Modern genetic analysis has revealed genetic differentiation across the south of Britain and Ireland. This structure demonstrates the impact of hegemonies and migrations from the histories of Britain and Ireland. How this structure compares to the north of Britain, Scotland, and its surrounding Isles is less clear. We present genomic analysis of 2,544 British and Irish, including previously unstudied Scottish, Shetlandic and Manx individuals. We demonstrate widespread structure across Scotland that echoes past kingdoms, and quantify the considerable structure that is found on its surrounding isles. Furthermore, we show the extent of Norse Viking ancestry across northern Britain and estimate a region of origin for ancient Gaelic Icelanders. Britain and Ireland are known to show population genetic structure; however, large swathes of Scotland, in particular, have yet to be described. Delineating the structure and ancestry of these populations will allow variant discovery efforts to focus efficiently on areas not represented in existing cohorts. Thus, we assembled genotype data for 2,554 individuals from across the entire archipelago with geographically restricted ancestry, and performed population structure analyses and comparisons to ancient DNA. Extensive geographic structuring is revealed, from broad scales such as a NE to SW divide in mainland Scotland, through to the finest scale observed to date: across 3 km in the Northern Isles. Many genetic boundaries are consistent with Dark Age kingdoms of Gaels, Picts, Britons, and Norse. Populations in the Hebrides, the Highlands, Argyll, Donegal, and the Isle of Man show characteristics of isolation. We document a pole of Norwegian ancestry in the north of the archipelago (reaching 23 to 28% in Shetland) which complements previously described poles of Germanic ancestry in the east, and “Celtic” to the west. This modern genetic structure suggests a northwestern British or Irish source population for the ancient Gaels that contributed to the founding of Iceland. As rarer variants, often with larger effect sizes, become the focus of complex trait genetics, more diverse rural cohorts may be required to optimize discoveries in British and Irish populations and their considerable global diaspora. First documented in the fourth century BCE by the Greek explorer Pytheas, who describes its 3 corners , the arcPrevious genome-wide investigations of British  and IrisUsing this sample, we sought to ask 3 questions: First, what is the fine-scale genetic structure of Scotland and its surrounding Isles? Second, using modern samples from Scandinavia, what is the Norse Viking contribution to these populations? Third, with the advent of ancient sampling of the Viking Period Gaelic settlers of Iceland, is it possible to trace their origins back to regions within Britain and Ireland?SI Appendix, Supplementary Data 1). Studies with comparable marker numbers have previously reported fine-scale structure in European-descent populations using haplotype information  were either too small or inadequately geographically labeled, we merged uninformative clusters into their nearest neighbor on the fineSTRUCTURE tree, leaving 43 “merged” fineSTRUCTURE clusters to further analyze. We denote cluster names in italics in order to differentiate them from the regions that they may be named after. Principal Component Analysis (PCA) and ADMIXTURE analyses demonstrate the primary sources of differentiation within the Isles  and Orkney/Shetland versus the rest .Both fineSTRUCTURE and t-SNE reveal a remarkable degree of differentiation across the Isles. Our fineSTRUCTURE genetic clusters describe geographic regions within the Isles A and B, SI Appendix, Fig. S3. This genetic division is apparent in our fineSTRUCTURE dendrogram  shows a similar profile across this cline, suggesting the primary source of differentiation is isolation by distance.We were able to categorize our expanded sample of Scotland into 6 groups of genetic clusters: the northeast, the southwest, the Borders, the Hebrides, Orkney, and Shetland A. The mandrogram A, the findrogram C, and inndrogram . ADMIXTUTayside-Fife and Aberdeenshire. It also includes the small cluster Buchan-Moray which exhibits characteristics of isolation: high fixation indices (FST) (Dataset S1) and high levels of short and long runs of homozygosity (ROH) , with a gene flow barrier in EEMS . This border phenomenon is reflected in our EEMS analysis , where we observe a genetic barrier separating the Borders from the rest of Scotland.The southern, Borders, region of Scotland forms the boundary between Scotland and England. Individuals with ancestry from this region separate from other Scottish clusters and instead cluster with individuals of English ancestry , and presents higher Northern Isles ancestry components in ADMIXTURE analysis . This suggests part of N Scotland’s differentiation is due to shared ancestry with the Northern Isles.The north of Scotland is a sparsely populated, mountainous region known as the Highlands. This isolation is demonstrated in our migration analysis  which shSI Appendix, Fig. S2A). In t-SNE analysis  and the low gene flow rates estimated via EEMS , and, secondly, Hebridean samples demonstrate Irish affinity along principal component 2 and have a higher proportion of ADMIXTURE ancestral components most frequent in Ireland . Interestingly, the cluster Argyll appears intermediate between Sco-Ire (which it shares a fineSTRUCTURE branch with) and the Hebrides; Argyll plots in between the Hebrides and Sco-Ire in t-SNE analysis .The Hebrides also appear genetically distinct from the rest of mainland Scotland. Hebridean individuals are separate from the other Scottish populations studied in PCA , extremely low gene flow . They form their own isolated “islands” in t-SNE analysis, and fineSTRUCTURE clustering reveals that the Northern Isles are the first branch in the tree separating from all other British and Irish populations. This suggests that, in addition to Norwegian-related admixture .Orkney and Shetland are the most differentiated populations in our sample of the British Isles and Ireland, in agreement with previous observations , 17–19. 17ene flow , and theSI Appendix, Supplementary Discussion.Beyond Scotland, we describe the genome-wide genetics of the Isle of Man, as well as capturing novel structure in the northwestern Irish county of Donegal. The Isle of Man is grouped with the southwestern Scottish individuals, and Donegal appears to be at the end of a genetic axis within Ireland. We discuss these results in greater detail in SI Appendix, Supplementary Data 6.Our sample of Britain and Ireland covers the historical range of Norse Viking influence in the north of Britain, and Ireland. Thus, to answer our second question, we investigated the extent of Norwegian ancestry in fineSTRUCTURE clusters whose geographic ranges overlapped with the historical range of Norse Viking activity. We investigated using 2 different methods, utilizing our dataset of 2,554 British and Irish individuals together with 2,225 additional Scandinavians. For more information about these Scandinavians, see England, ‘Wales’ , and Norway (N Scotland) and southwest (Argyll), and the Isle of Man, averaging 4%, little more than other parts of mainland Scotland. We estimate that Norwegian (as well as Danish/Swedish) ancestry is also markedly low in Ireland (average 7%) compared with previous estimates (Aberdeenshire and Tayside-Fife present more English-like ancestry. Isle of Man similarly presents relatively high (42%) English ancestry.We first performed a supervised ADMIXTURE  analysisd Norway A. These d Norway . Norwegistimates , 9 . We calculated the mean total ancestry contributions from reference to target . Using Human Genome Diversity Project (HGDP) Yorubans as an outgroup, we calculated the D statistic of 2 groups of ancient individuals to either British or Irish genetic regions, or modern Scandinavia , Ireland , and S Ireland (Munster) to increase sample sizes of those regions. The 2 groups of ancient Icelanders were those of predominantly Gaelic (n = 7) or Norse (n = 10) ancestry . All but one of the predominantly Norse ancient Icelanders share significantly (Z > 3) more drift with modern Scandinavian individuals than with the British sample , notably KNS-A1, who shows greater affinity to the south of Ireland. Our results provide genetic evidence either of Viking-mediated migration of Gaels from the northwest of the British Isles and Ireland or, at least, that these modern regions represent the best proxy of the true ancient Gaelic source populations in the absence of direct ancient DNA sampling.We first confirmed that our underlying modern British and Irish structure does not dramatically change the ancestry estimates of the ancient Icelanders , Belerion , and Orkas (Orkney). Previous sampling of the British Isles followed this lead with the 1000 Genomes Project British in England and Scotland (GBR) sampling these exact areas. With extensive representation across the archipelago, however, we describe the full extent of the northerly Norse pole of ancestry, adding to the known Saxon and Celtic poles in the southeast and west . The entire northeast branch of fineSTRUCTURE clusters describes the boundaries of the Pictish kingdoms, with the southwest branch mapping the Dark Age kingdoms of Strathclyde (Sco-Ire) and Dál Riata (Argyll). The Borders cluster coincides geographically with the Brythonic kingdoms of the Gododdin (modern Lothian and Borders) and Rheged (modern Cumbria). The legacy of the later Norse Jarldom of Orkney and its Scandinavian admixture drives the differentiation of the Northern Isles [for distributions of relevant historical groups, see Leslie et al.  and P-Celtic . Scotland, the main focus of our analysis, is defined by a southwest versus northeast division near the River Forth  ref: 05/S1404/89. GS:SFHS is a Research Tissue Bank, approved by the East of Scotland Research Ethics Service ref: 15/ES/0040. Ethical approval for SCOTVAR was from the Multi-Centre Research Ethics Committee for Scotland: MREC/00/0/17: Investigation of genetic characteristics of Scottish regional populations to assess their genetic ancestry and their suitability for genetic association studies. Favourable opinions are held VIKING from the South East Scotland Research Ethics Committee (12/SS/0151), for the Orkney Complex Disease Study (ORCADES) from the North of Scotland Research Ethics Committee (12 December 2003), for the Irish DNA Atlas from the Royal College of Surgeon Research Ethics Committee (REC0020563). Ethical approval for the study of the samples from the Isle of Man was given by the Isle of Man Ethical Committee on 17 January 1997.Supplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File


Oryza sativa) has been extensively studied, the exact origins of African rice (Oryza glaberrima) are still contested. Previous studies have supported either a centric or a non-centric geographic origin of African rice domestication. Here we review the evidence for both scenarios through a critical reassessment of 206 whole genome sequences of domesticated and wild African rice. While genetic diversity analyses support a severe bottleneck caused by domestication, signatures of recent and strong positive selection do not unequivocally point to candidate domestication genes, suggesting that domestication proceeded differently than in Asian rice–either by selection on different alleles, or different modes of selection. Population structure analysis revealed five genetic clusters localising to different geographic regions. Isolation by distance was identified in the coastal populations, which could account for parallel adaptation in geographically separated demes. Although genome-wide phylogenetic relationships support an origin in the eastern cultivation range followed by diversification along the Atlantic coast, further analysis of domestication genes shows distinct haplotypes in the southwest—suggesting that at least one of several key domestication traits might have originated there. These findings shed new light on an old controversy concerning plant domestication in Africa by highlighting the divergent roots of African rice cultivation, including a separate centre of domestication activity in the Guinea Highlands. We thus suggest that the commonly accepted centric origin of African rice must be reconsidered in favour of a non-centric or polycentric view.Rice is a staple food for the majority of the world’s population. Whereas Asian rice ( Oryza sativa L.) was domesticated from a single wild species (Oryza rufipogon Griff.) approximately 9000 years ago [Oryza barthii A. Chev., approximately 3000 years ago [Oryza glaberrima Steud.Rice is the world’s most important cereal crop. As a staple food for more than half of the world’s seven billion people, it is of crucial importance in providing food security for an exponentially growing population. Like other cereals, rice has been domesticated by humans multiple times independently. Yet, unlike other cereal species, these domestication events have origins on different continents—one in Asia, and one in Africa. Although the exact history of rice in Asia is still disputed, it is clear that Asian rice  that are not represented in . sativa . Loss ofO. barthii and later cultivation alongside O. sativa may have caused interspecific gene flow, which further complicates inferences about domestication origin [Two main competing hypotheses have been proposed concerning the domestication of rice in Africa. One proposes that plant domestication in Africa occurred in a non-centric  manner, over a protracted period of time , and hasn origin .O. sativa ssp. indica (which originated in India) and O. sativa ssp. japonica (which originated in China). These subspecies of rice have separate origins, although later domestication stages saw extensive gene flow between the two, which has been associated with the transfer of domestication alleles [O. glaberrima is visualised for illustration purposes, alongside the two other hypotheses in In addition to the centric and non-centric model, an additional theory about crop domestication stipulates multiple  defined centres, as has been observed in both Asian rice  and barl alleles . AlthougO. glaberrima to have 70% lower genetic diversity and hardly any population structure compared to its wild relative, supporting a single origin around the Inner Niger Delta [O. glaberrima and 9 O. sativa accessions. Two of the sub-populations showing interspecific admixture with O. sativa were later shown to be true O. sativa varieties [O. glaberrima and associated with different phenotypic traits, corresponding to the floating, non-floating and upland ecotypes, respectively [O. glaberrima accessions without any correlation to geography—in contrast to their wild relative, which clustered into three geographically distinct sub-populations [Several studies have tried to illuminate the question of how and where African rice originated, either implicitly or explicitly addressing the domestication hypotheses described above. A study of 14 unlinked nuclear genes in 40 individuals found er Delta . In conter Delta  found fiarieties . The threctively . The autulations . ContrarO. glaberrima underwent an extreme genetic bottleneck and is probably the least genetically diverse crop ever documented. This low level of diversity was confirmed by subsequent genomic studies. In 2014, Wang et al. [O. glaberrima (AGI1.1). Whole genome resequencing of 94 O. barthii and 20 O. glaberrima accessions revealed that domesticated rice consistently clustered with one of five sub-populations of wild rice, suggesting a single origin in the area of Senegal, Gambia, Guinea and Sierra Leone [O. glaberrima accessions fine-tuned this finding by providing evidence for geographically localised diversification within this region, specifically suggesting that reduced salt tolerance may have evolved in the tropical south of the western Atlantic coast in response to higher rainfall and reduced salinity [A potential shortcoming of these earlier studies is the type and quantity of genetic markers analysed, which leads to a low resolution of genetic diversity. This limitation is overcome with the introduction of genome-wide analyses and tools. Nabholz et al.  analysedg et al.  publishera Leone . A genomsalinity . In addiThus far, no conclusive evidence has been provided in favour of either the non-centric or the centric domestication hypothesis. Although the first genome study of African rice supported a single origin of domestication , the sugO. glaberrima accessions—and an almost equal number of O. barthii accessions—provide a wealth of genetic data that can be used to reconstruct the evolutionary history of African rice, and to compare the genetic diversity of O. glaberrima in its different localities.The available genomic data enable a reinterpretation of previous results and might clarify some of the present ambiguities regarding the origin and diversification of African rice. Thus, while many questions concerning the domestication and migration of African rice are still outstanding, the complete genome sequences of more than a hundred O. glaberrima in West Africa, we performed a critical reassessment of the publicly available whole genome resequencing data of O. glaberrima and O. barthii accessions from across the species range  confirm the genetic bottleneck in African rice as a result of domestication; 2) identify which alleles have been driven to (near) fixation as a result of artificial selection; 3) discover population structure and differentiation within and between the two species; 4) assess the influence of geography on the distribution of genetic variation; 5) explore discordances between the evolutionary histories of candidate domestication genes and the genome-wide species tree; and 6) predict the functional relevance of gene regions with divergent histories.In order to elucidate the origin and diversification of es range . These dO. glaberrima and O. barthii through a variety of statistics. The second objective was met by unfolding the site frequency spectrum and performing a selection scan. The third objective was met by estimating the number of ancestral populations to delimit extant populations and calculating the fixation index between them. The fourth objective was met by mapping a principal components analysis onto known geographic coordinates and measuring isolation by distance. The fifth objective was met by phasing gene haplotypes and comparing their relationships with the overall phylogeny based on genomic distances. Finally, the sixth objective was met by identifying high impact mutations based on computational analyses and previously published experimental results. These results were subsequently used to weigh the evidence for local versus global adaptation, discuss the taxonomic implications for species delimitation, argue for a single or multiple domestication events, and ultimately to reconsider the dominant domestication hypotheses. While the combined results do not support either a fully centric or fully non-centric origin, evidence for multiple origins of domestication traits hints at the complex domestication history of African rice and suggests that the commonly accepted narrative of a single geographic origin of domestication should be reconsidered in favour of a polycentric or non-centric view.The first objective was met by quantifying the relative genetic diversity of O. barthii (9.04 SNPS per kb) as in O. glaberrima (5.00 SNPs per kb). About half of the polymorphic sites in O. barthii are unique to this species. In contrast, the vast majority of polymorphic sites in O. glaberrima are shared with O. barthii, suggesting very little species-specific variation among the domesticated accessions. The ratio of synonymous to non-synonymous substitutions and the relative proportion of protein coding variation appear to be roughly the same . Both O. barthii and O. glaberrima are predominantly selfing plants, which is reflected in their low levels of heterozygosity, averaging around 5%. Removal of low coverage individuals (< 4X) in both species revealed a higher heterozygosity in O. barthii (9.29%) than in O. glaberrima (5.33%), consistent with a higher level of inbreeding.Joint variant calling and quality filtering resulted in a total of 3,923,601 SNPs. Average SNP density is almost twice as high in c = 0.0007) than in the wild species (πw = 0.0013) at p < 1.0E-05. The relative nucleotide diversity (πw/ πc) was found to be 1.87 across the genome, but was markedly higher in some regions (O. glaberrima (-0.6761) and positive in O. barthii (0.5172). The relative levels of Tajima’s D suggest that large parts of the O. glaberrima genome exhibit an excess of rare variants as compared to O. barthii. This is compatible with the general trend in π ratio (πw/ πc), which is usually well above one (Relative nucleotide diversity between the two species was significantly lower in the cultivated species (π regions . Tajima’bove one  and takeO. glaberrima is confirmed by the Minor Allele Frequency (MAF) spectrum  than the majority of O. barthii alleles, which are of intermediate frequency (MAF 0.01–0.05). The low levels of nucleotide diversity and the large number of rare variants found in O. glaberrima are consistent with a scenario of population expansion following a sudden drop in effective population size. These results are in congruence with previous findings [O. glaberrima occurred as a result of domestication.The excess of rare variants in spectrum , where Ofindings ,21,22 anThe previous statistics show a deviation from neutrality that could be caused both by changes in the effective population size as well as selection. Whereas demographic factors can usually account for extremely low frequencies of derived alleles, it is very unlikely that genetic drift alone can push derived alleles to extremely high frequencies. A U-shaped derived allele frequency spectrum is therefore used as evidence of positive selection, but has not been demonstrated in African rice to date.O. glaberrima shows a greater excess  than O. barthii  in the far-end of the spectrum (0.7–1.0). This excess is also caused by higher frequency classes in O. glaberrima  than in O. barthii . Over the whole spectrum, the discrepancy between observed and expected frequencies as observed in an empirical cumulative distribution plot, is greater in O. glaberrima than in O. barthii  than in O. barthii (0.93 on average). Barring a single shared outlier on chromosome 4, there is a remarkable lack of overlap of outliers between the two species, suggesting that the sweeps found in O. glaberrima are unique to the domesticated accessions  test was conducted. Overall, the genome-wide CLR was higher in cessions . More suterature , not a sterature . This isO. glaberrima and O. barthii could disqualify the hard sweep model, we re-examined the variation in both species. A joint ADMIXTURE analysis was performed to infer which ancestor fractions are shared between the species and which are unique. Cross-validation error estimates shows that model fit was optimised for both species at K = 8 and at K = 5 when either O. glaberrima or O. barthii were considered by themselves  was calculated between individuals separated by the 11°N and the 6°W cline, respectively. Genetic differentiation between O. glaberrima and O. barthii was calculated as a reference point for within O. glaberrima comparisons (O. glaberrima and O. barthii are differentiated greatly (FST > 0.181), the western and eastern groups were differentiated moderately (FST = 0.10) and the northern and southern groups were differentiated only a by a small degree (FST < 0.042). This is in line with previous results, proposing an early split between the eastern and western cultivation range, followed by a later split between the northern and southern diversification centres [To determine the degree of genetic differentiation between the geographic regions, the fixation index , but relatively larger for the three coastal populations (FST > 0.25) than for the two inland populations (FST 0.15–0.25). Genetic differentiation from O. barthii is the smallest for OG-IV and the largest for OG-II (Genetic differentiation from or OG-II . This paor OG-II . The oppr) of -0.35 (r) of only 0.02  was assessed among all West African accessions. Genetic IBD is explained by the accumulation of genetic differences by dispersal . We evalof -0.35 . This coof -0.35 . In contnly 0.02 . This suO. glaberrima from O. barthii in an east to west direction, is reflected in a genome-wide neighbour-joining (NJ) tree of all accessions (O. barthii or any of the inland accessions (OG-IV an OG-V). In contrast, the closest wild relatives of O. glaberrima (OB-C and OB-B) cluster primarily among the inland accessions, whereas OB-A and OB-B together form a monophyletic clade with much longer branches than the other O. barthii. This clustering of O. glaberrima and O. barthii populations is largely confirmed by PCA and multi-dimensional scaling of the genetic variation in both species, where the distinct nature of OB-A and OB-B is visible in its large distance from the other accessions.The progressive differentiation of cessions . Almost O. barthii implies that they share a common pool of genetic variation from which O. glaberrima was domesticated, and that the coastal lineages branched off at a later point in time. The smaller nucleotide diversity in the coastal populations, indicative of recovery from a genetic bottleneck, seems to support this scenario. Assuming that the sampling locations of the present accessions reflect their historical origins, this suggests that the origin of domestication lies east of the 6°W cline, and that O. glaberrima subsequently migrated westward. This is consistent with the TreeMix analysis performed by Meyer et al. [The clustering of inland samples with r et al. , and witr et al. .O. glaberrima could have had multiple origins, NJ trees were constructed for several domestication genes known from recent rice genetics literature [O. glaberrima subpopulations segregate into different haplotypes and whether they cluster with the expected OB-C and OB-D accessions.To assess whether certain domestication traits of terature ,25, a liO. glaberrima from a single subpopulation cluster together in smaller clades that are far removed from the larger O. glaberrima clade. In four of those , the segregating haplotype that is farthest removed from the major clade in these genes is composed O. glaberrima individuals that are exclusively from the OG-II population , which encodes a premature stop codon in almost all domesticated accessions. Polarisation of this SNP against O. meridionalis as an outgroup demonstrated that the variant responsible for truncation of the protein is derived, and the variant encoding the intact protein is ancestral. The ancestral variant was found to be represented exclusively in the OG-II haplotype. Since the non-shattering phenotype is a crucial trait in the domestication syndrome, the ancestral state of this substitution in a limited number of OG-II accessions presupposes that another variant—either in the same gene or in a different gene—might be causing the same phenotype. This has recently been confirmed by functional characterisation of another gene, called Sh3, that is on its own responsible for a non-shattering phenotype in African rice [To see what phenotypes might be associated with the segregating haplotypes in these genes, we evaluated the impact of the responsible substitutions using variant prediction software. No high-impact substitutions were detected apart from a variant in can rice .Sh4), both in silico and in vitro, the phenotypic consequences of haplotype variation in the other genes remain to be determined. Considering the extensive LD in O. glaberrima and the fact that strong candidates for high impact substitutions could not be identified, it cannot be excluded that associated functional mutations lie outside the intervals included in our phylogenetic analyses and that the gene haplotypes identified here may have hitchhiked on the selection of a different genomic feature altogether. Any claims regarding the adaptive significance of these phylogenetic patterns will therefore have to be complemented by experimental evidence.Although the functional relevance of these genetic differences has been demonstrated for one gene  of chromosome 1 [Interestingly, screening of homologs of additional agronomically important genes reveals that the NAC transcription factor olerance . Phylogence [Sd1 . A genomposition 0698514 oO. glaberrima. The sample sizes of both O. glaberrima and O. barthii, in combination with the genomic nature of the data, confer an advantage to the analyses presented here that previous studies did not have.This is the first time that the data sets of two large scale genomic studies on African rice ,22 have O. glaberrima as a result of domestication. This reduction in diversity is most likely caused by a combination of selection, favouring a small number of preferred alleles, and demographic history, causing a large drop in effective population size (Ne).The diversity measures reported in this study are consistent with previous reports ,22 and pO. glaberrima underwent population expansion following a severe bottleneck. While this demographic scenario can account for the large number of rare derived alleles, it is unlikely to account for the large number rare ancestral alleles. The fact that O. glaberrima shows a larger excess of high frequency derived alleles than O. barthii, as evidenced by their empirical cumulative distribution functions, is an indication that it at least underwent stronger positive selection than its wild relative.Both the negative values of Tajima’s D and the skewed MAF distribution in favour of rare alleles support the notion that COLD1), other genes that we expected to be among the outliers (such as Sh4) also lacked a clear signal, casting doubt on the assumptions of the employed method. Specifically, none of the currently available selection scans have been tested for their robustness under simulations of a very extreme and rapid bottleneck, as encountered in domestication events. Without additional modelling, therefore, we cannot be sure that demographic history of African rice did not interfere with the CLR test presented here. In addition, this test does not consider alternative forms of selection, such as balancing selection or diversifying selection. In recent years, more studies have focused on the existence of ‘soft’ selective sweeps, which are caused by positive selection on either pre-existing variants or multiple de novo mutation. We did not explore alternative models of selection in this study, because the existence of several haplotypes due to multiple novel mutations or standing variation renders the detection of soft sweeps exceedingly complex.Despite this evidence, the identification of exact regions in the genome that have been under positive selection is notoriously difficult due to the confounding effects of demographic history, which are known to produce local reductions in genetic diversity that can look remarkably like selective sweeps . AlthougO. glaberrima, is the population subdivision. A recent study of the AfricaRice gene bank collection also revealed exactly five genetic clusters based on a study of 27,560 SNPs across 2,179 accessions. These clusters are linked strongly to country of origin, but less so to ecotype [O. glaberrima [But perhaps the most compelling argument that can be made against the hard sweep model in  ecotype . The geo ecotype . In addi ecotype , resultiaberrima . Hence, O. barthii, while the reverse can be seen in the coastal populations, seems to suggest that the population bottleneck occurred in an east to west direction, followed by differentiation between the north and south in the coastal region. We therefore see strong evidence of Portères’ domestication theory in our population structure analysis.The population structure of African rice further sheds light on the geography of its domestication. Interestingly, all the accessions that originate from the proposed primary domestication centre of African rice, around the Inner Niger Delta , belong O. glaberrima does not form a monophyletic clade, however, calls into question the assumption that it speciated through a discrete domestication event. A possible explanation could be the rewilding of ancient O. glaberrima landraces, which have ‘gone feral’ and are now classified as the wild species. This would explain the shorter branch lengths of some of these closely related O. barthii accessions. In addition, O. glaberrima and O. barthii diverged so recently that hybridisation is still considered possible—if difficult—and that admixture between O. barthii and O. glaberrima even now should not be excluded. In fact, ‘weedy’ rice, which is a genetic mix between the wild and cultivated species, can result from interspecific crosses and has been observed in the case of African rice in both Mali and Cameroon [The fact that Cameroon .O. glaberrima still contains a part of the ancestral variation that is observed in O. barthii. For this reason, gene trees may not correspond to the overall genomic tree. Indeed, it is a widely observed phenomenon that incomplete lineage causes mixed phylogenetic signals [Another explanation for the observed phylogenetic patterns might be that the time that has passed since domestication (roughly 3000 years ago) has not been sufficient to establish complete lineage sorting. This could mean that  signals .Sh4 has been confirmed independently and proposed to “support the deep and separate roots of domestication practices in the west versus the eastern cultivation range” [O. glaberrima and is consistent with parallel adaptation.However, the recurrent pattern of gene haplotypes that are restricted to OG-II accessions from the Guinea Highlands suggests that domestication may have followed a different path in this area, possibly through local introgression from wild rice. This could have led to the observed genetic differences in various parts of the genome. Specifically, the ancestral character of a functionally important SNP in n range” . The exiThe results discussed so far thus shed new light on an old controversy concerning the process of plant domestication in Africa, in which two models have traditionally been competing: the rapid transition model proposed by  and the This study has aimed to resolve some of the confusion introduced in this debate. First of all, it is clear that the previous genomic studies address different aspects of the controversy: Wang et al.  merely pO. glaberrima, the results are strongly suggestive of an early domestication event in the eastern cultivation range, with subsequent genetic differentiation towards the west. The geographic origin of the closest wild relatives of O. glaberrima, however, correspond mostly with the Senegambian and Guinean forest regions. If we assume that these relatives are genuinely O. barthii and not ‘rewilded’ or intermediate forms, there are two plausible scenarios. Either African rice was first domesticated along the coast and subsequently migrated east. In that case, the coastal landraces must have undergone substantial differentiation in order to explain their larger genetic distance from O. barthii. Alternatively, the geographic separation of the inland populations and their wild relatives can be explained by domestication in the eastern cultivation range and a subsequent range shift of the wild progenitor from the east to the west.Based on the sampling origins of O. glaberrima and O. barthii migrated in the past and whether the present sampling locations truly reflect historical populations. In addition, while the sampling locations of the analysed O. glaberrima accessions are quite precise, for most O. barthii only the country was known and detailed coordinates were not available. This not only prevented accurate knowledge of the collection sites of these accessions, but also the detection of isolation by distance.To ascertain which of the two scenarios  eventually holds out, more knowledge is needed about the extent to which O. glaberrima points to geographic differentiation during or following domestication. The presence of separate OG-II haplotypes in multiple domestication genes might mean that the segregating landraces acquired domestication traits independently of the majority of O. glaberrima and would support a polycentric scenario as illustrated in O. barthii accessions provides evidence for a separate genetic origin that may have been caused by introgression or domestication from this otherwise seemingly unrelated wild population.Despite these limitations, the marked population structure observed in O. glaberrima underwent an extreme bottleneck. To the best of our knowledge, this bottleneck was most probably associated with domestication, thus supporting the rapid transition model. Although there are some indications of positive selection in terms of an excess of high frequency derived alleles, conclusive evidence of hard selective sweeps—especially in relation to known domestication traits—has been elusive. Whether this stems from methodological issues or from the population structure observed in O. glaberrima can only be demonstrated with improved knowledge of the demographic history of O. glaberrima and additional modelling.In light of the findings presented, we can now with some confidence assert that the centric hypothesis of African rice domestication is incorrect, or at least has some serious shortcomings. The diversity analyses unequivocally demonstrate that O. glaberrima is all but homogeneous, even compared to O. barthii. The subdivision of the species in coastal and inland populations is suggestive of geographic structure, as is the differentiation along a north-south gradient on the coast. Contrary to expectation, moderate isolation by distance was observed in three out of five genetic sub-populations. Due to data limitations, isolation by distance could not be assessed in O. barthii.Although it has been shown that cultivated African rice is much less genetically diverse than wild African rice, the ADMIXTURE analysis shows that O. glaberrima within O. barthii but shed new light on the relationships between sub-populations of the wild and domesticated species. O. glaberrima indeed shares characteristics with a subset of O. barthii individuals; however, the majority of the coastal accessions form a monophyletic clade that does not contain any wild relatives. This pattern breaks down when considering phylogenies at the level of individual genes; there we see that some landraces are far removed from the majority of O. glaberrima and cluster with a different O. barthii sub-population instead. These separate haplotypes demonstrate the divergent evolutionary trajectories among distant sub-populations, most notably the accessions from the Guinean forest region and the Middle and Lower Niger basin.Phylogenetic results confirm the clustering of O. glaberrima was domesticated in a single and discrete event has to be rejected. The observed population structure is partially consistent with Portères’ proposed primary and secondary domestication centres. However, evidence of persisting ancestral variation and multiple gene haplotypes among different sub-populations of O. glaberrima suggests that important functional traits may have arisen out of parallel evolution or local adaption, rather than single selective sweeps. This is corroborated by the effect of geographic distance on genetic relatedness and by experimental evidence confirming the phenotypic consequences of spatially restricted genetic variation [O. glaberrima. The protracted transition model with multiple domestication centres, or alternatively a polycentric view, might offer a valuable alternative perspective on the observed geographic distribution of genetic variation found in African rice.Whereas this study provides compelling evidence for the origin of African rice in the eastern cultivation range and its diversification along the Atlantic coast of West Africa, the overarching hypothesis that ariation ,26,33. HO. glaberrima are in fact hybrids or rewilded ancient landraces. A closer examination of the genetic diversity and signatures of selection of the hypothetical ancestor population in comparison to other O. barthii and O. glaberrima accessions might elucidate whether they are more similar to the domesticated or to the wild species. These genetic analyses will have to be balanced with suitable morphological evidence. Uncertainty in species delimitation could be further examined through phylogenetic networks and introgression analyses. This will aid our understanding of the precise evolutionary relationships between O. glaberrima and O. barthii.Future research into the origins of African rice should investigate the possibility that the closest wild relatives of O. glaberrima and in the western range for O. barthii, where collections of these species are presently scarce. Larger sample sizes will also increase the sensitivity of genome-wide association studies, which enable the identification of SNPs that are associated with traits of interest. While genome-wide data have already been used to explore the mutations associated with drought tolerance [In addition, focus should be given to more even sampling across the geographic range of both species, especially in the eastern range for olerance , these dLastly, more functional analyses are needed to improve the annotation of the African rice genome, which is still lacking in many ways in comparison to the Asian rice genome. This will help to predict the phenotypic consequences of domestication gene haplotypes and in linking phylogenetic patterns to the evolution of functionally significant traits. The complementation of computational studies with experimental data will be indispensable in the future—not just for understanding the broad patterns of evolution and domestication of African rice, but also to provide insights into the emergence of local adaptive traits connected with the diversification of this crop in its different geographic contexts.O. glaberrima and 94 O. barthii accessions. A list of all used accessions and their metadata can be found in O. glaberrima (AGI1.1) reference genome and subsequently filtered to remove false positives. The O. glaberrima reference genome [This study used publicly available whole genome data of 111 e genome  was retre genome . Untrimme genome  of the Be genome . Local re genome . The resHard filters were applied to the raw SNPs by removing SNPs falling outside the quality thresholds of several common annotations: DP, QD, MQ, MQRankSum, ReadPosRankSum and FS . The fixation index (FST) between O. glaberrima and O. barthii is based on the implementation of [p is the allele frequency in the total population,σ2T is the variance in allele frequency in the total population, and σ2S is the variance in allele frequency between the two sub-populations. Relative nucleotide diversity was calculated as the ratio of π in O. glaberrima to π in O. barthii, where π defined as:ij is the number of differences per site between sequences i and j, xi is the frequency of sequence i, xj is the frequency of sequence j and n is the total number of sequences in the data set. The results were deemed sufficiently comparable to proceed with both call sets  for both call sets. Large problematic regions were not detected. In order to make an informed decision as to which version of call set to use and whether or not to adjust the filtering parameters, we calculated additional statistics. Following the method of , both caation of  and calcall sets . In addiO. barthii and O. glaberrima separately, variants were split into two populations based on species identification. Monomorphic sites were removed. A total of 2,580,362 and 1,419,601 SNPs were used to calculate SNP density, π, and Tajima’s D in O. barthii and O. glaberrima, respectively, where Tajima’s D is defined according to [ω , the two estimators should equal each other and Tajima’s D equals 0. These statistics were computed in 100 kb regions with VCFtools (v0.1.14). Genome-wide average statistics were compared using the Kruskal-Wallis test. Minor allele frequencies were calculated using VCFtools (v0.1.14) in combination with a custom R script and plotted in R (v.3.3.2).In order to estimate the genetic diversity in rding to  and measOryza is a large genus and many reference genomes of related species are available. Three potential outgroups were identified to polarise SNPs, in order of decreasing genetic distance to O. glaberrima: Oryza punctata, Oryza meridionalis, and Oryza longistaminata A. Chev. & Roehr. . Oryza punctata was rejected because of its high genomic divergence (>5%) and associated data loss. Hence, we chose O. meridionalis as an outgroup.& Roehr. . These odistance . For thiO. meridionalis (v1.3) genome [O. meridionalis x O. glaberrima multiple alignment was retrieved from Ensembl Genomes (release 33) and parsed with mafTools [O. meridionalis genome were discarded. A total of 3,923,601 variants were screened, of which 2,332,467 either did not align to the outgroup species or did not pass the alignment quality filter. The derived allele frequency spectrum was calculated for all synonymous and noncoding variants of the remaining 1,591,134 SNPs.SNPs were polarised with reference to the ) genome  with a cmafTools . For eacSynonymous and non-coding SNPs were extracted using SnpSift (v4.0) . For theO. glaberrima genome, with r2 reaching half its maximum value at a distance of 175 kb and approaching baseline at 300 kb [Several CLR tests that are widely used for detecting ‘hard’ sweeps are available as open source software, including OmegaPlus , SweeD  and SweeW is the number of segregating sites, divided into two groups: one from the first to the l th polymorphic site on the left and the other from the (l + 1)th to the last polymorphic site on the right. L and R represent the left and right set of polymorphic sites, respectively, and r2ij is r2, a common measure of LD [ith and the jth site. The value of l that maximises ω defines the test statistic. .The ω test statistic is calculated as:re of LD , betweenobertson  as r2 = Values of ω were log-transformed, prior to creating Manhattan plots in R (v3.3.2) using the ‘qqman’ package . WindowsO. glaberrima reference genome on Ensembl (release 33). Genomic features containing putative moderate to high impact mutations within close proximity (< 25 kb) of candidate regions were extracted for closer inspection using a custom R script. Candidate sweeps (OmegaPlus outliers) and their associated genes harbouring high impact mutations can be found in Candidate regions were screened for potential causative mutations by examining related SNP content and genomic features. Variants were annotated using SnpEff (v4.0) . Genomic2 > 0.25 were pruned with PLINK (v2.0) in sliding windows of 500 SNPs, with a step size of 50 SNPs [O. glaberrima and O. barthii separately. Cross-validation error estimates of all three analyses can be found in O. glaberrima population was subsequently divided into five populations based on the ancestral population contributing the highest fraction of genetic variation.Population structure was determined with ADMIXTURE (v1.3.0) . In orde 50 SNPs . A totalThe geographic distribution of these populations in West Africa was visualised by plotting the coordinates of all West African accessions using the R packages ‘rworldmap’ and ‘raster’ ,56. The ST, as implemented in VCFtools (v0.1.14). FST between O. glaberrima and O. barthii was included as a baseline. To account for uneven sample sizes of these populations, an equal number of individuals (n = 15) was selected for each of the five sub-populations as identified with ADMIXTURE. To minimise the effect of missing data, an equal number of individuals (n = 15) that were sequenced at high coverage were selected from the O. barthii population. Pairwise FST and π were calculated between all six groups to obtain a more balanced estimate of allelic differentiation, taking uneven sampling and sequencing depth into account.Based on the results by Meyer et al. , four geIsolation by distance (IBD) was assessed by comparing the relatedness of individuals with the geographic distance separating their sites of collection. Kinship coefficients were estimated based on 1,419,601 SNPs, using the KING robust relationship inference method  as impleO. glaberrima within O. barthii, a whole genome phylogenetic tree was constructed based on 3,923,601 genome-wide SNPs. To avoid distortion of branch lengths, no outgroup was used. Pairwise genomic distances between all accessions were calculated using a custom perl script, implementing the method described in S3.2 of [aibi is the genotype at position i in X, and cidi is the genotype at position i in Y. The resulting distance matrix was used to construct an unrooted neighbour joining (NJ) tree, using the BioNJ algorithm of FastME (v2.0) with subtree pruning and regrafting (SPR) [To confirm the clustering of  S3.2 of . The divng (SPR) .O. glaberrima genome was determined using the O. sativa reference genome as a guide. O. sativa protein sequences were retrieved from the Rice Annotation Project Database (RAP-DB) and compared against the Ensembl Genomes O. glaberrima reference protein FASTA using BLAST+ (v2.6) [O. glaberrima reference genome on Ensembl (release 33). Gene structures of these genes were retrieved from Ensembl Plants (release 37).Gene trees were constructed of salient domestication genes that have been proposed to play a role in rice evolution ,25. Thes+ (v2.6) ,64. GeneTo ensure sufficient phylogenetic signal, gene intervals were extended with 5 kb flanking regions on either side using bedtools slop (v2.26.0) . Larger S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.


Podosphaera plantaginis employs both sexual and asexual reproduction to increase its chances of infecting the plant Plantago lanceolata.The fungal parasite  Related research article Laine A-L, Barrès B, Numminen E, Siren JP. 2019. Variable opportunities for outcrossing result in hotspots of novel genetic variation in a pathogen metapopulation. eLife8:e47091. doi: 10.7554/eLife.47091Most populations of animals produce genetically different offspring via sexual reproduction. A small minority of animals, however, use asexual reproduction to produce offspring that are genetically identical to the parent. Both strategies have their advantages and weaknesses, and some animals actually alternate between sexual and asexual reproduction .Most parasites reproduce asexually, but they can switch to sexual reproduction to encourage diversity and to remain infectious. Certain species of parasites can even sexually reproduce with other species, via a process called hybridization. For example, some schistosome flatworms can infect a wider range of hosts, as a result of a bovine-infecting species mating with a human-infecting species . This dePodosphaera plantaginis, which commonly infects the plant species Plantago lanceolata, commonly known as ribwort plantain  of the parasite . Howeverntaginis .P. plantaginis subpopulations in the Åland Islands of Finland over four consecutive years. Analyzing the DNA sequences of every P. plantaginis specimen revealed a number of new genotypes were being produced each year, suggesting that sexual reproduction is common within this fungal species. For sexual reproduction to occur at least two genotypically different parasites need to infect the same host so they can be close enough to exchange genetic material. Laine et al. showed the number of co-infected specimens was spatially varied across the population. P. plantaginis with an increased number of co-infections were also found to have a larger number of new genotypes, suggesting that there are localized hotspots of genetic diversity.Laine et al. tracked the life-cycle of Further work showed that these populations with a higher prevalence of co-infections  were also more likely to survive winter and initiate an infection the following season. This higher success indicates that these newly generated genotypes can start populations that are better equipped at infecting the host plant. When the growing season starts, this in turn could have an effect on the host population, and cause them to adapt a higher resistance.P. plantaginis and its host effects the number of resting spores that are sexually produced. This could be achieved by setting up a bank of P. plantaginis resting spores, and tracking their infections throughout the years to see how they relate to evolutionary changes (Sub-populations of hosts and parasites will co-evolve at different rates depending on their surrounding environment: for example, in one location the parasite may be ahead in the evolutionary arms race, while in another the host is winning. These differences in evolutionary rate are then reflected spatially, creating localized pockets of emerging new genotypes . By incr changes .Future challenges involve seeing if the results obtained from this study can be reproduced by laboratory experiments, and untangling the possible selective pressures and benefits of sexual reproduction in parasites. It is also possible that sexually generated genetic changes in parasites could influence important ecological processes: for example, genetic variation in parasites could make a change to the host’s shape, which then propagates throughout the population and disrupts the whole ecosystem. These changes in the ecosystem could then fuel evolutionary changes in the parasite, resulting in a full feedback loop of eco-coevolutionary interactions.


Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm.Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors  samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations.The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development.The online version of this article (10.1186/s12864-018-4969-2) contains supplementary material, which is available to authorized users. Triticum aestivum L.) feeds more than one third of the world’s population and is one of the most important staple crops in the world [T. turgidum L. ssp. dicoccon (Schrank) Thell.  with the wild diploid Aegilops tauschii Coss.  about 8,000 years ago in the Fertile Crescent [Ae. tauschii followed by limited gene flow from Ae. tauschii to hexaploid wheat led to limited D-genome diversity [Hexaploid (bread) wheat (he world . Bread wCrescent . GeneratT. turgidum) and from wild relatives (Ae. tauschii) through synthetic hexaploid wheat (SHW) production [, Ae. tauschii has many desirable genes/alleles for biotic and abiotic stress resistance for wheat improvement [One approach for broadening the genetic base of BW is utilizing genes from cultivated tetraploid wheat  , 10 and Ae. tauschii accessions from China, Iran, Kyrgyzstan, Jammu and Kashmir, and Turkmenistan developed by Kyoto University, Japan. The remaining (86) SHW (winter type) originated from the six winter durum parents from Ukraine and Romania  crossed with 10 different Ae. tauschii accessions from Azerbaijan, Iran, Russia, and Unknown , common bunt (Tilletia tritici and T. laevis) resistance, barley yellow dwarf virus resistance and resistance to soil-borne pathogens , and pest resistance including Hessian fly (Mayetiola destructor), sunny pest (Eurygaster integriceps), and Russian wheat aphid (Diuraphi noxia) resistance. Therefore, these SHWs under study are highly valuable lines for breeding purpose. These SHWs are maintained by the International Winter Wheat Improvement Program (IWWIP) at CIMMYT, Turkey [Initially, 139 SHWs were analyzed for genetic diversity and population structure  using BioSprint® 96 Plant Kit (QIAGEN). The GBS libraries were constructed in 96-plex following digestion with the restriction enzymes PstI and MspI  at WheatST) using AMOVA [Basic genetic diversity summary statistics including: effective number of alleles, observed heterozygosity, heterozygosity within population (gene diversity), standardized measure of population differentiation  of subpopulation, the genotypes were treated as an admixture population with the allele frequencies correlated model with a total of 100,000 burn-in periods followed by 100,000 Markov chain-Monte Carlo iterations for  K = 1 to 10 with five independent runs for each K. The structure output was visualized using StructureHarvester [Population structure was inferred using Bayesian clustering algorithm in the program STRUCTURE v 2.3.4  from thearvester  and the arvester . Kinshiparvester  implemenarvester .Ae. parents for a total of 101 SWHs. Once the cross is made and the chromosomes are doubled, it would be expected that the SWH should be homozygous. However, our phenotypic data and marker data suggested that heterozygous parents, outcrossing, mechanical mixtures, or misclassification occurred  and gene diversity (Hs) of the SHW genomes were similar and ranged from 0.31 (D genome) to 0.34 (B genome) with an average of 0.33. Expected nucleotide diversity or expected number of polymorphic sites (θ bp− 1) and observed heterozygosity (Ho) in SHWs were similar with an average observed heterozygosity of 0.19. Tajima’s D ranged from 2.04 (D genome) to 2.40 (B genome) with an average of 2.26. Tajima’s D [Summary statistics of various genetic diversity estimates for each genome of SHWs had similar values Table . The avejima’s D  test forAe. parents, respectively.The population structure of 101 SHW was first analyzed on the basis of the ABD genome to study them using all of their genetic diversity. Then the 101 SHWs were analyzed on the basis of the AB and the D genome separately to study genetic diversity of durum and The 101 SHWs showed clear evidence of population structure. Delta K values obtained from the STRUCTURE  output were used to classify subpopulation. The largest delta K was observed at K = 2 Fig. , suggestWhen comparing the grouping obtained from Bayesian clustering in the neighbor joining cluster Fig.  and prinAe. tauschii were studied separately to further understand how durum or Aegilops parents were grouped.The population structure of SHWs were mainly grouped based on the geographical location of durum parents and growth habit of the crop. Therefore, the population structure using durum and When looking at grouping based on the AB genome (durum parent) of SHWs, two groups were obtained from Bayesian clustering Fig.  and b. TAe. tauschii) of SHWs, two groups were obtained from Bayesian clustering  and unknown (5)  and unknown (12)  for the two subgroups identified in Bayesian clustering were similar whereas gene diversity (HS) was lower for Spring_SHW (0.18) group compared to Winter_SHW (0.31) group. The FST (F statistic) obtained from Bayesian clustering measures the divergence and heterogeneity within predefined subgroups and is obtained by estimating the correlation of alleles within the same subgroup [ST values were about 66% in Spring_SHW and 13% in Winter_SHW, which indicates population differentiation among genotypes in Winter_SHW was lower than Spring_SHW  to 0.32 (Winter_Durum) and Ae. subgroups (Aegilops1 and Aegilops2) was 0.30.The effective number of alleles across durum subgroups ranged from 1.22 to 1.54 and ST) estimated using an analysis of molecular variance (AMOVA) [ST) between Spring_SHW and Winter_SHW was 0.34, Spring_Durum and Winter_Durum was 0.39, and Aegilops1 and Aegilops2 was 0.22 (Table Pairwise population differentiation was obtained from  standard (AMOVA)  and this (AMOVA) , Jost’s  (AMOVA)  as an in (AMOVA)  as the sPopulation differentiation between SHWs (Spring_SHW and Winter_SHW) subgroups using analysis of molecular variance (AMOVA) found that 17.43% of the total genetic variance was explained by the differences between subgroups and 82.57% due to the variation within subgroups (Table O = 0.17 to 0.18) was slightly lower compared to SHW (HO = 0.19 to 0.20) for all genomes. The gene diversity was significantly lower for the BW cultivars (HS = 0.17 to 0.25) compared to SHWs (HS = 0.32 to 0.33) for all genomes. Percentage of SHW gene diversity was larger than BW and ranged from 32.0% (B genome) to 88.2% (D genome) higher than that found in BW cultivars. The overall three-genome and the unanchored scaffold (ABD + unmapped) gene diversity of SHW (0.33) was 50.0% larger than that found in the BW cultivars (0.22).For comparing the genetic diversity between SHW and BW, 34,887 high quality SNPs available after quality filtering were used for genetic diversity analysis  parents. This result agreed with the results of Lage et al. [Ae. tauschii) used in the SHW production which is the likely reason that SHW grouped together based on geographical location of tetraploid parent and growth habit of the crop.Two subgroups obtained from Bayesian clustering algorithm, neighbor joining cluster analysis, and PCoA were mainly divided based on the geographical location of the tetraploid (durum) parents (Romanian and Ukrainian durum in Winter_SHW group and USA durum in Spring_SHW group) rather than e et al. , where SAe. parents separately to understand how they clustered. Durum parents were divided into two subgroups mainly based on the type/pedigree of durum parents used and its origin. When comparing two subgroups of durum parents to two subgroups of SHWs, all entries of Spring_Durum were in Spring_SHW and all entries of Winter_Durum were in Winter_SHW. Similarly, the Ae. parents also clustered into two subgroups. When comparing two subgroups of Aegilops parents to two subgroups of SHWs, all entries of Aegilops1 were in Spring_SHW and all entries of Aegilops2 were in Winter_SHW. Most of the entries of Aegilops1 were Ae. tauschii ssp. strangulata and most of the entries of Aegilops2 were Ae. tauschii ssp. tauschii. However, there was no distinct clustering based on the area of origin and type of the Ae. taushii ssp. Similar results were obtained in the past [Ae. taushii, Ae. tauschii were divided into four subgroups and the same Ae. tauschii ssp. or from the same area of origin were not clustered together  [Ae. tauschii collections. However, in a different study of 477 Ae. tauschii accessions, Ae. tauschii were divided into two lineages (Ae. tauschii ssp. tauschii and ssp. strangulata) having little genetic overlap in the clusters [Further population structure analysis was performed using Bayesian clustering algorithm for durum and the past , 30. Forlusters) . This relusters)  or wrongclusters .S) for each subgroup showed that genetic variation in SHWs ranged from 0.18 (Spring_SHW) to 0.31 (Winter_SHW) with an overall gene diversity of 101 SHWs was 0.33. In Spring_SHW group, only one durum parent was used with different accessions of Ae. tauschii parents indicating that genetic variation observed within Spring_SHW was largely due to Ae. tauschii parents (D-genome diversity). Furthermore, the D genome gene diversity within 101 SHWs was 0.31 and genetic diversity of diploid parents (Ae. tauschii) from SHWs would expected to be very diverse and novel. The SHWs had a significantly higher level of gene diversity (HS = 0.32 to 0.33) compared to elite BW cultivars (HS = 0.17 to 0.25) in the present study. Similarly, higher gene diversity in SHWs have been reported in the past using AFLP [Analysis of molecular variation suggested that the population differentiation exists in two subgroups obtained from Bayesian clustering algorithm, where most of the variation was accounted by within population variance. Gene diversity  in our study ranged from 0.17 to 0.25. Similar results were obtained in a study of a diversity panel of 369 Iranian hexaploid wheat accessions. The gene diversity using SNP markers in Iranian wheat landraces and cultivars ranged from 0.14 to 0.20 . The genAe. tauschii originated from different countries were divided into two (Spring_SHW and Winter_SHW) distinct groups based mainly on geographical location of durum parents and growth habit of the crop. Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type/pedigree or origin of parents used. Although Ae. tauschii parents were divided into two groups mainly based on type of parent used, Ae. tauschii ssp. tauschii and ssp. strangulata did not separate entirely in each subgroup. The GBS derived SNPs were able to identify the inaccurate pedigree of synthetic hexaploid wheats based on misclassifications of some of the durum or Ae. parents from analyzing 139 SHWs and such misclassifications may have resulted due to heterozygous or heterogeneous parent lines, partial sterility and outcrossing, or seed handling error/mechanical mixing.The present study provided a detailed understanding of genetic diversity and population structure of 101 SHWs and revealed high genetic diversity in the SHW compared to elite BW cultivars. Population structure analysis revealed that SHWs developed from diverse accessions of durum wheat and This study found that the percentage of SNPs on the D genome was nearly equal to that of other two genomes (A and B), which is unique to the SHWs as compared to previous studies on BW that reported that the D genome had less than 50% of the SNPs compared to A and B genomes. This result indicated the presence of high variation in the D genome in the SHWs. Furthermore, the gene diversity of SHWs under study was higher in all three genomes compared to elite BW cultivars and the greatest increase in gene diversity (88.2%) was observed in the D genome of SHWs compared to BW cultivars. Such higher genetic diversity in SHWs suggests that the diversity could be utilized in the elite wheat-breeding program to broaden the genetic diversity and increase genetic gain. The markers with high genome coverage such as GBS derived SNP markers are helpful for elucidating the population structure and genetic diversity. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and facilitate the discovery of novel source of genes/genomic regions conferring resistance to multiple biotic and abiotic stresses from association mapping study to unravel economically important marker-trait associations.Additional file 1:Aegilops tauschii. (XLSX 22 kb)Details of 139 synthetic hexaploid wheats used and subgroups obtained from Bayesian clustering algorithm for SHWs, durum wheat, and Additional file 2:Aegilops tauschii parents obtained from Bayesian clustering algorithm. (DOCX 747 kb)Population structure analysis of 139 synthetic hexaploid wheats, durum and Additional file 3:Aegilops tauschii. (XLSX 17 kb)Details of 101 synthetic hexaploid wheats (SHWs) used and subgroups obtained from Bayesian clustering algorithm for SHWs, durum wheat, and Additional file 4:Crossing scheme of 86-winter synthetic hexaploid wheat under study. (XLSX 10 kb)Additional file 5:Kinship relationship matrix of 101 Durum wheat. (XLSX 116 kb)Additional file 6:Aegilops tauschii. (XLSX 117 kb)Kinship relationship matrix of 101 Additional file 7:Details of 12 bread wheat cultivars used in this study. (XLSX 9 kb)Additional file 8:Genotyping-by-Sequencing derived SNP markers of 101 synthetic hexaploid wheats used in this study. (XLSX 15100 kb)


Enterocytozoon bieneusi is the well-known species responsible for the most recorded cases of human microsporidian affections. The pathogen can colonize a broad range of mammals and birds and most of the animals surveyed share some genotypes with humans, posing a threat to public health. Based on DNA sequence analysis of the ribosomal internal transcribed spacer (ITS) and phylogenetic analysis, several hundreds of E. bieneusi genotypes have been defined and clustered into different genetic groups with varied levels of host specificity. However, single locus-based typing using ITS might have insufficient resolution to discriminate among E. bieneusi isolates with complex genetic or hereditary characteristics and to assess the elusive reproduction or transmission modes of the organism, highlighting the need for exploration and application of multilocus sequence typing (MLST) and population genetic tools. The present review begins with a primer on microsporidia and major microsporidian species, briefly introduces the recent advances on E. bieneusi ITS genotyping and phylogeny, summarizes recent MLST and population genetic data, analyzes the inter- and intragroup host specificity at the MLST level, and interprets the public health implications of host specificity in zoonotic or cross-species transmission of this ubiquitous fungus.Microsporidia comprise a large class of unicellular eukaryotic pathogens that are medically and agriculturally important, but poorly understood. There have been nearly 1,500 microsporidian species described thus far, which are variable in biology, genetics, genomics, and host specificity. Among those,  Enterocytozoon bieneusi is an important etiological agent of diarrhea in AIDS patients   . The typer (ITS) . Howeverer (ITS) . ITS genbieneusi . Thereforonments . The useronments . The preNosema bombycis) and honey bees (Nosema apis and Nosema ceranae) and fish . Chronic infections caused by the pathogens were a significant economic problem for silk farms, apiaries, and fisheries in the 1800s  are relevant to livestock veterinary medicine or pest control  became prominent in the field of human medicine as important causes of diarrhea and systemic disease in HIV-infected patients. In addition, colonization of microsporidia has been repeatedly reported to negatively affect immunocompetent individuals in recent years  are the best-known representatives with simple developmental cycles. They affect a wide variety of mammal hosts and together represent the most frequently reported causative agents of zoonotic microsporidian infections  to extremely complex   . NeverthIntracellular parasitism commonly leads to gene loss and genome reduction, which enable adaptation of microbial pathogens to the selective pressures of different niches . This prE. intestinalis has the smallest known eukaryotic nuclear genome of ∼2.3 megabases (Mb)  of E. cuniculi contains nearly 2,000 densely packed genes located on 11 chromosomes , II (mouse strain), and III (dog strain) (E. cuniculi a feasible model for comparative genomics.Microsporidia have relatively compact genomes compared with other eukaryotes . E. inteses (Mb) . Its sisomosomes . Anotherstinalis . Despite strain) , which m strain) . These fE. bieneusi, possesses a relatively more expanded genome (∼6 Mb) than E. cuniculi  in eukaryotic cells probably correlate with the ability of nucleoside transporters to catalyze the import of ATP from hosts and the enhancement of such processes by the specific interactions between the meront plasma membrane and host cell mitochondria , porcines, ruminants, companion animals, equines, carnivores, rodents, lagomorphs, birds, water, etc., and great potential for zoonotic or cross-species transmission  of which were determined to be appropriate for high-resolution typing of E. bieneusi  with varying genotype frequencies (1.4 to 36.1%)  and Nigeria (adults) and five isolates from olive baboons in Kenya were analyzed, leading to recognition of a total of 27 MLGs that differed by regions and hosts. Most of the MLGs are also different from those identified in Peru  in this important fungus.The MLST tool was applied to investigate 72 o 36.1%) . Likewis in Peru . Anotherpulation . Mixed iulations , 2013. Cof China . The 44 primates . In addin number ,b. DespiE. bieneusi isolates sampled in Peru and tested the intragenic and intergenic linkage disequilibrium , the standardized index of association , Fu’s neutrality, and genetic recombination event (Rm) to assess the population structure. The strong intragenic LD , significant pair-wise intergenic LD among all pairs of markers , ISA = 0.7043, lack of neutrality, and limited genetic recombination detected are evidences for a clonal structure in the population  , Nigeria , and Peru , and absence of significant recombination was determined  . In anottermined . Despitetermined . The samn humans . Howeverprimates .E. bieneusi Group 1 isolates with nucleotide sequence data available at all five loci  and recombination potential (far fewer Rms in SP2 than SP1) between the two subpopulations coupled with Wright’s fixation index  analysis confirmed the segregation of SP1 and SP2 and revealed their different genetic structures and transmission routes. SP1 with a clonal structure contains mainly isolates identified at ITS as zoonotic genotypes , whereas SP2 with an epidemic structure  comprises isolates defined at ITS as anthroponotic genotype A. In spite of the sequence diversity of MLGs as mentioned earlier, no significant geographic difference in population genetic structure was ascertained  showed a close genetic relationship between SP1 and SP5 (FST = 0.185 and Nm = 1.10) and the differentiation of SP1/SP5 from SP2/SP3/SP4 . Genetic network analysis showed directed evolution of SP5 to SP3/SP4 and SP1 to SP2 (ISA for SP2/SP3/SP4 = 0.3729/0.4709/0.4866) and a quite small number of Rms examined, are indicators of potential host adaptation in the three subpopulations (ISA for SP1/SP5 = 0.1191/0.2535) and enhanced genetic diversity in SP1 and SP5 might be consequences of host range expansion and ability to adapt to new host environment (E. bieneusi population SP6 (made up of zoonotic genotype D isolates) from SP1 to SP5 was perspicuously seen in E. bieneusi isolates (belonging to Group 1 genotypes SC01 and SC02) obtained from captive bears in China  geneE. bieneusi. Early assessment of recondite reproductive modes in microsporidia is mostly dependent on the patterns of the cell structure and life cycle development (N. apis and Nosema bombi) are considered to be asexual since they have no monokaryotic stage throughout life cycle and lack concerted evolution of rRNA genes in a single spore and even in a single nucleus  and zygomycetes constitute evidence for the occurrence of sex in microsporidia , Group 6 (genotypes horse2 and Nig3), and Group 10 (genotypes CHB1 and CSK1), an outlier genotype Nig5, and 27 Group 1 isolates with genotypes being D, EbpC, type IV, horse1, IH, and Nig2  or long (>6 bp) tandem repeat units using software Tandem Repeats Finder1, were run on the genomic scaffolds or contigs derived merely from a single E. bieneusi isolate (E. bieneusi isolates to uncover more complex factors that modulate the cross-species ability and host adaptation of the pathogen. Comparative genomic analysis of various E. bieneusi isolates that display phenotypic differences would provide useful information on the exploration of new polymorphic genetic markers.Analyses of host ranges of  Group 1 . MLST miand Nig2 . The isoand Nig2 . SP8 andand Nig2  and the eviously . Some MLeviously . Yet, iteviously . To date isolate . Thus, iE. bieneusi has shrinking genome, greatly reduced biological complexity, and robust host dependence. Extreme intraspecific genetic variability and varied levels of host specificity were ascertained within E. bieneusi at the ITS and MLST levels. Genetic analysis of population structure and substructure demonstrates that E. bieneusi might be transmitted differently in a given host.We briefly introduce taxonomy, biology, genetics, and genomics of microsporidia to understand the derived host-adapted characteristics and public health and veterinary importance. Microsporidia differ significantly from species to species in their life cycle, biology, genome organization, and host specificity, which makes the single-celled organisms ideal to elaborate the host-pathogen evolution and transmission. E. bieneusi and its implications to zoonotic and interspecies transmission of this ubiquitous pathogen. However, our understanding of the epidemiology and population genetic traits of the pathogen is still quite superficial. Additional E. bieneusi MLST and genomic data from other hosts and geographic areas are needed for in-depth assessment of the potential role of either host species or geography in genetic clustering of MLGs, host specificity, virulence or clinical presentations, and transmission risks.The data would be helpful in elucidating the host-adaptive mechanism of WL and LX conceptualized, read, and approved the manuscript. WL collected, analyzed the data, elaborated the figures, and drafted the manuscript. LX revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Orcinus orca) of the same population have uniform specialized diets that are non‐overlapping with other sympatric, genetically divergent, and socially isolated killer whale ecotypes. However, killer whales in Iceland show intrapopulation variation of isotopic niches and observed movement patterns: some individuals appear to specialize on herring and follow it year‐round while others feed upon herring only seasonally or opportunistically. We investigated genetic differentiation among Icelandic killer whales with different isotopic signatures and observed movement patterns. This information is key for management and conservation purposes but also for better understanding how niche specialization drives genetic differentiation. Photo‐identified individuals (N = 61) were genotyped for 22 microsatellites and a 611 bp portion of the mitochondrial DNA (mtDNA) control region. Photo‐identification of individuals allowed linkage of genetic data to existing data on individual isotopic niche, observed movement patterns, and social associations. Population subdivision into three genetic units was supported by a discriminant analysis of principal components (DAPC). Genetic clustering corresponded to the distribution of isotopic signatures, mtDNA haplotypes, and observed movement patterns, but genetic units were not socially segregated. Genetic differentiation was weak (FST < 0.1), suggesting ongoing gene flow or recent separation of the genetic units. Our results show that killer whales in Iceland are not as genetically differentiated, ecologically discrete, or socially isolated as the Northeast Pacific prey‐specialized killer whales. If any process of ecological divergence and niche specialization is taking place among killer whales in Iceland, it is likely at a very early stage and has not led to the patterns observed in the Northeast Pacific.Local adaption through ecological niche specialization can lead to genetic structure between and within populations. In the Northeast Pacific, killer whales ( At the l oceans .15N/14N, represented as δ15N), consistent with a diet predominantly composed of herring, while killer whales only seen seasonally  exhibited larger variation in δ15N, suggesting that some individuals have a diet including other prey  haplotype frequency and individual data on observed movement patterns, social associations, and isotopic signatures were used to measure the divergence of putative genetic units. We interpret the identified genetic patterns among Icelandic killer whales, discuss potential ecological and behavioral processes driving them and how they correspond to the patterns of the best‐studied Northeast Pacific ecotypes.22.1N = 60) were collected from a research vessel in both herring overwintering‐ (winter) and spawning‐ (summer) grounds where killer whales can be seasonally found, coinciding with seasonal herring migration into those locations. In the winter, samples were collected off Grundarfjörður and Kolgrafafjörður  was extracted from skin samples using standard proteinase K digestion and phenol/chloroform methods , 0.2 μM of each primer, 0.2 mM of mixed dNTPs , and 0.1 μl of AmpliTaq® DNA Polymerase . For the smaller fragment, the quantities were the same except for the primers, which were increased to 0.3 μM of each. PCR amplifications were performed using a G‐Storm GS1 thermal cycler (Gene Technologies) with an initial denaturation step at 95°C for 2 min, followed by a specific number of cycles  of denaturation at 95°C for 15 s, annealing for either 30 s at 54°C for the longer fragment or for 1 min at 57°C for the smaller fragment, and extension at 72°C for 1 min, followed by a final extension at 72°C for 5 min. Successful amplification was confirmed using agarose gel, EtBr staining, and UV visualization. Negative controls without DNA were included in all PCR plates to monitor for contamination during the PCR set up. Excess dNTPs and unincorporated primers from the completed amplifications were removed using either Illustra ExoProStar 1‐Step  or ExoSAP‐IT™ PCR Product Cleanup Reagent (Thermo Fisher Scientific Inc.).Two sections of the 5′ end of the mtDNA control region were amplified as in Foote et al. : a longePCR products were sequenced in both directions on an ABI 3730 DNA sequencer (Applied Biosystems) at Edinburgh Genomics (University of Edinburgh). Seven randomly selected samples (>10% of the dataset) were re‐sequenced to ensure consistency. All sequences were visually inspected using the software FinchTV v1.4.0  by two of the authors independently: forward and reverse readings of the same sample were compared and any inconsistency was corrected according to the result from the clearest sequence. For each sample, the sequences of the small and longer fragments were concatenated. To determine individual mtDNA haplotype, the final sequences were aligned against 11 previously published sequences for the same genetic regions in North Atlantic killer whales  performed in STRUCTURE v2.3.4 ) of each K, calculated in STRUCTURE HARVESTER v.0.6.94  value and the elbow in the BIC curve. Then, the genetic data were transformed using Principal Component Analyses and a linear discriminant analysis was performed on the retained principal components (no more than 80% were retained to avoid over‐fitting). Each individual was assigned to a genetic unit according to its maximum membership probability. The robustness of the division was confirmed by rerunning DAPC after removing one individual from the pairs of individuals showing a relatedness coefficient superior or equal to 0.45 as in Rosel, Hansen, and Hohn  , the mean number of alleles per locus (k) and the observed and expected heterozygosities  in CERVUS  in FSTAT : the mean sample size per locus . Individuals were considered “seasonal” or “year‐round” if photographed in Iceland only seasonally (either in the winter or in the summer) or if sighted in both herring overwintering‐ and spawning‐grounds, respectively. We tested for significant differences in the ratio of seasonal versus year‐round individuals among genetic units using a 2×3 Fisher's exact test in R , stable isotope ratios analyzed from skin samples  were available from Samarra, Vighi, et al. (2017). Differences in the isotopic niche width between putative genetic units were assessed using the area of standard ellipses corrected for sample size (SEAC) and statistically tested by comparing the probability distributions of Bayesian estimates of SEAC  with the SIAR . Tavares et al.  between males and females from different genetic units, were tested by generating null distributions with 10,000 permutations. Since only adults or sub‐adults were sampled, the whole dataset was used in this test. The mean number of successfully reproducing migrants per generation among movement patterns (Nm) was estimated using GENEPOP : 31 individuals  were seen in both seasonal herring grounds and the remainder of the individuals were seen only seasonally .3.2Sequences of the control region (~611 bp) were generated for all individuals and two different haplotypes were identified, haplotypes 33  from genetic units 1  and 2 , which were further separated by the second component  and all pairwise FST values were low but significant . Results were consistent when excluding closely related individuals. The randomization procedure confirmed that the pairwise FST values obtained were significantly greater than by sampling at random, with significant differences between the distribution of FST values of randomly selected individuals and the distribution of FST values for each pairwise comparison of the genetic units .Overall, microsatellite Ho) in relation to expected values, and low number of alleles per locus (k) and allelic richness (AR) .All genetic units showed high levels of observed heterozygosity , with almost all of the individuals seen year‐round assigned to genetic units 1 and 2 .Genetic unit 1 included 14 individuals seen year‐round in Iceland and seven only seen seasonally (six in the summer and one in the winter). Genetic unit 2 included 16 individuals seen year‐round in Iceland and nine only seen seasonally (seven in the winter and two in the summer). Genetic unit 3 was composed of 14 individuals seen seasonally (nine in the winter and five in the summer) and only one individual seen in Iceland year‐round. This unit included all six individuals that are known to travel to Scotland. The difference in the ratio of individuals seen year‐round to individuals seen seasonally was significantly different among units . The SEAC of genetic units 1 and 2 largely overlapped , particularly in the distribution of δ13C values.Stable isotope ratios were available for all 21 individuals of genetic unit 1, for 23/25 individuals of genetic unit 2 and for 12/15 individuals of genetic unit 3, including 5/6 individuals matched to Scotland in the summer . However, there were still several associations among individuals from different genetic units  of 4.75 migrants per generation, indicating gene flow among the genetic units. Recent migration rates estimated by BAYESASS were highly consistent among runs. High rates of recent gene flow were detected between genetic units 1 and 2 . In contrast, very low rates were detected between genetic units 1 and 3 . Unidirectional gene flow was detected from genetic unit 2 into genetic unit 3 . However, we infer these rates may not correspond to contemporary gene flow, as all individuals within each genetic unit had a high likelihood (≧88%) of being at least a 2nd generation migrant.There was no significant sex‐biased dispersal across observed movement patterns detected in FSTAT, since neither sex‐specific es Table . Individve Table , so no ep‐value = 0.005; genetic unit 3: p‐value = 0.001), but this was not verified under the two‐phase model . Furthermore, the allele frequency distribution had a normal L‐shape for both genetic units, which is not expected if a recent bottleneck has occurred .BOTTLENECK showed marginal detection of recent changes in effective size for genetic units 2 and 3. Under the infinite alleles model there was a significant heterozygosity excess , genetic units were not socially segregated and it is unlikely that association patterns were the main drivers of the genetic discontinuity observed. Since different social groups, as defined by Tavares et al. , were sa15N values suggesting a broad diet  and allelic richness (AR), were overall similar among genetic units: high Ho relative to expected values and low allelic richness. When populations experience a recent reduction of effective size, allelic diversity is reduced faster than heterozygosity  while a high number of migrants (Nm) indicated high gene flow.Genetic diversity indices, specifically observed heterozygosity  reported between Northeast Pacific mammal‐eating and resident populations  ongoing gene flow, with occasional outbreeding among genetic units, where the genetic differentiation is at an equilibrium state ; or (b) recent historical separation of the genetic units, where the divergence is at non‐equilibrium state, and the gene flow among gene units is non‐existent or decreasing, but has not yet resulted in greater genetic differentiation. Little or non‐existent current gene flow would suggest that the current population has evolved rapidly from a historically more panmictic population. Additionally, these mechanisms could be influenced by several processes, such as: (a) differing rates of outbreeding with other North Atlantic populations; (b) preferential mating within subgroups; (c) isolation‐by‐distance/time/adaptation; and/or (d) periodic changes in connectivity among demes due to decadal changes in herring stock migration routes and separation into different herring feeding, spawning and wintering grounds.The level of genetic structure within the Icelandic population suggests variation in gene flow among killer whales in Iceland. The lack of indications of sex‐biased dispersal and the find that individuals were likely to be at least 24.3Eubalaena australis) is maintained since site‐fidelity to feeding areas is transmitted along matrilineal lines  or a subtle temporal structure driven by the contemporary distribution of fragmented seasonal herring grounds. Geographic overlap in Icelandic herring grounds and the fluidity of the feeding aggregations  in the Sainte‐Marguerite river (Canada) showed that the population had significant substructuring in both space and time (Garant et al., Metapopulation dynamics can arise when patches of a fragmented habitat are momentarily occupied and are followed by local extinction when conditions become unsuitable and then reoccupied again sometime later (Beebee & Rowe, None declared.S.B.T., F.I.P.S., and P.J.O.M. conceptualized the work and S.B.T., S.P., and J.A.G. conceptualized the analyses. S.B.T., F.I.P.S., and P.J.O.M. collected samples. S.B.T. performed laboratory work. S.B.T., S.P., and J.A.G. analyzed data. S.B.T. wrote the paper. All authors approved the final manuscript.https://doi.org/10.5061/dryad.674k8j4.Mitochondrial haplotypes and microsatellite genotypes used in this study are available from the Dryad Digital Repository:  Click here for additional data file.