Milicia, currently divided into M. regia (West Africa) and M. excelsa (from West to East Africa). We used 435 samples collected in West, Central and East Africa. We genotyped SNP and SSR loci to identify genetic clusters, and sequenced two plastid regions  and a nuclear gene (At103) to confirm species’ divergence and compare species delimitation methods. We also examined whether ecological niche differentiation was congruent with sampled genetic structure.Species delimitation in closely related plant taxa can be challenging because (i) reproductive barriers are not always congruent with morphological differentiation, (ii) use of plastid sequences might lead to misinterpretation, (iii) rare species might not be sampled. We revisited molecular-based species delimitation in the African genus M. regia, West African and East African M. excelsa samples constituted three well distinct genetic clusters according to SNPs and SSRs. In Central Africa, two genetic clusters were consistently inferred by both types of markers, while a few scattered samples, sympatric with the preceding clusters but exhibiting leaf traits of M. regia, were grouped with the West African M. regia cluster based on SNPs or formed a distinct cluster based on SSRs. SSR results were confirmed by sequence data from the nuclear region At103 which revealed three distinct ‘Fields For Recombination’ corresponding to (i) West African M. regia, (ii) Central African samples with leaf traits of M. regia, and (iii) all M. excelsa samples. None of the plastid sequences provide indication of distinct clades of the three species-like units. Niche modelling techniques yielded a significant correlation between niche overlap and genetic distance.West African Milicia could be recognized. It is surprising that the occurrence of two species in Central Africa was not reported for this well-known timber tree. Globally, our work highlights the importance of collecting samples in a systematic way and the need for combining different nuclear markers when dealing with species complexes. Recognizing cryptic species is particularly crucial for economically exploited species because some hidden taxa might actually be endangered as they are merged with more abundant species.Our genetic data suggest that three species of The online version of this article (doi:10.1186/s12862-016-0831-9) contains supplementary material, which is available to authorized users. Species diversification and morphological evolution are not always correlated, as demonstrated by the existence of hidden genetic diversity in taxa previously considered as single species. In sexual organisms, a cryptic species complex in the sense of  designatHidden genetic diversity can sometimes be detected through direct observations, suggesting for instance reproductive barrier between individuals . Reproductive isolation between sympatric sibling species can be detected using genetic markers. In addition, several molecular markers are needed because different evolutionary processes such as incomplete lineage sorting can obscure the genetic divergence displayed by a single marker. DNA-based phylogenetic approaches are often used to delineate species, and reproductively isolated groups can be identified following the phylogenetic species concept if they segregate into different monophyletic lineages. However, the absence of monophyly is not conclusive because genetically isolated groups can be paraphyletic or polyphyletic , 7. HencA variety of such methods have been proposed . Flot etBesides nuclear DNA sequences, widely used co-dominant markers such as nuclear microsatellites  and single-nucleotide polymorphism loci (SNPs) are valuable to address population structure and species delineation in closely related taxa . AssignmMilicia is an important African timber genus that has received attention from scientists during the last decade, although the focus was on populations found in Ghana and Cameroon . Phylogeview in , 26 confof Gabon . Second,of Gabon , 29). Thof Gabon  so that clusters , 32.Milicia populations in Africa along with an assessment of its relationships with climate niche patterns using a more homogeneous sampling. We compare the ability of SSRs and SNPs for delimiting genetic clusters and species, and detect any hidden genetic diversity in Milicia populations, and we use a nuclear DNA sequence to assess phylogenetic relationships. More specifically, we addressed the following questions: (1) Does Milicia regia naturally occur in Central Africa as reported a century ago ) ollowing , we cons2 = 2μG , with μ ite loci , 48.At103 was employed for further analyses as plastid sequence data provided poor separation of the different genetic clusters (see results). Haplotypes were reconstructed with PHASE implemented in DnaSP [sensu [At103, we also constructed a phylogenetic tree based on At103 haplotypes through the Bayesian method implemented in *BEAST [Milicia at 8-41 mya (95% posterior estimate of the age distribution) and this was utilized as a prior for the analysis. A Yule tree prior and an uncorrelated relaxed molecular clock were assumed. The MCMC was run seven times for 500 million generations, each run being sampled every 50,000 generations, and the final tree had the highest posterior probabilities at the nodes.For nucleotide sequence data, we first constructed a median joining network for each sequence using NETWORK 4.6 . Thereafin DnaSP  and CHAMin DnaSP  for lengP [sensu  was consn *BEAST . From a n *BEAST , we dateMilicia range in Africa and because this gave the best models based on preliminary values of the Area Under the Curve . Analyses were performed with the R environment [First, we inferred putative geographical locations of each genetic cluster detected with TESS 2.3.1  through ironment .i (100-km2 each) [D developed by [D = 1 – 0.5 ∑ |pX,i - pY,i| where pX,i and pY,i stand for the probability assigned by the ENM  for genetic clusters X and Y, respectively, to cell i). The statistic D varies from 0 (no overlap between the two considered niches) to 1 . Finally, the correlation between genetic distances as expressed by DS or (δμ)2 from SSRs and niche overlap measure D, was tested by the means of a simple Mantel test in order to verify whether niche overlap was higher – or not – in genetically similar clusters. Recent studies revealed that genetic divergence of specific gene markers can be a good predictor of differentiation at quantitative trait loci in random mating populations [D and DS or (δμ)2 may reflect a genetic signature of divergent selection across some genetic clusters of Milicia, especially those in the Congo Basin where climate is quite similar across a large region. We chose genetic divergence measures from SSRs because they are superior at elucidating the genetic structure of Milicia populations (results). A total of 10,000 permutations were performed for testing the significance of the Mantel tests.Second, we applied a smoothing technique through a PCA that divides the environmental space  in cells and uses a kernel function to determine the smoothed density of occurrence for each genetic cluster to each cell m2 each) . Then weloped by  for pairulations . That isulations ). Hence Kmax displayed a steep positive slope to Kmax = 4 followed by a shallower positive slope with Kmax without an asymptotic trend  and Democratic Republic of the Congo (DRC). A genetic cluster K4 covered the Western part of Gabon and appeared only at Kmax = 5 for the SNPs and Kmax = 6 for the SSRs  according to SNPs  were isolated from the largest Central African cluster only at Kmax = 4 according to SSRs . As the most questionable genetic cluster was K6 given its disjoint distribution and its inclusion in K1 (M. regia) according to the SNPs, we verified the morphology of all individuals from that genetic cluster and samples from the neighbouring genetic clusters. Our observations confirmed that all the 13 individuals identified as M. regia according to the SNP display the specific leaf feature of M. regia   Fig. . Even whM. excelsa: the Kenyan cluster, K5, displayed the lowest values whereas genetic diversity was in the same order for the other clusters K2, K3 and K4. Markers differed strikingly when comparing the two species: SSR data provided the highest diversity values for the West African M. regia genetic cluster, K1, while SNPs displayed the opposite trend, with the clusters of M. excelsa exhibiting the highest diversity values ; that trend was reversed if we considered DS . As expected from divergent evolution in the clustering pattern , DS for the same pair of genetic clusters was 0.09  (Table DS better reflected the clustering pattern and was better correlated to (δμ)2 in microsatellites than did FST (Pearson’s R = 0.74 for DS-(δμ)2 vs. R = 0.55 for FST-(δμ)2  harboured specific haplotypes. Individuals of the cluster K6 shared their haplotypes with the other M. excelsa populations  presented specific haplotypes, H_1 and H_14  also displayed a specific FFR with eight haplotypes whereas M. excelsa populations from West and Central Africa  shared a unique FFR  tend to depart from the other genetic clusters (M. excelsa) but with a weak support  which was not significant (P = 0.159). A similar analysis using D and (δμ)2 resulted in b = -0.022 (R = -0.581) which was significant (P = 0.016).The occurrence range of the six genetic clusters was well explained by the following climatic variables correlated to the first PCA axis : annual mean precipitation, annual mean temperature, annual temperature range, solar radiation, and precipitation of the driest quarter . Owing to new populations included in the analyses and a more homogeneous geographic sampling with a lower number of individuals , we showed that both SSRs and a few dozens of SNPs are good marker candidates to reliably characterize the genetic structure within a taxon.According to the sampling scheme and the markers used genetic studies can either detect or miss hidden genetic diversity. In particular the sampling approach may be a major issue. Daïnou et al.  did not Milicia excelsa strongly diverge from the Central and West African populations, a pattern found in other species [Milicia genetic cluster K6 made of scattered Central African samples morphologically similar to M. regia. This species is known to occur only in West Africa westwards of Togo, hence one may think that these individuals could be remnants of historical plantations of M. regia (as supported by SNPs). However, we found no report of such plantations, while we rediscovered a century-old article reporting M. regia in Gabon [M. regia observed in Central Africa is strongly divergent from the other populations of Milicia: (i) the clustering pattern from SSRs that considered this group as a different genetic cluster; (ii) the absence of gene flow with the other clusters; and (iii) the haploweb outputs from the nuclear sequence At103 that identified all these K6-individuals as a separate field for recombination. Furthermore, phylogenetic reconstructions suggested that the genetic cluster K6 is probably as old as the ancestors of all Milicia populations. Divergence among Milicia genetic clusters looks to have been shaped by geographic isolation probably in relation to past ice ages but there was also a signal of habitat selection effect (significant correlation between niche overlap and the genetic distance (δμ)2).As hypothesized, East African populations of  species , 59, and species ). But thin Gabon . Furtherst century, barcoding techniques were used to detect hidden genetic diversity in the form of two or more phylogenetically distinct clades corresponding to slightly different phenotypic groups or having distinct geographical distributions [In case of weak morphological differentiation among taxa the discovery of cryptic species is most of the time a matter of chance , unless ibutions , 64. Theibutions . Detectiibutions ) or any ibutions –68. As aMilicia is not a young genus [M. regia. Although not employed as much as trnH-psbA, trnC-ycf6 got a certain success when combined with the former . Howeveng genus  and as ir . trnH-pL + matK  and showL + matK  includinK [Ficus . TherefoK [Ficus ). But itM. excelsa individuals from chloroplast sequences may suggest either a strong relatedness between those populations along with incomplete lineage sorting, or past chloroplast capture. If we remove from consideration the West African cluster of M. regia K1, the divergence time of K6 and its relatedness to Kenyan cluster K5 composed of M. excelsa 2 and niche overlap D performed in the present study resulted in a substantial and significant correlation (R = -0.58). This should reflect signs of selection acting for the differentiation between the genetic clusters of Milicia, even at intraspecific level for M. excelsa [Milicia genetic clusters could be unreliable or incomplete. Indeed, the outputs of those approaches, especially Maxent technique, can be biased by samples provided for the modelling [Niche modelling techniques offer a good way to verify relationships between population genetic divergence and environmental selection. Daïnou et al.  already  excelsa , and thiodelling . The WesM. excelsa and M. regia in West Africa . As the clustering solution of SSRs was clearly supported by the At103 sequences that demonstrated that K6 bears exclusive haplotypes, SNPs appeared less powerful than SSRs to discriminate genetic clusters in Central Africa.Due to the biallelic character of most of SNPs these markers are usually considered as less informative than polymorphic microsatellites to highlight a genetic structure, for a similar number of loci. Hess et al.  found thviduals; ). HoweveM. regia cluster K1 (He = 0.72 compared to He in the range 0.32 to 0.54 for the other genetic clusters), SNPs displayed much lower diversity values in both M. regia clusters K1 (He = 0.06) and K6 (He = 0.03) as compared to M. excelsa genetic clusters (He in the range 0.12 to 0.33). Ascertainment biases due to marker discovery protocols can explain those differences. In microsatellites, the hypothesis of length ascertainment bias states that the median or mean allele size of microsatellites is the greatest in the species or population that has served for the development of the markers. Homologous loci in sister species may have different evolutionary histories so that a locus characterized in a sister species may not be as polymorphic as in the one from which SSRs have been derived [Milicia excelsa individual (sampled in the area of K2) and their polymorphism was evaluated in a sample composed of 30 trees of M. excelsa and 10 of M. regia from Ghana [M. excelsa individuals from K2 and K5, and the step of polymorphism screening for final marker selection involved only 17% of West African M. regia trees . As by definition SNPs are identified based on their polymorphism in the initially screened samples, ascertainment bias can be strong and this likely explains the much lower genetic diversity recorded in M. regia populations. As a SNP generally results from a unique mutation event and SNPs were assessed between the M. excelsa populations K2 and K5, only polymorphisms that appeared before the differentiation between M. excelsa and M. regia could remain polymorphic in both species. A comparison of SNP loci in morphologically assigned M. regia genetic clusters showed that among the 48 loci which were polymorphic in K1, only 14 were also polymorphic in K6. Only one locus was found polymorphic in K6 and not in K1. This clear ascertainment bias highlights that particular care should be made for the selection of SNPs for genetic structure characterization and that starting from a broad genetic basis is preferable.Another important difference between SSRs and SNPs was observed in the trend of genetic diversity among genetic clusters for each type of marker. Whereas SSRs exhibited the highest sequence diversity in the West African  derived . In the om Ghana . First, om Ghana . The SNPMilicia phenotypically resemble to the other species. In particular, floral and fruit characters should be meticulously examined. Additional file The present work highlights the value of large-scale genotyping of genera to discover cryptic species as well as highlight their hidden diversity at the intra-specific level. It is notable that, for a well-known timber tree, the occurrence of two species in Central Africa was not reported by botanists for a century although diagnostic leaf characters were known. Additional morphological investigations are required to evaluate at which extent the Central African new species of rbcL + matK sequencing. However, SNPs are prone to ascertainment bias than SSRs, at least when assessing genetic diversity, so that their development should ideally start from a large sample size. We recommend to collect and genotype hundreds of samples covering the distribution range of the taxon investigated.We suspect that many similar cases remain, and that the floristic diversity of tropical forests remains underestimated. Recognizing cryptic species is particularly important for exploited species, like timber trees, as some of them might be endangered and require a special management policy while they are currently confused with a less vulnerable species. To identify cryptic species we showed that nuclear SNPs and SSRs can both be utilized and show similar resolution, while plastid markers are less reliable, a problem for current DNA barcoding in plants based on


Columba livia domestica speciesusing 11 microsatellite markers and to investigate the success of these markers amplification across another eight pigeon species.This study aimed to analyze the genetic diversity and relationships of 10 Egyptian pigeon populations belonging to C. livia domestica but belonging to another eight pigeon species were genotyped. The polymerase chain reaction (PCR) products were electrophoresed on an ABI 3130xl DNA Sequencer. The basic measures of genetic diversity and phylogenetic trees were computed using bioinformatics software.Genomic DNA was isolated from feather samples of179 pigeon samples from 10 Egyptian breeds: Asfer Weraq (n=14), Austoraly (n=20), Reehani (n=21), Messawed (n=17), Nemssawy (n=27), Otatti (n=12), Morasla (n=17), Tumbler (n=22), Halaby Asfer (n=10), and Karakandy (n=19) in addition to Japanese feral pigeons (n=30). Genotyping was done using 11 specific polymorphic microsatellite makers. Moreover, 37 samples not belonging to STF coefficient which indicates fixation coefficients of subpopulations within the total population for the 11 loci varied from 0.318 to 0.114 with a relatively high mean (0.226). In our study, the ISF showed a relatively high average(0.037). The pairwise Reynolds’s genetic distance between the11 studied pigeon populations recorded lower values between Otatti and Austoraly (0.025) and between Morasla and Japanese feral pigeons (0.054). These results are supported by clustering pattern either by the neighbor-joining phylogenetic tree or by a Bayesian clustering of STRUCTURE with the admixture method.Across the 10 studied Egyptian populations, the number of alleles per locus ranged from 3 to 19 and the average number of alleles observed was 9.091. The lowest value of expected heterozygosity (0.373) was obtained for the Reehani breed, and the highest value (0.706) was found for Morasla breed. The overall expected heterozygosity of Egyptian pigeons was 0.548. The CliµD17, CliµT17, CliµD16, CliµD32, CliµT13, CliµD01, PG1, PG2, PG4, PG6, and PG7 microsatellite markers among Egyptian domestic pigeons and across other pigeon species using cross-species amplification method. The information from this study should be useful for genetic characterization and for developing conservation programs of this important species.We confirm the applicability of the  Assessment of farm animal genetic diversity is important to identify native populations useful for food security and rural development. It allows breeders to identify, select, and develop new breeds in response to the changeable conditions such as climatic change, disease threats, human nutritional requirements, and changing market needs ,2. NativDarwin wrote about the high degree of phenotypic variation among domestic pigeons and their differences from their wild ancestors. Pigeon is considered as a model organism because it is easy to breed and study in the laboratory; it helped Darwin to understand how evolution works in general . Pigeon C. livia, Streptopelia, and other related taxa [de novo marker development. Some early studies have shown the applicability of microsatellite markers among closely related species using cross-species amplification [The microsatellite is a marker of choice for studying the genetic diversity and relationships among closely related livestock breeds ,12. MtDNted taxa . The crofication ,15.This study aimed to analyze the genetic diversity and relationships of 10 Egyptian pigeon populations  using 11 microsatellite markers. Also, to investigate the success of these markers’ amplification across other pigeon species such as Oriental turtle dove, White-bellied green, Emerald dove, Whistling green pigeon, Blue-crowned pigeon, Japanese wood pigeon, Victoria crowned pigeon, and Pied imperial pigeon. Such information can be useful to develop a sustainable genetic improvement and conservation programs for this valuable species.All aspects of the study were performed according to theguidelines established by the Ministry of Education, Culture,Sports, Science, and Technology in Japan (Notice No. 71). The protocol was approved by the Committee on the Ethics of Animal Experiments of the Wildlife Research Center of Kyoto University (Permit No. WRC-2017-002A).A total of 179 pigeon feather samples from 10 Egyptian local breeds: Asfer Weraq (n=14), Austoraly (n=20), Reehani (n=21), Messawed (n=17), Nemssawy (n=27), Otatti (n=12), Morasla (n=17), Tumbler (n=22), Halaby Asfer (n=10), and Karakandy (n=19) in addition to Japanese feral pigeons (n=30) were obtained. Moreover, 37 feather samples were collected from eight wild pigeon species as shown in COI sequence was analyzed for one sample of the 10 Egyptian breeds and of Japanese feral pigeons. Moreover, one sample was sequenced from the four wild pigeon species: Emerald dove , Oriental turtle dove , Whistling green pigeon (Treron formosae), and white-bellied green pigeon (Treron sieboldii) for comparison. The primers and polymerase chain reaction (PCR) condition are the same as described by Ramadan et al. [Mitochondrial https://www.megasoftware.net) [The amplified products were purified using PCR Purification Kit , and the resultant products were sequenced using the same primers and the Big Dye Terminator ver. 3.1 Cycle Sequencing Kit  according to the standard protocol and electrophoresed on an ABI PRISM 3130xl sequencer (Applied Biosystems). The MEGA 6 Software (are.net)  was usedare.net)  methods are.net) .CliµD17, CliµT17, CliµD16, CliµD32, CliµT13, and CliµD01 from the study of Traxler et al. [PG1, PG2, PG4, PG6, and PG7 from the study of Lee et al. [et al. [Eleven labeled microsatellite markers  were us [et al. . The PCRAN and eN) and observed and expected heterozygosity  using GENALEX version 6.0 [PIC) was calculated using CERVUS version 3 software [F-statistics  in addition to pairwise STF [K with 50,000 iterations following a burn-in period of 50,000. Pairwise comparisons of the 20 solutions of each K value were run along with 20 permutations using CLUMPP software [H value was graphically displayed for the selected K value using DISTRUCT software [Genetic diversity was assessed by calculating the observed and effective number of alleles (sion 6.0 . Polymorsoftware . F-statiwise FST  across twise FST . Geneticwise FST . A phylowise FST  method [wise FST . The robwise FST . We invewise FST . We did software . CLUMPP software .C. livia sequence retrieved from GenBank (accession number GQ481605). The branching pattern of other wild species reflected their phylogeny. The low sequence divergence among Egyptian and Japanese feral pigeons together with GenBank sequence of C. livia confirms that all of these breeds belong to the same species (C. livia) and the mtDNA COI sequence divergence is more suited for the analysis of among species divergence than within species divergence [We amplified and sequenced 693 bp for one sample of the 10 Egyptian breeds and of Japanese feral pigeons in addition to the four species of wild pigeons for confirmation of the species of Egyptian pigeon. After alignment, there were only one substitution sites among the 10 Egyptian and the Japanese feral pigeons. From the NJ phylogenetic tree , these 1vergence .PG6) to 19 (CliµD01), and the average number of alleles observed was 9.091. Locus PG6 had the lowest values for both of EH (0.371) and PIC (0.364), whereas locus CliµD01 had the highest values for EH (0.731) and PIC (0.806), and locus PG2 recorded the highest OH (0.727) as shown in PG6 showed lower polymorphism not only in our study but also in previous studies [ISF for each population are shown in et al. [et al. [et al. [Across the 10 studied Egyptian populations, the number of alleles per locus ranged from 3  to 0.194 (Messawed) with a mean 0.031. The inbreeding coefficients (ISF) were positive for all investigated population except Asfer Weraq and Halaby Asfer which showed negative ISF values. The higher values for inbreeding coefficient might indicate a departure from random mating, which could be expected for breeding flocks due to artificial selection of mating pairs. There were 16 breeds specific alleles observed among the 11 populations. The number of breed-specific alleles ranged from 0  to 5 (Morasla) as shown in As a measure of deviation from Hardy-Weinberg equilibrium, the ISF, ITF, and STF) for each locus  to 0.114 (Cliµd16), with a relatively high mean 0.226, which indicate that there is a genetic differentiation among the 10 populations. This means that about 22.6% of the total genetic variation is due to population’s differences, while the remaining 77.4% is due to differences among individuals. This STF value is comparable with those of Ramadan et al. [et al. [STF of our study was higher than the value of 0.147 reported by Biała et al. [Genetic differentiation across the 11 studied populations was examined by fixation indices in addition to seven loci  recorded a deficiency of heterozygosity; this might indicate that these six loci are under selection for some favorable morphological or productive characteristics.The to 25.3% . For theSTF values was the lowest in Otatti–Austoraly (0.029) and in Morasla–Japanese feral pigeons (0.039) as shown in K= 5 where the 11 studied pigeon populations showed the most probable structure clustering, Otatti and Austoraly populations were clustered together, and also Morasla and Japanese feral pigeon populations were also clustered together forming admixed mosaic clusters  and between Morasla and Japanese feral pigeons (0.054). Similarly, the genetic differentiation indicated by pairwise clusters . Nemssawet al. [STF. Moreover, they found a close relationship between European free-living populations with a former racing breed called Modena. Modena pigeon breed was developed in Italy since 2000 years ago [et al. [The close relationship between Otatti and Austoraly might be attributed to that Austoraly is a synthetic breed originated from crossing between Safi with Zagel and Otatti breeds . The cloet al.  found liears ago . They su [et al.  found a C. livia species across eight wild pigeon species by means of cross-species amplification were amplified and showed polymorphism across six of the seven studied Corvidae species.We investigated the applicability of 11 microsatellite markers of ification. We obtaWe confirm the applicability of microsatellite markers among Egyptian domestic pigeons and across wild pigeon species by means of cross-species amplification. Relatively reliable diversity results could be obtained even with a small number of polymorphic microsatellites, as shown in our study and confirmed by other similar studies . The infSR and MI planned and designed the study. AD, OE, and AMN collected the feather samples and provided the help for statistical analysis. SR performed microsatellite genotyping and drafted the manuscript under the guidance of MI. ME and AMN provided the help for the data analyzed and interpretation of laboratory results. All authors participated in draft and revision of the manuscript. All authors read and approved the final manuscript.


N⩽5553, longitudinal assessments at 8, 11, 14 and 17 years) using the Social Communication Disorder Checklist. Data on clinical ASD  and schizophrenia  were either obtained through the Psychiatric Genomics Consortium (PGC) or the Danish iPSYCH project. Overlap in genetic influences between ASD and social communication difficulties during development decreased with age, both in the PGC-ASD and the iPSYCH-ASD sample. Genetic overlap between schizophrenia and social communication difficulties, by contrast, persisted across age, as observed within two independent PGC-SCZ2 subsamples, and showed an increase in magnitude for traits assessed during later adolescence. ASD- and schizophrenia-related polygenic effects were unrelated to each other and changes in trait-disorder links reflect the heterogeneity of genetic factors influencing social communication difficulties during childhood versus later adolescence. Thus, both clinical ASD and schizophrenia share some genetic influences with impairments in social communication, but reveal distinct developmental profiles in their genetic links, consistent with the onset of clinical symptoms.Difficulties in social communication are part of the phenotypic overlap between autism spectrum disorders (ASD) and schizophrenia. Both conditions follow, however, distinct developmental patterns. Symptoms of ASD typically occur during early childhood, whereas most symptoms characteristic of schizophrenia do not appear before early adulthood. We investigated whether overlap in common genetic influences between these clinical conditions and impairments in social communication depends on the developmental stage of the assessed trait. Social communication difficulties were measured in typically-developing youth (Avon Longitudinal Study of Parents and Children,  Recent cross-disorder genetic analyses highlighted the continuity of psychiatric phenotypes beyond current diagnostic boundaries.6 The nature of shared genetic influences between childhood neurodevelopmental disorders, such as ASD, and adult-onset psychiatric illnesses, like schizophrenia, however, remains less well understood.The phenotypic overlap between autism spectrum disorder (ASD) and schizophrenia is complex and dates back to Kanner in 1943.8 Core features include deficits in social interaction and communication, as well as highly restricted interests and/or stereotyped repetitive behaviours.2 By contrast, schizophrenia is an adult-onset psychiatric illness with a typical first-time diagnosis between 16 and 30 years. The disorder has a lifetime prevalence of ~1%9 and is characterised by hallucinations, delusions, disorganised speech or behaviour, apathy and lack of emotional reactivity.2 Both ASD and schizophrenia are highly heritable11 and recent studies have linked different types of genetic variation including common variants,13 as well as rare inherited15 and de novo variation17 to risk of illness in both conditions. Contemporary research strongly supports a genetic overlap between ASD and schizophrenia for rare copy number variants18 and rare de novo mutation events16 with converging evidence for gene sets involved in synaptic function.16 The role of shared common genetic risk between ASD and schizophrenia, however, is less clear. Common genetic influences account for 25 to 33%19 of total liability to schizophrenia and up to 49% of total liability to ASD.12 Despite this, the common genetic overlap between ASD and schizophrenia is small compared with the overlap between psychiatric adult-onset only disorders.20ASD represent a group of neurodevelopmental conditions with a typical age of onset before the age of 3 years affecting ~1 to 2% of children.21 Common disorders, due to their polygenic architecture, can be understood as quantitative traits.22 For ASD, following the findings of earlier twin studies,24 there is now molecular evidence for shared common genetic influences with social communication difficulties during childhood.25 The genetic continuity of social interaction and communication deficits in schizophrenia has not yet been observed though it can be hypothesised that such common genetic links exist given the impairments in social cognition within first-degree relatives of schizophrenia patients.3The framework of Research Domain Criteria (RDoC), including social communication difficulties, now actively facilitates the study of functional dimensions spanning the full range of human behaviour from normal to abnormal and across development.-h2=0.74)26 and a large part of these genetic influences can be captured through common single-nucleotide polymorphisms .27 Beside some stable genetic influences,28 genetic factors underlying social interaction impairments and social communication difficulties vary during development,28 especially for common variation.27 Thus, we hypothesise that also the genetic overlap between social communication difficulties and clinically recognised disorder may change during childhood and adolescence.Impaired abilities in social communication in affected children are heritable .31The primary aim of this study is to examine the nature of common polygenic influences in ASD and schizophrenia through their genetic overlap with phenotypic symptoms in the general population that are shared between both conditions, but differ according to developmental stage. We predict that if social communication difficulties are part of a common shared aetiology between ASD and schizophrenia, trait-disorder relationships for both conditions should follow similar patterns. Dissimilar patterns due to independent genetic influences would be expected for a non-shared genetic aetiology. Here, we report developmental profiles in common genetic overlap for both ASD and schizophrenia with respect to longitudinal measures of social communication difficulties within the general population. Analyses are based on the largest publicly available genome-wide data for ASD31 Ethical approval was obtained from the ALSPAC Law-and-Ethics Committee (IRB00003312) and the Local Research-Ethics Committees, written informed consent was obtained from a parent or individual with parental responsibility and assent was obtained from child participants.Genome-wide association studies (GWASs) were carried out in ALSPAC participants, a UK population-based longitudinal pregnancy-ascertained birth cohort (estimated birth date: 1991–1992).http://www.1000genomes.org/)32 32 .26 The SCDC is a brief screening instrument of social reciprocity and verbal/nonverbal communication , with high reliability and good validity,26 which has been extensively investigated.33 Higher SCDC scores reflect more social communication deficits and are positively skewed .y skewed . Mother-y skewed  and are 34 and then rank-transformed .Genome-wide single marker summary statistics were generated by regressing rank-transformed residuals on allele dosages using SNPTESThttp://www.med.unc.edu/pgc/). An ASD diagnosis was confirmed using research standard diagnoses and expert clinical consensus diagnoses. 94.1% of all ASD cases had also a diagnosis of autism from the Autism Diagnostic Interview-Revised37 and/or the Autism Diagnostic Observation Schedule.38 Genome-wide data were imputed to a 1000 Genomes reference (PhaseI_v3) and genetic association studied using a case and pseudo-control design.29 This design is robust to population stratification as pseudo-controls are based on un-transmitted parental alleles, and thus cases and pseudo-controls are ancestrally matched. To replicate findings, we analysed ASD GWAS summary results in the Danish iPSYCH project  using samples from the Danish Neonatal Screening Biobank hosted by Statens Serum Institute (39) in their medical record. iPSYCH-ASD has been genotyped using the Illumina Infinium PsychArray BeadChip and genotypes were imputed to a 1000 Genomes template (PhaseI_v3). This study has been approved by the Danish research ethical committee system.The Psychiatric Genomics Consortium (PGC) has completed a genome-wide scan of 5305 ASD cases and their parents (PGC-ASD), all of European ancestry  has been included in this study .13 (Summary results at http://www.med.unc.edu/pgc/). Cases met diagnostic criteria for either schizophrenia or schizoaffective disorder.13 Here, we investigated two non-overlapping schizophrenia subsets: (1) PGC-SCZ1 , constructed as part of the first PGC mega-analysis of schizophrenia,40 and (2) PGC-SCZ2i, containing novel PGC-SCZ2 cases and controls not included in PGC-SCZ1 .13 In addition, we studied the combined PGC-SCZ2 sample  of the second PGC mega-analysis of schizophrenia.13 As PGC-SCZ2 contains 1836 cases and 3383 controls from East Asia, we also studied a PGC-SCZ2 sample of European ancestry only . Genome-wide data were imputed to a 1000 Genomes template (PhaseI_v3).A large PGC mega-analysis on schizophrenia has been carried out studying individuals of predominantly European descentThe studied population-based and clinical samples  contain To analyse the specificity of genetic overlap between SCDC scores and schizophrenia, we studied further adult-onset psychiatric disorders, such as major depressive disorder (MDD) and bipolar disorder BIP; .41 was applied to estimate the cumulative effect of common SNPs on either variation in developmental SCDC scores or risk to disorder (SNP-h2), using GWAS statistics and exploiting LD patterns in the genome. LD score correlation20 analysis was carried out to estimate genetic correlations (rg) between SCDC scores and clinical conditions, or among clinical conditions, that is, the extent to which two phenotypes share common genetic factors, based on GWAS statistics. All analyses were performed with LDSC software41 using HapMap3 markers42 (Linkage disequilibrium (LD) score regressionarkers42 .44 were analysed to estimate the explained phenotypic variance in social communication difficulties due to risk-increasing alleles for clinical disorder. Using a range of P-value thresholds (0.001<PT⩽ 1), PGS for ASD (based on PGC-ASD), schizophrenia (based on PGC-SCZ2) and schizophrenia subsamples (based on PGC-SCZ1 and PGC-SCZ2i) were generated in ALSPAC  consistent with current guidelines45 using PLINK,46 excluding duplicate SNPs (https://cran.r-project.org/), and the proportion of phenotypic variance explained by each PGS predictor reported as adjusted regression R2. Note that assuming an infinitely large clinical 'discovery' sample, the regression R2 is equivalent to the product of rg squared and the heritability of the explained Z-standardised trait.44Polygenic risk scores (PGS)n ALSPAC  using imate SNPs . Rank-tr47 Models included fixed effects for ASD-PGS and schizophrenia-PGS, sex, age at assessment, as well as random intercepts. Beta-coefficients for PGS quantify here the increase in natural-log SCDC scores for each increase in one standard deviation of PGS. Differences in sample-dropout across time were accounted for through bootstrapping, generating parametric 95%-bootstrap confidence intervals (NBootstrap=500). We have not estimated adjusted R2-related measures due to the difficulty of defining the residual variance for non-Gaussian responses, especially within a mixed model context.48Mixed Poisson regression (R:lme4 library) was utilised to test the trend in common genetic overlap longitudinally using untransformed SCDC scores. Repeatedly assessed SCDC score counts were regressed on ASD- and schizophrenia-PGS with overdispersion being accounted for through the random error part.50 was utilised to estimate SNP-h2 and genetic correlations among SCDC scores, as published previously,27 for comparison only son only .Attrition analysis in ALSPAC studied the relationship between SCDC-missingness at each assessed age and PGS for clinical ASD and schizophrenia .h2 intercepts were consistent with one, ranging from 0.988 (s.e.=0.0067) to 1.009 , 0.17 (s.e.=0.07) and 0.30 (s.e.=0.11), respectively (h2=0.08 (s.e.=0.06)). These LDSC-based findings mirrored closely GCTA-h2 estimates using GREML (51 SCDC scores shared furthermore genetic factors across development (GREML rg=0.38 (s.e.=0.16) to 0.95 (s.e.=0.34), Pmin=2 × 10−7), as previously reported,27 with lower correlations across wider age gaps )25 and iPSYCH-ASD ),25 with strong evidence for similar polygenic architectures among samples (rg=0.74 (s.e.=0.07), P<10−20).25 Also, it is known40 that common genetic factors influence schizophrenia liability. Liability-scale LDSC-SNP-h2 estimates for PGC-SCZ1, PGC-SCZ2i, PGC-SCZ2Eur and PGC-SCZ2 were 0.31 (s.e.=0.02), 0.24 (s.e.=0.01), 0.25 (s.e.=0.01) and 0.25 (s.e.=0.01), respectively , with strong evidence for shared genetic factors among independent samples (PGC-SCZ1 and PGC-SCZ2i: rg=0.96 (s.e.=0.024), P<10−20).A common genetic basis for ASD has been described earlierg=0.34 (s.e.=0.15), P=0.027)25 and attenuated by 17 years (rg=0.01 (s.e.=0.12), P=0.94). This pattern was replicated in iPSYCH-ASD (rg=0.35 (s.e.=0.13), P=0.008 and rg=0.02 (s.e.=0.10), P=0.81, respectively, g=0.20 (s.e.=0.08), P=0.01) and was strongest at 17 years (rg=0.24 (s.e.=0.08), P=0.004; g=0.15 (s.e.=0.06), P=0.011, g=0.18 (s.e.=0.06), P=0.003, g=0.18 (s.e.=0.06), P=0.004, g=−0.05 (s.e.=0.11), P=0.65 and BIP-rg=0.04 (s.e.=0.08), P=0.62, g=0.20 (s.e.=0.05), P=0.00011) were modest, compared with considerably stronger links between schizophrenia and other adult-onset disorders , P=6.5 × 10−70, 20As part of a two-stage analysis design , we usedg-point estimates were, overall, in close correspondence with constrained estimates, but had wider standard errors.For comparison, we also analysed trait-disorder overlap using LD score correlation without constraining intercepts . In the 44 , P=0.019, 95%-bootstrapped confidence interval: −0.0057 to −0.00035), while the effect of schizophrenia-PGS increased (schizophrenia-PGS x SCDC-age: Beta=0.0029 (s.e.=0.0014), P=0.030, 95%-bootstrapped confidence-interval: 0.00047 to 0.0054). Consistent with the findings for rank-transformed scores, ASD-related polygenic influences on SCDC score counts were strongest during childhood (age 8: Beta=0.047 (s.e.=0.017), P=0.0056; age 17: Beta=0.019 (s.e.=0.018), P=0.29), while schizophrenia-related polygenic effects were more pronounced during later adolescence (age 8: Beta=0.046 (s.e.=0.017), P=0.0080; age 17: Beta=0.072 (s.e.=0.018), P=0.000056). Similar developmental changes in genetic overlap were also found for other PGS thresholds , P=0.82), although there was support for an association with schizophrenia-PGS (age 17: odds ratio=1.10 (s.e.=0.03), P=0.000050), consistent with previous studies.52Analyses of SCDC-missingness in ALSPAC were carried out to investigate potential sources of bias . Using fThis study provided evidence for shared common genetic overlap between social communication difficulties and both ASD and schizophrenia, but does not imply a shared genetic susceptibility between these clinical conditions. Instead, we identified distinct patterns in genetic trait-disorder relationships, largely consistent with the onset of clinical symptoms. Genetic links were driven by independent polygenetic influences and showed opposite trends in magnitude with progressing age of the population-based trait, as supported by longitudinal analyses.g~33%), in line with recent cross-sectional studies,25 while those with schizophrenia was strongest for social communication difficulties during later adolescence (rg~18%). Complementary estimates were provided by polygenic scoring analyses. Up to 0.13% phenotypic variation in social communication difficulties could be explained by ASD risk-increasing alleles during childhood and up to 0.43% phenotypic variation by schizophrenia risk-increasing alleles during later adolescence, independently of each other. The genetic overlap with social communication difficulties during later adolescence was not observed for other adult-onset disorders, such as BIP, despite their strong genetic links with psychosis,12 making unspecific age-related influences unlikely. Thus, our findings suggest that social communication impairments are part of the genetically influenced phenotypic spectrum of schizophrenia.Genetic overlap with ASD was strongest for social communication difficulties during middle childhood  was only 0.58 compared with 0.99 for schizophrenia-PGS , assuming a liability-scale SNP-h2 of 0.23 for ASD and 0.25 for schizophrenia, a disease prevalence of 0.01, a type-I-error rate of 0.05 and a population-based target sample of 5000 individuals. Under attrition, such as a decrease of ~1000 ALSPAC participants during later adolescence, the power of ASD-PGS would further drop (to 0.49), while the power of schizophrenia-PGS remains largely unaffected (0.99). Longitudinal analyses, adjusting for differences in population-based sample numbers across time through bootstrapping, suggested, however, that the observed developmental changes in polygenic risk effects are robust, even in the presence of sample dropout.Changes in genetic overlap over time need to be viewed within the context of cohort-specific sampling properties and clinical sample power. For instance, it is possible that the genetic overlap between schizophrenia and social communication difficulties has been underestimated, as SCDC-missingness, and more generally study non-participation,21 within a developmental context. The lack of support for shared polygenic effects between ASD and schizophrenia, with respect to social communication impairments, is in agreement with recent studies. Molecular analyses of PGC samples reported modest correlations between ASD and schizophrenia29 (rg~0.20), confirmed within this study, and twin research suggested little genetic overlap between autistic traits and psychotic experiences.53 The absence of shared aetiological factors strengthens furthermore positions suggesting that the exact nature of social deficits implicated within ASD and schizophrenia differs from each other.54 Here we show that common genetic variation underlying complex disorders can be dissected through temporal changes in the genetic architecture of behavioural symptoms that are shared between disorders. Thus, a developmental analysis of genetic relationships between population-based and clinical samples can be informative with regard to the dimensional nature of psychiatric illness without discarding the aetiology of different disorders, a concern often raised with respect to RDoC.21Our results have direct relevance for the definition of RDoC27 This may suggest developmental (but not rapid) changes in the phenotype capture by the SCDC with progressing age. For example, it is possible that the behavioural phenotypes influencing SCDC scores at age 8 or 11 years are, in terms of average composition, different from those influencing the scale at age 17. Social communication abilities comprise many components, such as social interaction, social cognition, pragmatic and language processing skills (http://www.asha.org/), some of which will vary during child and adolescent development, including changes in the social-cognitive understanding of friendship and peer interaction.55 One might envisage that these phenotypic changes reflect distinct genetic factors driving different stages of postnatal brain development.56 In addition, social communication difficulties have been linked to behavioural problems.57 Note that the SCDC has a high sensitivity but a lower specificity in discriminating ASD from the non-ASD patients in the presence of other clinical disorders.26 Thus, the SCDC is likely to capture multiple behavioural and cognitive dimensions related to social communication problems during the course of child and adolescent development, spanning around 10 years, which give rise to distinct patterns in trait-disorder overlap. This poses questions on the nature of genetic influences affecting variation in social communication impairments across development that will require exploration with longitudinal genome-wide approaches and biological network analyses.The identification of distinct patterns in common genetic overlap between social communication difficulties and psychiatric illness is consistent with the presence of multiple distinct genetic influences contributing to variation in social communication behaviour during development. While genetic factors underlying SCDC scores across ~3-year intervals are stable and shared by at least 80%, only ~50% of common genetic influences are shared across 10 years intervals.Social communication difficulties are phenotypically shared with both ASD and schizophrenia and show common genetic overlap with both disorders. These polygenic links manifest, however, as distinct developmental profiles and do not imply a shared genetic susceptibility between these clinical conditions.


Promenetus exacuous and Valvata tricarinata are freshwater snail species with widespread distributions throughout North America. Information regarding their genetic diversity and population connectivity are currently lacking. We utilized next generation sequencing to develop the first microsatellites for each species to investigate genetic diversity within and differentiation among populations.P. exacuous and V. tricarinata, respectively, and tested in a total of 43 P. exacuous and 48 V. tricarinata from two lakes approximately 183 km apart in New York State, USA. Fifteen P. exacuous loci were polymorphic in at least one lake and possessed 1–23 alleles and observed heterozygosities of 0.00–0.96 within individual lakes. Seventeen polymorphic V. tricarinata loci possessed 2–19 alleles and observed heterozygosities of 0.04–0.96 within lakes. Bayesian clustering using 12 loci for each species identified two distinct genetic populations, reflecting the two lakes. High assignment scores for individual snails to the lakes they were collected from supported strong population structure with minimal admixture at the scale of this study. These loci will be useful for investigating the genetic diversity and population structure of these species and indicate genetic differentiation may be common among their populations.Sixteen and seventeen microsatellite loci were developed for  Promenetus exacuous  and Valvata tricarinata . Microsatellites were developed for these particular species for future studies comparing their genetic connectivity throughout New York State based on the species’ disjunct and often sympatric populations throughout the region, their phylogenetic divergence and differing biological characteristics offering comparative insights into gastropod population connectivity throughout New York, and their current lack of genetic data and available microsatellite loci [Despite their diversity, importance to ecosystems, and conservation concerns, basic taxonomic and biological information is lacking for many freshwater gastropods throughout North America , 2. We rite loci . In addiite loci –6.Valvata tricarinata and P. exacuous were collected from Round Lake  and Lake Saratoga  using dip nets. Both lakes are in New York State and are approximately 183 km apart. To develop microsatellite loci, genomic DNA for next generation sequencing was extracted from the foot of a single V. tricarinata from Round Lake and a single P. exacuous from Lake Saratoga using a Qiagen Dneasy® Tissue kit. DNA was eluted with 75 µl H2O and quantified using a NanoDrop 2000 (Thermo Scientific). Approximately 3 µg of RNA-free genomic DNA from each species was sent to the University of Wisconsin-Madison Biotechnology Center for DNA fragmentation and sequencing. Next generation sequencing was performed using an ION Torrent PGM system with each species allocated approximately 25% of a 318 chip. Raw genomic data were converted to FASTA format using Geneious ver. 8.1 [ver. 8.1 , 10. PriV. tricarinata snails from each lake and 24 and 19 P. exacuous from Round Lake and Lake Saratoga, respectively. For each DNA extraction, the foot was removed and transferred to 400 µl of 5% Chelex containing 0.1 mg/ml proteinase K. The solution was incubated for approximately 8 h at 60 °C followed by eight minutes at 95 °C. DNA was utilized directly from these extractions. Microsatellite loci were amplified using three primer polymerase chain reactions (PCRs) [DNA was extracted from 24 s (PCRs)  on indivs (PCRs) .P. exacuous: Pex577, Pex757, Pex1009, Pex1877, Pex2091, Pex2181, Pex2263, Pex2471, Pex2889, Pex2908, Pex2958, and Pex2972; V. tricarinata: Vtr99, Vtr565, Vtr828, Vtr835, Vtr980, Vtr1099, Vtr1279, Vtr2328, Vtr2388, Vtr2492, Vtr4154, and Vtr4287) were used for STRUCTURE analyses. STRUCTURE runs used an admixture model with five iterations, a burnin length of 100,000 and 100,000 steps in the Monte Carlo Markov Chain (MCMC). Separate runs for each species utilized LnPD as the selection criterion and the number of genetic populations (K) was allowed to range from 1 to 6.MICRO-CHECKER ver. 2.2  was usedP. exacuous loci possessed 1–23 alleles and observed heterozygosities ranged from 0.00 to 0.96 within individual lakes , revealing minimal admixture between these populations for both species , reflecting the two sample locations Fig. . All snaies Fig. . The higThe development of microsatellites in these two understudied, distantly related species will enable researchers to examine the factors impacting the genetic diversity within and population structure among their populations, and gain additional insights into the biology, evolution, and conservation of freshwater gastropods. While our interests are primarily the dispersal and connectivity of these species throughout New York State and surrounding areas, these microsatellites may be used by other labs to address diverse questions in other regions. For example, although both species are globally secure, there are conservation concerns for specific populations throughout their range –6, and tDue the potential for variation in regions flanking microsatellite loci and the relatively widespread distribution of both species, some of these loci may not amplify in populations throughout their range.


Eunicella verrucosa and Alcyonium digitatum, in the northeast Atlantic , using two panels of 13 and 8 microsatellite loci, respectively. Our results identified regional genetic structure in E. verrucosa partitioned between populations from southern Portugal, northwest Ireland and Britain/France; subsequent hierarchical analysis of population structure also indicated reduced gene flow between southwest Britain and northwest France. However, over a similar geographical area, A. digitatum showed little evidence of population structure, suggesting high gene flow and/or a large effective population size; indeed, the only significant genetic differentiation detected in A. digitatum occurred between North Sea samples and those from the English Channel/northeast Atlantic. In both species the vast majority of gene flow originated from sample sites within regions, with populations in southwest Britain being the predominant source of contemporary exogenous genetic variants for the populations studied. Overall, historical patterns of gene flow appeared more complex, though again southwest Britain appeared to be an important source of genetic variation for both species. Our findings have major conservation implications, particularly for E. verrucosa, a protected species in UK waters and listed by the IUCN as ‘Vulnerable’, and for the designation and management of European MPAs.Connectivity is an important component of metapopulation dynamics in marine systems and can influence population persistence, migration rates and conservation decisions associated with Marine Protected Areas (MPAs). In this study, we compared the genetic diversity, gene flow and population structure of two octocoral species,  Population connectivity has emerged as a key factor in the sustainable management of marine resources (Crassostrea virginica) on the Atlantic coast of North Carolina and showed that if marine reserves were too small – relative to the mean dispersal distance of the oyster– local retention of larvae was reduced; likewise, if reserves were spaced too far apart, connectivity became limited. While early landmark studies of genetic connectivity in the marine environment  is often listed as a factor in the designation of an area as a Marine Conservation Zone (MCZ). However, population genetic studies of octocorals across this area, and the northeast Atlantic in general, are limited. Previous research in this region has had either a phylogenetic  in their designation. Ireland . In BritAlcyonium digitatum (‘dead man’s fingers’) has a ubiquitous presence along rocky upper and circalittoral zones, typically to a depth of 200 m, and it can be found around most British and Irish coasts  and pelagic larvae can survive up to 14 weeks and beyond (E. verrucosa occurs towards the end of summer (August–September), though its pelagic larval duration is unknown  what is the genetic diversity of each species and is it uniform across the sampling range; (ii) do both of these species show population genetic structure indicative of departures from panmixia; and (iii) what are the levels of gene flow and effective population size for each species? Finally, we consider the conservation and potential management implications of our findings for these species, both in terms of connectivity between existing MPAs and with regard to the designation of future MPAs.In this study, two panels of microsatellites  were useE. verrucosa (N=922) were collected from 27 sites ranging from southern Portugal to northwest Ireland, including sites around Brittany in northwest France, Lyme Bay in southern England and southwest Wales (A. digitatum (N=655) were collected from 20 sites across a similar geographic area ; samples from two additional sites in the North Sea . Colonies of E. verrucosa were sampled by removing a 3 cm terminal branch using sea-snips. This species is protected in UK waters, and all UK sampling complied with licenses granted by Natural England and the Marine Management Organisation (see Acknowledgements). Colonies of A. digitatum were sampled by removing a 1 cm3 section of tissue from a terminal thumb-like ‘branch’ using sea-snips. After removal, individual colonies were placed into mesh bags, brought to the surface, and quickly immersed in 95–100% ethanol for storage. In both species, samples were taken from individual colonies spaced at least 1 m apart to avoid sampling clones; previous studies of hard corals have identified potential clones at spatial scales up to 5 m apart  from E. verrucosa. Microsatellites were amplified for both species and alleles were scored using GENEMAPPER v3.7 . Full details of DNA extraction and microsatellite amplification conditions and multiplexing are given in a primer note for each species: E. verrucosa  following the manufacturer’s protocol. Polyps were removed from colonies using forceps, or by using a scalpel to shave a portion of ~1 cmCERVUS v3.0.3  and results were considered significant if the P-value was <0.010.Duplicate genotypes were identified in POP v4.2  using dePOP v4.2 . To iden Lositan . LositanHexp) and the inbreeding coefficient (FIS) for each population were estimated using the diveRsity package  and private allelic richness (PAr) were calculated in HP-RARE v1.1  was performed using a matrix of codominant genotypic genetic distances in GenALEx v6.5 , the delta K statistic (K) were examined in the POPHELPER R package  matrices and were compared with the logarithm of geographic distances (km). Negative FST values were converted to zero for this analysis. Geographical distances were estimated in Google Earth by measuring the shortest in-water distance between sites in a straight line or by calculating the shortest distance following coastlines.Population differentiation was analysed using pairwise FST  and G”STST  and the population parameter theta (4Ne*μ) in Migrate-n v3.6  and the shape of the histograms in the output files were examined.Contemporary (within the last few generations) and historical gene flow was estimated using two methods. Contemporary gene flow was analysed using BayesAss v3.0.4 , which ee-n v3.6 . MigrateNe). This was calculated from the optimum value of theta using the equation Ne=theta/4 μ, assuming a microsatellite mutation rate (μ) of 10−4 per generation, as used in a previous study of a Mediterranean cup coral  was omitted from the original microsatellite panel of ; consequently, no loci were discarded on the basis of linkage disequilibrium and 13 were used for subsequent analyses. For A. digitatum, three loci  were discarded from the original microsatellite panel of For E. verrucosa, 17 individuals with duplicate genotypes were identified in nine samples  from north Cornwall, in which four duplicate individuals (across three genotypes) were identified out of a sample of only 11 individuals successfully genotyped.A relatively low number of duplicate genotypes were identified in both species. In  samples , while i samples . DuplicaE. verrucosa samples were monomorphic at several loci, but this was not consistent in all populations at the same locus. In comparison, A. digitatum was monomorphic at only one locus (Adig007) in three populations. For E. verrucosa, two loci (Ever013 and Ever014) were identified as outliers under the island model and one (Ever013) under the hierarchical island model  to 0.459 (Hand Deeps) and were generally consistent within regions, with minor differences between some regions  to 2.79 (The Heroine Wreck); overall, both measures were slightly lower in the samples from Ireland. For A. digitatum, Hexp and Ar measures were also relatively uniform within and between regions  to 0.668 (Norfolk) and Ar ranged from 3.99 (Laonegued Taer) to 4.34 (Roscoff2). Private allelic richness (PAr) was also consistently higher for A. digitatum than for E. verrucosa; values for A. digitatum ranged from 0.075 (The Lucy Wreck) to 0.193 (Roscoff1), while values for E. verrucosa were between 0.001 (Camel Estuary) to 0.059 (Laonegued Taer). The majority of FIS values for both species were positive; overall, however, few were significant, though generally at least one site in each region showed a significant positive FIS coefficient  are discussed below. A small sample of E. verrucosa from the Camel Estuary (Cam) had a significantly negative FIS, indicating an excess of heterozygotes at this site.After removal of duplicate genotypes, genotypes of 905 individual specimens of  regions . A simil regions  and werefficient  and 2. TFST and G”ST measures across all populations of E. verrucosa were 0.012 and 0.023, respectively   . For botE. verrucosa, the largest significant pairwise FST (0.059) value was observed between Faro and the Camel Estuary, while the highest significant pairwise G”ST (0.089) value observed was also between populations from Portugal and southwest Britain: Portamao2 and the Heroine Wreck  for A. digitatum were between populations from southwest Britain and the North Sea: Trenemene Reef and Norfolk  and France, Ireland, Britain, and the North Sea (A. digitatum). In both species, global tests revealed that the majority of variation was explained by variation within populations . Similarly, a significant  amount of variation was explained by differences between regions for A. digitatum .For the AMOVA, populations were grouped by geographical region for each species: Portugal, France, Ireland and Britain  and delta K statistics indicated K=3 as the most probable number of discrete populations within the data set  shared some allelic similarities with E. verrucosa from Portugal. To explore potentially finer-scale population structure (<500 km distance between sites) in populations from Britain and France, a hierarchical STRUCTURE analysis was conducted using data from only these regions. The most likely number of populations was identified as K=2  suggested panmixia   . For A. sing FST , howevernificant .E. verrucosa, gene flow from Britain was predominantly into France, whereas in A. digitatum gene flow from southwest Britain into the North Sea, Ireland and France was observed. In comparison, contemporary gene flow into Britain appeared very limited for both species. For E. verrucosa, little genetic material was exchanged between Ireland and any other region; likewise, gene flow to/from Portugal was minimal, except for some minor gene flow from Portugal into France. Little or no gene flow from France was detected, suggesting that E. verrucosa in both France and Ireland are effectively sinks. In contrast, for A. digitatum, some gene flow from France to other regions was apparent, although gene flow from the North Sea and Ireland to other study areas was all but absent. For both species, estimates of historical gene flow (using Migrate-n) were somewhat more complex, with populations from Britain again acting as the main source of gene flow for both species, and with only limited gene flow into southwest Britain  for both species indicated that the majority of gene flow originated from sample sites within regions . However Britain . OverallNe were run using the same groupings as used in the gene flow analyses. Estimates of contemporary effective population sizes were infinite for both species  were generally uniform across the sampling ranges of each species  only two or three alleles were detected during initial testing (Hexp and Ar) detected between the two species studied were markedly consistent and may, at least in part, be explained by higher genetic connectivity in A. digitatum.Genetic diversity measures  observed for E. verrucosa in the current study suggests that the frequency of inbreeding is low, variable between sites and likely due to site-specific factors. Regarding the use of FIS, while the coefficient is typically referred to as measuring the degree of inbreeding within a population, it actually measures homozygosity excess relative to Hardy-Weinberg expectations, and other processes, for example, the inadvertent combining of data from populations with different allele frequencies  can also drive significant positive FIS results. Such a consideration is relevant when seeking to explain the higher number of significant positive FIS values obtained for E. verrucosa populations  were due to Wahlund effects, such findings would suggest even less inbreeding within the species than the small amount currently postulated. Additionally, the generally low FIS values observed also accord with the low proportion of duplicate genotypes detected in E. verrucosa (<2%) across the study; the number of E. verrucosa individuals at a site with duplicate genotypes ranged from 0 (most samples) to 4 in a small sample  from north Cornwall. Interestingly, the north Cornwall sample was one of the few samples not collected by our dive teams, and the relatively high proportion of duplicate genotypes at this site may be a reflection of sampling practice rather than biological reality.otential ; if corrotential  and the ulations , as thisulations  than didigitatum  across tum , suggesting only very limited inbreeding in this species; likewise, only a very low proportion (~1%) of all individual A. digitatum successfully genotyped had duplicate profiles. While such findings might be expected for broadcast spawning corals , leading them to conclude that widespread inbreeding was apparent in this species in the eastern Pacific.For g corals , exceptiFIS estimates reported here for E. verrucosa and A. digitatum is globally low: ten significant FIS estimates at 27 sites for E. verrucosa , suggesting restrictions to gene flow between populations in different geographical regions. This finding was also supported by analyses of contemporary gene flow , as demonstrated by the limited exchange of genetic material between regions, except for some gene flow between Britain and France ; this differentiation appeared to be the product of IBD  were infinite for both species  and Bayesian clustering (STRUCTURE) analyses. This pattern has been reported previously in a number of taxa, including bivalves  by reference to founder effects and low numbers of recolonisers  are in line with those of E. verrucosa also makes it difficult to infer the precise origins of the populations in northwest Ireland, as gaps in our knowledge concerning the genetic identity of all possible source populations limits the accuracy of any putative recolonisation hypotheses. Furthermore, to what degree the contemporary distribution of E. verrucosa reflects the extent of the species at the last glacial maximum is unknown, but, to date, its distribution appears not to have extended to areas known to be under ice during the last glacial maximum , implying that the transfer of genetic material can potentially occur up to distances of ~480 km. For A. digitatum, gene flow was evident at an even larger spatial scale, suggesting that genetic material can be transferred greater distances, potentially more than 1050 km (Payne’s Rock—Norfolk). These results suggest that genetic connectivity is high at an intra-regional scale in both species. However, as observed in many marine species with similar life history traits, large effective population size can also act to reduce (or eliminate) genetic structure, sometimes even in situations were gene flow is limited  less than that separating some British populations; however, minimal differentiation was evident for A. digitatum across this area. The effects of mid-channel currents and local near-shore eddies  to regional  scales.oitation . It is aE. verrucosa populations from Ireland underpins an argument for protecting particular sites. Marginal populations often contain rare alleles , but may recruit more slowly, and may be genetically isolated, implying vulnerability and reduced resilience  global distribution and, although contemporary connectivity between British populations appears to be a high, at regional spatial scales it could be argued that the genetic distinction of these populations, coupled with their possible role as source populations that act to maintain broader connectivity across this area of the northeast Atlantic, may be sufficient to warrant international conservation efforts , it exhibited relatively high genetic diversity with little evidence of any major barriers to gene flow. Overall, coupled with the high prevalence of this species in UK waters, these factors imply that this species may be a low priority for protection in its own right, and it is likely to receive only patchy, incidental protection based upon the location of current MCZs designated on the basis of other features, although arguments for consideration of this species in design guidelines could still fit both the ‘representativity’ and ‘replication’ principles . However, reduced heterozygosity and impaired sexual reproduction have been reported in another cnidarian species subjected to trawling damage  was detected for either of the species studied. This may be indicative of genetic connectivity between these populations, but could also represent a lack of power in the genetic markers used. Exploration of alternative marker systems may deliver improved resolution (for example, The results from the present study suggest that populations of 40–80 km . In lighE. verrucosa, whereas in A. digitatum, it was slightly higher, but still lower than that reported for two species of Mediterranean octocorals (E. verrucosa, indicative of departures from panmixia at large spatial scales; in contrast, in A. digitatum, apart from some genetic differentiation between populations from the North Sea and those from the English Channel/eastern Atlantic, we found little population structure, suggesting high gene flow and connectivity in this species in the western part of the range sampled. Contemporary and historical estimates of effective population size were contrasting and generally difficult to interpret, and for both species the potential role of large Nes in masking a lack of gene flow cannot be ruled out. Patterns of gene flow were complex, but indicated Britain as a source of genetic variants for both species. Several populations of both species are represented in the UK MPA network and, given the ecological importance of both species, continued monitoring and assessment of their genetic diversity within and beyond protected sites could be a useful measure of the efficacy of the existing network, and a valuable guide to the designation of new MCZs.In conclusion, genetic diversity appears to be uniform across the range studied in both species; however, genetic diversity was low in Eunicella verrucosa and Alcyonium digitatum are available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.nj0v4.Microsatellite genotypes for


Oxalis lacinata is endemic to the Patagonia region, including high mountains and steppe, and even grows on the coast. Its marked polymorphism made us think that the genetic variability behind this would explain both its morphological variability and its adaptability to different ecological niches. Based on molecular analysis, we detected non-random organization of genetic variability. We propose two refugia and we were able to estimate the diversification centre, which coincides with the centre of the distribution. Distribution modelling suggested the possibility of occurrence in the Central Andes, in addition to its known distribution. Oxalis laciniata, a representative of Oxalis, which is an important emerging model in evolutionary biology and phylogenetic studies. We analyse genetic correlation, haplotype diversity and genetic structure. In this study, cpDNA reveals the presence of 16 haplotypes, connected in tree networks. Genetic diversity is high and polymorphism is low among populations based on ISSR markers. Both clustering and analysis of the structure of the population indicate two different groups. Distribution modelling predicts two potential distribution areas. Our main conclusions are: (i) The phylogeographical pattern demonstrates non-random organization of genetic variability since two distinct groups can be distinguished; (ii) two refugia are proposed: one is situated in the SE, holding the most ancestral haplotypes; and the second one is situated in the SW; (iii) we propose an in situ diversification hypothesis for the populations located in the steppe; (iv) the centre of diversification coincides with the centre of the distribution; (v) distribution modelling shows a strong correspondence with the distribution of the species but it also suggests the possibility of occurrence in the Central Andes.Phylogeographical patterns provide valuable insight into the historical processes underlying diversification, and may provide a better understanding of biodiversity, dispersal modes, diversification times, extinctions, refuge areas and other species-/population-level processes. Here, we examine the genetic structure of  Phylogeographical studies can provide a better understanding of the biodiversity, dispersal modes, diversification times, extinctions, refuge areas and other species-/population-level processes . The phyThe current geographical distribution of a species can be estimated using niche-based models that predict the presence or absence in certain places . Such moOxalis laciniata, is distributed in Chubut, Santa Cruz and Tierra del Fuego e Islas del Atlántico Sur Provinces (from 45°35′38″ to 53°41′28″S and from 65°55′4″ to 72°16′4″W) and inhabits both the Patagonian ecoregions of the High Andes and the Steppe.Patagonia Argentina extends from Neuquén and Río Negro provinces  to Cape Horn (56°S) and it is currently partitioned into two main ecoregions. The High Andean–Patagonian ecoregion is mountainous, cold with precipitations above 300 mm per year; and the Patagonian Steppe ecoregion extends eastward to the Atlantic Ocean and is mostly low-lying, cold, dry and characterized by scattered herbs and shrubs .The admixture model was used, which assumes correlated frequencies. The log-likelihood probability of the data was calculated for each possible K value from 1 to 13 using 5 runs of 15 × 106 MCMC iterations following a burn-in period of 500 000 iterations. The best fit number of clusters was calculated according to X/K) for successive Ks (referred to as Delta K) against a range of K values, and selecting the true K based on where the maximal value of this distribution occurs. The CLUMPP 1.1.2 .PCR products were sequenced by Macrogen, Inc. (Korea). Assembly and editing of sequences used the software Chromas Pro 1.34 . Sequences of  7.0.9.0 . We alsoWe used ANeCA 1.2  to calcuO. adenophylla Cav., O. enneaphylla Cav. and O. loricata Dusén). Phylogenetic trees were calculated in TNT 1.1  for current and past conditions were downloaded from WorldClim database (http://www. worldclim.org/). We tested the two global climate models for the Last Glacial Maximum (LGM): the Community Climate System Model (CCSM) and the Model for Interdisciplinary Research on Climate (MIROC). We cut the grid layers with DivaGis 7.5.0.0  were parsimony informative. Based on this variation, 16 different haplotypes were determined . For discussion of the results, we adopted the hypothesis obtained from Bayesian analysis since it was the method with the highest resolution . From thOxalis Sect. Palmatifoliae. The estimated diversification times for the three main clades were the following: Clade A = 1.62; Clade C = 1.5 and Clade D = 1.42 Ma. The estimated times would place the diversification of O. laciniata in a period between the Andean orogeny (15–10 Ma) and Great Patagonian Glaciation (GPG), estimated of 1–1.2 Ma.The tree obtained by Bayesian inference was calibrated using a molecular clock with the proposed date of diversification for O. laciniata predicts that the area encompassing southern Argentina, from Chubut River down to the top tip of Tierra del Fuego e Islas del Atlántico Sur Province, provides the most suitable environment for this species  of  stricta  and 10 %d allies . In addiO. laciniata exhibits non-random organization of genetic variability suggesting a division of the populations into two primary groups. We propose that the larger group is composed of closely related populations, which not only share common alleles supported by ISSR results, but also the genealogy of chloroplasts. The other group includes populations that are more isolated, and our hypothesis is that their grouping is due not to sharing common alleles but to having private alleles. High DW values are expected in long-term isolated populations, which accumulate population-specific mutations that are quickly fixed but not dispersed to other populations , a hybridization process could be possible. Nevertheless, further detailed study in this region, using nuclear molecular data, including all the other taxa of Sect. Palmatifoliae is needed to clarify this finding more precisely. The refugia in the SE is supported by both the cpDNA and ISSR results, whereas the refugia in the SW is strongly supported by the ISSR data but not by the cpDNA data since H1 is present in 33 individuals belonging to 4 different populations. The results suggest that populations with low DW values may have survived during the last glaciations in the same place as they currently inhabit, supporting the hypothesis of in situ diversification.To infer the origin of populations from their present distribution pattern we used cpDNA data to test hypotheses about both, glacial refugia and The analysis of population 7 allows us to infer that the centre of diversification of the species could have been in the steppe , where haplotypes H6 and H10 are present. If we consider the position in the network and the geographical location of these haplotypes, we can propose that they would be ancestral with respect to H16 (Pop 3), H8 and H9 (Pop 1), H15 (Pop 6), and that the most derived haplotypes would be H11, H12, H13 and H14, all present in the population 8. In contrast, population 10, located in Co. Huiliches, also holds an exclusive haplotype (H5) that does not appear to be linked to any of the others, constituting an isolated network by its own.Oxalis Sect. Palmatifoliae. The locality data obtained from herbarium material of O. laciniata coincide with the area that predicts our distribution modelling for the species, ranging from the Chubut River to the tip of Tierra del Fuego e Islas del Atlántico Sur Province. Furthermore, the probability of being found in the High Andes regions of the North of Argentina and Chile showed a new suitable habitat for this species. Outstandingly, the highest probability of occurrence, indicating the most suitable environment, is in correspondence with our proposed centre of diversification.Our results of distribution modelling are in agreement with the present distribution of all the species of O. laciniata within this period. Therefore, these results are preliminary and require more detailed studies of the biological, geological and climatic variables of the past.Glacial climate fluctuations had a substantial impact on the diversification, distribution and demography of the Patagonian species. A scenario of multiple periglacial Pleistocene refugia and subsequent multiple recolonization routes, from eastern Patagonia to the Andean flanks, were proposed by several authors to explain the phylogeographical patterns of the present day distribution . In the O. laciniata compared with other Oxalis species such as O. tuberosa and allies  the genetic structure revealed low level of polymorphism among populations and high genetic variation within populations; (ii) the phylogeographical pattern demonstrated non-random organization of genetic variability and two distinctly groups could be distinguished; (iii) two refugia are proposed: one situated in the SE that also holds the more ancestral haplotypes; and the second one situated in the SW; (iv) we also proposed in situ diversification hypothesis for the populations located in the steppe; (v) the centre of diversification is in coincidence with the centre of the distribution; (vi) distribution modelling shows a strong correspondence with the distribution of the species but also suggests the possibility of occurrence in the High Andes regions.Here, we provide the first range-wide assessment of haplotype diversity and genetic structure of O. laciniata are (i) outcrossing facilitated by tristyly that includes both a complex mating system and self-incompatibility; (ii) widespread distribution including heterogeneous environments (High Andean and Steppe ecoregions). The intriguing question whether such genetic variation might be linked with incipient polyploid speciation should also be tested by assessing the strength of reproductive isolation among the spatially, genetically and ecologically divergent lineages. Further investigations are necessary to test whether genetic structure and genetic variation within and among populations resulted from random population genetic processes or are the result of historical processes occurring in the Patagonia.We proposed that some of the reasons behind the high genetic variation of A.L. acknowledges the National Research Council of Argentina (CONICET) as researcher and M.G.B. as postdoctoral fellowship holder. The project was supported by Préstamo BID PICT2013-0291, and PIP-112201301 00 124CO. A.L. conceived the idea, collected the samples and performed the laboratory work; M.G.B. performed the molecular and statistical analyses; A.L. and M.G.B. analysed the data and led the writing.None declared.


Pungitius pungitius collected from brackish and freshwater habitats. Based on inter‐ and intraspecific comparisons, we estimated that 84% and 68% of the nonsynonymous polymorphisms in the freshwater and brackish water populations, respectively, are weakly or moderately deleterious. Using in silico prediction tools , we subsequently identified nonsynonymous polymorphisms with potentially harmful effect. Both prediction methods indicated that the functional effects of the fixed nonsynonymous substitutions between nine‐ and three‐spined stickleback were weaker than for polymorphisms within species, indicating that harmful nonsynonymous polymorphisms within populations rarely become fixed between species. No significant differences in mean estimated functional effects were identified between freshwater and brackish water nine‐spined stickleback to support the hypothesis that reduced osmoregulatory energy demand in the brackish water environment reduces the strength of purifying selection at OXPHOS genes. Instead, elevated frequency of nonsynonymous polymorphisms in the freshwater environment  suggested that purifying selection is less efficient in small freshwater populations. This study shows the utility of in silico functional prediction tools in population genetic and evolutionary research in a nonmammalian vertebrate and demonstrates that mitochondrial energy production genes represent a promising system to characterize the demographic, life history and potential habitat‐dependent effects of segregating amino acid variants.Both effective population size and life history may influence the efficacy of purifying selection, but it remains unclear if the environment affects the accumulation of weakly deleterious nonsynonymous polymorphisms. We hypothesize that the reduced energetic cost of osmoregulation in brackish water habitat may cause relaxation of selective constraints at mitochondrial oxidative phosphorylation (OXPHOS) genes. To test this hypothesis, we analyzed 57 complete mitochondrial genomes of  ConcurrNe) is low  via oxidative phosphorylation (OXPHOS). Traditionally, mitochondrial DNA (mtDNA) has been used as a neutral genetic marker in phylogenetic and phylogeographic studies Avise, . Howeverw Lynch, . Thus, w Kimura,  and/or i Kimura, . For exaIn contrast to terrestrial species, aquatic organisms face unique osmoregulatory challenges to maintain their optimal body fluid homeostasis at various salinities. To compensate for passive water loss, marine teleosts consume seawater and actively secrete salt, whereas freshwater fish balance the passive water gain by producing diluted urine and actively absorbing salt through their gills. Maintaining body fluid homeostasis is a complex and energetically expensive physiological process, and it is estimated that 20% to over 50% of the total energy budget in fish is used for osmoregulation  collected from both freshwater (populations with a relatively low Ne) and brackish water (populations with a relatively high Ne) environment in the Baltic Sea region. In order to infer the role of purifying selection on the mitochondrial OXPHOS genes, we evaluated the proportion of nonsynonymous and synonymous polymorphisms and substitutions within and between species, respectively, and used recently developed in silico tools that predict the harmful effect of nonsynonymous mutations  and six freshwater locations within the Baltic basin , or from tiny streams/ditches inflowing or outflowing from these ponds using standard electrofishing equipment. The individuals from the brackish water habitats were sampled in the Baltic Sea near the shores of the Gulfs of Finland and Riga and from the Archipelago Sea using beach seine.In total, 478 nine‐spined stickleback individuals Figure  belonginn = 26) and brackish water (n = 31) habitats were used for complete mitochondrial genome sequencing (Table P. pungitius sequence (AB445130) using MIA  and Western Europe (KT989571), P. sinensis (EU332748), P. kaibarae (EU332749), Culaea inconstans (AB445125), Gasterosteus wheatlandi (AB445129), G. aculeatus (AP002944), Hypoptychus dybowskii (AP004437), Aulichthys japonicus (AB445127), and Aulorhynchus flavidus (AP009196) were obtained from GenBank to infer phylogenetic relationships. The evolutionary relationships were analyzed under Bayesian inference using MrBayes v.3.1.2 . Basic descriptive sequence statistics for the complete mitogenome and mitochondrial OXPHOS genes were calculated using DnaSP v.5.1  and six brackish water (n = 31) populations were pooled for further statistical analyses to obtain separate mtDNA diversity estimates for freshwater and brackish water habitats.In total, 57 nine‐spined sticklebacks from freshwater (ter n = 3 habitatsng Table . Sequencng Table . The reap < .001). Altogether, 48 individuals were genotyped from each location with the exception of the Saulepi population, where 30 samples were genotyped (Table HE), observed heterozygosity (HO), and allelic richness (AR), were estimated as implemented in FSTAT v.2.9.3.2  were estimated based on multilocus microsatellite genotypes using coalescent‐based Bayesian inference implemented in Migrate 3.6.11 /HE brackish. We assumed that the genetic diversity in the brackish water populations mirrored the diversity in the early phases of freshwater colonization. In other words, the brackish water populations were the source from which the freshwater populations were derived. Thus, the F parameter describes the loss of diversity due to genetic drift since the colonization of freshwater after the last glaciation.In addition to mitochondrial genome sequencing, 462 individuals from five freshwater and five brackish water populations were initially screened at 12 microsatellite loci  and synonymous (Ps) polymorphisms within P. pungitius with the ratio of nonsynonymous (Dn) and synonymous (Ds) substitutions between the nine‐ and three‐spine sticklebacks (G. aculeatus) using the following formula: δ = 1 − (DnPs/DsPn). To obtain confidence intervals for δ, we used a bootstrap procedure by resampling both within species and fixed between‐species substitutions .We estimated the proportion of harmful polymorphisms at OXPHOS genes in the ramanian , 2012. T2.3P. pungitius were classified as derived alleles. The ancestral status of more frequent amino acid variants was confirmed using phylogenetic information  constructed as described above. When the estimated ancestral amino acid at a particular position was identical to observations in either the nine‐ or three‐spined stickleback, we quantified the functional effect of the amino acid change from the ancestral to the derived state. By contrast, when the ancestral amino acid at a particular position (11 cases) was different in both the nine‐ and three‐spined sticklebacks, we estimated the functional effect of the amino acid change separately for both species. The distributions of the functional scores for both the inter‐ and intraspecific comparisons were visualized using the Gaussian kernel density estimator with default bandwidth as implemented in the R package stats v.3.2.5  reflecting smaller Ne in the former . On the other hand, the loss of genetic diversity at mtDNA in freshwater was less pronounced at population level . Despite the lower genetic variability and smaller Ne of freshwater populations, multiple distinct mitochondrial haplotypes segregated within several freshwater nine‐spined stickleback populations , suggesting that purifying selection might be less efficient in freshwater populations with lower Ne values (Tables p = .0047). Similarly, estimated dN/dS ratios across 13 OXPHOS genes in the nine‐spine stickleback were almost twice as large in the freshwater (dN/dS = 0.188) compared with the brackish water habitat  synonymous substitutions fixed between the nine‐ and three‐spine sticklebacks were observed, whereas only a small proportion (n = 141) of nonsynonymous substitutions were fixed between the two stickleback species . By assuming that the ratio of nonsynonymous to synonymous polymorphisms within species is similar to the ratio of nonsynonymous to synonymous substitutions between species under neutral evolution  was identical in both habitats .We detected 145 synonymous and 41 nonsynonymous polymorphisms in s Tables . Further3.3P. pungitius was strongly shifted toward low values compared with the predicted effects of all potential variants, indicating a pervasive role of purifying selection on the mitochondrial genome . In total, 15 and 13 of 65 amino acid variants in P. pungitius showed ≥0.6 and >0 functional scores for MutPred and SNAP2, respectively, implying potential functional effect  but not for the interspecific comparison between P. pungitius vs. G. aculeatus .The distribution of the SNAP2 and MutPred scores for polymorphisms in l effect . Both toU‐tests p > .05; p = .027).The majority of nonsynonymous polymorphisms occurred at low frequencies . The distribution of the functional scores calculated for singletons was similar to the one observed for variants that occurred at a higher frequency  for both SNAP2 and MutPred (both Mann–Whitney (F‐test p = .056), variance in pathogenicity scores compared with their freshwater conspecifics  was slightly steeper in the freshwater environment than in the brackish water habitat for both prediction tools  compared with brackish water habitat (Pn/Ps = 0.283) and slightly higher proportion of weakly deleterious mutations in freshwater habitat  supported the hypothesis that a reduced effective population size may drive the efficacy of purifying selection at mtDNA OXPHOS genes in nine‐spined stickleback. This finding is in line with analysis in mammals, which demonstrates more efficient purifying selection in species with large Ne . Furthermore, the nine‐spined stickleback samples used in this study were collected from multiple populations showing different, albeit low, levels of genetic divergence  between two prediction methods when all possible variants were compared within both P. pungitius and G. aculeatus, which most likely is related to congruent prediction of highly deleterious variants. On the other hand, SNAP2 and MutPred scores for observed fixed variants between P. pungitius and G. aculeatus were not correlated, which likely reflects the lack of strongly deleterious fixed substitutions between species. The variable outcomes of the prediction tools also highlight the value of large scale sequencing efforts to identify sites in the genome that are highly conserved. For example, recent analysis of over 10,000 human genomes enabled to the pinpointing conserved positions in the genome that are under strong purifying selection .None declared.AV conceived the study and wrote the first draft of the manuscript. AV, MB, JS, and OT performed sampling and laboratory work. MO, JS, MB, OT, HM, and AV analyzed the data. Click here for additional data file.