Rhinolophus monoceros). Recordings of bats from across Taiwan revealed that females called at higher frequencies than males; however, we found no effect of environmental or morphological factors on call frequency. By comparison, variation showed clear population structure, with frequencies lower in the centre and east, and higher in the north and south. Within these regions, frequency divergence was directional and correlated with geographical distance, suggesting that call frequencies are subject to cultural drift. However, microsatellite clustering analysis showed that broad differences in constant frequency among populations corresponded to discontinuities in allele frequencies resulting from vicariant events. Our results provide evidence that the processes shaping genetic subdivision have concomitant consequences for divergence in echolocation call frequency.The origin and maintenance of intraspecific variation in vocal signals is important for population divergence and speciation. Where vocalizations are transmitted by vertical cultural inheritance, similarity will reflect co-ancestry, and thus vocal divergence should reflect genetic structure. Horseshoe bats are characterized by echolocation calls dominated by a constant frequency component that is partly determined by maternal imprinting. Although previous studies showed that constant frequency calls are also influenced by some non-genetic factors, it is not known how frequency relates to genetic structure. To test this, we related constant frequency variation to genetic and non-genetic variables in the Formosan lesser horseshoe bat ( HoweverBoth explanations are expected to lead to concordance between variation in call and neutral genetic variation. Trends between genetic subdivision and variation in vocalizations have to date been studied mostly in birds and humans, both of which exhibit cultural vocal learning. In birds, the expectation that dialect boundaries should correlate with genetic discontinuities has received mixed empirical support  represent an ideal model system for studying the determinants of geographical variation in vocal signals for three main reasons. First, the CF component of the call can be measured accurately to within 1 kHz and so assessing call variation does not rely on qualitative comparisons of spectrograms, as is the case for many animal vocalizations. Second, cultural learning has previously been established in this genus, with the fine-tuning of call frequency determined in part by vertical transmission from mother to offspring  component , which affspring . Third, mequinum . Kingstorrelated .Hipposideros ruber might be due to the attenuation of higher frequencies in moist air Bats were captured between June 2002 and September 2003 at 20 roosts (hereafter referred to as populations 1–20) across Taiwan . For eac(b)Bats were recorded in the summer (June–October). Additionally, individuals from seven populations  were recorded in the winter (January–March) to allow seasonal comparisons. Juveniles captured at one population (2) were used to compare call frequencies between adults and juveniles. Pregnant females were excluded from analyses.For echolocation call recording, bats were held 30 cm from a microphone attached to a D980 Pettersson Elektronik bat detector . Time-expanded (10 × ) calls were recorded onto a Sony WM-D6C cassette recorder. Echolocation calls were analysed using the sound analysis software BatSound Pro . The maximum energy (in kHz) of the dominant (second) harmonic of each CF call was determined from a power spectrum of a call. A 4096-point fast Fourier transform (FFT) and a Hanning window were used within a 5 kHz frequency range to give a frequency resolution of 64 Hz. We checked for call frequency variation within individuals by comparing 10 randomly chosen calls for 20 bats. Inter-individual variation was small, typically around 0.2 kHz (standard deviations ranging from 0.035 to 0.098 kHz). Therefore, single calls per individual were used in subsequent analyses.(c)n = 20). A longer-term study of environmental conditions at three sites revealed little seasonal variation . We predicted high humidity would be associated with lower frequencies, because atmospheric attenuation increases with humidity and frequency. Bats would therefore need to call at lower frequencies at higher humidity levels to get similar echo strengths from a given target , body mass (g) and a standard index of adult body condition defined as the residual in a linear regression of body mass versus forearm length  in Minitab (Minitab Inc.). To test for an effect of geographical location and sex, we tested for differences in call frequency among localities and between sexes using a GLM in which both variables were treated as factors. Call frequency data collected from both sexes were tested for normality using an Anderson–Darling test.We first tested for an effect of age using a To assess the impact of other non-genetic determinants of call frequency, we constructed a generalized linear mixed model (GLMM) using the software S-PLUS (Insightful Inc.). Morphological  and environmental variables  were fitted as fixed effects whereas population identity was coded as a random effect. This approach overcomes the potential problem of non-independence among measurements that are spatially correlated (i.e. from the same population).(d)To test for a relationship between neutral genetic structure and call frequency, we analysed multi-locus microsatellite data. Wing-membrane biopsy punches (3 mm diameter) of recorded individuals were taken and stored in ethanol (see FST/(1 − FST) values against corresponding mean pairwise call frequency differences, analogous to an isolation-by-distance model (genetic divergence versus geographic distance). The correlation coefficient was derived using a Mantel test and significance was tested by permutation (10 000 times) in Arlequin  in the data by assigning individuals to population groupings so that linkage-disequilibrium in the dataset is reduced. We used a burn-in length of 20 000 and a run length of 1 million without prior population information, and undertook 20 independent runs for each K from 1 to 8. To estimate the number of clusters present in the data we inspected the value of K that maximized the posterior probability of the data, given by p(K|X) and also derived values of ΔK, defined as the mean of the absolute value of the second-order rate of change of L(K) with respect to K divided by the standard deviation of L(K). This value has been shown to be useful in detecting the number of clusters present where L(K) increases monotonically  following the method outlined by lumpp with the FullSearch algorithm. Pairs of runs that yielded similarity scores of less than 0.95 were removed.The outcomes of independent runs for the most likely value of 3.(a)Rhinolophus monoceros comprising 240 adult females, 296 adult males and 18 juveniles of both sexes . Females were significantly larger than males according to both forearm length  mm; males: 37.02 ± 1.00 mm) and body mass .We recorded 554 individuals of t-test: females: t55 = 5.40, p < 0.001; males: t22 = 5.13, p < 0.001). Call frequency did not fluctuate between summer and winter  and, therefore, samples from summer and winter were pooled for subsequent analyses.An Anderson–Darling test showed that distributions of call frequency did not deviate from normality for all sex and age combinations. Adult males and females showed significantly higher frequencies than juveniles of the same respective sex . Central and eastern populations consistently displayed lower call frequency compared to their more northern and southern counterparts. The highest average divergence between populations was 6.22 kHz in females and 6.53 kHz in males, although individuals showed greater differences . The effect of sex itself was also highly significant : females called at higher frequencies than males , although there was no evidence of a silent band that could facilitate sex recognition. Sex differences in call frequency also occurred within juveniles . No interaction was detected between population and sex, suggesting that the calls of both sexes responded in the same manner across localities.Mean population echolocation call frequency varied markedly across Taiwan in both sexes . A GLM rt518 = 3.846, p < 0.001). In addition, sex remained significant in the full model . We found no effect of elevation; however, R. monoceros is typically restricted to low elevations areas, and, although we sampled from a wide range of available elevations , this might have limited the power of detecting an effect. A plot of humidity versus call frequency a), although this appeared to be due to the combined effects of three sets of points, reflecting pairwise distances that included bats from (1) populations 1–16, (2) population 17, and (3) populations 18–20. These three groups broadly correspond to different clades in a mtDNA haplotype network with contrasting phylogeographic histories . In contrast, comparisons between populations 17–20 (from the south and southwest) and the more northerly populations showed a negative trend. The fact that the two main groups of pairwise differences (circles and squares) show positive and negative trends against geographical distance, respectively, reveals drift-like directionality in the change in call frequency over geographical distance within each region.For analyses of genetic structure, genotype data of females and males were pooled for each population, and only populations with sample sizes of over five were included. A plot of call frequency difference against geographical distance among pairs of populations approximated to a negative binomial distribution a, althouistories . The oveistories . Populatr2 = 0.133, d.f. = 90, p < 0.01) (b); however, this was not significant after correcting for the effect of geographical distance . On the other hand, the correlation between geographical distance and call frequency difference remained significant after controlling for genetic distance .A plot of call frequency difference against pairwise genetic distance also showed a positive trend ( < 0.01) b; howeveK = 2. A monotonic decrease in likelihood values from K = 2 to K = 8 precluded the application of the ΔK method for further resolution of K. Of 20 independent runs for K = 2, ten were characterized by high pairwise similarity scores (>0.95), and were subsequently used to estimate average individual cluster membership for each population. This revealed that the two clusters of sampled populations of R. monoceros in Taiwan are broadly separated along a latitudinal gradient .The results of our cluster analyses revealed that the most likely number of multiple clusters, based on the outcomes of replicate runs, was ient see . Populat4.R. monoceros both within and especially among populations. On average, females produced higher frequency calls and were larger than males. This trend, also seen in the morphologically similar R. hipposideros (R. ferrumequinum offspring. Therefore, variation within populations may be attributable to postnatal learning, variation in proportions of sex and age classes, and by physiological differences among individuals.We recorded substantial variation in echolocation call frequency in osideros . Althougosideros . The marosideros . We alsoSignificant inter-population variation in call frequency associated with geographical distance shows remarkable similarities with the results of work on human language. R. monoceros breaks down due to a change in the direction of drift in call frequency in the southern populations, which also coincides with a discontinuity in allele frequencies between this and other regions. Similar discontinuities in allele frequency within broadly continuous horseshoe bat populations have previously been shown to reflect suture zones between different refugial populations (R. monoceros indicated that although the Taiwan population is monophyletic, the south has experienced both different demographic and evolutionary histories compared with other populations (The relationship between human language similarity and geographical distance may also be disrupted by physical barriers to diffusion, which are expected to lead to greater vocal divergence . Our resR. monoceros corresponds to a range of wavelengths of 2.94–3.23 mm, assuming the speed of sound to be 345.67 m s−1 at a temperature of 22°C and a relative humidity of 80 per cent. It is therefore not meaningful to interpret the intra-specific variation in terms of partitioning diet by prey size (Rhinonicteris aurantia, where call frequency appears not to relate to humidity, but instead shows concordance with phylogenetic distinctiveness, and seems to be evolving in different directions among isolated populations (Cultural drift is traditionally considered to be directional, whereas genetic drift is random. In our study, however, call frequencies in the south appear to be drifting in the opposite direction to those of their nearest sampled populations, and it is unclear whether this variation, and particularly the higher frequencies in both the north and south, are due to chance drift or are of adaptive significance. The frequency differences reported are unlikely to have consequences for diet or habitat use because they correspond to small differences in wavelength. Indeed, the recorded range of calls in Rhinolophus. A sex-recognition role for echolocation calls has also been posited, although this is based on stationary captive bats that might nevertheless be relevant to roosting wild bats (Variation in call frequency among populations might also stem from social divergence. Work on birds and mammals supports the theory that cultural drift or social selection can restrain and maintain local vocal repertoires . This hyild bats . Given tild bats  and can R. monoceros might have arisen by maternal transmission followed by cultural drift or selection during past periods of isolation, whereas smaller-scale population differences will be more dependent on the extent of local mixing as well as the nature of colonization. Rhinolophus cornutus populations on Okinawa, Japan, in light of female-biased philopatry gene flow, greater nuclear gene flow and a small population size.Drivers of geographical acoustic variation based on vicariant events, adaptation to environmental conditions and social selection are unlikely to be mutually exclusive but instead probably act in concert, with their relative importance varying across different spatial scales. For example, regional differences in call frequency in These emerging results highlight the need for further work on the relationship between bat echolocation calls and population history and, in particular, clarification of the correspondence between patterns of echolocation call design and genetic structure.


Near the junction of three major continents, the Caucasus region has been an important thoroughfare for human migration. While the Caucasus Mountains have diverted human traffic to the few lowland regions that provide a gateway from north to south between the Caspian and Black Seas, highland populations have been isolated by their remote geographic location and their practice of patrilocal endogamy. We investigate how these cultural and historical differences between highland and lowland populations have affected patterns of genetic diversity. We test 1) whether the highland practice of patrilocal endogamy has generated sex-specific population relationships, and 2) whether the history of migration and military conquest associated with the lowland populations has left Central Asian genes in the Caucasus, by comparing genetic diversity and pairwise population relationships between Daghestani populations and reference populations throughout Europe and Asia for autosomal, mitochondrial, and Y-chromosomal markers.We found that the highland Daghestani populations had contrasting histories for the mitochondrial DNA and Y-chromosome data sets. Y-chromosomal haplogroup diversity was reduced among highland Daghestani populations when compared to other populations and to highland Daghestani mitochondrial DNA haplogroup diversity. Lowland Daghestani populations showed Turkish and Central Asian affinities for both mitochondrial and Y-chromosomal data sets. Autosomal population histories are strongly correlated to the pattern observed for the mitochondrial DNA data set, while the correlation between the mitochondrial DNA and Y-chromosome distance matrices was weak and not significant.The reduced Y-chromosomal diversity exhibited by highland Daghestani populations is consistent with genetic drift caused by patrilocal endogamy. Mitochondrial and Y-chromosomal phylogeographic comparisons indicate a common Near Eastern origin of highland populations. Lowland Daghestani populations show varying influence from Near Eastern and Central Asian populations. The populations of the Caucasus region have complex histories of isolation and gene flow. The region as a whole has served as a gateway between continents, with waves of human migration leaving rich cultural and linguistic diversity in their wake ,2. The CWe have identified five populations from Daghestan that have been influenced by both physical and cultural barriers to gene flow. Three are highland isolates, while two lowland populations represent admixed groups influenced by Turkic and Mongolian migrants. We investigate whether the geographic barrier of altitude, the cultural barrier of patrilocal endogamy, or the introduction of migrants from a great distance have left detectable patterns in the genetic diversity of these populations. Specifically, we ask 1) whether geographic isolation and patrilocal endogamy have caused more genetic drift in highland than lowland populations, and 2) whether lowland populations show evidence of admixture from Turkic and Mongolian migrants.Alu insertions among Daghestani, Central/Northern European, and East Asian populations. We compare measures of haplogroup diversity and pairwise distance for the mtDNA and NRY markers, then compare these to genetic distances from the autosomal data, looking for evidence of genetic drift and shared origins. Our results demonstrate that the cultural practice of patrilocality and historic population movements have shaped genetic variation in these Caucasus populations.We test these hypotheses by comparing mitochondrial DNA (mtDNA) and Y-chromosome  haplogroup frequencies among Daghestani, Near Eastern, Central Asian, Central/Northern European, and East Asian populations, as well as autosomal variation in 100 polymorphic The Avar, Dargin, and Kubachi populations sampled in our study live in the highlands of Daghestan Figure . They eath to the 15th centuries and may be descended from the Kipchaks, an ancient Turkic population [th century, which arrived in Daghestan in the 13th and 14th centuries [The Nogai and Kumiks of the Daghestani lowlands have a history of admixture. These populations speak different languages belonging to the Kipchak division of the Turkic language family and are more exogamous than the highland populations ,12. The pulation ,12. The enturies . Althougenturies ,12, suggOverall, the five ethnic groups sampled for our study are representative of the genetic variation to be found in Daghestan, as these groups represent approximately one-half of the country's population . DNA samTo place the genetic variation observed within Daghestan into a regional perspective, we include mtDNA and NRY data obtained from the literature for several Near Eastern and Central Asian populations in our analyses. Near Eastern populations include Crimean Tatars ,13, GeorWe are unable to include these Near Eastern and Central Asian population in our analyses of autosomal Alu insertion variation, as comparable data have not been published. We therefore include Central/Northern European and East Asian populations in our study of mtDNA, NRY, and Alu insertion variation. Our Central/Northern European sample includes 15 French and 57 Utahns of Central/Northern European ancestry, while the East Asian population includes 9 Cambodians, 13 Han Chinese, 16 Japanese, 5 Malay, 3 Taiwanese, and 9 Vietnamese individuals. These populations are geographically and genetically more distant from the Daghestani populations than the Near Eastern and Central Asian populations. As such, they act as extreme values in pairwise distance calculations and principal components analyses. These populations place the observed differences between Daghestani and other populations into a broader perspective. Distances between Daghestani populations and the Central/Northern European and East Asian populations serve as a yardstick against which distances within Daghestan, and between Daghestani populations and populations from the Near East and Central Asia may be measured.We have genotyped 62 Avar, 28 Dargin, 25 Kubachi, 26 Kumik, 33 Nogai, 54 East Asian, and 71 Central/Northern European individuals for the following 24 mtDNA haplogroup-defining single nucleotide polymorphisms (SNPs) using ABI SNaPshot  multiplexes designed by WSW : A1811G,In a similar fashion, we have genotyped the males of the above sample  for the following 19 NRY haplogroup-defining SNPs using ABI SNaPshot  multiplexes designed by WSW : M122, MGenotypes for 100 Alu insertions have been previously published  for the h statistic [ST. Genetic distances were tested for significant differences from zero with 10,000 permutations in a randomization test . PrinciMitochondrial haplogroup frequencies and diversity values are presented in Table h value of zero, as all males sampled belong to NRY haplogroup F.NRY haplogroup frequencies and diversity values are presented in Table ST values of zero, while the Nogai have low genetic distances to the Near Eastern and Central Asian populations, for both the mtDNA and NRY data sets.Genetic distances based on mtDNA haplogroup frequencies are shown in the upper triangle of Table The autosomal pairwise genetic distances calculated from the 100 Alu loci Table  are simiFigure The third principal component, explaining 13% of the variance, identifies the lowland Kumik and Crimean Tatar populations as the extremes. This third principal component almost divides the Near Eastern populations from the Central Asian populations. Highland Avars cluster with the Near Eastern reference populations, while the highland Dargins and lowland Kumiks are intermediate between the Central/Northern European and Turkish populations. The lowland Nogai lie between the Near Eastern and Central Asian populations.Figure Figure Cultural restrictions on marriage practices have proved to be powerful barriers to gene flow, as evidenced by the historical caste system in India  and patrWe see no significant correlation between the mtDNA and NRY distance matrices. Y chromosomes of highland Daghestanis appear to have undergone genetic drift independent of the population history of mitochondrial and autosomal loci, suggesting that restrictions on marriage, and not geographic isolation, are the causal agents for NRY drift among highland populations. This is consistent with previous research , which fThe highland Kubachi form an endogamous isolate of nearly 3,000 individuals , restricPatrilocal endogamy should have resulted in each highland population drifting to different extremes, as the stochastic process of genetic drift occurs independently in each population. Instead, we see that all Daghestani highland populations share high frequencies of NRY haplogroup F in contrast to their neighbours. When we break haplogroup F down to include haplogroups G and J, greater detail emerges. The highland Avar, Dargin, and Kubachi exhibit high frequencies of haplogroup J , though haplogroup F is still present among the Kubachi (0.25), and haplogroup G is observed among the Avar (0.06), Kumik (0.10), and Nogai (0.06) populations. The NRY haplogroup frequencies among highland Daghestani populations are consistent with a shared common ancestral population dominated by NRY haplogroup J and other haplogroups observed among Near Eastern and Indian populations -33.Phylogeographic analysis of the mtDNA haplogroups found in highland Daghestani populations also suggests Near Eastern and Caucasus-specific origins ,32,34,35ST distances to the Turkish population equalling zero for both analyses. This is consistent with historical evidence of a Turkic origin of the Kumik population.Genetic variation among the lowland Kumik and Nogai populations reveals a diverse heritage influenced to varying degrees by Turkic and Mongolian migrants. Both the Kumiks and Nogai possess high levels of mtDNA and NRY haplogroup diversity, consistent with multiethnic origins. Kumiks show genetic affiliation with Near Eastern populations for all measures of mtDNA and NRY genetic variation, with FThe Nogai population is defined by its relationship to the administration of the Mongol Empire in the Caucasus, when Mongolians ruled over the native peoples of the Caucasus region ,12. We mN = 16) sample of Nogai males is not representative of the full spectrum of NRY genetic variation in the population. Asymmetric gene flow and differing effective population sizes across loci could also cause differing population pairwise distances to Central/Northern European and East Asian reference populations for these markers.The Nogai do show clear evidence of an Asian heritage. Approximately half of the mitochondrial and NRY haplogroups observed among the Nogai are of Asian origin  ,35. TheyThis study describes the effects of culture, geography, and gene flow on genetic diversity among Daghestani populations. Highland populations show reduced Y-chromosome diversity, but autosomal and mtDNA variation is not reduced, reflecting the effects of a patrilocal mating system. Mitochondrial and Y-chromosomal phylogeographic inferences suggest a Near Eastern or Caucasus-region origin of the highland populations. Our results, including haplogroup sharing, genetic diversity, and patterns of population pairwise distance lead us to confirm that the lowland Kumik and Nogai populations have been influenced by gene flow from local and migrant Central Asian populations, as suggested by history. Overall, our results demonstrate that population history, geographic isolation, and patrilocality have all left detectable signatures on the genetic landscape of the Caucasus.EEM designed the study, genotyped the Daghestani individuals, performed the analysis, and drafted the manuscript. WSW designed the multiplex technology used to genotype the mitochondrial and Y-chromosome haplogroups and genotyped the Central/Northern European and East Asian individuals. KB collected the DNA samples from Daghestani populations. HCH and LBJ participated in study design, supervision, and revision of the manuscript.Mitochondrial DNA Haplogroup Definitions. Describes how Daghestani, East Asian, and Central/Northern European samples were assigned to haplogroups.Click here for fileY-chromosome Haplogroup Definitions. Describes how Daghestani, East Asian, and Central/Northern European samples were assigned to haplogroups.Click here for file


Anopheles (Kerteszia) cruzii was the most important vector of human malaria in southern Brazil between 1930–1960. Nowadays it is still considered an important Plasmodium spp. vector in southern and south-eastern Brazil, incriminated for oligosymptomatic malaria. Previous studies based on the analysis of X chromosome banding patterns and inversion frequencies in An. cruzii populations from these areas have suggested the occurrence of three sibling species. In contrast, two genetically distinct groups among An. cruzii populations from south/south-east and north-east Brazil have been revealed by isoenzyme analysis. Therefore, An. cruzii remains unclear.timeless gene (~400 bp), a locus involved in the control of circadian rhythms, was used as a molecular marker to assess the genetic differentiation between An. cruzii populations from six geographically distinct areas of Brazil.In this study, a partial sequence of the timeless gene revealed that An. cruzii from Itaparica Island, Bahia State (north-east Brazil), constitutes a highly differentiated group compared with the other five populations from south and south-east Brazil. In addition, significant genetic differences were also observed among some of the latter populations.The timeless gene among An. cruzii populations from different areas of Brazil indicated that this malaria vector is a complex of at least two cryptic species. The data also suggest that further work might support the occurrence of other siblings within this complex in Brazil.Analysis of the genetic differentiation in the  Anopheles cruzii is one of the few mosquito species belonging to the subgenus Kerteszia. Immature stages of this species are found associated with water trapped in the interfoliar space of plants from the Bromeliaceae family, which are abundant in the Brazilian Atlantic forest [c forest -3. Accorc forest ,5.The adults are found in a variety of habitats, from sea level in coastal areas to the mountains. Females are strongly anthropophilic and preferably bite during the evening ,6,7, perAn. cruzii together with Anopheles bellator and Anopheles homunculus, which also belong to Kerteszia, were considered the main vectors of malaria when the disease was endemic in southern Brazil. Vector control measures have significantly reduced or even interrupted malaria transmission in some areas, but eradication of the pathogen was not achieved and An. cruzii is still responsible for several oligosymptomatic malaria cases in southern and south-eastern Brazil.Between 1930 and 1960, Plasmodium vivax and Plasmodium falciparum, and imported cases are frequently reported in different states due to emigration from this region [An. cruzii and Anopheles (Nyssorhynchus) spp. are considered the main vectors of the disease, respectively [An. cruzii as a malaria vector in south-east Brazil, another recent study in Espírito Santo State, including the locality of Santa Teresa, suggested that this species is the potential vector of recent autochthonous cases of malaria in this state [The Amazon region is highly endemic for human malaria, caused by s region ,12. Howeectively ,7,13,14,ectively . Reinforis state .Anopheles cruzii is also a natural vector of simian malaria in Rio de Janeiro and São Paulo States [An. cruzii in coastal São Paulo State demonstrated high vertical mobility from ground level to tree tops, with significantly more activity in the uppermost branch layer of the forest [Plasmodium species [o States . Studiese forest . This be species ,20.Epidemiological surveillance and the use of control measures are required to avoid the expansion or introduction of malaria in areas where vector species are abundant and susceptible humans are present. Thus, assessment of the epidemiological status of such localities as well as knowledge concerning the biology, behaviour and the genetic characteristics of the vector species are relevant to prevent the occurrence of outbreaks or to lead control strategies, especially in formerly endemic areas.An. cruzii [Anopheles cruzii is polymorphic for chromosome rearrangements. Differences in inversions frequencies, and X chromosome banding patterns from south-eastern and southern Brazil, have suggested the existence of three sibling species [An. cruzii from south-eastern and southern Brazilian localities [Despite its epidemiological importance, there are only a few population genetic studies of . cruzii ,21, and  species . On the  species . Finallycalities .The activity and feeding rhythms of insect vectors are very important to disease transmission. These patterns are controlled by endogenous circadian clocks, which are under genetic control . FurtherKerteszia were formerly studied by Pittendrigh [An. cruzii [timeless gene is involved in the control of activity rhythms in Drosophila [Drosophila species [The circadian rhythms of malaria vectors belonging to the subgenus tendrigh  and, rec. cruzii . The timosophila , and con species .An. cruzii timeless gene was used as a molecular marker to assess intraspecific variability and genetic divergence among six populations of An. cruzii captured in different locations within the geographic distribution range of this species in Brazil.In the present study, a fragment of ~400 bp of the All mosquitoes used in this study were females captured at the following localities along the Brazilian Atlantic forest: Florianópolis, Santa Catarina State (SC) (27°31'S/48°30'W), Cananéia and Juquitiba, São Paulo State (SP) (25°01'S/47°55'W and 23°57'S/47°03'W), Itatiaia, Rio de Janeiro State (RJ) (22°27'S/44°36'W), Santa Teresa, Espírito Santo State (ES) (19°56'S/40°35'W) and Itaparica Island (Jaguaripe), Bahia State (BA) (13°05'S/38°48'W)  for 90 s and 72°C for 60 s, following 20 cycles of 94°C for 60 s, 50°C for 90 s and 72°C for 60 s. The products obtained were then purified and cloned in either Zero Blunt TOPO PCR cloning kit (Invitrogen) or pMOS Blue vector blunt-ended cloning kit (Amersham Biosciences). Sequencing of positive clones was carried out in an ABI Prism 377 or ABI Prism 3730 DNA sequencer at the Oswaldo Cruz Institute using the ABI Prism Big Dye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems). The identity of the cloned fragments was determined by BlastX analysis using the GenBank [timeless gene fragment in An. cruzii, a specific forward primer (5'darltim02a) based on a fragment of the Anopheles darlingi timeless gene  was used in combination with the specific reverse primer previously designed for An. cruzii (3'cruziitim03) in a PCR that amplified a fragment of ~450 bp. This 450 bp fragment from the An. cruzii genome was then purified, cloned and sequenced as above. After checking the sequence identity, two new specific forward primers named 5'cruziitim02 and 5'acbatim02a  and primers 5'acbatim02a or 5'cruziitim02 and 3'cruziitim03  and ClustalX software [ROSEQ programs [FST was calculated as described by Hudson et al [The software . Analysesoftware  and PROSprograms . FST wason et al  and signon et al  using thAn. cruzii timeless gene (see Methods). Figure An. cruzii compared to the TIMELESS protein of other insect species . A fairly high degree of inter-specific similarity is observed, but the putative protein encoded by 5' end of this fragment is variable, presenting some amino acid changes among the species compared. Figure An. cruzii used for the population genetics analysis described below.Different PCR schemes were tested to amplify a fragment of the An. cruzii used in this study is shown at Figure timeless gene was amplified in all An. cruzii populations analyzed, with the exception of samples from Bahia State (Itaparica Island), which revealed a ~400 bp amplification product, indicating the existence of length variation among the studied populations. The sample from Bahia, however, displayed lower amplification in some cases using these primers, and so a new internal forward primer named 5'acbatim02a . A full alignment of all sequences is shown in Additional file A total of 24 sequences from Florianópolis, 24 from Cananéia, 22 from Juquitiba, 24 from Itatiaia, 12 from Santa Teresa and 28 from Itaparica (Bahia State) populations were obtained. The sequences were submitted to GenBank (accession numbers: An. cruzii population studied (n) and the number of polymorphic sites (S). The values in parentheses were calculated using only coding regions of the timeless gene fragment. Based on the sequences, two measures of nucleotide diversity were calculated for each population: π, based on the average number of pair-wise differences and θ, based on the total number of mutations (Table D or Fu & Li's D and F statistics were non-significant (P > 0.10) indicating no deviations from neutrality.Table ns Table . The popFST) between all An. cruzii populations. In all cases the FST values were significant, except for the comparison between Juquitiba and Santa Teresa when the coding regions of the timeless gene fragment were used. Very high FST values were found between Bahia State and the others (0.8353 – 0.8761). The average number of nucleotide substitutions per site (Dxy) and the number of net nucleotide substitutions per site between populations (Da) are shown in Table S1 and S2), the number of shared polymorphisms (Ss) and the number of fixed differences (Sf) between populations are also included in Table Table The values using only coding regions   [timeless gene between these two Drosophila species based on the third codon positions is 0.03030. Based on the Da value (0.05426), the estimated time of divergence between An. cruzii populations from south and south-east Brazil and that from Bahia State, is approximately 1.5 MYA.The estimate of the time of divergence between ctively) . The divAn. cruzii populations using the Kimura 2-parameter distance and the timeless gene sequences. The resulting tree showed no clear separation between the sequences of the populations from Florianópolis, Cananéia, Juquitiba, Itatiaia and Santa Teresa, but some differentiation was evident since the sequences do not appear at random in the tree, especially in the case of Itatiaia. The An. cruzii sequences from the Bahia population, however, were clearly separated on an isolated branch.Figure An. cruzii from Rio de Janeiro and Santa Catarina States, suggesting that An. cruzii could represent more than a single species. A moderately high FST value between Florianópolis (Santa Catarina State) and Itatiaia (Rio de Janeiro State) populations was reported here. In addition, comparison of Itatiaia with the other populations (excluding Bahia) revealed even higher FST values, perhaps suggesting that this population is indeed in a process of differentiation and incipient speciation. Moreover, sequences from Itatiaia showed some clustering in the Neighbour-joining tree , suggesting a process of incipient speciation acting on An. cruzii populations. Among the localities analysed in this study, only Juquitiba and Cananéia were also investigated by Ramirez and Dessen [timeless gene between these two populations is not high, the FST value is significant and does not contradict the results of the chromosomal analysis. The relatively low differentiation in timeless among most populations from south and south-east Brazil might reflect introgression at this locus. It would be interesting to analyse the same populations with an X-linked molecular marker to see whether a higher level of differentiation is found.In a recent review, Ayala and Coluzzi  argue thd Dessen ,24, studd Dessen ,24. Theyd Dessen ,24. Althet al [An. cruzii populations from south and south-east Brazil. Although, they found some differences between sequences from different localities, including Juquitiba and Cananéia, they considered premature to conclude based on their results that there are distinct sibling species in the areas they investigated. Similar results were observed by Calado et al [Recently, Malafronte et al  compareddo et al  using PCFST values detected between this population and the other five populations studied, as well as the higher number of fixed differences observed, show that Bahia represents a different species. This population also presented lower levels of variability than those from south and south-east Brazil, possibly indicating a smaller population size or past founder effects. However, although the isoenzyme heterozygosity reported for Bahia is lower than Cananéia it is similar to that observed in Florianópolis [Very strong evidence was presented here that confirms the existence of a different species in Bahia State, a finding that supports a previous isoenzyme study . The extanópolis .An. cruzii possibly occurred around 1.5 MYA, during the Pleistocene. Climate changes during this period such as an intense precipitation decrease and more arid conditions fragmented the Brazilian Atlantic forest [Sabethes albiprivus [An. cruzii is also a forest-obligate species, it is possible that the Bahia and southern populations of this species complex suffered fragmentation due a constriction of the forest. Although Tajima's D and Fu & Li's D and F statistics were non-significant, they were negative in most cases and that is consistent with population expansion following the forest recovery after the Pleistocene. Analysis of a number of other molecular markers will allow more precise estimates of the divergence time between the Bahia population and those of south and south-east Brazil. It may also help in determining whether further An. cruzii siblings exist in the latter area.A very rough estimate suggests that the divergence between the Bahia population and the more southern populations of c forest  creatingc forest , tree frc forest . Forest biprivus . Since APlasmodium spp. transmission in this area is An. darlingi and not An. cruzii, the most important vector in the southern states. This suggests that the differentiation observed within the An. cruzii complex might also explain aspects of the vectorial capacity of these mosquitoes, however further studies are needed to confirm or reject this hypothesis.Finally, although malaria cases are reported annually in Bahia State, the main vector implicated in timeless gene among Brazilian populations of An. cruzii indicates that this malaria vector is a complex of at least two cryptic species, one occurring in the north-east (Bahia State) and another in south and south-east Brazil. In addition, the data also suggest that populations of the latter region might also constitute different incipient species and that further work might support the occurrence of other siblings within this complex in Brazil.Analysis of the molecular polymorphism and genetic differentiation of the The authors declare that they have no competing interests.An. cruzii timeless gene fragments. ECG participated in the DNA sequencing and helped to write the manuscript. AAP is the principal investigator, participated in its design and coordination, and helped to write the manuscript. All authors read and approved the final manuscript.LDPR participated in data generation and analysis, and drafted the manuscript. She also helped capture mosquitoes in Florianópolis. CJCP carried out the capture and morphological identification of mosquitoes collected in Florianópolis and Itaparica. CG participated in the cloning of An. cruziiAlignment of the DNA sequences of . Alignment of the DNA sequences from the timeless gene fragment from all populations of An. cruzii analysed. The translated amino acid sequence is shown above the alignment and the introns are presented in the darkened regions. Dots represent the identity of the first nucleotide sequence and asterisks represent the identity of all sequences. The non-synonymous changes found among the sequences are highlighted in yellow boxes. Flo: individuals from Florianópolis; Can: Cananéia; Juq: Juquitiba; Ita: Itatiaia; San: Santa Teresa; Bahia: individuals from Itaparica Island, Bahia State.Click here for file


Anopheles albimanus is an important malaria vector in some areas throughout its distribution in the Caribbean and the Pacific regions of Colombia, covering three biogeographic zones of the neotropical region, Maracaibo, Magdalena and Chocó.An. albimanus populations because knowledge of vector population structure is a useful tool to guide malaria control programmes. Analyses were based on mtDNA COI gene sequences and four microsatellite loci of individuals collected in eight populations from the Caribbean and the Pacific regions of Colombia.This study was conducted to estimate intra-population genetic diversity, genetic differentiation and demographic history of COI haplotypes from each region. A star-shaped statistical parsimony network, significant and unimodal mismatch distribution, and significant negative neutrality tests together suggest a past demographic expansion or a selective sweep in An. albimanus from the Caribbean coast approximately 21,994 years ago during the late Pleistocene. Overall moderate to low genetic differentiation was observed between populations within each region. However, a significant level of differentiation among the populations closer to Buenaventura in the Pacific region was observed. The isolation by distance model best explained genetic differentiation among the Caribbean region localities: Los Achiotes, Santa Rosa de Lima and Moñitos, but it could not explain the genetic differentiation observed between Turbo , and the Pacific region localities . The patterns of differentiation in the populations from the different biogeographic provinces could not be entirely attributed to isolation by distance.Two distinctive groups were consistently detected corresponding to An. albimanus populations as measured by MS loci which may be mainly influenced by semi-permeable natural barriers in each biogeographical region that lead to the genetic differences and effective population sizes detected. The relatively high genetic differentiation in the port city of Buenaventura may be the result of specific ecological conditions, human migration and activities and/or differences in effective population sizes. This knowledge could serve to evaluate and coordinate vector control strategies in these regions of Colombia.The data provide evidence for limited past gene flow between the Caribbean and the Pacific regions, as estimated by mtDNA sequences and current gene flow patterns among  Anopheles albimanus is considered an important malaria vector in rural and periurban areas throughout its distribution in Colombia, along the Caribbean and Pacific Coasts and on San Andres Island .The uz et al , were geAn. albimanus were determined per locus using both haplotype frequencies of mtDNA COI gene and allelic frequencies of MS loci. Haplotype and nucleotide diversities were generated using DnaSP version 4.50.2   ,75. To asoftware . To obtasoftware -79. Alsosoftware  and a nesoftware  based onsoftware  were perk 1.2.02 .An. albimanus COI gene corresponding to positions 1,802-2,859 of Anopheles gambiae s.s. mitochondrion complete genome and aligned with RefSeq NC 002084 [An. albimanus mosquitoes from eight localities , 101 transitions and 14 transversions were observed. The best-fit DNA substitution model selected by the hierarchical likelihood ratio test (hLRT) was TrN+I+G [I = 0.7675) and Gamma distribution shape parameter (G: 0.9555). Using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the best-fit model was the Transitional model: TIM+I+G [I = 0.7664) and Gamma distribution shape parameter (G: 0.8906), A-T composition of the COI sequences was 39.7% and 29.5%, respectively, similar to that found in other Anopheles species [A 1,058-bp sequence of the s Figure , Table 2 TIM+I+G , with in species , and theAlthough diversity values were low overall, Pacific region populations had higher nucleotide diversities  than Caribbean populations , and haplotype diversity values were similar. There were 112 unique haplotypes  per locus ranged between three in MON and MTU (6-41) to eleven, also, in MTU (2-25). STU showed the lowest average allelic richness (6.125), while BUE showed the highest (8.144) compared to all other localities. The expected heterozygosity (He) across all samples ranged from 0.503 (ACH) to 0.87 (MTU), while the average expected heterozygosity ranged from 0.681 (STU) to 0.784 (TUR). Of 32 tests performed for HWE, 12 (37.5%) remained significant after the sequential Bonferroni correction (p < 0.01); all of them were associated with heterozygote deficits, in each locus as follows: 6-41 (STU), 1-90 (TUR and BUE), 2-14  and 2-25 . Of 48 tests conducted, no locus was at linkage disequilibrium after the sequential Bonferroni correction (p < 0.05)  and the average Ne for all Caribbean populations (including TUR) was an infinite number of individuals , while for all Pacific populations it was 256 individuals (CI 136- ∞). SRL showed the lowest value , and TUR, NUQ, STU and MTU showed an infinite result (∞). Under the HE model all of populations showed infinite values  and heterozygosity tests based on MS data for An. albimanus).For MS analyses, 280 d Figure . The numd Figure . Allelicd MTU 6-4 to elevep < 0.05) of the total variance was explained at the among regions level. When populations were separated into two groups, including TUR in the Pacific region, only 3.86% (p < 0.05) of the total variance was due to the variation among regions , and most of the pairwise comparisons of FST between localities were significant; however, no significant FST values at p < 0.01 were observed in comparisons between SRL, MON and TUR from the Caribbean coast, or between NUQ, PIZ and BUE from the Pacific coast (Table FST) and gene flow (Nm) among populations using MS data are shown in Table FST values were observed between population belonging to the same region, Caribbean or Pacific. Of 28 differentiation tests, 18 were significant (p < 0.01). Comparisons between ACH and STU showed the highest degree of differentiation. An. albimanus samples collected from TUR, located in the middle of the collection range, showed lower differentiation with its nearest localities: NUQ (Pacific) and MON and SRL (Caribbean). Nm estimates among An. albimanus populations ranged from 4 to 59 individuals, presenting an average value of 21 individuals.The 7 p < 0.0, and mosCOI and MS data to test the Isthmus of Panama as a putative barrier between the Caribbean and Pacific regions, including and excluding TUR. The AMOVA corresponding to two groups: the Caribbean and the Pacific samples (excluding TUR), showed the highest percentage of the total variance and FST value, 4.3% (p < 0.05) and 0.06049 (p < 0.05), respectively  ACH, SRL and MON, 2) TUR, 3) NUQ, 4) BUE, 5) STU, 6) MTU Figure . In agrely Table .FST/(1- FST), and the geographic distance (Ln) was statistically significant, although with low values, for the whole dataset . The IBD model best explains genetic differentiation among populations from the Caribbean region: ACH, SRL and MON (excluding TUR), but it could not explain the low differentiation observed within the Pacific region populations . No statistically significant correlation was detected when tests of IBD were carried out separately for the Caribbean (including TUR) or the Pacific region (with or without TUR).A correlation test between genetic distance based on MS loci, measured by n = 27), exclusively Caribbean, and B (n = 24), exclusively Pacific. Three localities  share haplotypes H and M from both regions , which suggested a possible past demographic expansion event. The distribution of the number of differences (mismatches) between pairs of DNA sequences from Caribbean populations demonstrated the expected unimodal distribution for Caribbean all populations together and also for each population. The expected distribution did not differ significantly from the sudden-expansion model. The distribution for each locality from the Pacific region and for the grouped Pacific region showed a multimodal distribution typical of populations at equilibrium. Although the r  and SDD  values were not significant for Caribbean populations, the estimated values were small, also supporting a population expansion. As in previous Anopheles studies [Drosophila (D. melanogaster and D. yakuba) mutation rate values of 10-8/site/year [An. albimanus, using τ = 4.654, this is approximately 21,994 years ago  during the late Pleistocene.Both neutrality tests found the Caribbean populations have significant negative values, and one test was significant for the Pacific populations  that the Pacific populations are older relative to the Caribbean ones, perhaps most closely related to populations from Cuba and Central America, which had similar values using ND5 sequence [Nucleotide diversity values for  America , An. gam gambiae  and otheisozymes . This diisozymes . Nucleotsequence .An. albimanus (within ~665 km) had been observed in Central America, Costa Rica and Panama, Colombia and Venezuela. It appears that An. albimanus populations have had different periods of isolation followed by secondary contact throughout the species range in America [An. albimanus did not detect a significant correlation between genetic and geographic distance [Panmictic populations of  America -29. The An. albimanus populations from TUR, located in the Magdalena province as described by Morrone [An. albimanus populations may indicate different demographic histories for this species in these two regions of Colombia, and the AMOVA support for variance resulting from these two groups is significant, albeit low. Population expansion for An. albimanus from the Caribbean coast in Colombia is estimated to the late Pleistocene, similar to An. darlingi [The neutral model is rejected for the Caribbean populations, indicating possible previous population expansion and/or natural selection. In general, all tests illustrate two distinctive groups, corresponding to haplotypes from the Caribbean and from the Pacific regions. The data did not show significant differences between  Morrone , and thedarlingi .An. albimanus polytene chromosomes, consequently, their location on chromosome inversions is unknown and neutrality cannot be assumed [An. albimanus and it is conspecific along its distribution [An. albimanus in Colombia.Microsatellite loci used in this study have not been physically mapped in  assumed . Chromosribution ,92; howeet al [Significant departures from HW equilibrium were detected associated with heterozygote deficits Table , similaret al , observeFST) was not observed with isozyme markers [FST), but at the same time, the FST is the estimate presenting the lowest differentiation bias [COI data, the TUR population clustered with the Pacific region populations. Low admixture was detected between the cluster represented by ACH, SRL and MON from the Caribbean region and the other five populations. Thus, the largest genetic differentiation was observed in comparisons among the Caribbean and the Pacific region, and SAMOVA analyses placed TUR, NUQ, BUE, STU and MTU in five different groups perhaps suggesting a specific barrier that reduces gene flow between these populations.The moderate level of population differentiation detected , among the Caribbean and the Pacific region of Colombia is comparable to that reported among countries in South America and between Cuba and continental An. albimanus populations  [Anopheles nuneztovari s.l from Cordoba, Norte de Santander and Valle in the east, west and southeast of Colombia (FST = 0.024-0.06), using RAPDs (Randomly Amplified Polymorphic DNA) [An. darlingi populations from Cordoba, Meta and Chocó in the northwest and west of Colombia, using AFLPs (Amplified Fragment Length Polymorphism) and RAPDs , results suggested that the observed genetic differences could be the result of the biogeographic characteristics of each particular region [The level of differentiation (ctively) . Similarhic DNA) . In thisr region . The datNe, that could have increased gene flow and decreased population genetic structure [In general, in the Caribbean region, there was low genetic differentiation, partly explained by the high tructure . IBD wastructure , and by An. gambiae [FST), is not discarded given the low sample number [A significant level of differentiation among the populations closer to BUE was detected, probably influenced by ecological characteristics  that cou gambiae ,103,104. gambiae ; howevere number ,105.An. gambiae and Anopheles arabiensis [Anopheles dirus [Anopheles moucheti [An. darlingi [Most population structure analyses assume equilibrium, however, in several studies on anopheline vectors this has been violated, for example for abiensis , Anopheles dirus , Anophelmoucheti  and An. darlingi . In thisdarlingi . Under tNe calculated under the HE model assumes equilibrium [Ne values were obtained for all localities and on average, the effective population size  was three fold higher than in the study with An. albimanus from Central and South America  [Ne was obtained under both models. According to Donnelly et al. [Ne could contribute to the levels of population genetic differentiation seen in this study. Similarly, Ne heterogeneity has been reported for An. darlingi [An. gambiae complex [An. gambiae, the Ne heterogeneity observed in the savana cytotype was demonstrated to be region specific [Ne might be associated with a wide availability of breeding places that are suitable for An. albimanus, such as lakes, ponds, mine excavations and fish ponds [An. albimanus.The ilibrium . Some ofviduals) . For TURy et al.  heterogedarlingi ,34 and w complex -111. In sh ponds . The higsh ponds , could cAn. albimanus detected using COI gene sequences appears to be mostly influenced by demographic events in An. albimanus populations during the late Pleistocene. The data indicated little to moderate genetic differentiation among An. albimanus populations from the Caribbean and the Pacific regions. Current gene flow patterns may be mainly influenced by semi-permeable natural barriers in each biogeographical region that lead to the genetic differences and effective population sizes found in this study.The genetic diversity of Caribbean and Pacific populations of An. albimanus populations from the Caribbean and Pacific regions. It would also be of interest to examine additional populations of other Anopheles species to evaluate the generality of these patterns and to test the hypothesis that Caribbean expansion occurs among the Anopheles species whose distributions include these Colombian geographic areas.Current hydrological and climatic variation in Colombia is a factor associated with malaria outbreaks for multiple localities throughout endemic regions of the country . TherefoAn. albimanus populations from Colombia. This knowledge could be applied to existing vector control strategies since data on vector population genetics allow the inference of the spread of genes important for insecticide resistance or refractoriness to malaria parasites. In addition, this information contributes to the understanding of the epidemiology and the dynamics of disease transmission [In summary, this study showed current patterns of gene flow among the different smission . MoreoveThe authors declare that they have no competing interests.COI molecular procedures and analysis, co-participated in MS genotyping and analysis and drafted the manuscript. NJN contributed in the design of the study, was involved in field surveys, conducted procedures for microsatellite genotyping and analysis and helped in manuscript preparation. AVC co-participated in the design of the study and performed all molecular identification assays. CEM, SL and JEC co-participated in the design of the study and helped drafting the manuscript. MMC participated in the design of the study, data analysis, draft of the manuscript, general supervision of the research group and funding acquisition. All authors read and approved the final document.LAG contributed in the design of the study, extracted DNA, performed all Ne) and heterozygosity tests based on MS data for An. albimanusEstimates of effective population size  and heterozygosity tests based on MS data for An. albimanus from eight sites from the Caribbean and the Pacific regions of Colombia.Click here for fileCOI sequences of An. albimanus from ColombiaResults of neutrality tests based on . The table provided describe the estimated values of neutrality tests based on COI sequences of An. albimanus from eight sites from the Caribbean and the Pacific regions of Colombia.Click here for file


Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP–based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance.Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback ( Oceanic threespine stickleback have invaded and adapted to freshwater habitats countless times across the northern hemisphere. These freshwater populations have often evolved in similar ways from the ancestral marine stock from which they independently derived. With the exception of a few identified genes, the genetic basis of this remarkable parallel adaptation is unclear. Here we show that the parallel phenotypic evolution is matched by parallel patterns of nucleotide diversity and population differentiation across the genome. We used a novel high-throughput sequence-based genotyping approach to produce the first high density genome-wide scans of threespine stickleback populations and identified several genomic regions indicative of both divergent and balancing selection. Some of these regions have been associated previously with traits important for freshwater adaptation, but others were previously unidentified. Within these genomic regions we identified candidate genes, laying the foundation for further genetic and functional study of key pathways. This research illustrates the complementary nature of laboratory mapping, functional genetics, and population genomics. F statistics Population genetics provides a rich and mathematically rigorous framework for understanding evolutionary processes in natural populations. This theory was built over the last hundred years by modeling the processes of selection, genetic drift, mutation and migration in spatially distributed populations Gasterosteus aculeatusAn excellent opportunity for this type of population genomics approach exists in the threespine stickleback, http://www.ensembl.org/Gasterosteus_aculeatus/Info/Index).Because of their extreme diversification some stickleback populations are actually incipient Stickleback can be crossed in the laboratory to produce viable offspring and genetic mapping crosses STDTo address this question we have performed the first analysis of genome-wide patterns of polymorphism and differentiation using densely spaced single-nucleotide polymorphism (SNP) markers in replicate derived freshwater and ancestral oceanic stickleback populations. We used a novel and efficient genotyping approach based on Illumina sequencing of libraries of Restriction-site Associated DNA (RAD) tags RAD tag sequencing provided a genome-wide distribution of over 45,000 single nucleotide polymorphisms (SNPs) that were simultaneously identified, scored, and used in a genome-wide scan of 100 individuals, 20 each from two oceanic and three freshwater stickleback populations ; Table 1From these SNP genotype data we identified significant genetic variation within and across populations, with average genetic diversity (π) equal to 0.00336 across all populations and 0.0020–0.0027 within each population . These fST = 0.0076; ST were observed in pairwise comparisons among freshwater populations and between freshwater and oceanic populations (0.05–0.15), which is generally interpreted as low to moderate amounts of population structuring  using a Gaussian kernel smoothing function across each linkage group (H) heterozygosity largely correspond, they differ at a few genomic regions . Genomic regions that exhibit significantly (p<10−5) low levels of diversity and heterozygosity  and heterozygosity . In addition, a 250 kb unassembled genomic contig (scaffold 131) contains a block of six MHC class II genes . Nucleotide diversity , heterozygosity , and freshwater-oceanic differentiation  averaged over this scaffold are somewhat consistent with a hypothesis of balancing selection.Evidence for balancing selection on Major HistoCompatibility (MHC) loci is somewhat weaker. An MHC Class II gene (ENSGACG00000017967) falls nearly 580 kb outside the interval of maximum nucleotide diversity on LG III, although both π and ST values described above. In agreement with the genome-wide results of little genetic structuring among the oceanic populations, we found no genomic regions that exhibit either significantly elevated or reduced (p<10−5) differentiation between the two oceanic populations .Examining more closely the height and location of peaks in Fmparison . A strikmparison , where aST values at individual SNPs where population differentiation in the overall oceanic-freshwater comparison is significant at the α = 10−20 level  following false discovery rate correction of individual G-tests  and shows virtually no differentiation among the freshwater populations. In contrast, the second peak and neighboring region to 22.5 Mb shows more variation among the freshwater populations and is substantially lower in the overall oceanic-freshwater comparison .In contrast, some of the genomic regions that show consistent differentiation in all of the individual freshwater populations do not exhibit a peak in the overall oceanic-freshwater comparison. An example of this situation is observed on LG II , where sST = 0.3092, p<0.02 vs. RS; FST = 0.2737, p<0.01 vs. RB) that is not observed to the same extent in the other two populations. Correspondingly, there is a peak in differentiation among the freshwater populations at this location. This contrasts with the peak at ∼8.3 Mb on the same linkage group, which is consistent across the three populations and also observed in the overall oceanic-freshwater comparison , but not present in the comparison among freshwater populations.Finally, there are peaks of differentiation observed in one or two, but not all three, freshwater populations. One example of this is seen at 11.5–12 Mb on LG VIII , where tDD is negative overall in the oceanic populations, perhaps as a result of demographic processes affecting the entire genome equally. However, regions of significantly negative D correspond with peaks of freshwater-oceanic differentiation. In addition, we examined the genomic distribution of the density of private alleles–alleles that are found in only a single population or group of populations in a comparison. Overall, the private allele density (ρ) is higher in oceanic populations compared to freshwater than vice versa (ST peaks in the freshwater-oceanic comparisons (with the exception of the peaks on LG I and XI). Thus the peaks in FST are largely the result of alleles that we did not detect in the oceanic populations. The hypothesis that these are new mutations in the freshwater populations is rejected by the absence of corresponding peaks in private allele density among the freshwater populations , implicated in the loss of the lateral plate phenotype ST values and highly significant SNPs. Other regions outside the nine most significant peaks also exhibit a correspondence with QTL studies. For example, the peak on LG XII ; the orthology relationships of the remaining genes, those for which no gene name is yet listed, were further analyzed by a BLAST comparison of the predicted protein sequence for each of them against the NCBI protein database. We then assessed the ontological relationships of all protein coding genes in each interval with respect to skeletal biology and to osmoregulation, two axes of the phenotype known to change drastically as stickleback evolve in response to freshwater environments with very different ecological and chemical conditions than the ocean. To evaluate potential candidate genes, we identified all loci overlapping the boundaries of the nine most consistent peaks  lie just adjacent to Peak 1 of LG IV and partially overlapping Peak 2 of LG VII. Of these, EBF1, IL12B and ADRB2 are skeletal trait candidates Population genomic studies depend on having a very high density of markers that can be scored across many individuals. Depending upon demographic factors such as population size and structure, and the strength and nature of selection 2 or backcross mapping family, they could be ordered to produce a high-density linkage map. This genetic map could then be used to perform genome scans, as well as to help order a physical map from subsequent genome sequencing projects. Such data may be useful even when a preliminary genome assembly already exists. For instance, our approach revealed that nearly 60 Mb - equivalent to two of the largest chromosomes - of the stickleback genome are segregating alleles and show significant signatures of selection, but have not been incorporated into the existing assembly of 21 linkage groups . A forthcoming RAD genetic map will help incorporate this nearly 10% of the genome into its proper locations. In sum, RAD sequencing has the potential to combine population genetic and genomic studies with genetic and association mapping in populations of both model and non-model organisms, and in addition can help quickly produce or enhance essential genomic resources for organisms that presently have few.Although we used the stickleback reference genome sequence for the alignment of RAD tags, this tool can be used for population genomic studies in organisms that do not yet have a sequenced genome. Instead of aligning against a genome, the sequence reads can instead be aligned to one another, with SNPs identified and zygosities scored for individuals in the same manner as we describe here (Hohenlohe and Cresko unpublished data). Although these identified RAD sites are initially unanchored with respect to one another, if scored in an FWe produced genome-wide estimates of population diversity and differentiation for five stickleback populations that have been the focus of intense previous research. These data are largely in agreement with previous estimates of genetic diversity for stickleback, and support the view that oceanic stickleback populations have differentiated little from each other due to extensive gene flow over long distances. Each freshwater population exhibits a greater amount of divergence from the oceanic populations and from the other freshwater populations, but the overall amount is generally moderate and in line with previous estimates of population genetic divergence derived from microsatellite markers Furthermore, we were able to determine the distribution across the genome of genetic diversity and differentiation among the replicate populations. Identifying genomic regions of significantly increased or decreased diversity and differentiation allows us to make inferences about evolutionary processes, and to generate hypotheses about the evolutionary role of specific loci. Overall, the genome-wide patterns showed remarkable consistency across replicate populations and across pairwise comparisons. For example, the region with the most substantially elevated nucleotide diversity, observed on LG III, was consistent across populations and also exhibited increased heterozygosity and greatly reduced differentiation among populations. This pattern indicates balancing selection. This situation is best known for the vertebrate Major HistoCompatability (MHC) loci, which encode proteins responsible for tagging and presenting antigens to the immune system ST across this LG XIII interval in stickleback provides a second example of balancing selection acting at a TRIM cluster locus and bolsters the hypothesis that the largely unstudied mammalian TRIM14 and TRIM35 genes may be involved in immune response Similarly, the interval of increased nucleotide diversity on LG XIII overlaps a region rich in TRIM family genes, and includes a TRIM14 and three TRIM35 genes. Antiviral gene TRIM5alpha provides a rare example of balancing selection in primates We can draw further evolutionary inferences by focusing on the patterns of differentiation among replicate oceanic and freshwater stickleback populations, taking advantage of the rapid and often parallel phenotypic evolution coupled with little background population genetic structuring. In comparisons between freshwater and oceanic populations, we found numerous regions of the stickleback genome that exhibit significantly greater differentiation than observed in the rest of the genome, providing clear signatures of divergent selection distributed across numerous linkage groups. Although there were several instances in which a private signature could be observed in just one population, the strikingly common pattern is one of very similar regions being selected in all three independently derived populations. We can thus answer the question posed in the Other researchers In the case of replicate freshwater stickleback populations, we can identify instances of parallel hard sweeps, in which the same one or a few haplotypes present in the ancestral oceanic population were selected to high frequency independently in multiple freshwater populations. Alternatively, non-parallel sweeps are observed when different alleles from the oceanic standing variation are swept to high frequency in different derived freshwater populations, producing a hard sweep pattern within each freshwater population. The distinctions between these cases are apparent in the overall oceanic-freshwater comparison and in the comparison among freshwater populations. In fact, the ability to differentiate between parallel and non-parallel hard sweeps is a particular strength of natural systems with multiple replicate populations like stickleback. For example, the examination of parallel hard sweeps in several populations may help identify causative mutations if each sweep is only partially overlapping, narrowing the search to the region common in all populations.ST results, private allele density was significantly elevated in freshwater relative to oceanic populations , which has specifically been implicated in the parallel loss of bony lateral plates in freshwater populations On LG IV, the three regions of differentiation between oceanic and freshwater populations that we observed  were prePitx1 as a likely candidate responsible for loss of the pelvic structure In contrast to the lateral plate QTL on LG IV, the major pelvic structure reduction QTL exhibits a very different pattern with respect to signatures of selection. The major locus for pelvic loss was mapped to the very distal end of LG VII in two of these three populations In addition to these two major armor QTL, others have been identified in stickleback crosses for a variety of traits. Previous QTL mapping analyses, using crosses between oceanic and freshwater stickleback populations or among freshwater ecotypes, uncovered genomic regions co-segregating with various morphological traits, including the aforementioned presence or absence of lateral plate or pelvic armor elements and aspects of head and body geometry These examples demonstrate the ways in which QTL mapping and population genomic studies complement each other. While QTL studies can implicate genomic regions and specific genes in the evolution of particular phenotypes, population genomic results such as those presented here can provide evidence for the adaptive significance of these genomic regions in natural populations. A population genomics approach covering multiple replicate populations provides further insight into the standing genetic variation, types of selective sweeps, and extent of parallel evolution across natural populations for genes previously linked to particular phenotypes. A population genomics approach may also narrow a region of interest previously identified in mapping studies, especially when blocks of linkage disequilibrium in natural populations are smaller than in laboratory crosses. Even situations in which a population genomic approach does not implicate a genomic region previously identified as a QTL, as here on LG VII, are informative. The type of soft sweep postulated for the pelvic structure locus may lead to a bias against detecting selection on some previously identified loci with a genome scan. In addition, the converse situation is also informative: population genomic studies can identify putative regions of adaptive significance and candidate genes that no previous mapping approach has identified.We identified a list of candidate genes within peaks of parallel divergence among stickleback populations that may be important for adaptation to freshwater. Most work on adaptation to freshwater in stickleback has focused on genes and pathways associated with bone development and skeletal morphology. Changes in teeth, jaw and gill elements correlate with feeding mode in some lacustrine threespine stickleback populations Orthologs of many genes known to affect bone development by modulating specification, differentiation, proliferation, migration and patterning of skeletogenic tissues fall within genomic regions associated with differentiation between oceanic and freshwater stickleback. In other vertebrates, profound effects on the developmental patterning of the teeth, jaw, and other branchial arches result from changes in expression of EDA, EYA1, FBLN1, NFYB, RDH10, and Wnt5a genes Anadromous fish such as salmon undergo smoltification, a set of morphological and physiological changes that prepare the juvenile fish for the demanding transition from freshwater to marine. Stickleback entrained in freshwater lakes have lost this portion of their life history, and are probably no longer under strong selection pressure to maintain tolerance and physiological adaptability to saline conditions. On the other hand, fish adapted to freshwater must contend with limited access to minerals  and with a steep gradient of internal to external ion concentration. Peaks of oceanic-freshwater differentiation on LG IV, VII and XXI in stickleback contain genes associated with acute physiological adaptation to hypo- or hyperosmotic conditions in other species of fish, namely PRL2, a hormone controlling osmoregulation, and CA4 and ATP6V1A, important for ion transport across the gill epithelium and skin  [146]–[1Evolved responses to the host of physical and biological constraints that differ between freshwater and oceanic life histories are expected to be genetically complex. It is not surprising, therefore, that we find many genomic regions displaying strong patterns of differentiation between populations. What is surprising is the consistency of the regions of differentiation and the number of compelling candidate targets for selection they contain, suggesting the possible co-selection of functionally related, multilocus genotypes.This work represents the first whole-genome analysis of threespine stickleback in which high-density SNP markers reveal signatures of selection in natural populations. The patterns we detected confirm findings from earlier studies that used QTL analysis in controlled crosses or research that used microsatellite markers in natural populations to scan the genome. However, because of the dense coverage of SNPs across the genome, and our ability to sample numerous individuals in multiple populations, our findings are a significant extension of previous work. The present investigation complements these prior efforts by exposing new genomic regions that had not yet been recognized as important in the transition from oceanic to freshwater life histories. In particular, we find remarkably similar patterns of conservation and differentiation between three independently derived freshwater populations as compared to a common oceanic ancestor. Our data support the view that these patterns are driven in part by alleles that are repeatedly selected for in freshwater populations, and maintained at low frequency in oceanic populations by a balance between gene flow from freshwater and selection against them in the ocean. Previous work supported the role of parallel genetic evolution associated with parallel phenotypic evolution in a small number of traits. Our data indicate that this pattern is not limited to these traits, and that parallel phenotypic evolution in stickleback may be underlain by extensive, genome-wide, parallel genetic evolution.Threespine stickleback were collected from five populations in Alaska: Rabbit Slough (oceanic), Resurrection Bay (oceanic), Bear Paw Lake (freshwater), Boot Lake (freshwater), and Mud Lake (freshwater) . Fish weSbfI (New England Biolabs). RAD tag libraries were created as in Baird et al. 5′/5Phos/GATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCAGAACAA3′; P2-2 bottom oligo 5′ CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT 3′) was used in the production of all libraries, libraries produced for the May 2009 run and thereafter used P1 and P2 adapters modified with a phosphorothioate bond between the last two 3′ nucleotides on both oligos of the P1 adapter and the bottom oligo of the P2, adaptor ligated DNA was subjected to fewer rounds (14 or 16) of PCR amplification and PCR products were gel purified by excising a DNA fraction of 400–600 bp. Each Illumina sequencing lane contained a library representing approximately equal amounts of DNA from 16 individual fish .Genomic DNA was purified from 20 individuals from each of the five populations. DNA from each fish was digested with high fidelity refer to . SequencSequence reads from the Illumina runs were filtered as follows: reads with a barcode that did not match one of the expected barcodes (i.e. a sequencing error in the barcode), and sequence reads of poor overall quality, were removed from the analysis. Sequence reads were then sorted by barcode and aligned to the stickleback genome using Bowtie n be the total number of reads at that site. Let n  =  n1 + n2 + n3 + n4, where ni is the read count for each possible nucleotide at the site (disregarding ambiguous reads). For a diploid individual, there are ten possible genotypes (four homozygous and six heterozygous genotypes). We calculate the likelihood of each possible genotype by using a multinomial sampling distribution, which gives the probability of observing a set of read counts  given a particular genotype. For example, the likelihoods of a homozygote (genotype 1/1) or a heterozygote (1/2) are, respectively:n1 be the count of the most observed nucleotide, and n2 be the count of the second-most observed nucleotide, then the two equations in (1) give the likelihood of the two most likely hypotheses out of the ten possible genotypes. For all the analyses that follow, we assigned a diploid genotype to each site based on a likelihood ratio test between these two most likely hypotheses with one degree of freedom. If this test was significant at the α = 0.05 level, we assigned the most likely genotype at the site. If this test was not significant, we did not assign a genotype at the site for that individual. This effectively removes data for which there are too few sequence reads to determine a genotype, instead of establishing a constant threshold for sequencing coverage. We account for the resulting variance in sample size among sites in the analyses below.Diploid genotypes at each nucleotide site for each individual were determined in a maximum likelihood statistical framework as follows. For a given site in an individual, let This basic multinomial-based statistical framework has been proposed elsewhere in is the count of allele i in the sample, and H was calculated as the proportion of diploid genotypes in the sample that are heterozygotes. To estimate differentiation among populations, we adapted a formula for FST from jn is the number of alleles sampled in population j, πj is the nucleotide diversity within population j from equation (2), and ST to others, including the analysis of variance approach of ST (not shown). Finally, for each population in a comparison we assessed whether each single nucleotide polymorphism (SNP) was the result of a private allele. Here ρj = 1 if an allele at the SNP is found only in population j and at least one individual was genotyped at that nucleotide site in each population, and ρj = 0 otherwise.We first calculated four population genetic measures at each nucleotide site for the population(s) under examination. To estimate nucleotide diversity, we calculated π  asc, the contribution of the population genetic statistic at position p to the region average was weighted by the Gaussian function kn is the number of alleles sampled at site kH were weighted and averaged across all nucleotide sites; FST and private allele density ρ were weighted and averaged across all SNPs.To generate smooth genome-wide distributions of these four population genetic measures, we used a kernel-smoothing moving average. For each genomic region centered on a nucleotide position Dn was taken to be the mean of kn across all sites within the window.We also estimated the allele frequency spectrum within populations or groups of populations using Tajima's ST values with a goodness-of-fit G test statistic We assessed statistical significance at two levels. At individual SNPs, we estimated the significance of FH, FST, and ρ, as well as window values of Tajima's D, we used bootstrap resampling within each population comparison. For each nucleotide position  or SNP position (for FST or ρ) within each truncated Gaussian window described above, we randomly sampled with replacement from across the entire genome a value for the statistic  and the corresponding sample size (kn). We calculated the weighted average as above for each replicate. For Tajima's D, for each nucleotide position within the 2σ window we randomly sampled with replacement from across the genome and calculated the overall D for the re-sampled dataset. For computational efficiency, at each region we began with 100 (for π or H), 1,000 (for D), or 10,000 (for FST or ρ) replicates and stepped up to 1 million  or 10 million (FST or ρ) replicates as necessary to provide accuracy in the tails of the distribution. Essentially this bootstrapping technique gives a null distribution of expected genomic region averages, accounting for the observed genome-wide average of each statistic in a given population or population comparison, but assuming no correlation among neighboring positions. It thus indicates genomic regions that differ significantly from the genome-wide average as a result of the combination of linkage disequilibrium and evolutionary or demographic processes. Significance values (p) given in the text and tables represent proportions of these bootstrap distributions exceeding the particular statistic.To assign significance values to moving average values of π, −5 for π in the combined 5-population dataset, including positions within 2σ ( = 300 kb) of the outer positions. For FST, we identified all genomic regions for which p<10−5 in the overall freshwater-oceanic comparison as well as in all six of the pairwise freshwater-oceanic comparisons. We then delineated the region of interest using the overall freshwater-oceanic comparison, +/− 2σ as above. Note that this 2σ margin includes locations that may contribute to a highly significant average value of a statistic, even if the value for the genomic region directly over the gene is not as significant , these issues are not likely to bias the distribution of these measures along the genome. Also, they should not bias measures of population differentiation (FST), assuming that these sources of error affect different population samples equally.For several reasons, we believe that our method may provide an underestimate of nucleotide diversity within populations. First, we expect polymorphism in RAD sites, such that the restriction enzyme recognition site is missing in some haplotypes and a RAD tag sequence will not be obtained for this allele. Individuals homozygous for absence of a RAD site will lack any sequence information for those two RAD tags; individuals heterozygous for the presence of a RAD site will be represented by one of only two possible sequences for each tag, so they will likely be scored as homozygous for all nucleotide positions in those tags. . We removed sequence data within the restriction enzyme recognition site prior to analysis. However, to the extent that presence/absence of a RAD site is in linkage disequilibrium with SNPs in the adjacent RAD tag sequence, this polymorphism will be underestimated. Second, RAD tags with low coverage are not assigned a genotype by the method above if the likelihood ratio test is not significant. Because of the multinomial sampling process, true heterozygotes may be more likely to go unscored than true homozygotes at the same, low level of sequencing depth. Third, we have some evidence that there is bias in number of reads and read quality between alternative alleles at heterozygous sites during library construction and/or Illumina sequencing (unpublished data). As described above, our method does not account for these unknown sources of bias, but they could also lead to the analysis assigning homozygous genotypes to heterozygous sites. We are currently exploring ways to account for all of these issues in the analysis . In any case, we believe that while our method may lead to an underestimate of nucleotide diversity measures within groups  and reduced  values. Vertical gray shading indicates boundaries of the 21 linkage groups and unassembled scaffolds, and gold shading indicates two consistent peaks of elevated nucleotide diversity. (A) RS. (B) RB. (C) OC (RS + RB). (D) BP. (E) BL. (F) ML. (G) FW (BP + BL + ML).Nucleotide diversity within single and groups of populations. Nucleotide diversity (π) across the genome, with colored bars indicating significantly elevated (p≤10(2.85 MB TIF)Click here for additional data file.Figure S2−3, blue; p≤10−5, red) or reduced (p≤10−3) values, assessed by bootstrap resampling. Vertical gray shading indicates the 21 linkage groups and unassembled scaffolds, and gold shading indicates the nine consistent peaks of population differentiation. (A) Private allele density in FW compared to OC. (B) Private allele density in OC compared to FW.Private allele density in the overall freshwater-oceanic comparison. Each plot shows density of private alleles (ρ), with colored bars indicating regions of significantly elevated (p≤10(1.38 MB TIF)Click here for additional data file.Figure S3Gasterosteus aculeatus genome database . Vertical gray shading indicates the 21 linkage groups and unassembled scaffolds.Density of annotated and predicted genes along the stickleback genome. Count of genes in each 1-Mb window, taking each gene's position to be its lower bound as given in the (0.66 MB TIF)Click here for additional data file.Table S1Illumina sequencing runs used in this analysis.(0.06 MB DOC)Click here for additional data file.Table S2A complete list of the protein coding genes that fall in genomic regions associated with differences between oceanic and freshwater populations. Gene names are listed, where available from Ensembl (release 55.1j). Where gene names were lacking, ortholog names are listed for candidate genes from (0.10 MB XLS)Click here for additional data file.Table S3Candidate genes related to skeletal morphology and osmoregulation in additional regions of differentiation on Linkage Groups IV, VII, and XII.(0.11 MB DOC)Click here for additional data file.