Corophium volutator  and the annelid Hediste diversicolor , which were introduced from Europe to North America, have limited natural dispersal capabilities, are abundant in intertidal sediments, but not commonly found in modern water ballast tanks. We detect similar levels of genetic subdivision among introduced North American populations and among native European populations. Phylogenetic networks and clustering analyses reveal population structure between sites, a high degree of phylogenetic reticulation within ranges, and phylogenetic splits between European and North American populations. These patterns are inconsistent with phylogeographic structure expected to arise from natural dispersal alone, suggesting human activity eroded ancestral phylogeographic structure between native populations, but was insufficient to overcome divergent processes between naturalized populations and their sources. Our results suggest human activity may alter species' evolutionary trajectories on a broad geographic scale via regional homogenization and global diversification, in some cases precluding historical inference from genetic data.The same vectors that introduce species to new ranges could move them among native populations, but how human‐mediated dispersal impacts native ranges has been difficult to address because human‐mediated dispersal and natural dispersal can simultaneously shape patterns of gene flow. Here, we disentangle human‐mediated dispersal from natural dispersal by exploiting a system where the primary vector was once extensive but has since ceased. From 10th to 19th Centuries, ships in the North Atlantic exchanged sediments dredged from the intertidal for ballast, which ended when seawater ballast tanks were adopted. We investigate genetic patterns from RADseq‐derived SNPs in the amphipod  n = 121; 4,870 SNPs) and the annelid Hediste diversicolor , which were introduced from Europe to North America, have limited natural dispersal capabilities, are abundant in intertidal sediments, but not commonly found in modern water ballast tanks. Phylogenetic networks and clustering analyses reveal population structure between sites, a high degree of phylogenetic reticulation within ranges, and phylogenetic splits between European and North American populations, which is inconsistent with phylogeographic structure expected to arise from natural dispersal alone and suggests human activity eroded ancestral phylogeographic structure between native populations but was insufficient to overcome divergent processes between naturalized populations and their sources.The same vectors that introduce species to new ranges could move them among native populations, but how human‐mediated dispersal impacts native ranges has been difficult to address because human‐mediated dispersal and natural dispersal can simultaneously shape patterns of gene flow. We investigate genetic patterns from RADseq‐derived SNPs in the amphipod Corophium volutator ( Until recently, they were thought to be native in North America  if historical human‐mediated movement facilitated range expansion but had little impact on native populations, native populations are expected to exhibit phylogeographic structure and introduced populations are expected to bear high genetic similarity to their sources; (b) if historical human‐mediated movement facilitated gene flow among native populations, it is therefore expected that native populations would exhibit population genetic structure due to contemporary isolation, but not phylogeographic structure due to historical gene flow; and (c) if historical human‐mediated movement facilitated gene flow among native populations, the relative similarity of introduced populations to their sources is expected to decrease due to gene flow between the source and other native populations, thereby increasing measures of between‐range divergence relative to within‐range divergence. Our study provides evidence of the historical impact of human exploration and trade on diversification in introduced invertebrates and draws attention to the potential for human activity to reshape evolutionary trajectories across introduced and native ranges alike.To investigate the net effect of diversifying and homogenizing aspects of human‐mediated dispersal on species' evolutionary trajectories, we compare patterns of genetic divergence using reduced representation genomic sequencing of native European and naturalized North American populations of 22.1C. volutator from 21 sites and 78 individuals of H. diversicolor using a trowel and forceps at 15 sites , the North Sea (n = 5), and North America outside of the Bay of Fundy (n = 17) as cryptic species related to H. diversicolor from mitochondrial DNA   to reduce errors introduced through sequencing. We compared results for several combinations of nucleotide mismatches per individual allowed to group stacks  and nucleotide mismatches allowed among stacks to form catalog loci , which showed a plateau in number of loci retained at M = 6 and n = 6. To reduce physical linkage, we retained only a single SNP per stack, and to reduce bias from missing data, we retained loci genotyped in 100% of populations (p = 1) and at least 50% of individuals per population (r = .5) and filtered rare variants by removing SNPs with minor allele frequencies <0.01 , 15 , and 16 (eN = 200). Reduced values of eN were included to visualize the effect of increased drift. We used two generations per year, reflecting reproductive cycles of C. volutator and H. diversicolor  active in this system over 1,800 generations  to be an order of magnitude less than, equal to, and an order of magnitude more than the magnitude of natural migration (NM), followed by a return to natural migration matrices for 200 generations (~100 years).To investigate the impact of human‐mediated migration on phylogeographic patterns, we conducted simulations following a complex demographic history with varying degrees of natural and human‐mediated migration, assessing genetic patterns in one of these simulations at different points in time. We simulated SNPs from 10,000 unlinked neutrally evolving diploid loci of 85 base‐pairs each under scenarios of differing magnitudes of human‐mediated migration in continuous time simulations with fastsimcoal2 v.2.6  was 0.5 (SD = 0.6) in North America and 3.5 (SD = 2.3) in Europe for C. volutator, and 4.2 (SD = 1.9) in North America and 8.6 (SD = 7.8) in Europe for H. diversicolor  was 6.4 in North America and 34.1 in Europe for ns Table .3.3FST supports strong genetic structuring between North American and European populations for C. volutator  and H. diversicolor . Calculation of FST on simulated data shows no bias with low sample sizes , but not for H. diversicolor . The proportion of variation between individuals within populations was relatively low for both C. volutator (5.2%) and H. diversicolor (2.3%). In C. volutator, the best‐fit number of genetic clusters (K = 2) differentiates North American from European individuals, and the best‐fit value for each continent separately is K = 1 for North America and K = 2 for Europe, with two populations from Ireland (ROS & BAL) and one from Denmark (BAH) forming a separate cluster . Each of these clusters analyzed separately has a best‐fit value of K = 1. In H. diversicolor, the best‐fit number of clusters (K = 4) differentiates North American individuals from three genetic clusters in Europe, and analyses of these clusters separately have a best‐fit value of K = 1. Increasing K beyond its optimal value reveals increasingly fine‐scale clustering that corresponds to geography in both species, particularly among native populations, but does not group North American individuals with European clusters  than for H. diversicolor . IBD in the native range was less positive and correlated than in the introduced range for C. volutator  and H. diversicolor . Pairwise estimates of FST among European populations was similar to that observed among North American populations for C. volutator  and for H. diversicolor .Phylogenetic networks and genetic clustering recover nearly all population groups with high fidelity Figure , consist3.5C. volutator or H. diversicolor, and populations from the European continental coast did not cluster strictly according to geographic location , with similar values for simulated data when human‐mediated migration was high  and one population in France (LAV) are interspersed in two different clusters.Long‐standing isolation between continental Europe and the British Isles and stepping‐stone migration between populations distributed along >2,500 km of European coast is expected to produce branching networks that reflect colonization history, geographic location, and major barriers to dispersal Figure . Contrar3.6To investigate how different scenarios of human‐mediated and natural dispersal influence phylogeographic and population genetic structure, we simulated neutrally evolving populations with two generations per year undergoing postglacial expansion and colonization, isolation between three regions in the native range, introduction to a new range (400 years ago), and varying strengths of natural (stepping‐stone) and human‐mediated  gene flow at times corresponding to ballast sediment use in Europe . The effects of human‐mediated gene flow depend on the magnitude of natural gene flow between populations, but phylogenetic networks of simulated data show two emergent patterns Figure : (a) inc3.7C. volutator, H. diversicolor, and data from simulations using two methods reported to perform well regardless of demographic history: OutFLANK  and H. diversicolor (23% of SNPs), but these results were well within the 13%–46%  range of false‐positive ratios identified in simulated data  and Normandy, France , form two distinct genetic groups with individuals from each population in both clusters ; data curation (lead); formal analysis (lead); funding acquisition (supporting); investigation (lead); methodology (lead); project administration ; visualization (lead); writing – original draft (lead); writing – review & editing (lead). Linley K. Jesson: Conceptualization (supporting); supervision ; writing – review & editing (supporting). Jason A. Addison: Conceptualization (supporting); funding acquisition (lead); project administration ; supervision ; writing – review & editing (supporting).Appendix S1Click here for additional data file.


Ephemeroptera, one eurytopic and therefore widespread (Afroptilumsudafricanum) and two stenotopic and thus endemic  species, all of which co-occur in the southern Great Escarpment, South Africa. Mitochondrial DNA was analysed to compare the genetic diversity between the habitat generalist and the two habitat specialists. Afroptilumsudafricanum showed no indication of population genetic structure due to geographic location, while both Demoreptus species revealed clear genetic differentiation between geographic localities and catchments, evident from phylogenetic analyses and high FST values from AMOVA. In addition, the phylogenetic analyses indicate some deeper haplotype divergences within A.sudafricanum and Demoreptus that merit taxonomic attention. These results give important insight into evolutionary processes occurring through habitat specialisation and population isolation. Further research and sampling across a wider geographic setting that includes both major mountain blocks of the Escarpment and lowland non-Escarpment sites will allow for refined understanding of biodiversity and associated habitat preferences, and illuminate comparative inferences into gene flow and cryptic speciation.This study investigates genetic diversity in three species of  Greater genetic diversity within a lineage is regarded as increasing its resilience to environmental change , which gAquatic insects have a winged adult stage that is generally considered to have relatively strong dispersal ability . This isEphemeroptera, show varied degrees of habitat-specificity, with some species being completely restricted to a certain habitat and others occurring in a range of habitat types  areas in the Eastern Cape and KwaZulu-Natal  (KP438135 and KP438160) and 16S rRNA (16S) (KP438109 and KP438119) gene regions.A related species of nce data  were obtnce data  for bothDNA was extracted using the Invisorb Spin Tissue Mini Kit following manufacturer’s protocol . Extraction was non-destructive, using internal body digestion, which ensured the preservation of the exoskeleton for future morphological analysis COI) and small subunit ribosomal RNA (16S). A 528-bp section of the COI regions of D.natalensis and D.capensis was successfully amplified with the standard ‘universal’ primer pair, LCO1490 and HCO2198 (A.sudafricanum. A new forward primer (5’–GGT GGA TGG GCA GGA ATG GTA GGA–3’) was designed and used with HCO2198 to successfully sequence the remaining samples of A.sudafricanum. The 16S region was amplified with the primer pair 16Sar (5’–CGC CTG TTT ATC AAA AAC AT–3’) and 16Sbr (5’–CCG GTC TGA ACT CAG ATC ACG T–3’)  . HoweverPCR) was performed in a 50 μl volume using the following thermal regime: 95 °C for 5 min, 35 cycles of 95 °C for 45 s, 50 °C for 45 s, and 72 °C for 90 s, followed by a final extension period of 72 °C for 5 min. PCR amplifications were checked for the presence of amplified PCR products by gel electrophoresis (0.5% agarose gel stained with SYBR green) and viewed with a UV-transilluminator. Successful PCR products were cleaned up using the Invisorb PCRapace® Quick purification kit  and cycle-sequenced in both directions using the primers used for amplification, the ABI Big Dye Sequencing kit v.3.1 (following manufacturer’s instructions (Applied Biosystems)), and a ABI Genetic Analyzer 3500 (Applied Biosystems).The polymerase chain reaction (http://www.genecodes.com). The sequences were then aligned in MEGA v.6  in PAUP* (Congruence between the in PAUP*  with 100BI) analyses were conducted with MrBayes v.3.1.2  analyses were conducted with 2 000 bootstrap replicates using the GARLI (Genetic Algorithm for Rapid Likelihood Inference) on XSEDE via the CIPRES (Cyberinfrustructure for Phylogenetic Research) Science Gateway v3.3 (SDSC) and the University of California (UC San Diego). Models of molecular evolution for each dataset were selected using the Akaike information criteria (AIC) as implemented by jModeltest 2.1.6   branch-swapping was used to find the approximate length of the shortest trees, with one tree kept with each random addition. To investigate nodal support, all of the trees from this search were used as starting trees for a second heuristic search with MAXTREES set to 5 000. The Demoreptus analysis used FASTBOOTSTRAP with 10 000 replicates.Parsimony analyses were performed in PAUP* version 4.0b10  using thCOI datasets. The number of variable sites (S), number of haplotypes (Hap) and haplotype diversity (Hd), Nucleotide diversity (p) and neutrality tests (Tajima’s D and Fu’s FS) were calculated in DNAsp (AMOVA) in ARLEQUIN ver. 3.5.2 (ST (fixation index) values were calculated between localities to determine whether putatively conspecific populations differed significantly in their genetic composition. For all AMOVA analyses (listed in Table a priori groups were defined by each site where the insects were collected. Haplotype networks were illustrated with a median-joining network (MJN) algorithm (ε = 0) (Molecular diversity was investigated using the in DNAsp . Populatr. 3.5.2 . FST (fi (ε = 0)  using th (ε = 0)  to analyCOI sequences (649 bp) were obtained from 86 individuals and 16S sequences (542 bp) obtained from 59 individuals of A.sudafricanum. Shorter (528 bp) COI sequences were obtained for 24 Demoreptus individuals . DNA characteristics for each gene dataset are summarised in Table COI and 16S datasets were not significantly incongruent (P = 0.3890) and could therefore be combined for analysis. The COI+16S molecular dataset consisted of 59 specimens and 1191 nucleotides including the outgroup. No evidence of saturated substitution was found for either gene (data not shown).uences 54 bp obtaiA.sudafricanum consistently retrieved six distinct clades and an unresolved grade of specimens (referred to hereafter as the “widespread grade”) from a wide range of sites for all analyses  grouped together exclusively, otherwise all other sites are mixed.pes Fig. , Table 3pes Fig. , which ipes Fig. . Haplotypes Fig. . The numpes Fig.  , six of which were singletons and D.natalensis retrieved six haplotypes  including three singletons  for D.capensis and Haplotype 4 (N = 3) for D.natalensis (study areas Barkly Pass and Rhodes), which are both found in the Eastern Cape Drakensberg main mountain block in the Escarpment  are reported in Table Demoreptus spp. make the estimates imprecise. Neutrality tests (Tajima’s D and Fu’s FS) were not significant for A.sudafricanum, D.capensis or D.natalensis indicating that the nucleotide patterns of variation are consistent with the neutral theory of evolution. Fu’s FS statistic for the widespread grade of A.sudafricanum was negative (FS = –11.544) and significant (P < 0.02), indicating a recent population expansion  while 47.67%  was explained by variation within populations  and 60.57%  explained by variation within populations. In contrast, the AMOVAs for the habitat-restricted species, D.capensis and D.natalensis, indicated a higher proportion of variation among populations: 94.83%  and 95.39% , respectively  for D.natalensis.The ST) was much lower for A.sudafricanum compared to the Demoreptus species  sites across the sampling area. Afroptilumsudafricanum was best represented with a haplotype network , across various mountain chains and differing in altitude by over 900 m . Even if A.sudafricanum is treated as a species complex and assessed for mitochondrial genetic differentiation, results indicate low divergence between populations, suggesting that gene flow is not particularly limited within catchments and across the geographic range. Although mayflies are traditionally thought to have limited dispersal ability due to weak flight and short adult lifespans ; and Kamdebooberg was unresolved. Demoreptuscapensis had a similar result, but the Rhodes area returned a separated clade with a well-supported, long branch. Suggestively, the samples of A.sudafricanum and D.capensis collected at Rhodes both occupy long branches in their respective phylogenetic analyses ; these characters will be documented in a subsequent taxonomic study. Other areas in the Drakensberg range in KwaZulu-Natal and Lesotho should be sampled to investigate the range of this mitochondrial clade and whether it occurs throughout high altitude, mountainous areas. A caveat is that the Demoreptus population analyses involve limited sample sizes from few localities, which can produce misleading clustering . However, the evidence available for this niche glaciation is considered by some as ambiguous and unclear (A.sudafricanum).The observed deep haplotype divergences in all three species studied and the recent population expansion in g slopes . Small r unclear . Cyclica unclear  could haHowever, because they are asexually and maternally inherited, strongly divergent haplotypes that originated in relict populations may not reflect contemporary mating pattern if those isolated populations’ ranges subsequently expand to restore potential panmixis . More saThese results help to illuminate some of the evolutionary processes occurring in mayfly species and highlight the effect of habitat-specificity on haplotype diversity and partitioning within a species. While all three species have qualitatively similar levels of dispersal potential in terms of flight, they show differences in gene flow, suggesting that other processes, such as species-specific habitat requirements, may contribute to genetic population structure. These results have implications for the conservation of riverine organisms, the reintroduction of locally extinct taxa and the rehabilitation of disturbed environments .D.capensis and D.natalensis further highlight the genetic distinctiveness of populations between catchments, further corroborating the value of using catchments in conservation, management and legislative frameworks. These genetically distinct populations form an important component in the evolutionary legacy of a species. Therefore, the development of inter-basin water transfer schemes poses a threat to both D.capensis and D.natalensis and many other species by potentially connecting historically isolated and genetically distinct populations (In South Africa, it is legislated that catchments are used as management units . Previouulations In addition, dispersal among adjacent catchments has implications for the recovery of lotic systems following disturbance . These fA.sudafricanum and new species of Demoreptus affect the field of aquatic research in South Africa. Mayflies form a very important component of applied aquatic biology, particularly biomonitoring, the presence of cryptic taxa is being discovered at an increasing rate and poses challenges for some aquatic ecosystem monitoring methods. With bioassessment methods gaining increasing popularity, a detailed understanding of commonly collected species will aid in further development of assessment methods and clarify species identification (This study highlights the importance for future studies on community structure, biodiversity, and biomonitoring, where the taxonomic accuracy of species identification is crucial . The idefication . In addification  and widefication . This ap


Oncorhynchus kisutch) to address two objectives. We demonstrate that a single glacial refugium is the source of most of the present-day genetic diversity, with detectable inputs from a putative secondary micro-refugium. We found statistical support for a scenario whereby ancestral populations located south of the ice sheets expanded recently, swamping out most of the diversity from other putative micro-refugia. Demographic inferences revealed that genetic diversity was also affected by linked selection in large parts of the genome. Moreover, we demonstrate that the recent demographic history of this species generated regional differences in the load of deleterious mutations among populations, a finding that mirrors recent results from human populations and provides increased support for models of expansion load. We propose that insights from these historical inferences should be better integrated in conservation planning of wild organisms, which currently focuses largely on neutral genetic diversity and local adaptation, with the role of potentially maladaptive variation being generally ignored.A thorough reconstruction of historical processes is essential for a comprehensive understanding of the mechanisms shaping patterns of genetic diversity. Indeed, past and current conditions influencing effective population size have important evolutionary implications for the efficacy of selection, increased accumulation of deleterious mutations, and loss of adaptive potential. Here, we gather extensive genome-wide data that represent the extant diversity of the Coho salmon ( Reconstruction of a species’ past demographic history from genetic data can highlight historical factors that have shaped the distribution of genetic diversity along its genome and its geographic range. Here, we combine genotyping-by-sequencing with demographic modelling to address these issues in the Coho salmon, a Pacific salmon of conservation concern in some parts of its range, notably in the south. Our demographic reconstructions reveal a linear decrease in genetic diversity toward the north of the species range, supporting the hypothesis of a northern route of postglacial recolonization from a single major southern refugium. As predicted by theory, we also observed a higher proportion of deleterious mutations in the most distant populations from this refugium. Beyond this general pattern, among-site variation in the proportion of deleterious mutations is consistent with different local trends in effective population sizes. Our results highlight the potential importance of understanding historical factors that have shaped geographic patterns of the distribution of deleterious mutations in order to implement effective management programs for the conservation of wild populations. Such fundamental knowledge of human historical demography is now having major impacts on health sciences, and we argue it is time to integrate such approaches in conservation science as well. Historical climate variation has had a major influence on the current distribution of species genetic diversity . The PleIn this context, understanding the impacts of historical climatic oscillations on the demographic history of a given species is crucial. Namely, it is important to understand how historical demographic events influenced the present-day geographic distribution of the within-species genetic diversity across its range. Here it is noteworthy that while the majority of studies on wild populations have focused on patterns of neutral and potentially adaptive genetic diversity, the importance of potentially maladaptive variation (e.g. patterns of deleterious mutation load) has generally been ignored. By disentangling past and current drivers of range-wide genomic diversity, this information can inform management and conservation decisions . Beyond Ne) , corresponding to the onset of the last glacial retreat , supporting the idea that the Thompson R. has evolved in isolation from the remaining populations for a long period of time.Our demographic inferences provided highly consistent divergence time estimates among all models and populations . Accordingly, the Ne values were reduced to approximately 20% of their initial values depending on the considered population . Obviously, the magnitude of the reduction would require further investigation as quantifying the effect of linked selection is likely more complicated than a simple rescaling of Ne. Still, these results provided increased support for a role of linked selection affecting the estimation of demographic parameters [S6 Table).Our models account for the confounding effects of selection at linked sites and that of the accumulation of local barriers to gene flow in the genome, two potential confounding factors that can lead to erroneous interpretations when they are not explicitly taken into account ,17. All bination . Our inframeters  and pote.To consolidate inferences made from ∂a∂i we constructed a global model based on ∂a∂i parameter estimates in Fastsimcoal v2.6. To limit the confounding of barriers to gene flow , linked In summary, our results best supported a scenario whereby contemporary populations mainly originated from a single major ancestral refugium located south of the ice sheets in Cascadia/California, followed by a postglacial expansion and population divergence along the South to North recolonization axis. As a consequence, we expect that the South-North expansion should favour the accumulation of deleterious mutations at the expansion front toward the north ,66, but N/πS,S8 Table), a commonly used proxy for the deleterious mutation load [N/πS was significantly and negatively correlated with Tajima’s D . Considering our demographic inferences, this result illustrates how the postglacial recolonization changed local effective populations and shaped the present-day deleterious load variation among these populations. Interestingly, even after excluding the two most highly loaded populations (Clakamas and Upper Pitt River in Cascadia and BC respectively), the relationship between the πN/πS and the Tajima’s D still remain strong . Our results suggest that the Clackamas River populations exhibit high levels of admixture with the Thompson R populations . As a consequence, we hypothesize that the post-glacial secondary contact with this population contributed to the introgression pulses of maladaptive alleles that are still detectable nowadays. This raises exciting future research questions regarding the role of past introgression and the maintenance of deleterious alleles [S14 Fig), suggesting additional sources of variation that remain to be identified and their effect evaluated.Our above demographic reconstruction revealed expansion from a major southern refugium. Consequently, we hypothesized that such a history has generated significant differences in the deleterious mutation load among populations, consistent with patterns documented in human populations –67. Therion load . Accordi alleles . It mustOne possible consequence of postglacial recolonization is that neutral and deleterious mutations may increase in frequency (i.e.surf) at the expansion front . This siST and highest diversity) and the recessive load , validating theoretical expectations from range expansion [Oncorhynchus tshawytscha) [O. mykiss) and 3) Atlantic salmon  [2 = 0.02, p = 0.96, Fig 6B). Since PROVEAN is primarily designed for Human and Mouse . After excluding Clackamas and Upper Pitt populations (hypothesized to display increased load due to admixture), the correlation significantly increased . Third, we observed significant differences in the derived allele frequency (DAF) at predicted deleterious non-synonymous mutations among regions  as well as among populations  with higher values indicative of less efficient purging . To the best of our knowledge it is the first time that such correlations between different proxies of the load and distance to the putative refugial origin are reported other than in studies on humans [First, we found a nearly linear relationship between the distance to the candidate source (defined as the one with the lowest βxpansion . This laxpansion  based onwytscha) , 2) Raino salar)  , we n humans ,66 and an humans ,76 or ban humans . As suchp < 0.01) (S11 Table), a result that is in line with our finding of increased expansion load in Fig 6A. When considering the total load of derived deleterious mutations, we found that, on average, there were indeed significantly more putatively deleterious variants per individual in California, Cascadia, and Haida Gwaii populations than in fish from Alaska, British Columbia, or the Thompson R. watershed , although these differences were modest. Accordingly, we observed that πN/πS varies as a function of the levels of nucleotide diversity (πS). We indeed found a positive relationship between πN/πS and πS . We note again the same two populations (Clackamas River & Upper Pitt) were strongly contributing to the linear regression. However, the relationship remained significant after their exclusion . This result provides increased support for our above findings that populations with higher effective population size also displayed a higher deleterious load when considering mildly deleterious variants in heterozygous states (those that are more likely to contribute to πN). This suggests strongly that deleterious mutations are efficiently purged in populations of large Ne while mildly deleterious mutations would still segregate at appreciable frequency [Finally, a general prediction from population genetic theory is that deleterious mutations in a heterozygous state should be more frequent than in a homozygous state, especially in populations with higher effective sizes, where selection should be more effective at purging mutations with increased homozygosity . Accordirequency , as obserequency ,22,26The role of historical demography in generating differences in the mutation load at the population level remains debated among evolutionary biologists, particularly among human geneticists ,28,29,78Fig 7). Using the Coho salmon as a case study, we demonstrated how these approaches are meaningful for conservation genomics and provide opportunities to disentangle the impacts of historical vs. contemporary drivers of population declines. Such investigations also allow assessing the distribution of all selected variants, including not only the beneficial, but also deleterious ones.In this study, we provided rare evidence of how demographic modelling can provide valuable information for conservation genomics. We found considerable statistical support for a recent postglacial demographic history in the Coho salmon, consistent with a main southern refugium, which generated relatively simple latitudinal gradients in nucleotide diversity and mutation load. Complex demographic processes including past population expansions or periods of isolation and secondary contact, have also been reconstructed with state-of-the-art demographic modelling methods. This was made possible by jointly considering the confounding impact of linked selection and barrier to gene flow on such demographic reconstructions. Altogether, this postglacial history, as well as some additional demographic changes, have influenced the efficacy of selection and favoured the accumulation of deleterious mutations in the most distant populations from the main refugium . DNA was extracted from all individuals and sequenced using a GBS method . Reads ailed in . Samtoolailed in  was usedailed in . To do sD v0.930  was usedD v0.930 . We firsD v0.930  in ANGSDST [ST. Weir and Cockerham’s FST estimator θ [ST, FST and the distance to the southernmost site using linear models with the lm function implemented in R. We also verified the relationship between FST and the distance to the southernmost site using Mantel tests with 10,000 permutations. Vcftools was also used to identify singletons . The power to discover singletons is however dependent upon the sample size. Therefore, we reduced the size of each population to the smallest size of 13 by randomly sampling individuals in each population. We repeated the procedure 200 times to obtain standard deviations of the distribution of singletons  resulting in 40,632 SNPs. K-values ranging between 1 and 60 were tested and cross-validations were obtained using the cross-entropy criterion with 5% of masked genotypes. The default value for the regularization parameter was used to avoid forcing individuals into groups and hence underestimating admixture. Similar results were obtained from Admixture [https://github.com/QuentinRougemont/treemix_workflowLevels of ancestry and admixture proportions were inferred with the snmf function implemented in the R package LEA , allowindmixture  and are dmixture  based ondmixture  using thdmixture  R packagWe tested alternative hypotheses of divergence from a single southern or northern refugium versus divergence from two or more refugia . In the first case, by comparing different regional groups, we expected a model of divergence with ongoing migration (IM) to be supported, or alternatively a model of divergence with ancient migration if gene-flow has stopped recently (AM) following the divergence and recent colonization of rivers. In the case of divergence into multiple refugia we expected models of secondary contact between putative groups to be best supported. We further expected postglacial gene flow between the historical refugia to leave detectable traces that should correspond to the signals of secondary contact. In the unlikely case of no gene flow between regional groups, we expected a model of strict isolation to be most likely. All divergence scenarios are represented in anc, splits at time Tsplit into two daughter populations of size N1 and N2. Under the SI model, no gene flow occurs between the two populations. Under AM, gene flow occurred between Tsplit and Tam and is followed by a period of strict isolation. Under IM, gene flow occurs at a constant rate at each generation between the two populations. Gene flow can be asymmetric, so that two independent migration rates m12 (from population 2 to 1) and m21 (from population 1 to 2) were modeled. Under the SC model, the population evolved in strict isolation between Tsplit and until Tsc where a secondary contact occurs continuously up to present time. Gene flow is modeled as M = 2Nref.m. In ∂a∂i, heterogeneous introgression was modeled using two categories of loci occurring in proportions P  and 1-P  across the genome. The same procedure was used to account for linked selection by defining two categories of loci with varying effective population sizes . To quantify how linked selection affects reduced Ne, we used a Hill-Robertson scaling factor (Hrf) to relate the effective population size of loci influenced by selection (Nr = Hrf * Ne) to that of neutral loci (Ne). A hierarchical approach was used to avoid over-fitting: first we compared models assuming constant effective population size. Second, the best identified models were modified to incorporate population expansion or decline, as expected given the observed distribution of genetic diversity. Population expansion was implemented using two additional parameters for population 1 and population 2, allowing each population to either grow or decline exponentially.The models shared the following parameters: the ancestral populations of size Nhttps://github.com/QuentinRougemont/DemographicInference.Models were fitted using the diffusion theory implemented in ∂a∂i  and inclWhile the ∂a∂i analyses provided valuable results regarding the demographic history and how it is confounded by linked selection and barriers to gene flow, this analytical framework is currently limited to pairwise comparisons, which does not allow painting a global scenario of the species divergence history. Therefore, we build upon the ∂a∂i models and other observed statistics to construct a global scenario of population expansions from the South to the North with a postglacial secondary contact between Thompson and the main distribution. This scenario was then tested using Fastsimcoal v2.6  and is dSalmo salar SRA Bioproject: SRP059652). Every individual was aligned against the Coho salmon V1 reference genome (GCF_002021745.1) using GATK UnifiedGenotyper and calling every SNP using the EMIT_ALL_SITES modes. We then used a python script to determined the ancestral state of the GBS SNPs if 1) the SNP was homozygous in at least two of the three outgroups, and 2) match one of the two alleles identified in Coho salmon. Otherwise, the site was inferred as missing and not used in subsequent analyses.We identified derived and ancestral allele using the genomes of three outgroup species, the chinook salmon, the rainbow trout, and the Atlantic salmon, to classify SNPs as ancestral or derived. These informations were subsequently used to analyze the load of deleterious mutation. Whole genome data for the chinook salmon  were provided by one of us (B. Koop), whereas rainbow trout (n = 5) and Atlantic salmon (n = 5) data were downloaded from NCBI Sequence Read Archive  and 1) πS used as a proxy of the long-term effective population size; 2) Tajima’s D used as a proxy for historical change in population size and 3) distance to the Southernmost site. Significance of the correlation were tested using the lm function in R. Plots were drawn using ggplot2 [https://github.com/QuentinRougemont/gatk_haplotypeandhttps://github.com/QuentinRougemont/piNpiS.The ratio of non-synonymous diversity to synonymous diversity is a commonly used metric to quantify the deleterious mutation load e.g. . To do s ggplot2 . All piphttps://github.com/QuentinRougemont/gbs_synonymy_with_genome. We analysed the data in two ways: first we counted the total number of putative homozygous deleterious alleles per individual as well as the total number of deleterious alleles (both in homozygous and heterozygous states) using: Ntotal = 2 Χ Nhomo + Nhetero [While the computation above provides insights into the deleterious mutation load, it does not allow distinguishing among recessive and additive load nor testing for differences in allelic frequencies. We therefore further tested differences in mutation load among populations as follows. The software Provean  was used Nhetero . These t Nhetero ,67. ThesA permit number SIRUL 111722 was obtained to work on DNA sequences. (i) No specific guidelines were followed as we did not work with animals per se and obtained all of our samples from a tissue/DNA repository of the department of Fisheries and oceans Canada; (ii) the study was approved by the following committee "Comité de protection des animaux de l'Université Laval (CPAUL)" (iii) the approval number "SIRUL 111722" was the project label at University Laval. Following answering the CPAUL questionnaire, we did not get a permit because we did not need one since there were no animals manipulations involved.10.5061/dryad.h44j0zph8. All code to reproduced the analyses is available at: https://github.com/QuentinRougemont/Raw data will be deposit on NCBI together with Short Read Archive (SRA) accession number PRJNA647050. The vcf file is available at dryad doi:S1 FigEach points represents a sample site and is colored by region.(TIF)Click here for additional data file.S2 FigThe branch (grey) represent shared polymorphism between sample site and are proportional to levels of sharing. Each point represents the number of private polymorphism and is colored by region. Computation were based on a sample of size 100 in each region to enable comparison. Regional groups were chosen based on the literature regarding expected ancestral refugia.(TIF)Click here for additional data file.S3 FigShown is the distribution observed across 200 dataset obtained by randomly sampling individuals across populations. Black dots with errors bars represent the mean ±1 standard deviation.(TIF)Click here for additional data file.S4 FigFST as a function of the distance to the southernmost site. Each point represents a sample site and is coloured by region. The FST was computed between the southernmost site and all other remaining sites.Increasing (TIF)Click here for additional data file.S5 FigFST-based Hierarchical tree depicting relationship among samples. Colors represent the major region. B. Heatmap of FST values among samples ordered from North to South on the X and Y-axis.A. (TIF)Click here for additional data file.S6 FigThe Axis 3 and axis 4 are displayed.(TIF)Click here for additional data file.S7 FigEach point represents an individual site and is colored by region.(TIF)Click here for additional data file.S8 FigA. Admixture Barplot obtained from LEA for various K-values. B. Progressive decrease of LEA cross-entropy criterion. Lower cross-entropy values indicates the number of cluster compatible with the data (here from 30 to 60).(TIF)Click here for additional data file.S9 FigA. Proportion of variance explained (y-axis) as a function of the number of migration edge (x-axis) B. Treemix tree inferred without gene flow. C. Treemix tree inferred with four migration edges. D) Residuals for Treemix tree without gene flow E) Residuals for Treemix tree with four migration edges.(TIF)Click here for additional data file.S10 FigThe site indicated as “blacklisted” are sites that were not included in the ∂a∂i analyses. These corresponds to potentially admixed sites between BC and Alaska and display a reduced number of individuals to constitute a coherent unit for demographic comparison.(TIF)Click here for additional data file.S11 FigNanc, N1, N2: effective population size of the ancestral population, of the first and second daughter population. M1 and M2 represent the effective migration rates per generation that is (M = 2.Nref.m) with m the proportion of population made of migrants from the other population and Nref the size of the reference population. Tsc is the number of generations since gene flow started (secondary contact) after a period of isolation. Tam is the number of generations since the two populations have diverged without gene flow until present. Each model is declined in alternative version allowing homogeneous or heterogeneous effective size and homogeneous or heterogeneous gene flow to account for the effect of linked selection (affecting Ne) and barrier to gene flow (affecting m) respective.Strict Isolation (SI), Isolation with constant Migration (IM), Ancient Migration (AM) and Secondary Contact (SC). The models shared the following parameters: Tsplit: number of generation of divergence (backwards in time). (TIF)Click here for additional data file.S12 FigEach plot displays the observed jSFS (data), the modeled jSFS (model) and the residuals. Left part: Best model without population size change. Each best model was inferred using ΔAIC and AIC weights. Right panel: The same model as the left but including the possibility for population size change of the diverging daughter populations.(TIF)Click here for additional data file.S13 FigFor easier interpretations of admixture coefficient among the 58 samples site a PCA was performed to summarized the distribution of admixture. Populations with lower cos2 contributed weakly to the plot and hence have higher admixture.(TIF)Click here for additional data file.S14 FigNo significant relationship was observed , suggesting that each local population has undergone different evolutionary trajectory in post-glacial time.(TIF)Click here for additional data file.S15 FigData are normalized for a sample of size n = 102 corresponding to the smallest size for the combined samples in Haida Gwaii.(TIF)Click here for additional data file.S16 Fig(TIF)Click here for additional data file.S17 Fig: A) Distribution of the count of homozygous derived deleterious alleles in each major group. B) Distribution of the count of total derived deleterious alleles in each major group(TIF)Click here for additional data file.S18 FigDisplayed are the mean derived allele frequencies of polymorphic deleterious sites in each region +/- 2 standard deviation.(TIF)Click here for additional data file.S19 Fig(TIF)Click here for additional data file.S1 TableAbbreviation, with corresponding river of sampling, Region and coordinates (Longitude and Latitude) of each sites used in the GBS data with the number of individuals provided (nb. Inds).(TIF)Click here for additional data file.S2 Table95% confidence intervals obtained after 1000 bootstraps.(TIF)Click here for additional data file.S3 TableAIC, ΔAIC and AIC weights are provided for each pairwise comparison and model. AM = Ancient Migration, IM = Isolation with Migration, SI = Strict Isolation, SC = Secondary Contact, the simplest models assume homogeneous migration and homogeneous effective population size. 2N suffix = heterogeneous effective population size, 2M suffix = heterogeneous migration. Model with both suffix assumes that both effective population size and migration are heterogeneous. Model with a single suffix assumes that either migration or effective population size are heterogeneous. In one cases  the SC2N, SC2m and SC2N2m provided similar fit. For consistency the SC2N2m was considered for parameter estimates. Its parameter estimates were more consistent than those from either SC2N or SC2m. All tested models included the possibility for expansion or contraction of the daughter populations (not displayed here).(TIF)Click here for additional data file.S4 TableNe1 and Ne2, effective population size of the compared pair. m1 ← 2 and m2 ← 1, migration from population 2 to population 1 and migration from population 1 into population 2. me12 and me21, effective migration rate estimated in the most differentiated regions of the genome Ts: Split Time of the ancestral population in two population; Tsc: duration of the secondary contact P: proportion of the genome freely exchanged ; Q: proportion of the genome with a reduced effective population size due to selection at linked sites; hrf = Hill-Robertson factor representing the reduction of Ne in the region Q with reduced Ne.(TIF)Click here for additional data file.S5 Table(TIF)Click here for additional data file.S6 Table(TIF)Click here for additional data file.S7 Table(XLS)Click here for additional data file.S8 Table(TIF)Click here for additional data file.S9 Table1)Derived Allele Frequency (DAF) of deleterious mutation, after averaging by rivers and then by major regional group. 2) Count of deleterious mutations in each river and then averaged by major regional group. 3) Number of homozygous derived deleterious mutations by individual, after averaging by rivers and then by major regional group. 4) Number of heterozygous mutations by individuals, after averaging by rivers and then by major regional group 5) Total load of derived deleterious mutations by individuals, after averaging by rivers and then by major regional group.(TIF)Click here for additional data file.S10 Table(TIF)Click here for additional data file.S11 Table(TIF)Click here for additional data file.S1 TextThe tested model is displayed in (PDF)Click here for additional data file.


Drosophila melanogaster have been the focus of several studies aiming at inferring demographic and adaptive processes based on genetic variation data. However, in these analyses little attention has been given to gene flow between African and European samples. Here we present a dataset consisting of 14 fully sequenced haploid genomes sampled from a natural population from the northern species range . We co-analyzed this new data with an African population to compare the likelihood of several competing demographic scenarios for European and African populations and show that gene flow improves the fit of demographic models to data.European and African natural populations of  Drosophila melanogaster originated in sub-Saharan Africa where it diverged from its sister species Drosophila simulans approximately 2.3 million years ago1. The species has a strong dispersal capacity and long-distance migration has been reported in field experiments2. Previous genetic analyses of European and Asian samples indicated that non-African populations started expanding beyond their ancestral range around 13,000 years ago, eventually colonizing large areas in Europe and Asia6. By contrast, the introduction of the species in the Americas and Australia is very recent (within a couple of hundred years ago) and has been well documented by early entomologists (Keller7). Interestingly, demographic analyses of North-American and Australian populations revealed significant African ancestry (between 15 and 40%) in a dominantly European background11. However, less attention has been given to gene flow between African and European samples .Drosophila melanogaster, international research groups and consortia are generating large-scale genomic datasets from natural populations, which are densely sampled through time and space14. The major aim of these studies is to understand the evolutionary history of broadly distributed populations and to identify signals of adaptation in response to spatially or temporarily varying environments20. Analyses of comprehensive genomic datasets have, for example, only recently revealed previously unknown population structure along the East–West axis in Europe21 or the predominant effect of inversions on clinal variation along the East coasts in North American22 and Australia23. However, a major hurdle of such population genetic approaches is the urgent need for accurate null models which require a fundamental understanding of the demographic history24. We believe that our analyses, which provide new and more accurate estimates of key demographic parameters, will prove useful in future genome-wide studies to investigate the evolutionary history of non-African Drosophila populations and to distinguish signals of adaptation from neutral evolution.Recent advances in sequencing technology have led to a dramatic increase of genome-wide studies of genetic variation in natural populations. Particularly in 25 and sequenced haploid genomes of 14 Drosophila melanogaster embryos obtained from a Swedish population. We describe patterns of genetic diversity and compare them to previously available data from a Zambian population located in the ancestral range of the species. We use this new dataset to test for the importance of migration in the demographic history of European populations and show that accounting for historical gene flow in demographic models of European and African populations significantly improves the fit to the data compared to models without gene flow.Here, we followed a protocol proposed by Langley et al.26 and that the colonization was associated with a population size bottleneck27. We conducted a principal component analysis (PCA) to explore whether our new genome-wide data is consistent with those expectations t, for which high genetic differentiation has already been reported between standard and inverted haplotypes28. Therefore, SNPs from chromosomal arm 2L were excluded from further demographic analyses. Summary statistics of polymorphism data were in line with previously published values for European samples, with lower nucleotide diversity in Sweden (θw = 0.003) than Zambia .Previous studies showed that ancestral lineages of current European flies diverged from sub-Saharan populations at the end of the last glacial maximumons Fig. . Our resons Fig. . One impdadi30 assumes that the genetic variation used for model optimization is not affected by negative selection, which can create biases in demographic analyses31. Because the magnitude of indirect negative selection is known to depend on the local recombination rate32 we partitioned our data into three classes of recombination  based on published estimates33 and compared the site frequency spectrum  between recombination classes to identify signatures of linked negative selection. In the low-recombination class, we identified a significant skew in the SFS of the autosomal data towards low-frequency classes , which is consistent with the predicted effect of negative selection on linked neutral variants34. Consequently, we restricted our analyses to regions with local recombination rates larger than 1.5 cM/Mb.The demographic inference methodology implemented in dadi showed that models with migration provided a better fit than models without migration, for both the autosomal and X-linked data  implements symmetrical migration between the populations, starting immediately after the split and lasting until the end of the simulation (present). Model 3 (ASYMIG) is like model 2 but allows for asymmetrical migration rates. Finally, Model 4 (RASYMIG) is similar to model 3 except that asymmetrical migration only starts at time Tmig. For autosomal and X-linked data, the best model was ASYMIG in which asymmetrical migration is ongoing since the divergence time. The identification of gene flow as a relevant parameter in demographic models of Drosophila melanogaster has been reported before36 and leads to older estimates of population split times, compared to estimates obtained using models without migration  estimated a generation time for natural populations of D. melanogaster of 15 generations per year, which is supported by larger empirical evidence than the previously used estimate of 10 generations per year. Demographic estimates based on autosomal data for our best model were in line with previously published estimates when using the same estimates for mutation rate and generation time (Table 13). Naturally, the use of a larger mutation rate and a shorter generation time yields smaller population sizes and younger ages for the split between the ancestral lineages of the European and African samples  as well as the African expansion t extends several megabases beyond the inversion’s breakpoint42. Therefore, we excluded all of chromosome 2L for the demographic analyses in this study and recommend that future demographic studies of natural populations of Drosophila melanogaster address the potential effect of this inversion prior to demographic inference. We also identified an excess of singletons among low-recombining regions  in the autosomal data of our Swedish sample, which is likely caused by linked negative selection and should therefore be filtered out when conducting demographic inference.Our PCA for chromosome 5. However, Li and Stephan4 already predicted that accounting for gene flow would lead to older divergence times, owing to the homogenizing effect of migration on allele frequencies in isolated populations. Our results confirm this prediction  cannot detect such population size bottlenecks under specific conditions. This could be investigated by testing whether MSMC2 can identify a bottleneck when used to analyze simulated datasets obtained from the ASYMIG model. Another promising avenue is RELATE, a recently published demographic inference method based on ancestral recombination graph (ARG) reconstruction43. Similarly to MSMC2, RELATE can be used to estimate continuous changes in population sizes but it also allows for the analyses of significantly larger sample sizes, which is expected to improve the quality of demographic inferences. It is also noteworthy that MSMC2 appears to confirm the existence of a recent admixture event between African and European lineages while the RASYMIG model in the dadi analysis (in which gene flow could start after population divergence) provided a poorer fit to the observed data than the model with ongoing gene flow. More work is needed to identify whether this corroborates the earlier description of a recent event of cosmopolitan pulse-admixture into the African gene pool12 or rather reflects a loss of statistical signal in the most recent past. This work could be achieved by evaluating the performance of dadi and MSMC2 using simulations with recent gene flow and by applying the recently published ARG-based method, which facilitate the estimation of gene flow and local admixture mapping44. MSMC2 and dadi rely on different summarization of population genomic variation, which in principle could be capturing different aspects of the evolutionary signal; but it remains unclear how precisely this may contribute to the differences observed in this study.A major difference between the demographic histories estimated using D. melanogaster were sampled from Umeå in northern Sweden in August 2012. Then, full-sibling mating was performed for 10 generations, which yielded 80 inbred lines. Out of these, 14 lines were randomly selected from which haploid embryos were generated following the protocol described by25. Standard genomic libraries were constructed using up to 10 μg (~ 200 ng/μl) of DNA. Library construction and sequencing of one haploid embryo for each of the 14 haploid-embryo lines were carried out on an Illumina HiSeq 2000 sequencer at GATC Biotech . In addition to the newly established and sequenced inbred lines from Umeå/Sweden, we randomly chose 10 lines not carrying any inversions from the DPGP3 dataset. They were collected in Siavonga/Zambia in July 2010 and sequenced as haploid embryos similar to our data. Since four of the Swedish lines carried the chromosomal inversion In(2L)t, we additionally chose four lines at random from Zambian lines that also carried In(2L)t to match the number and distribution of inversion karyotypes in our Swedish dataset. Accession numbers are listed in table A total of 96 inseminated female bigsnpr45. This algorithm uses clumping instead of pruning to thin SNPs based on linkage disequilibrium, removes SNPs in long-range LD regions, and uses the thinned data to perform dimensionality reduction by singular value decomposition. Analyses were conducted on the full data and on each chromosomal arm separately.Principal component analyses were conducted with the “auto_SVD” function from the R package 28). Additionally, long runs of Identity-By-Descent were masked from the African lines using a perl script available from the DPGP website (http://www.johnpool.net/genomes.html). Genomic regions identified as of European ancestry in the DPGP2 and DPGP3 datasets were not masked, because our demographic analyses intended to evaluate the possibility of gene flow between the two populations. All coordinates were transformed to D. melanogaster genome reference version 6 using an in-house python script. In total, 4,020,733 bp  were used for the three autosomes together and 5,859,268 bp  for the chromosome X. We used the software dadi30 to test four different demographic scenarios. In all models, the ancestral African population experienced a stepwise expansion at time Texp. After the expansion, (forward in time) the European population splits from the African population at time Tsplit. After the split, the size of the new European population is instantaneously reduced to a population size Nbot, whereas the size of the African population does not change. After the bottleneck, the European population can recover exponentially until it reaches its current size Neu. The four scenarios differ in the modeling of migration following the population split. For every scenario, at least 10 independent runs with different initial parameter values were performed and the run achieving the highest likelihood was kept for parameter estimation and model choice. Model choice was done by comparing the Akaike information criteria (AIC) between models46. Confidence Intervals (CI) were calculated using the following procedure: First, 150 datasets were simulated using the best demographic model. These simulations were treated as pseudo-observed data and used to re-estimate demographic parameters under the best model. The set of 150 estimates for each demographic parameter was then used to construct the confidence intervals. Confidence intervals were calculated as the 2.5–97.5% percentiles. Nucleotide diversity, Tajima’s D, Fst, and the observed 1D and 2D site frequency spectra presented in Figure dadi.We used SNPs from introns with a local recombination rate larger or equal to 1.5 cM/Mb; autosomal and X-linked data were treated separately. Genomic regions spanned by common inversions were excluded from the analyses /.Historical changes in population size were inferred with the program Supplementary Information.


Ancestry informative single nucleotide polymorphisms (SNPs) can identify biogeographic ancestry (BGA); however, population substructure and relatively recent admixture can make differentiation difficult in heterogeneous Hispanic populations. Utilizing unrelated individuals from the Genomic Origins and Admixture in Latinos dataset , we designed an 80 SNP panel (Setser80) that accurately depicts BGA through STRUCTURE and PCA. We compared our Setser80 to the Seldin and Kidd panels via resampling simulations, which models data based on allele frequencies. We incorporated Admixed American 1000 Genomes populations , into a combined populations dataset to determine robustness. Using multinomial logistic regression (MLR), we compared the 3 panels on the combined dataset and found overall MLR classification accuracies: 93.2% Setser80, 87.9% Seldin panel, 71.4% Kidd panel. Naïve Bayesian classification had similar results on the combined dataset: 91.5% Setser80, 84.7% Seldin panel, 71.1% Kidd panel. Although Peru and Mexico were absent from panel design, we achieved high classification accuracy on the combined populations for Peru , and Mexico  as evidence of the portability of the Setser80. Our results indicate the Setser80 SNP panel can reliably classify BGA for individuals of presumed Hispanic origin. Clinically, a more complete description of the various Hispanic BGAs may result in identification of rare variants that may not have been previously described when grouping all Hispanic populations together3, or for controlling for population substructure in clinical trials5. Hispanic individuals are known to have differential predispositions for various diseases and ignoring this diversity restricts the generalizability of the results6. In forensics, BGA data could be used to investigate the origin of unidentified human remains (UHR)7, or locate the rightful parents/guardians of a child who is unable to identify where she/he is from8. It is the heterogeneous nature of Hispanic populations that has previously deterred full characterization of their substructure. However, in the past decade, there has been a movement to explore global human diversity and a variety of genetic panels have been designed for this purpose.It is important to study the genetics of Hispanic populations to avoid oversimplifying this heterogeneous ethnicity into a single conglomerate. The identification of specific biogeographic ancestries (BGA) has implications both in clinical9, Galanter et al.’s 44610, Kidd5511, EUROFORGEN12, Genetic Atlas13, Genographic Project14, Cuba by Marcheco-Teruel et al.15, and Cuba by Fortes-Lima et al.16. Although these studies assessed continental ancestry proportions (e.g. Seldin128)9, highly differentiated populations may be detected within continental panels, even identifying admixed populations such as Gujarati Indians in Houston, TX and Mexican ancestry from Los Angeles, CA17. The ability to separate small admixed populations among larger more homogenous populations supports the notion that continental SNPs with high genetic differentiation may still be informative on a more specific country level. The simultaneous description of highly divergent populations alongside less specific populations using the same SNP panel is central to the goals of our study. However, dual level analysis of admixed populations within continental panels is rare, as it tends to decrease the panel’s performance17.Early ancestry informative marker (AIMs) panels are “continental” in nature, focused on admixture mapping to determine from which of the six inhabited continents an individual has ancestry; these include: Seldin12814 assessed populations worldwide (though sparsely in the Americas), their in-house geographic population structure (GPS) algorithm is capable of identifying country of origin. EASTASAIMS was one of the first non-European AIMs panels focusing on 22 East Asian populations using 1,500 AIMs and was able to separate the five largest populations in the region18. Zeng et al.19 created a panel of 23 AIMs using FST focusing on the four major US populations from HapMap 320: African ancestry from Southwest United States (ASW), Utah residents with Northern and Western European ancestry (CEU), Chinese from Metropolitan Denver, Colorado (CHD), and Mexican ancestry from Los Angeles, CA (MEX). And more recently, Huerta-Chagoya et al.21 reported 32 AIMs within Mexican mestizo populations, to estimate admixture proportions in various regions of Mexico.Other panels target more specific, country BGA beginning in European populations before extending to other regions of the world (e.g. Denmark within Northern Europe). Although the Genographic Project14. Additionally, of the 12,476 reference samples used to select 40,000+ SNPs in their panel, only 9% were from American/Amerindian populations22, which limits the utility of their panel for resolving Hispanic ancestry. The size of this panel14, the proprietary nature of the SNPs on their Genochip22, and poor representation of the Western hemisphere, has prompted us to create a small, efficient, and publicly available SNP panel concentrated on BGA of Central America, South America, and the Caribbean.Highly accurate BGA predictions are possible with up to 83% accuracy, but at the expense of panel size, requiring 40,000–130,000 SNPs as used in the Genographic Project23 and Cuba16 have attempted to describe the diversity of their populations. The British Isles were ideal candidates for national differentiation due to their relative homogeneity and the presence of a geographic barrier which has historically restricted continuous gene flow with continental Europe and other island populations. In contrast, studies by Marcheco-Teruel et al.15, and Fortes-Lima et al.16 superficially appear to differentiate between the fifteen Cuban provinces on a national level, but their real focus was measuring admixture proportions using a subset of Galanter et al.’s 446 SNPs10, making their studies better described as continental and highlighting the need for a within country panel. Overall, at least 21 AIMs panels have been reported; however, of the 1,397 SNPs identified by Soundararajan et al.24, only 46 Consensus SNPs were in common to three or more SNP panels.Within one country, both Great Britainet al.’s 446 Latin American AIMs10, our purpose was to classify BGA, not to estimate the ancestral proportions contributed from 3–4 continental populations. The majority of AIMs panels and genetic ancestry studies have a heavy concentration of populations in Europe and Asia and far fewer in Central America, South America, and the Caribbean18. Our country panel addresses this gap in knowledge and focuses on these same populations.At present, there is no AIMs panel that focuses on the determination of BGA between countries in the Americas. Despite the overlap of our region of interest with the Galanter 9 and Kidd5511 to separate heterogeneous Hispanic populations in the GOAL dataset  using STRUCTURE26 and principal components analysis (PCA). With the STRUCTURE26 results, we applied the Evanno method27 which optimized the computer-determined (K) populations; the highest likelihood for the Setser80 was at K = 4 while Seldin1289 and Kidd5511 were optimized at K = 3 , DOM (Cluster 2 = 0.6976), and COL (Cluster 3 = 0.6562)   versus the Seldin128= 3 Fig. . The gen2) Table . Using t9 PCA showed HUR and COL separated together but apart from the other populations across PC1, and CUB and DOM separated together along PC2  for the AIMs panels in the GOAL population Fig. . In the PC2 Fig. . The KidPCA Fig. , not for 26 Fig.  (Table 125 and 1000 Genomes Project28 (1000 G) allele frequencies, we modeled populations to determine classification accuracy using the Snipper 2.5 app suite29. Snipper uses naïve Bayesian likelihood ratios and multinomial logistic regression (MLR) for prediction of unknowns via −log(likelihood)29. Despite the different algorithms, both analyses had similar results.Based on the GOALAs expected, the Setser80 had the highest overall accuracy across the three panels in the simulated GOAL dataset (98.4%) by naïve Bayesian classification implemented via leave-one-out cross-validation. Additionally, the Setser80 achieved 90% accuracy in the 1000 G dataset and 91.5% in the 7 Populations Combined dataset, both of which include populations not involved in our SNP ascertainment Table . In the Naïve Bayes analysis of the actual 1000 G genotypes revealed the Setser80 had the highest specificity in CLM (98.4%), the highest sensitivity in MXL (84.4%), and similar specificity in PUR (85.2%) and PEL (97.7%) in comparison to the Seldin  and Kidd  panels Table . In all Utilizing the MLR algorithm, Setser80 had the highest accuracy in GOAL and 7 Populations Combined ; the Setser80 and Seldin panel had equal accuracy in 1000 G (93.8%); and the Kidd panel had 80.5% in GOAL, 71.4% in 7 Populations Combined, and 82.2% overall in 1000 G  and DOM emerged first in STRUCTURE26 and PCA, followed by COL at K = 4, which separated from CUB & PUR, indicating three distinct populations . Within a country, the United States HapMap 3 populations20 used PCA with receiver operating characteristics curve (ROC)19, and the Mexican mestizos panel used nested subsets with high SNP weights followed by the lowest number of SNPs with the highest PC121. Similar to Kidd et al.11, we prioritized SNPs that distinguished populations with lower mean FST per country. However, we focused on differentiating Hispanic instead of continental populations. Kosoy et al.9 (Seldin128) also concentrates on continental differentiation, but they also evaluated their AIMs on African American, Puerto Rican, and Mexican/Mexican American populations.Many panels have sought country-level ancestry determination, using a variety of SNP ascertainment methods29 that provided two classification methods: a naive Bayesian classifier and MLR32. This web-based classifier was designed for classification of externally visible characteristics37 and ancestry41, particularly in forensics. Snipper has successfully analyzed admixed South American populations43, similar to those used here.We used the Snipper 2.5 app suite9. The Kidd panel emphasized capturing diversity by using 63 global populations11 including seven isolated Amerindian populations; they continue to add more populations via ALFRED44. The size of the Kidd panel and the ratio of SNPs to the number of samples  suggests the number of SNPs, rather than SNP ascertainment population size, is the higher contributing factor to population differentiation. However, the number of individuals per population may also be a factor.The classification accuracy of the Seldin and Kidd panels is due to both the composition of their SNP ascertainment datasets and the size of the panels. The Seldin panel  was more accurate in MXL than the Setser80  in the 7 Populations Combined and 1000 G datasets, respectively. Its success is likely because 199 of their 825 samples were from admixed Latin American and Amerindian individuals 25 study genechip45 was built on 270 African (YRI), Caucasian (CEU), and East Asian population (CHB and JPT) samples from HapMap 146, without any Amerindian component. Although, our SNP ascertainment dataset was small it was not inconsistent with other studies20 where the larger overall size was coupled with small sub-populations. Therefore, we combined the GOAL25 dataset with the 1000 Genomes Admixed American dataset (n = 347)28, merging COL with CLM (n = 147) and PUR with PUR (n = 122) due to negligible allele frequency differences, to create the 7 Populations Combined.Our study’s limitations include: genechip design, sample size and its effect on allele frequencies. The GOALST and selection of SNPs with LD < 0.7. Using a dilution series of 234 to 44 SNPs, we evaluated the effect of panel size on classification accuracy in relation to Seldin and Kidd sized panels and found 80 SNPs to be sufficient. Therefore we chose 80 SNPs from 247 candidates by selecting SNPs such that ~20% could be attributed to each country. It is possible that other panels informative of Hispanic ancestry could be selected from the same candidates, but testing multiple different panels was beyond the scope of this study. Residual LD is possible despite our threshold where four pairs of SNPs had r2 > 0.5; however, removing one of each pair and classifying two separate 76 SNP subsets had negligible effect on classification accuracy via naïve Bayes  to create the panel, the Setser80 had similar accuracy overall in GOAL25 and 7 Populations Combined. The Setser80 is robust as it clusters well with Bayesian model-based clustering and PCA, and classifies equally well in naïve Bayes classification and MLR. The Setser80 is portable and, to our knowledge, is the first BGA AIMs panel specifically for the Caribbean and surrounding mainland countries. In comparison to Seldin1289, Kidd5511, and 46 Consensus SNPs24, our 80 AIMs for Hispanic BGA is unique, both exact and by linkage disequilibrium. Therefore, it is our intention that the Setser80 be integrated into a future Western Hemisphere panel.Our findings indicate that the Setser80 can predict BGA of individuals of presumed Hispanic origin with high confidence. By selecting additional SNPs attributed to countries with lower average country attributable F25. These samples were collected in South Florida and genotyped using the Affymetrix 6.0 gene chip of 906,600 predetermined SNPs45.Here we downloaded the GOAL dataset and used 160 unrelated individuals including Honduran , Dominican Republican , Colombian , Cuban , and Puerto Rican  populations with three of four grandparents from the same countryhttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000750.v1.p1&phv=202273&phd=4443&pha = &pht=3936&phvf = &phdf = &phaf = &phtf = &dssp=1&consent = &temp=1. Funding support for the GOAL Study was provided by the National Institute of General Medical Sciences (1R01GM090087). Additional support for sample collection was provided by a grant from the Stanley J. Glaser Foundation and the Dr. John T. Macdonald Foundation Department of Human Genetics.The Genomic Origins and Admixture in Latinos (GOAL) dataset analyzed during the current study is available in the dbGaP repository, accession number phs000750.v1.p1, found at: 28, accessed through the UCSC Genome Browser52. These include Colombia in Medellin , Peru in Lima , Puerto Rico , and Mexican Living in Los Angeles 28. The 1000 Genomes Project dataset is available via the UCSC Genome Browser, found at: http://genome.ucsc.edu/.For further comparison, we used fully sequenced individuals from the 1000 Genomes Project Phase 3 Admixed American populations (n = 347)45, we filtered the GOAL dataset by linkage disequilibrium (LD) ≤ 0.7, missingness ≤ 0.1, and minor allele frequency (maf) ≥ 0.01 using PLINK v.1.954 and retained 494,886 SNPs. After calculating FST31 by Weir & Cockerham’s algorithm30 in PLINK v.1.9 (https://www.cog-genomics.org/plink/1.9/basic_stats#fst)55, 1509 SNPs with FST ≥ 0.15 for at least one pairwise comparison were retained.We created our AIMs panel by applying a series of quality control algorithms. Beginning with 897,336 autosomal SNPs on the genechipST for each of the five countries and assigned each SNP to a country based on the highest mean FST. The next highest mean FST was designated the 2nd country mean FST. For example, rs3777908 is attributed to HUR because the average of HUR vs. DOM, HUR vs. COL, HUR vs. CUB, and HUR vs. PUR is [(0.27318 + 0.19754 + 0.19560 + 0.28808)/4] = 0.23860, which was the highest country mean FST value for rs3777908. The 2nd highest country mean FST = 0.07442, corresponded to PUR , DOM (mean FST = 0.16901), COL (mean FST = 0.14212), CUB (mean FST = 0.10803), and PUR (mean FST = 0.10272).We binned the 1509 SNPs by the 111. Here, we refer to the panel developed by Kosoy et al., 2009 as the Seldin1289, and the 55 ancestry informative SNPs developed by Kidd et al., 2014 as the Kidd5511. We performed each analysis on the Setser80 in parallel with the Kidd and Seldin panels to evaluate the utility of our Hispanic AIMs panel.To assess the value of our panel, we compared it to two commonly sited AIMs panels45 were pre-determined and not all SNPs were included in the ABI Taqman assay used to genotype the Seldin1289 and Kidd5511; therefore, we imputed these two panels into the GOAL dataset25 using IMPUTE256 on the full 250 individuals using a 5 Mb window centered on each SNP and an effective population size of 20,000 as seen in Instructions for IMPUTE version 257. We used 2,504 individuals from 1000G28 for the genetic map and legend and the strand alignment from dbSNP batch query. Given the use of genome builds NCBI35/hg17 to GRCh38/hg38, we converted all components to GRCh37/hg19 for analysis.The SNPs on the Affymetrix 6.0 gene chip45 was based on an early genome build (NCBI35/hg17) which did not have all the tag SNPs necessary (in comparison to the 1000 G Project) to reliably impute ~30 of the SNPs from Seldin1289 and 11 from Kidd5511 for each individual. We assessed the accuracy of the imputation using the concordance tables provided by IMPUTE2; of the ~160 imputed SNPs from 20 chromosomes the mean concordance = 92.6% and range = 85.3% to 96.4%. Of the ~30 SNPs with missingness >10%, there was no obvious pattern between missingness proportion and concordance. Despite multiple attempts with different intervals, rs10954737 from the Seldin1289 was unable to be imputed due to the lack of Panel 2 SNPs. Because STRUCTURE and PCA ignore missing data50, the full Seldin1289 and Kidd5511 were used in these analyses. However, since the resampling approach to simulations is dependent upon the reliability of allele frequencies in our real data58, we applied the same <10% missingness filter used in the development of the Setser80; this resulted in 96 SNPs in the Seldin panel and 44 SNPs in the Kidd panel after imputation.However, the gene chip used26 to compare the self-reported to computer-determined (K) populations. We performed STRUCTURE analysis at K = 2 to K = 7 for each dataset/panel at 10 iterations each using the admixture model, no LOCPRIOR, 10,000 burn-in, and 100,000 Markov Chain Monte Carlo (MCMC) repetitions. The final STRUCTURE diagrams for each SNP panel were optimized and averaged through STRUCTURE Harvester59, CLUMPP60, and Distruct61 to create the diagrams in Fig. We evaluated ancestry by the Bayesian model-based clustering method used in STRUCTURE v.2.3.49, and Kidd5511 on the GOAL dataset by PCA using EIGENSOFT v.6.1.462 and plotted the first three eigenvectors. Genesis63 was used for improved visualization of clustering as seen in Fig. We analyzed the Setser80, Seldin12864, we compared the Setser80 to the Seldin1289 and Kidd5511, and the 46 Consensus SNPs described in a review article by Soundararajan et al.24. We used r2 > 0.7 as the threshold to evaluate whether any SNP in the Setser80 was in strong LD with SNP(s) from Seldin1289 and Kidd5511 (tested together) or the 46 Consensus SNPs appearing in more than 3 of 21 panels of AIMs24.Using the web-based tool LDmatrix58. We simulated a randomly mating population of 100–125 individuals within each country. Next, we assigned a genotype to individuals by generating a random number between 1 and 0 and comparing this number to the maf for the country at the specified locus. Any random number above the maf was assigned the major allele. All genotypes were created from 2 separate allele generations for each locus. The simulation of each population was performed at least 5 times for the GOAL and 1000 G countries. The 7 Populations Combined dataset was created by merging the countries from the 1000 G and GOAL simulations without regard to simulation number. We verified our model using a chi-square test for each panel and found the allele frequencies from the simulation sets were not significantly different from the true allele frequencies at α = 0.05 after Bonferroni correction.To model the data for BGA prediction of unknown individuals, we used a resampling approach based on calculated allele frequencies of the three SNP panels on each dataset29 is a web-based Naïve Bayes classifier, found here (http://mathgene.usc.es/snipper/), which calculates −log(likelihood) with leave-one-out cross-validation and multinomial logistic regression (MLR) options. Cross-validation divides a set of data into a training set and a testing set, and rotates the samples until all samples have been in the testing set. Using the “Thorough analysis of population data with a custom Excel file” option, Snipper calculated likelihood ratios (LR) of population vs. not the population and selected the country that corresponded to the highest LR. MLR is similar to STRUCTURE32, which calculated genetic proportions of individuals (as percent admixture) instead of whole populations, and categorized individuals based on those probabilities. We used 100–125 micro-simulations  each from population for references and selected 10% of profiles from a separate set of micro-simulations to predict unknowns. We evaluated potential overlap of MLR classification using the confusion matrix and assessed the validity of our classification by sensitivity, specificity, and positive predictive value from the naïve Bayes classification of the actual 1000 G genotypes .Snipper 2.5 app suiteet al. (2013)25, and the 1000 Genomes Project28 datasets was approved under University of North Texas Health Science IRB 2013-201. As this manuscript only used pre-existing genetic data from Moreno-Estrada, A. et al. (2013)25, where their “Informed consent was obtained from all participants under approval by the University of Miami Institutional Review Board (study no. 20081175)”. The 1000 Genomes Project data was only included in the International Genome Sample Resource if the submission was in accordance with the Consent, Ethics Review and Sampling Process of the 1000 Genomes Project28.This research study using the Genomic Origins and Admixture in Latinos (GOAL) from Moreno-Estrada, A. Supplementary Information.