy, that r2 estimates from genotype data will not be identical to haplotype frequencies but are typically very similar (Purcell 2009). VCFTOOLS was used to calculate nucleotide diversity (π), SNP density, and FST (Danecek et al. 2011). Sliding window analyses were computed using custom scripts written in the python programming language (www.python.org), except FST, which was computed with a sliding window tool within VCFTOOLS. The software SNPEFF (Cingolani et al. 2012) was used to determine the SNP effects by chromosomal arm. Figures were created using GNUPLOT (www.gnuplot.info). For the sliding window plots, the approximate locations of the telomeres, centromeres, and known inversions were taken from coordinates derived from An. gambiae s.s. as detailed in vectorbase (www.vectorbase.org).

Results

To assess nucleotide diversity, population structure and LD, in An. arabiensis, we conducted Illumina 100-bp paired end whole-genome sequencing on 24 samples at low coverage; 20 from three villages in Tanzania and four from a single village in Cameroon. In addition, three of these samples (one from Cameroon, two from Tanzania) were sequenced at greater coverage to create a reference of high-confidence SNP sites. The number of reads generated per sample varied between 8 and 19 million reads for the low-coverage sequencing and 47 and 62 million reads for the high coverage sequencing.

We aligned sequences to the An. gambiae s.s. genome. An. arabiensis and An. gambiae s.s. are closely related sister taxa that occasionally hybridize to produce fertile female offspring (Slotman et al. 2005) and have been shown to have relatively low divergence across much of the genome (Neafsey et al. 2010). Nonetheless, it is noteworthy, that An. gambiae s.s. and An. arabiensis differ by a number of inversion polymorphisms (Coluzzi et al. 2002). In particular, on the X chromosome An. gambiae is fixed for the Xag inversion (which is absent in An. arabiensis), whereas An. arabiensis is fixed for Xbcd which represents a complex of three fixed autapomorphic compound inversion arrangements. Furthermore, the 2La inversion which is fixed for the inverted arrangement in An. arabiensis (2La) is polymorphic in An. gambiae s.s. (2La/+a) and fixed for the standard arrangement (2L+a/+a) in the An. gambiae s.s. strain that was sequenced and used as a reference here.

Following alignment to the reference, we assessed coverage after excluding duplicate, low-quality, and poorly mapping reads. On the autosomes, coverage was high with 73–80% of bases exhibiting ≥30× combined coverage for the three high coverage samples, with 97–99% of the genome showing coverage overall (Table 1; Figure 1). On the X chromosome, coverage was lower, with only 38% of the genome exhibiting a combined coverage ≥30× for the three high coverage samples, and ~12% of the X chromosome showing no coverage (Table 1 and Supporting information, Figure S1). Similar findings were observed for the low-coverage samples (data not shown). Lower coverage on the X chromosome was particularly associated with the Xag inversion (Table 1, Figure 1, and Figure S1). Coverage also decreased proximal to the centromere on all chromosomes.

Table 1. Combined coverage and percentage of bases with 0 and ≥30× coverage across the three high-coverage samples per chromosomal arm.

% Bases X 2L 2R 3L 3R
0× 12.5 3.2 1.7 2.6 2.6
≥30× 38.3 73.8 80 77.8 77.5
Combined coverage 17,047,842 41,483,784 53,138,329 34,900,555 45,541,133
Open in a new tab

Figure 1.

Figure 1

Open in a new tab

Sliding window analysis (bin 100 kb, step 50 kb) of combined coverage across the three high-coverage samples. Boxes depict approximate location of telomeric (T) and centromeric (C) regions (gray), and known inversions (black) based on An. gambiae.

An. arabiensis is polymorphic for a number of inversions on the 2R and 3R chromosomes. To assess the inversions present in the Kilombero Valley of Tanzania we conducted karyotyping on ovarian nurse cells extracted from 37 blood fed An. arabiensis females collected from Lupiro in 2011, as ovarian nurse cells were not available for the Tanzanian samples sequenced here (see the section Materials and Methods). This revealed polymorphism for the 2Rb inversion (2R+b/+b = 2, 2Rb/b = 15, 2R+b/b = 20), and 3Ra inversions (3R+a/+a = 15, 3R+a/a = 21) which is consistent with other studies of An. arabiensis in Tanzania (Petrarca et al. 2000).

Genetic diversity

In total we identified 712,707 biallelic SNPs among our 24 low-coverage samples. This should be viewed as a conservative estimate because we applied a set of stringent filters to ensure a set of high-quality and high-confidence SNPs. Specifically, from the 2,574,223 SNPs remaining after standard quality filters were applied (see Materials and Methods), we excluded any SNPs that were: (1) not called in all 24 low-coverage samples (excluded 1,142,750 SNPs); (2) not present in the three high-coverage samples (excluded a further 317,549 SNPs); (3) where the nonreference allele was not observed in ≥5 samples (excluded a further 321,714 SNPs); and (4) any multiallelic SNPs, which includes SNPs biallelic within An. arabiensis but for two nonreference alleles (excluded a final 80,115 SNPs). Of the 712,707 SNPs in our final data set, we found these to be predominately in intergenic (34%) or intronic (21%) regions or ±5 kb of a gene (38%), with only 6% occurring in coding regions.

In comparison with the autosomes, diversity was an order of magnitude lower on the X chromosome (X – density 1/2365 bp, π = 0.000213, autosomes − density ≥ 1/318 bp, π ≥0.00202; Table 2). Among the autosomes, SNP diversity was very similar for the 2R, 3L, and 3R arms (density = 1/221−1/227 bp, π = 0.00282−0.00294) but slightly lower on the 2L (density = 1/318 bp, π = 0.00202). Diversity was highly variable across chromosomal arms. On the X chromosome, diversity was lower in the telo/centromeric regions and within the Xag inversion, but higher outside of these regions (Figure 2). For the autosomes, lower diversity was associated with the centromeres as well as the 2La inversion, but not the other inversions.

Table 2. Number of SNPs, SNP frequency, and nucleotide diversity (π) by chromosomal arm for 24 low-coverage samples.

X 2L 2R 3L 3R
No. SNPs 6308 126305 229666 150246 200380
SNP / × bases 2365 318 227 226 221
π 0.00021 0.00202 0.00293 0.00282 0.00294
Open in a new tab

SNP, single-nucleotide polymorphism.

Figure 2.

Figure 2

Open in a new tab

Sliding window analysis (bin 100 kb, step 100 kb) of diversity (π) by chromosome for the 24 low-coverage samples. Boxes depict approximate location of telomeric (T) and centromeric (C) regions (gray), and known inversions (black) based on An. gambiae.

Population differentiation

Our set of genome wide SNP markers did not detect population structure among the three villages in Tanzania, which are located 14−57 km apart (FST = 0, Table S1). It is noteworthy though, that during filtering we excluded all multiallelic SNPs (see Materials and Methods), including SNPs where samples were biallelic for two non-reference alleles. As we had aligned our sequences to the An. gambiae s.s. genome, these biallelic nonreference SNPs were likely the most informative for identifying divergence within An. arabiensis. As such, we cannot exclude the possibility that some genetic sub-structuring exists between these locations, but on a finer scale than could be detected here with our conservative SNP dataset. Nonetheless, we did detect population structure between the Tanzania and Cameroon sites (FST = 0.057, Table S1), which is consistent with expectations for restricted gene flow between populations separated by great distances (>3000 km here, e.g., Donnelly and Townson 2000). However, it is important also to note that the Tanzanian and Cameroonian samples were collected in different years (2005 and 2012), which means temporal genetic changes also may have contributed to apparent geographical differentiation. To identify the genomic regions contributing the most to divergence between the Tanzanian and Cameroonian samples, we estimated FST independently for each chromosomal arm. These analyses highlighted that divergence on the 3R (FST = 0.092−0.130) was higher in comparison to the other chromosome arms (FST = 0.016−0.051). Sliding window analyses showed that in contrast to other regions, divergence was elevated across most of the 3Ra inversion (mean 3Ra = 0.144), as well as on the 2Rc inversion (mean 2Rc FST = 0.131, Figure 3). An 8-Mb region up and downstream of the chromosome 3 centromere also showed elevated FST (Figure 3). SNP density was too low on the X chromosome for sliding window analysis.

Figure 3.

Figure 3

Open in a new tab

Sliding window analysis (bin 100 kb, step 50 kb) of FST plotted along the chromosome between the Cameroonian and Tanzanian samples. Windows with less than 100 values were excluded, which resulted in reduced representation in the centromeric regions, where SNP density was lower. Boxes depict approximate location of telomeric (T) and centromeric (C) regions (gray), and known inversions (black) based on An. gambiae.

Linkage disequilibrium

We assessed linkage among the 20 Tanzanian samples. The four Cameroonian samples were excluded from this analysis to limit bias due to the confounding effects of population structure. The decrease in LD (r2) with physical distance for these samples is shown in Figure 4. These data show that average LD (r2) decayed rapidly to less than 0.2 within 200 bp in An. arabiensis (Figure 4), and this rate of decrease was very similar across all of the chromosomes.

Figure 4.

Figure 4

Open in a new tab

Decay of linkage (r2) by chromosome across the 20 Tanzanian samples.

To assess genomic patterns of LD, we plotted LD for all SNPs separated by 1−10 kbp by genomic location (Figure 5). This showed that although LD was generally low along the chromosome, it was increased around the centromeres and some inversions. Specifically, LD was elevated across the 2Rb and c inversions and particularly at the proximal (nearer to centromere) 2Rb breakpoint, as well as the proximal breakpoint of the 3Ra inversion (Figure 5). Otherwise, elevated LD was limited to small sporadic regions. Elevated LD at the location of the 2Rc inversion was unexpected because this inversion was not recorded in the small subset of samples from Lupiro in 2011 assessed here or in other studies from Tanzania (reviewed in, Petrarca et al. 2000). However, the number of samples and sites assessed in these studies was low, and therefore the inversion may have been missed. We did not find LD to be increased across the 2La inversion, but this inversion is not polymorphic within An. arabiensis (only An. gambiae s.s.). SNP density on the X chromosome was too low for sliding window analyses.

Figure 5.

Figure 5

Open in a new tab

Sliding window analysis (bin 100 kb, step 50 kb) of linkage (r2) for SNPs separated by 1−10 kb in the 20 Tanzanian samples. Windows with less than 100 values were excluded, which resulted in reduced representation in the centromeric regions, where SNP density was lower.

Discussion

Genetic diversity

Consistent with expectations for an outbreeding species with large population sizes, we found high levels of polymorphism in An. arabiensis. Diversity estimates (Table 2) were slightly lower than reported for An. gambiae s.s. [density 1/35-250bp, π= 0.006−0.0208, Ensemble (Cohuet et al. 2008; Lawniczak et al. 2010; Morlais et al. 2004; Wilding et al. 2009)]. However, direct comparisons should be treated with caution, given the sampling differences between studies and the fact that the aforementioned estimates were derived from detailed studies of candidate genes or select loci (8−660) rather than low-coverage, whole-genome sequencing. Moreover, it is very likely that the stringent SNP calling thresholds we applied, combined with alignment to a sister genome, under estimated the true diversity levels in An. arabiensis.

Genomic diversity in An. arabiensis fluctuated across the genome; a pattern also seen in An. gambiae s.s. (Cheng et al. 2012; Cohuet et al. 2008; Holt et al. 2002; Wilding et al. 2009). Specifically, lower diversity was associated with the X chromosome, 2La inversion, and telomeric and centromeric regions (Figure 2). The X chromosome plays a major role in postzygotic isolation between An. gambiae s.s. and An. arabiensis (Slotman et al. 2004, 2005), and is more diverged between these taxa than the autosomes (e.g., Besansky et al. 2003; Cohuet et al. 2008; Neafsey et al. 2010). In particular, An. gambiae s.s. (reference genome) and An. arabiensis are fixed for alternative arrangements of the Xag inversion which covers ~60% of the X chromosome (Cohuet et al. 2008; Neafsey et al. 2010). Consequently, reduced X-chromosome diversity may reflect poorer alignment or mapping to the An. gambiae s.s. genome. However, reduced X diversity has also been shown in An. gambiae s.s. (Cohuet et al. 2008; Holt et al. 2002; Wilding et al. 2009), which indicates that additional biological factors, such as reduced introgression on the X chromosome, may be involved (Cohuet et al. 2008; Holt et al. 2002; Slotman et al. 2004, 2005).

Lower coverage attributable to mapping difficulties likely also explains the reduced diversity in the centro/telo-meres, which are highly repetitive regions and the 2La inversion, which exhibits high divergence (Figure 1 and Figure 2). Specifically, the An. gambiae s.s. reference was derived from specimens fixed for the standard arrangement (2L+a/+a), which is highly diverged from the inverted arrangement (2La) which is fixed in An. arabiensis (Cheng et al. 2012; Neafsey et al. 2010). Indeed, Cheng et al. (2012) reported lower levels of coverage in An. gambiae s.s. specimens with the inverted 2La arrangement. Finally, reduced diversity in the 2La inversion may relate to the fact that this inversion is fixed in An. arabiensis, whereas the other inversions which did not exhibit reduced diversity are polymorphic and thus gene flux may contribute to diversity between arrangements (Cheng et al. 2012).

Population structure

Cryptic population structure can result in spurious associations if not accounted for (Kang et al. 2008). Complex population structure has been found within An. gambiae s.s. (e.g., Coluzzi et al. 1985; Favia et al. 2001; Lanzaro et al. 1998; Riehle et al. 2011) but fewer data are available for An. arabiensis (e.g., Donnelly et al. 1999; Ng’habi et al. 2011). We found average FST between Tanzanian and Cameroonian An. arabiensis populations (FST = 0.057) to be lower than between Cameroonian populations of An. gambiae s.s. (FST =0.129, whole-genome sequence data, Cheng et al. 2012) which is consistent with suggestions that population structure is weaker in An. arabiensis than An. gambiae (e.g., Kamau et al. 1999). Our findings are also consistent with studies showing little local population structure in An. arabiensis within East Africa (e.g., Czeher et al. 2010; Lee et al. 2012; Nyanjom et al. 2003) but strong population structure at greater distances (i.e., Cameroon and Tanzania) (Donnelly and Townson 2000). However, these data contrast with Ng’habi et al. (2011), who found significant FST values of 0.08−0.1 between villages within the Kilombero Valley Tanzania (20−50 km apart), and detected two subpopulations. Given that our studies did not assess the same sites (except Lupiro), this may reflect sampling. Alternatively, as mentioned previously, our stringent filtering of variants was biased toward removing the SNPs most likely able to detect fine scale structure. Assessments involving larger sample sizes and more study sites would be useful in clarifying local population structure.

Our analyses detected great variability in differentiation across the genome, with greater levels found on the 3R, particularly the 3Ra inversion, as well as the 2Rc inversion (Figure 3). Few studies have assessed inversions in An. arabiensis (Petrarca et al. 2000), but extensive research in An. gambiae s.s. indicates they are important for adaptation (e.g., 2La and 2Rb with aridity) and are under strong selection (Cheng et al. 2012; Lee et al. 2009; Powell et al. 1999; Touré et al. 1998; White et al. 2007a). Moreover, partial reproductive isolation and niche differentiation has been detected between population subgroupings (chromosomal forms) with specific combinations of inversions (Coluzzi et al. 1977; Manoukis et al. 2008; Taylor et al. 2001). Our data suggest recombination and gene flow is restricted between standard and inverted arrangements of some inversions in An. arabiensis. Elevated divergence for specific inversions (2La FST = 0.247, 2Rb FST = 0.149) among low overall genomic divergence (FST = 0.129) has also been reported in An. gambiae s.s. (Cheng et al. 2012) as well as Drosophila (e.g., Corbett-Detig and Hartl 2012). The values here for the An. arabiensis inversions with elevated FST (mean FST 2Rc = 0.13108, 3Ra = 0.14345) were slightly lower than those reported for An. gambiae s.s. (above), but this could result from unbalanced representation of the inversions among our samples; something that we could not control or test for as our samples could not be karyotyped prior to sequencing (see Materials and Methods). If as suggested by our data, chromosomal inversions contribute to population structure in An. arabiensis, it will be critical for this to be accounted for in association studies particularly given the potential for the inversions to cosegregate with the trait of interest (Weetman et al. 2010).

Linkage disequilibrium

The extent of LD in a genome is a key factor influencing the feasibility of association mapping studies (Lewis and Knight 2012). Overall, we found that LD decays rapidly within 200 bp in An. arabiensis (Figure 4), but we also detected elevated LD in regions of reduced recombination such as near centro/telo-meres (Figure 5). In particular, elevated LD extended ~8 Mb from the 3L centromere, as also observed in A. gambiae s.s. (~6 Mb, Weetman et al. 2010). Similarly, we observed elevated LD for the chromosomal inversions (Figure 5), which exhibit suppressed recombination due to a lack of crossing over in heterokaryotypes (Kirkpatrick 2010; Navarro et al. 1997). We found greater LD associated across the smaller 2Rb and 2Rc inversions and particularly at the proximal 2Rb breakpoint, whereas LD was only elevated at the proximal breakpoint of the larger ~24Mb 3Ra inversion (Figure 5). This pattern is indicative of gene flux, whereby double cross-over events or gene conversion results in recombination between arrangements near the center of large inversions, despite recombination rates near the breakpoints remaining near zero (Andolfatto et al. 2001; Navarro et al. 2000; Navarro et al. 1997).

The rapid decay of LD in An. arabiensis is broadly consistent with the short LD reported for An. gambiae s.s. immunity genes (<1 kb, Harris et al. 2010; Weetman et al. 2010) but is shorter than genome wide LD estimates of An. gambiae s.s. (<3 kbp, Neafsey et al. 2010). The difference with the latter study may be explained by the larger number and density of markers here, which allowed for greater resolution of LD in An. arabiensis than in An. gambiae s.s. (average spacing of SNPs ~3 kb, Neafsey et al. 2010). Such short LD contrasts with the LD structure of humans (>10 kb, Hinds et al. 2005; Pe’er et al. 2006), and many domesticated or selfing plants (~10 kb Arabidopsis thaliana and >100 kb in rice, Oryza sativa; Huang et al. 2010; Kim et al. 2007) but is consistent with findings for flies (Drosophila melanogaster, <30 bp; Mackay et al. 2012) and many outbreeding taxa for example the grapevine (300 bp, Vitis vinifera, Lijavetzky et al. 2007) and Norway spruce (100-bp Picea abies; Heuertz et al. 2006). Moreover, our results fit with expectations for wild species with high diversity, large outbreeding populations and recent population growth (Ardlie et al. 2002; Gaut and Long 2003; Lewis and Knight 2012), Nonetheless, the rapid LD decay presents considerable challenges for association mapping experiments as the number of markers and samples needed for there to be sufficient power to identify causal variants, would be prohibitive in most cases, except perhaps where causal variants are located in genomic regions with elevated LD (e.g., regions with selective sweeps, Weetman et al. 2010). Similar limitations inhibited attempts to utilize association mapping in wild populations of Drosophila which also exhibit very rapid LD decay. In this case, resources such as the Drosophila melanogaster Genetic Reference Panel (Mackay et al. 2012) and Drosophila Synthetic Population Resource (King et al. 2012) have been produced in order to artificially create LD through the formation of a population of inbred recombinant lines that can be used for whole genome association mapping of traits. The future of association mapping in An. arabiensis likely rests on the production of a similar type of resource which could be used to dis-entangle the genetic basis of traits that can be phenotyped in a laboratory setting.

Supplementary Material

Supporting Information
supp_4_1_121__index.html (1.3KB, html)

Acknowledgments

We are grateful to Anicet Kihonda, Masoud Kilalangongono, Noely Makungwa, Isaya Matanila, Tokosa Sanga, Robert Kasubiri, and Morsad Nywage of the Ifakara Health Institute for assistance with field collections in Tanzania; Travis Collier and Bradley Main for help with python scripts; and Michelle Sanford for assistance during the earlier stages of this project. This work was funded by National Institutes of Health grant R01AI085175-03. Tanzanian ethical approval for this research was granted by ethics clearance certificate number Institutional Research Board/Ifakara Health Institute /No:16-2013. Samples were exported from Tanzania under permit MTA 2013-252M and imported into the United States under Centers for Disease Control and Prevention permit 2011-06-040.

Footnotes

Communicating editor: J. M. Comeron

Literature Cited

  1. Andolfatto P., Depaulis F., Navarro A., 2001.  Inversion polymorphisms and nucleotide variability in Drosophila. Genet. Res. 77: 1–8. [DOI] [PubMed] [Google Scholar]
  2. Ardlie K. G., Kruglyak L., Seielstad M., 2002.  Patterns of linkage disequilibrium in the human genome. Nat. Rev. Genet. 3: 299–309. [DOI] [PubMed] [Google Scholar]
  3. Besansky N. J., Krzywinski J., Lehmann T., Simard F., Kern M., et al. , 2003.  Semipermeable species boundaries between Anopheles gambiae and Anopheles arabiensis: evidence from multilocus DNA sequence variation. Proc. Natl. Acad. Sci. USA 100: 10818–10823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Boyko A. R., Quignon P., Li L., Schoenebeck J. J., Degenhardt J. D., et al. , 2010.  A simple genetic architecture underlies morphological variation in dogs. PLoS Biol. 8: e1000451. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cheng C., White B. J., Kamdem C., Mockaitis K., Costantini C., et al. , 2012.  Ecological genomics of Anopheles gambiae along a latitudinal cline in Cameroon: a population resequencing approach. Genetics 190: 1417–1432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cingolani P., Platts A., Wang L. L., Coon M., Nguyen T., et al. , 2012.  A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster. Fly (Austin) 6: 80–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cohuet A., Krishnakumar S., Simard F., Morlais I., Koutsos A., et al. , 2008.  SNP discovery and molecular evolution in Anopheles gambiae, with special emphasis on innate immune system. BMC Genomics 9: 227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Coluzzi M., Sabatini A., Petrarca V., Dideco M. A., 1977.  Behavioral divergences between mosquitoes with different inversion karyotypes in polymorphic populations of Anopheles gambiae complex. Nature 266: 832–833. [DOI] [PubMed] [Google Scholar]
  9. Coluzzi M., Sabatini A., Petrarca V., Di Deco M., 1979.  Chromosomal differentiation and adaptation to human environments in the Anopheles gambiae complex. Trans. R. Soc. Trop. Med. Hyg. 73: 483–497. [DOI] [PubMed] [Google Scholar]
  10. Coluzzi M., Petrarca V., Di Deco M., 1985.  Chromosomal inversion intergradation in incipient speciation in Anopheles gambiae. Boll. Zool. 52: 45–63. [Google Scholar]
  11. Coluzzi M., Sabatini A., Della Torre A., Di Deco M. A., Petrarca V., 2002.  A polytene chromosome analysis of the Anopheles gambiae species complex. Science 298: 1415–1418. [DOI] [PubMed] [Google Scholar]
  12. Corbett-Detig R. B., Hartl D. L., 2012.  Population genomics of inversion polymorphisms in Drosophila melanogaster. PLoS Genet. 8: e1003056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Czeher C., Labbo R., Vieville G., Arzika I., Bogreau H., et al. , 2010.  Population genetic structure of Anopheles gambiae and Anopheles arabiensis in Niger. J. Med. Entomol. 47: 355–366. [DOI] [PubMed] [Google Scholar]
  14. Danecek P., Auton A., Abecasis G., Albers C. A., Banks E., et al. , 2011.  The variant call format and VCFtools. Bioinformatics 27: 2156–2158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Derua Y., Alifrangis M., Hosea K., Meyrowitsch D., Magesa S., et al. , 2012.  Change in composition of the Anopheles gambiae complex and its possible implications for the transmission of malaria and lymphatic filariasis in north-eastern Tanzania. Malar. J. 11: 188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Donnelly M. J., Townson H., 2000.  Evidence for extensive genetic differentiation among populations of the malaria vector Anopheles arabiensis in Eastern Africa. Insect Mol. Biol. 9: 357–367. [DOI] [PubMed] [Google Scholar]
  17. Donnelly M., Cuamba N., Charlwood J., Collins F., Townson H., 1999.  Population structure in the malaria vector, Anopheles arabiensis Patton, in East Africa. Heredity 83: 408–417. [DOI] [PubMed] [Google Scholar]
  18. Ersoz E., Yu J., Buckler E., 2009.  Applications of linkage disequilibrium and association mapping in maize, pp. 173–195 in Molecular Genetic Approaches to Maize Improvement, edited by Kriz A., Larkins B. Springer, Berlin, Heidelberg, Germany. [Google Scholar]
  19. Favia G., Lanfrancotti A., Spanos L., Siden-Kiamos I., Louis C., 2001.  Molecular characterization of ribosomal DNA polymorphisms discriminating among chromosomal forms of Anopheles gambiae s.s. Insect Mol. Biol. 10: 19–23. [DOI] [PubMed] [Google Scholar]
  20. Broad Institute, 2012 GATK Best practices variant detection with the GATK. Available at: http://www.broadinstitute.org/gatk/guide/best-practices. Accessed November 25, 2013.
  21. Gaut B. S., Long A. D., 2003.  The lowdown on linkage disequilibrium. Plant Cell 15: 1502–1506. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gentile G., Slotman M., Ketmaier V., Powell J., Caccone A., 2001.  Attempts to molecularly distinguish cryptic taxa in Anopheles gambiae s.s. Insect Mol. Biol. 10: 25–32. [DOI] [PubMed] [Google Scholar]
  23. Gordon D., Finch S. J., 2005.  Factors affecting statistical power in the detection of genetic association. J. Clin. Invest. 115: 1408–1418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Gore M. A., Chia J.-M., Elshire R. J., Sun Q., Ersoz E. S., et al. , 2009.  A first-generation haplotype map of maize. Science 326: 1115–1117. [DOI] [PubMed] [Google Scholar]
  25. Hall D., Tegström C., Ingvarsson P. K., 2010.  Using association mapping to dissect the genetic basis of complex traits in plants. Brief. Funct. Genomics 9: 157–165. [DOI] [PubMed] [Google Scholar]
  26. Harris C., Rousset F., Morlais I., Fontenille D., Cohuet A., 2010.  Low linkage disequilibrium in wild Anopheles gambiae s.l. populations. BMC Genet. 11: 81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Heuertz M., De Paoli E., Källman T., Larsson H., Jurman I., et al. , 2006.  Multilocus patterns of nucleotide diversity, linkage disequilibrium and demographic history of Norway spruce [Picea abies (L.) Karst]. Genetics 174: 2095–2105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hindorff, L. A., J. MacArthur, J. Morales, H. A. Junkins, P. N. Hall et al., 2013 A catalog of published genome-wide association studies. Available at: www.genome.gov/gwastudies. Accessed November 25, 2013.
  29. Hinds D. A., Stuve L. L., Nilsen G. B., Halperin E., Eskin E., et al. , 2005.  Whole-genome patterns of common DNA variation in three human populations. Science 307: 1072–1079. [DOI] [PubMed] [Google Scholar]
  30. Holt R., Subramanian G., Halpern A., Sutton G., Charlab R., et al. , 2002.  The genome sequence of the malaria mosquito Anopheles gambiae. Science 298: 129–149. [DOI] [PubMed] [Google Scholar]
  31. Huang X., Wei X., Sang T., Zhao Q., Feng Q., et al. , 2010.  Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42: 961–976. [DOI] [PubMed] [Google Scholar]
  32. Hunt R. H., 1973.  A cytological technique for the study of the Anopheles gambiae complex. Parassitologia 15: 127–139. [PubMed] [Google Scholar]
  33. Kamau L., Mukabana W. R., Hawley W. A., Lehmann T., Irungu L. W., et al. , 1999.  Analysis of genetic variability in Anopheles arabiensis and Anopheles gambiae using microsatellite loci. Insect Mol. Biol. 8: 287–297. [DOI] [PubMed] [Google Scholar]
  34. Kang H. M., Zaitlen N. A., Wade C. M., Kirby A., Heckerman D., et al. , 2008.  Efficient control of population structure in model organism association mapping. Genetics 178: 1709–1723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kim S., Plagnol V., Hu T. T., Toomajian C., Clark R. M., et al. , 2007.  Recombination and linkage disequilibrium in Arabidopsis thaliana. Nat. Genet. 39: 1151–1155. [DOI] [PubMed] [Google Scholar]
  36. King E. G., Macdonald S. J., Long A. D., 2012.  Properties and power of the drosophila synthetic population resource for the routine dissection of complex traits. Genetics 191: 935–949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kirkpatrick M., 2010.  How and why chromosome inversions evolve? PLoS Biol. 8: e1000501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lanzaro G. C., Toure Y. T., Carnahan J., Zheng L. B., Dolo G., et al. , 1998.  Complexities in the genetic structure of Anopheles gambiae populations in west Africa as revealed by microsatellite DNA analysis. Proc. Natl. Acad. Sci. USA 95: 14260–14265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lawniczak M. K. N., Emrich S. J., Holloway A. K., Regier A. P., Olson M., et al. , 2010.  Widespread divergence between incipient Anopheles gambiae species revealed by whole genome sequences. Science 330: 512–514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lee Y., Cornel A., Meneses C., Fofana A., Andrianarivo A., et al. , 2009.  Ecological and genetic relationships of the Forest-M form among chromosomal and molecular forms of the malaria vector Anopheles gambiae sensu stricto. Malar. J. 8: 75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lee Y., Seifert S. N., Fornadel C. M., Norris D. E., Lanzaro G. C., 2012.  Single-nucleotide polymorphisms for high-throughput genotyping of Anopheles arabiensis in East and Southern Africa. J. Med. Entomol. 49: 307–315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lewis C. M., Knight J., 2012.  Introduction to genetic association studies. Cold Spring Harb Protoc 2012: 297–306. [DOI] [PubMed] [Google Scholar]
  43. Li H., Durbin R., 2009.  Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics 25: 1754–1760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lijavetzky D., Cabezas J., Ibanez A., Rodriguez V., Martinez-Zapater J., 2007.  High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology. BMC Genomics 8: 424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lyimo I. N., Ferguson H. M., 2009.  Ecological and evolutionary determinants of host species choice in mosquito vectors. Trends Parasitol. 25: 189–196. [DOI] [PubMed] [Google Scholar]
  46. Lyimo I. N., Haydon D. T., Russell T. L., Mbina K. F., Daraja A. A., et al. , 2013.  The impact of host species and vector control measures on the fitness of African malaria vectors. Proc. Biol. Sci. 280: 20122823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Mackay T. F. C., Richards S., Stone E. A., Barbadilla A., Ayroles J. F., et al. , 2012.  The Drosophila melanogaster Genetic Reference Panel. Nature 482: 173–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Manoukis N. C., Powell J. R., Touré M. B., Sacko A., Edillo F. E., et al. , 2008.  A test of the chromosomal theory of ecotypic speciation in Anopheles gambiae. Proc. Natl. Acad. Sci. USA 105: 2940–2945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., et al. , 2010.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20: 1297–1303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Morlais I., Poncon N., Simard F., Cohuet A., Fontenille D., 2004.  Intraspecific nucleotide variation in Anopheles gambiae: new insights into the biology of malaria vectors. Am. J. Trop. Med. Hyg. 71: 795–802. [PubMed] [Google Scholar]
  51. Navarro A., Betran E., Barbadilla A., Ruiz A., 1997.  Recombination and gene flux caused by gene conversion and crossing over in inversion heterokaryotypes. Genetics 146: 695–709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Navarro A., Bardadilla A., Ruiz A., 2000.  Effect of inversion polymorphism on the neutral nucleotide variability of linked chromosomal regions in Drosophila. Genetics 155: 685–698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Neafsey D. E., Lawniczak M. K. N., Park D. J., Redmond S. N., Coulibaly M. B., et al. , 2010.  SNP genotyping defines complex gene-flow boundaries among African malaria vector mosquitoes. Science 330: 514–517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Ng’habi K., Knols B., Lee Y., Ferguson H., Lanzaro G., 2011.  Population genetic structure of Anopheles arabiensis and Anopheles gambiae in a malaria endemic region of southern Tanzania. Malar. J. 10: 289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Nyanjom S. R. G., Chen H., Gebre-Michael T., Bekele E., Shililu J., et al. , 2003.  Population genetic structure of Anopheles arabiensis mosquitoes in Ethiopia and Eritrea. J. Hered. 94: 457–463. [DOI] [PubMed] [Google Scholar]
  56. Pe’er I., Chretien Y. R., De Bakker P. I. W., Barrett J. C., Daly M. J., et al. , 2006.  Biases and reconciliation in estimates of linkage disequilibrium in the human genome. Am. J. Hum. Genet. 78: 588–603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Petrarca V., Nugud A. D., Ahmed M. E., Haridi A. M., Di Deco M. A., et al. , 2000.  Cytogenetics of the Anopheles gambiae complex in Sudan, with special reference to An. arabiensis: relationships with East and West African populations. Med. Vet. Entomol. 14: 149–164. [DOI] [PubMed] [Google Scholar]
  58. Powell J. R., Petrarca V., Della Torre A., Caccone A., Coluzzi M., 1999.  Population structure, speciation, and introgression in the Anopheles gambiae complex. Parassitologia 41: 101–113. [PubMed] [Google Scholar]
  59. Purcell, S., 2009 PLINK v1.07. Available at: http://pngu.mgh.harvard.edu/purcell/plink/. Accessed November 25, 2013.
  60. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. R., et al. , 2007.  PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81: 559–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Riehle M. M., Guelbeogo W. M., Gneme A., Eiglmeier K., Holm I., et al. , 2011.  A cryptic subgroup of Anopheles gambiae is highly susceptible to human malaria parasites. Science 331: 596–598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Russell T. L., Govella N. J., Azizi S., Drakeley C. J., Kachur S. P., et al. , 2011.  Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. Malar. J. 10: 80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Scott J., Brogdon W., Collins F., 1993.  Identification of single specimens of the Anopheles gambiae complex by PCR. Am. J. Trop. Med. Hyg. 49: 520–529. [DOI] [PubMed] [Google Scholar]
  64. Slotman M., Torre A. D., Powell J. R., 2004.  The genetics of inviability and male sterility in hybrids between Anopheles gambiae and An. arabiensis. Genetics 167: 275–287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Slotman M., Torre A. D., Powell J. R., 2005.  Female sterility in hybrids between Anopheles gambiae and A. arabiensis and the causes of Haldane’s rule. Evolution 59: 1016–1026. [PubMed] [Google Scholar]
  66. Taylor C. E., Touré Y. T., Carnahan J., Norris D. E., Dolo G., et al. , 2001.  Gene flow among populations of the malaria vector, Anopheles gambiae, in Mali, West Africa. Genetics 157: 743–750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Tenaillon M. I., Sawkins M. C., Long A. D., Gaut R. L., Doebley J. F., et al. , 2001.  Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays ssp mays L.). Proc. Natl. Acad. Sci. USA 98: 9161–9166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Touré Y., Petrarca V., Traoré S., Coulibaly A., Maiga H., et al. , 1998.  The distribution and inversion polymorphism of chromosomally recognized taxa of the Anopheles gambiae complex in Mali, West Africa. Parassitologia 40: 477–511. [PubMed] [Google Scholar]
  69. Weetman D., Wilding C. S., Steen K., Morgan J. C., Simard F., et al. , 2010.  Association mapping of insecticide resistance in wild Anopheles gambiae populations: Major variants identified in a low-linkage disequilibrium genome. PLoS ONE 5: e13140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. White B. J., Hahn M. W., Pombi M., Cassone B. J., Lobo N. F., et al. , 2007a Localization of candidate regions maintaining a common polymorphic inversion (2La) in Anopheles gambiae. PLoS Genet. 3: e217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. White B. J., Cheng C., Sangare D., Lobo N. F., Collins F., et al. , 2007b Population genomics of trans-specific inversions in Anopheles gambiae. Genetics 183: 275–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. White G. B., Magayuka S. A., Boreham R. F. L., 1972.  Comparative studies on sibling species of the Anopheles gambiae Giles complex (Dipt., Culicidae): binomics and vectorial activity of species A and species B at Segera, Tanzania. Bull. Entomol. Res. 62: 295–317. [Google Scholar]
  73. Wilding C., Weetman D., Steen K., Donnelly M., 2009.  High, clustered, nucleotide diversity in the genome of Anopheles gambiae revealed through pooled-template sequencing: implications for high-throughput genotyping protocols. BMC Genomics 10: 320. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information
supp_4_1_121__index.html (1.3KB, html)
supp_g3.113.008326_008326SI.pdf (703.2KB, pdf)
supp_g3.113.008326_FigureS1.pdf (555.9KB, pdf)
supp_g3.113.008326_TableS1.pdf (299.6KB, pdf)

Articles from G3: Genes|Genomes|Genetics are provided here courtesy of Oxford University Press

RESOURCES