Lynx rufus) populations in coastal southern California that appear isolated due to major highways and dense urban development. Divergence dates among FIV phylogenetic lineages in several cases reflected historical urban growth and construction of major highways. We found strong FIV phylogeographic structure among three host populations north‐west of Los Angeles, largely coincident with host genetic structure. In contrast, relatively little FIV phylogeographic structure existed among two genetically distinct host populations south‐east of Los Angeles. Rates of FIV transfer among host populations did not vary significantly, with the lack of phylogenetic structure south‐east of Los Angeles unlikely to reflect frequent contemporary transmission among populations. Our results indicate that major barriers to host gene flow can also act as barriers to pathogen spread, suggesting potentially reduced susceptibility of fragmented populations to novel directly transmitted pathogens. Infrequent exchange of FIV among host populations suggests that populations would best be managed as distinct units in the event of a severe disease outbreak. Phylogeographic inference of pathogen transmission is useful for estimating the ability of geographic barriers to constrain disease spread and can provide insights into contemporary and historical drivers of host population connectivity.Urban development has major impacts on connectivity among wildlife populations and is thus likely an important factor shaping pathogen transmission in wildlife. However, most investigations of wildlife diseases in urban areas focus on prevalence and infection risk rather than potential effects of urbanization on transmission itself. Feline immunodeficiency virus (FIV) is a directly transmitted retrovirus that infects many felid species and can be used as a model for studying pathogen transmission at landscape scales. We investigated phylogenetic relationships among FIV isolates sampled from five bobcat ( Implementing Bayesian viral phylogeography in discrete space, we quantify FIVLru connectivity and assess the permeability of several major highway and urban barriers to pathogen spread. In doing so, we aim to understand whether viral phylogenetic patterns reflect the expansion of urban development in one of the most rapidly urbanizing landscapes in North America. We predict weak FIVLru phylogeographic structure with respect to host populations, and thus, that FIVLru transmission is occurring among bobcat populations in this region despite minimal host gene flow between habitat fragments. We also expect that more genetically isolated bobcat populations will have a lower diversity of FIVLru strains and may exert differential selection pressure on FIVLru relative to the less‐isolated populations. This study provides novel insights into host and pathogen connectivity with respect to major urban barriers.Here, we test whether patterns of viral genetic structure are consistent with known anthropogenic barriers to host gene flow, and whether viral genetic structure can provide insights into host movements that are not detected from host genetic analysis. To address this, we sequence FIV22.1n = 265) and tissue (n = 27) samples were collected from 292 bobcats in three adjoining areas in coastal southern California  to explore trends in FIVLru prevalence among populations. A second logistic regression was used to test for differences in FIVLru prevalence among sexes and age categories, to assess variation in FIVLru prevalence over the sampling period, and to identify differences in detectability between blood and tissue samples.Bobcats infected with FIVe et al.  to ampliLru samples, we analysed a region of the highly variable env gene encompassing the entire domain encoding the transmembrane protein and approximately half the domain encoding the surface protein  was conducted using nested PCR primers and protocols developed by Lee et al.  according to the manufacturer's protocol. Purified PCR products were sequenced at Macrogen USA on an ABI 3730xl using internal forward and reverse primers  for each sequence. All sequences were trimmed to include only env to a final length of 1,257 bp, removing the sequenced 3′ LTR. Stop codons were removed from the end of all ORFs, and sequences were aligned using the MUSCLE translation alignment. The final alignment included sequences genotyped in this study together with 13 GenBank sequences trimmed from full genomes sequenced by Lee et al.  was estimated using DnaSP  version 1.10.4, using BEAUti to construct the input files .After confirming temporal signal, we tested several BEAST models comprising different substitution models and molecular clocks and selected the most appropriate using marginal likelihood estimation with path and stepping‐stone sampling  all transitions among states occur at the same rate (null model); (b) all transitions among states occur at different rates; and (c) all transitions among states occur at different rates but are symmetrical . We conducted likelihood‐ratio tests of support for each variable‐rate hypothesis in explaining the observed distribution of transitions among host populations across our virus maximum clade credibility tree, relative to the null model. These hypotheses were tested both before and after removal of branches undergoing selection to ensure that the spatial distribution of selection was not producing false signals of homogeneity (or heterogeneity) in road crossings.To complement the above phylogeographic analysis and further investigate the extent to which rates of FIVLru connectivity as a driver of phylogenetic patterns, phylogeographic structure in pathogens can occur due to local adaptation, arising from spatial variation in host immunity or environmental conditions that can affect the fitness of viral lineages . FIVLru infection was more likely among males than females , and less likely among young individuals than adults . Likelihood of FIVLru infection did not change with respect to sample date  or sample type . Five individuals screened for FIVLru lacked precise location data, including one FIVLru‐positive individual, and were thus not included in the above estimates.In total, we tested 292 bobcats for FIV% Figure . Prevaleenv gene, including the aforementioned 13 previously published sequences  or south‐east of Los Angeles . The clade identified south‐east of Los Angeles was characterized by relatively deeper divergences among isolates, with many long branches comprising relatively few isolates each. The earliest of these divergences likely occurred prior to 1900 (PP = 0.83). The two largest subclades south‐east of Los Angeles likely diverged around 1918 (95% HPD = 1842–1972) with high posterior support (PP = 0.98).We identified and subsequently removed one sequence as significantly biasing temporal estimates  were more closely related to each other than to isolates from East‐5. These West‐5 isolates diverged from East‐5 isolates in approximately 1980 . Another divergence among West‐5 and East‐5 isolates occurred in approximately 1996 .Within the clade south‐east of Los Angeles, there was an absence of phylogeographic structure in FIVLru isolates sampled north‐west of Los Angeles show strong phylogeographic structure with respect to host populations. This clade forms two major groups that diverged, with high posterior support (PP = 1.00), in approximately 1934 (95% HPD = 1869–1980). This divergence occurred between a group comprising all isolates sampled from the South‐101 and East‐405 populations and another group containing all FIVLru isolates sampled from the North‐101 population. All East‐405 samples form a single clade that diverged from South‐101 in approximately 1997 (95% HPD = 1984–2001). Two isolates sampled from south‐east of Los Angeles  diverged from the north‐west LA group separately from the other south‐east samples but at approximately the same time—around 1876 . These two isolates (from East‐5) were sampled from similar locations at the far eastern extent of our study area and may represent a distinct geographic group.In contrast to the clade south‐east of Los Angeles, FIVp = .50, x2 = 12.4, df = 19) or variable asymmetrical rate  state change models over the equal rate state change model (i.e. highway crossing rates do not vary among host populations). Tests following removal of branches undergoing selection produced similar results. Thus, despite increased transition rates being evident among some pairs of populations, overall, the observed patterns of FIVLru spread across urban barriers are best explained by equal rates of FIVLru exchange among each pair of host populations.Discrete trait transition (i.e. transmission among populations) rates were the highest for transitions from the East‐5 host population to West‐5 1.36) and from South‐101 to East‐405 (1.31), with both of these rates receiving decisive Bayes factor support. We found substantial support for a transition rate of 1.09 between North‐101 and South‐101, and of 0.90 between East‐5 and North‐101, which was the lowest transition rate across all pairs of populations. All other pairs of populations had poorly supported transition rates ranging between 0.91 and 1.03  and West‐5  having higher nucleotide diversity than North‐101 , South‐101  and East‐405 . BUSTED analysis showed evidence for positive selection acting on this gene region (p < .0001). aBSREL tested all 103 branches for evidence of positive selection and identified two branches showing signatures of selection across the sequence  were not substantially higher than those observed for Interstate 405 (north‐west of Los Angeles) and did not vary significantly across all pairs of populations. Instead, the greater number of inferred highway crossings south‐east of Los Angeles may be due to the greater depth of branches here compared with north‐west of Los Angeles, thus having a greater probability of observing crossings over this longer time frame. On the other hand, the low number of samples from the West‐5 population may have limited our power to detect differences in discrete trait transition rates. Compared with populations north‐west of Los Angeles, the East‐5 FIVLru population has substantially higher genetic diversity and a higher population size owing to a large host population size and its openness to unsampled populations to the east that may serve as potential sources for diverse FIVLru strains , we believe it likely that highways/development are leading to the observed patterns.All six isolates sampled from the East‐405 population diverged from the majority of isolates in the South‐101 clade in ~1997, providing evidence that the Interstate 405 has acted as a barrier to FIVLru exchange are comparable to those among more highly structured populations north‐west of Los Angeles. However, we did observe two lineages of FIVLru in the West‐5 population that originated in the East‐5 population, diverging in ~1980 and 1996, consistent with previous studies in this area  should be carefully scrutinized before conclusions are drawn about rates of transmission among contemporary host populations.In conclusion, our results demonstrate that the degree of congruence among host and pathogen spatial population structure can vary among populations with comparable landscape characteristics. However, we show that where incongruence occurs, this does not necessarily indicate substantially increased rates of pathogen transmission among host populations. In general, FIVNone declared. Click here for additional data file. Click here for additional data file.


Aedes albopictus is one of the most dangerous invasive species. Its worldwide spread has created health concerns as it is a major vector of arboviruses of public health significance such as chikungunya (CHIKV). Dynamics of different genetic backgrounds and admixture events may have impacted competence for CHIKV in adventive populations. Using microsatellites, we infer the genetic structure of populations across the expansion areas that we then associate with their competence for different CHIKV genotypes. Here we show that the demographic history of Ae. albopictus populations is a consequence of rapid complex patterns of historical lineage diversification and divergence that influenced their competence for CHIKV. The history of adventive populations is associated with CHIKV genotypes in a genotype-by-genotype interaction that impacts their vector competence. Thus, knowledge of the demographic history and vector competence of invasive mosquitoes is pivotal for assessing the risk of arbovirus outbreaks in newly colonized areas.The mosquito  Aedes albopictus affect its competence for the CHIKV. Using microsatellites, they found that the demographic history influences the competence for the virus and is an important feature to assess the risk of outbreaks in new areas.Anubis Vega-Rúa, Michele Marconcini et al. study how the dynamics of genetics and admixture events of  Aedes albopictus (Skuse 1894), the Asian tiger mosquito, is an emblematic example of an arbovirus vector that has rapidly and successfully invaded much of the globe supported by its life history traits and high propagule pressure driven by human activities. This mosquito took four decades to invade the world, while its counterpart Aedes aegypti did so in four centuries1. From its home range in tropical Southeast Asia, where it was originally a zoophilic forest species2, Ae. albopictus spread first to the islands in the Indian and Pacific Oceans3 and, during the 1980s, rapidly extended its range across temperate regions in Europe, the Americas and Africa4. This mosquito is able to tolerate climate/environment interactions that differ from its home range7. Its ability to diapause during unfavourable seasons and to lay desiccation-resistant eggs has facilitated its expansion in temperate regions11. It is listed among the top hundred most dangerous invasive species identified12. Its spread has created public health concerns as experimental infections have shown the species’ competence for at least 20 arboviruses and it is considered the main vector for chikungunya (CHIKV), and to a lesser degree for dengue (DENV) and Zika (ZIKV) viruses21. Anthropogenic activities, by creating new breeding and trophic niches of adaptation close to human habitations23, strengthened the association of Ae. albopictus with humans, with important public health implications. Indeed, beyond its original range, it has been increasingly involved in local autochthonous transmission of chikungunya in many places where it has become established, including La Réunion, continental Europe, Africa, the Americas and Japan, determining major CHIKV epidemics of severe, persistent, debilitating arthralgia27. Chikungunya virus , first discovered in 1952 in Tanzania28 and generally transmitted by Aedes mosquitoes, comprises three genetic lineages: Asian, West African and East/Central/South African (ECSA) which apparently evolved independently in different geographic areas29. The fact that Ae. albopictus is becoming the major vector of CHIKV is due to a series of adaptive mutations in the CHIKV ECSA lineage, the most known being an alanine to valine substitution at position 226 of the E1 glycoprotein (ECSA E1-A226V). This adaptive mutation, established in the Indian Ocean region in 2004–2005, dramatically increased the vector competence of Ae. albopictus for CHIKV, with respect to the previously more typical vector, Ae. aegypti. In association with the high invasive potential of Ae. albopictus, the ECSA E1-226V variant rapidly spread even in temperate areas such as Europe, causing CHIKV outbreaks.Understanding the genetic processes associated with species introduction and establishment in new environments is fundamental to infer the evolutionary mechanisms underlying the dynamics and history of invasive processes. But, when an invasive species is a threat to human health, such as a disease vector, knowledge of population dynamics and demography is useful to interpret the causes and predict the risks of disease outbreaks. 7. Several genetic, ecological and behavioural studies were performed to unravel different aspects of the invasion process and to infer the degree of genetic connectivity among populations at the micro- and macro-geographic levels36. However, vector competence for CHIKV has been found to be highly variable within and between populations, with transmission efficiencies that are strictly dependent on specific combinations of mosquito genome, viral genetic characteristics and temperature in a genotype-by-genotype-by-environment interaction39. On this basis, we previously proposed that admixture events and dynamics of different genetic backgrounds during the invasion process of this mosquito may have impacted competence for CHIKV in adventive populations32. In this study, we tested this hypothesis. We first provided a picture of the major events that determined, at the macrogeographic level, the demographic history of the adventive populations out of Southeast Asia starting from the 18th century. Secondly, we notably found that the population demographic history impacted their vector competence for CHIKV. We have demonstrated that the demographic history of populations was tightly related to CHIKV genotypes in a sort of adaptive coevolution, where genotype-by-genotype interactions influenced the corresponding Ae. albopictus vector competence phenotypes for this virus.The identification of the main dispersal routes of this mosquito vector can help in mitigating its spread and preventing/reducing the risks of arboviral outbreaks as once a vector is introduced, autochthonous transmission of its associated arboviruses may rapidly follow. But this implies knowledge of the demographic history of invading populations, their degree of connectivity and their vectorial capacitiesy = 1e−06× + 0.0805; R2 = 0.015) nor within regions . Moreover, the population differentiation has been found to be heterogeneous not only between but also within geographical regions , which maintains low levels of differentiation with La Réunion and the Mediterranean Basin (FST 0.041 – 0.140), and with North America (FST 0.056 – 0.124). The Mediterranean area appears to be heterogeneous (FST 0.081 – 0.186). Within North America, Florida (VRB) was the least differentiated population (FST 0.065 − 0.070), sharing connectivity with the populations from Asia, La Réunion, and the Mediterranean area. It was the only population that maintained genetic connectivity with Central and South America. These latter two regions were the most heterogeneous based on FST values. The Brazilian and Argentinian populations had no genetic affinities with those from Southeast Asia, the Mediterranean area or North America, with the exception of Manaus (MAN) in the Brazilian State of Amazonas that, in turn, maintained low levels of genetic differentiation with the populations from Congo in Africa.AMOVA analyses of molecular variance across mosquitoes from 25 localities spanning from Asia to Mediterranean area, the Americas and Central Africa Table , indicatK1–K6 in Table K6. The other Asian population, Thailand, was fragmented mainly between K4 (55%) and K6 (24%). The ancestry heterogeneity of La Réunion and Mediterranean populations was obvious. The La Réunion mosquitoes from the southwest coast (RE) were mainly structured between K6 and K4 together with the majority of Mediterranean samples. By contrast, the two La Réunion populations from the north-eastern part (PROV and STD) were clustered with the French Mediterranean sample (BL) in K2. Hawaii was also fragmented between K6 and K4. North America appeared as a transition area in which populations such as Virginia (VA) still maintained membership in the two Asian clusters K4 (63%) and K6 (23%), while others such as Florida (VRB) and Missouri (TYS) exhibited ancestry profiles that transitioned to those typical of Central America (K3) and South America (K1). The Brazilian Amazonian (MAN) was fragmented across K1 (24%), K3 (17%) and notably the Southeast Asian K5 (45%). It is noteworthy that high percentages of K5 outside of Asia were recovered only in Manaus and in Congo, Africa (79%).Six clusters/lineages represent the most parsimonious partitioning of the ancestry among individuals from the 25 populations . The direct derivation of the northern PROV from the south-western RE was the most supported hypothesis. The STD population appeared to have a similar derivation. Manni and colleagues32 suggested that La Réunion/St Pierre (RE) contributed to the origin of the North Italian IT1 population (Cesena) through an admixture event with another North Italian population IT2, (previously derived from the North American Virginia (VA) population). On this basis, we tested whether La Réunion contributed also to the establishment of another Mediterranean population, Bar-sur-Loup (BL) in southern France. The hypothesis that La Réunion (PROV) in admixture events (74%) with the Italian IT1 (26%) contributed to the French Bar-sur-Loup population was strongly supported.Mosquitoes from the southwest coast of La Réunion, St Pierre (RE), have previously been suggested as having an East Asian derivationAe. albopictus according to historical records  and Colòn in Panama (PAN) were considered under the hypothesis of a North American and/or a Brazilian derivation , those with major K6 , K2 (BL-France), K5 (Brazilian MAN and CONG1) and K1 (South America) ancestries showed significantly higher dissemination efficiencies  to the derived K2 , to K5  to K1 (South America) and K3  which displayed similar efficiencies. Another approach consisted in introducing all 6 K-ancestries when dichotomised. In univariate analysis, levels of K1, K5 and K6-ancestries >0.25 were significantly associated with DE. It appeared that these three dichotomised factors defined only four distinct population profiles ; to simplify model interpretation, a new variable was defined allowing comparison of these 4 populations  showed disseminated infection. CHIKV genotypes, dpi and blood titre were significantly associated with DE in univariate analysis Table . When poies Fig. . DissemiP = 0.0002) and ECSA (E1-226A) at a lower degree, while no association with TE was detected according to dpi and blood meal titres  displayed the greatest TEs  for CHIKV was evaluated in 1159 mosquitoes of which 58.0% 672) could transmit the virus. CHIKV genotypes were significantly associated with TE, with ECSA E1-226V strains being more efficient  admixture events had a major role in shaping the genetic makeup of the globally distributed adventive populations, impacting their demographic histories, and (2) the population demographic history impacted their ability to transmit CHIKV. It follows that knowledge of the relationship between demographic history and vector competence is crucial for assessing the risk of arbovirus outbreaks in areas where this mosquito has become established.This study provides a picture of the major events which determined, at the macrogeographic level, the demographic history of the adventive populations established in the Indian Ocean island of La Réunion, in the Mediterranean area, in the Americas and in Central Africa, as a consequence of the rapid west-oriented invasive processes of K6 (red) and K4 (green) were the first ancestral lineages that historically emerged from Southeast Asia. K6, highly represented in China, is the lineage that may correspond to the primary invasion route within East Asia (Thailand and Japan) and, out of this area, to Indian Ocean La Réunion in the 18th century, to Hawaii in 1895, to the Mediterranean area starting from 1979 and to the USA in 198540. K4 is highly present in Thailand and contributed to the ancient colonization of the west coast of La Réunion, to the USA, and to the recent outbreaks in the Mediterranean area, Greece and partially to Cesena (IT1)32. A spatial extension of K6 and K4 from the western side of La Réunion  to the eastern part of the island (PROV and STD) gave rise to the K2 lineage diversification (orange). Habitat structuring and strong climatic differences between the east and western coasts of La Réunion may have supported this diversification42. K2 expanded its range out of La Réunion and became established in the French Mediterranean area of Bar Sur Loup (BL) in 2004. La Réunion is a major trading partner of Continental France and most of this international trade passes through the main port and the international airport at St Denis on the north-east coast where K2 was established (PROV and STD). K6 and K4 were almost lost in the invasion process of the Americas. In North America, they contributed to the diversification of the K3 lineage  in Brazil with its subsequent diffusion within that country and in Argentina. In South America, the Rio Grande do Norte (PNM) and Rio de Janeiro (JRB) states appear to have played a role in the spread of the K1 lineage. A particular case is provided by the genetic signature of Brazilian mosquitoes present in the Central Amazonian state (Manaus) since 2002. Unlike the other Brazilian mosquitoes, they display a higher proportion of the ancient Southeast Asian K5 lineage (0.450), with respect to the K1 Brazilian component which is unexpectedly low (0.240) and that of North Central America, K3, is even lower (0.167). The high K5 lineage component may be explained by the contribution that Southeast Asia/Japan provided to the origin of Manaus mosquitoes46. Manaus is one of the four free trade areas in Brazil and its port is a commercial centre for ocean-going shipping and the main transport hub for the Amazon basin. We found the K5 lineage well established in mosquitoes from Congo in Central Africa as a derivation from Amazonian Manaus. How this area could be implicated in the introduction of this mosquito to Congo is an open question although it is noteworthy that this inference was derived from a sample collected in 2011; the year of the first record of this mosquito in this area. Several authors have also proposed that propagules originating from South America could have contributed to an expansion in Africa.As far as it concerns the demographic history of this mosquito, six gene pools/lineages (clusters) were detected across the invaded regions at the macrogeographic level. The data clearly suggest that the invasion process of this mosquito fits a demographic model of historical lineage diversification through admixture events among different genetic compositions as summarized in Fig. Finally, as summarised in Fig. Ae. albopictus populations, highlighting that distinct barriers to dissemination and to transmission may operate even at small geographic scales depending on the interactions between the vector’s genetic background and the viral genotype47. Our data demonstrated that the ability of an Ae. albopictus population to disseminate or transmit the different CHIKV genotypes is strongly associated with their degree of ancestry within a genetic lineage. It follows that if populations from different geographical areas are related by their demographic history notably in terms of ancestry, they will display similar levels of vector competence for a given CHIKV strain. This is true for the La Réunion PROV, STD and the French Mediterranean BL populations in K2; the Brazilian Amazonian Manaus and Congo populations in K5, and the North and Central American populations in K3 . However, there is a progressive and drastic loss of permissiveness of the mosquito barriers for CHIKV dissemination  regarding the Asian and ECSA E1-226V genotypes that parallels the historical lineage divergence from the ancestral Asian lineage K6 to K2, to K5 and finally, to the American K1 and K3 lineages. Indeed, the higher abilities to disseminate CHIKV Asian and ECSA E1-226V genotypes were observed in populations with higher levels of the ancestral K6 and K5 lineages  and the E1-226 position49. The highest transmission efficiency was displayed by La Réunion K2 lineage followed by K6 which retains around two-thirds of transmission efficiency. Interestingly, the K2 lineage differentiated in La Réunion where the CHIKV E1-226V genotype was identified for the first time in 2005 and established as an adaptive mutation of ECSA lineage for this mosquito51. ECSA E1-226V, with a valine instead of an alanine at position 226 A of the E1 glycoprotein, significantly increased viral replication in midgut infection and transmission. The association of this mutation with Ae. albopictus invasiveness impacted CHIKV diffusion in a sort of convergent adaptive evolution between vector populations and viral genomes49. Moreover, the ECSA E1-226V mutation is thought to have arisen independently in other areas where Ae. albopictus is present, supporting this convergent evolution hypothesis52. We have found that the higher the level of the K2 lineage in a population is, the higher is its ability to transmit the ECSA E1-226V genotype. After 2007, the ECSA E1-226V genotype was identified in Southeast Asia and quickly spread, supported by the transmission efficiency of the Ae. albopictus ancestral lineages (K6 and K5), inducing major chikungunya epidemics53. Prior to 2007, Ae. albopictus had never been incriminated in outbreaks involving Asian CHIKV strains, despite the massive presence of the mosquito in this region. These outbreaks were vectored by Ae. aegypti which is also abundant in the area. One plausible reason would be that, as explained above, Asian strains of CHIKV were constrained in their ability to adapt to Ae. albopictus via acquisition of the E1-A226V mutation49. This inability could have facilitated the establishment of the Ae. albopictus-adapted CHIKV strains that harbour the E1-226V substitution in the region, leading to the ongoing shift from Asian to ECSA E1-A226V genotypes in Southeast Asia49.If we consider the historical dynamics of Ae. albopictus to low temperatures (20 °C) significantly increased the extrinsic incubation period and reduced the transmission efficiency of Asian CHIKV strains, while for strains from ECSA lineages these parameters remained unaffected54. Although other factors influencing vectorial capacity may interact and determine that an infectious bite is received by a susceptible human host7, these results firstly highlight the importance of three-way interactions between temperature, the mosquito population and the viral genotype. Secondly, they suggest a higher risk for ECSA strains to emerge in Europe. In addition, the presence in this old-colonized area of different Ae. albopictus lineages that are highly competent for ECSA genotypes 7 may challenge risk prediction for arboviruses and should be taken into account in further studies.In the Mediterranean area, no CHIKV Asian genotype has been related to autochthonous transmission despite the intense influx of travellers returning from Southeast Asia and the Americas. It has been shown that exposure of European K1 (South America) and especially, the North and Central American lineage K3, displayed the lowest CHIKV dissemination and transmission efficiencies of all studied populations. Indeed, K3 is a heterogeneous lineage with USA populations such as Florida/Vero Beach that displayed lower CHIKV dissemination and transmission efficiencies  performed comparative analyses of vector competence using Ae. albopictus and Ae. aegypti populations across the Americas. All the populations of both species were susceptible to infection and dissemination of the different CHIKV genotypes. However, the transmission rates were heterogeneous even among populations from the same species with Ae. albopictus from Brazil, and generally from South America, having higher transmission efficiency for ECSA genotypes when compared to their counterparts from Florida, USA. The first detection of CHIKV in the Americas was recorded in the Caribbean in late 2013, followed by a dramatic spread of the virus throughout the continent. It has been stated that CHIKV arrived in Brazil through two independent introductions: the Asian genotype from the Caribbean entered through the northern region, while the African ECSA genotype was imported through the north-east region64. Following their initial introduction, both genotypes established and expanded their range with CHIKV-ASIA being the prevalent genotype causing several outbreaks in relation to the presence of Ae. aegypti that was the main vector implicated in the transmission37. In 2014, local transmission of CHIKV-ASIA was detected in the Amazon region. A distinct lineage CHIKV-ECSA was detected simultaneously and it rapidly spread inducing replacement of the Asian genotype in the Brazilian Amazonian region65. Whether this replacement was due to the spread of Ae. albopictus in this region and its transmission capacity for these particular ECSA strains, which do not harbour the E1-226V mutation, is an open question that deserves further investigation.The most recent divergent American lineages, K5 lineage is differentiated in the African populations from Congo that became established in 2011 as a derivation of the K5 Amazonian component. Vazeille and colleagues in 201638 demonstrated that Ae. albopictus newly introduced in Congo were unable to transmit the ECSA E1-226V genotype with the same efficiency as La Réunion mosquitoes, highlighting that genetic differentiation among these two populations may have impacted their vector competence. Our results are in agreement with this hypothesis: Congo and La Réunion belong to two well differentiated lineages K5 and K2, respectively, with Congo having half the ECSA E1-226V transmission efficiency when compared to the La Réunion K2 lineage.The ancestral Ae. albopictus populations is tightly related to CHIKV genotypes in a sort of adaptive coevolution, where genotype-by-genotype interactions influenced the corresponding Ae. albopictus vector competence phenotypes for this virus. It follows that the knowledge of the vector demographic history dynamics combined with vector competence data can provide an accurate risk map for CHIKV. However, in addition to these genetic interactions, we must consider that the spread of this mosquito occurred across different environmental conditions. Thus, the variation in competence between the eco-geographic populations may be impacted by the interactions between unexplored genomic factors67 within genomes and lineages, and complex environmental landscapes.In conclusion, it is clear from these data that the demographic history of 32. Their integration in this analysis aimed at obtaining a deeper genetic portrait of the demographic history of this species during its invasion process. For the 25 collection sites, samples were collected as eggs at the peak of density to avoid any seasonality effects due to the presence of diapause or pre-diapause periods10. As described in Manni and colleagues32, a standardized sampling protocol was adopted to minimize inbreeding effects at each breeding site. In each locality, 15–17 ovitraps were placed at a distance of least 500 m one to another, and at least 40 eggs/ovitrap were obtained.Twenty-five mosquito populations were considered. Three were from the ancestral East Asian area and 22 were sampled across much of the expansion area: La Réunion in the Indian Ocean, the Mediterranean basin, Hawaii in the Pacific Ocean, North, Central and South America, and Central Africa Table . The sam68. The mosquito egg collections in the Americas, La Réunion, France, the North of Italy and in Congo/Central Africa were separated in two batches: one batch was used for population genetic studies and the remaining batch, that gave rise to several hundred individuals, was amplified in the laboratory under controlled conditions to obtain the offspring that was used in vector competence assessments for CHIKV  and having sufficient genetic diversity to provide statistical power, as we previously assessed31.We used previously validated SSRs as highly polymorphic markers to provide continuity with our published 69.We used 11 highly informative SSR loci applied to 702 individuals from 25 localities to produce a SSR genotyping dataset with no major bias in statistical analyses70 was genotyped at the following SSR loci: Aealbmic1, 2, 3, 5, 6, 9, 11, 14, 15, 16 and 1731. These loci were chosen due to their high polymorphism and because they are spread across the genome ). They have proven to be efficient markers to detect variability, even in relatively small samples32. PCR amplifications and fragment identifications were performed as previously described31. In order to account for genotyping errors, automated binning of allele lengths was performed with TANDEM v1.0972 followed by manual checking. When microsatellite amplification was not successful, or allele scoring was unclear, a new DNA extraction was performed. The generated SSR genotype dataset, in addition to offering continuity with previous studies, allows the formulation of reliable models of recent evolutionary history because of their well‐documented mutation rates73, high molecular diversity and high minor allele frequencies.On this basis, genomic DNA extracted from each mosquito74. The statistical significance was assessed following Bonferroni corrections75. Variation within each locality was estimated in terms of average number of alleles (na), private alleles (np) and their respective frequencies (Ap) using GenAlEx 6.576. The average number of alleles (na/n) and private alleles (np/n) were also computed at the individual level. Gene diversity (Hs) and allelic richness (Rs) were computed with the program FSTAT V.2.9.3.277. Observed (Ho) and unbiased expected heterozygosity (uHe) values and Pairwise-FST were computed using Microsatellite Analyzer (MSA) V.4.0578. The statistical significance of each FST value was assessed by comparison of the assessed value with the value obtained using 10,000 matrix permutations and Bonferroni corrections.Linkage disequilibrium between pairs of loci in each sample  and deviation from within-population Hardy–Weinberg equilibrium (HWE) at each locus/sample combination were tested with GENEPOP V. 4.2K), 20 independent runs were repeated. The cluster (K) interval was set between 1 and 25 (i.e. the number of considered populations).The structure and degree of ancestry in the 25 population samples were inferred using the Bayesian clustering analysis implemented in STRUCTURE V 2.3.2 under the admixture model and assuming independent allele frequencies. The “burn-in” phase was set to 500,000 iterations, then 1,000,000 Markov Chain Monte Carlo (MCMC) replications were considered. For each number of possible clusters (79 and the most likely number of clusters was determined by plotting the log probability (L(K))80, as well as the ΔK over all the runs81. Once the best number of clusters, K, was inferred, CLUMPP software82 was used to merge the 20 runs and the results were plotted in DISTRUCT83.The results were analysed using STRUCTURE HARVESTERAe. albopictus at the macro-geographic level. This approach allows comparisons between competing hypotheses regarding the divergence of the populations on a global scale. DIYABC v2.1 implements a standard ABC method (ABC-LDA) which, although being more expensive computationally, was shown to perform comparably (for posterior probability) or slightly better (for prior error rate) than the more recent random forest method (ABC-RF)84 when large reference tables are used85, as in our case. Because of the huge number of possible evolutionary scenarios  that could be tested with the 25 populations considered, we adopted a step-by-step approach considering subgroups of populations, starting from the ancestral Southeast Asian region to the invaded areas. Based on our published data, we considered the previously characterized samples from China, Thailand and Japan as representative populations from countries within the Southeast Asian native range.The Approximate Bayesian Computation (ABC) method, as implemented in the software DIYABC v2.1, was used to disentangle the complex invasion patterns of 32. We took into account the effective population size, the time range in which split or admixure events occurred, the number of founders that contributed to the establishment of adventive populations, the duration of the eventual bottlenecks occurring during colonization and the rate of admixture, if considered32. When no prior information was available, the prior parameters were kept deliberately broad. For the effective population sizes, broad priors  with uniform distributions were chosen for all the populations. For each event, the time range chosen was less broad if it was supported by historical data. The timing of events was expressed in numbers of generations back in time. Considering that the number of generations/year is dependent on bioclimatic conditions, we assumed around 4–7 generations/year in temperate regions, and 12–17 generations/year in tropical regions. In the cases where the climate situation is not so clear-cut, the broadest interval was considered (i.e. 4–17 generations/year).Several competing invasion scenarios were considered taking into account the available historical information regarding the first records in invaded countries and population genetic data. The posterior probability for each scenario was computed. The competing scenarios were set using prior definitions and distribution of demographic parameters, as described in Supplementary Table 32, although Ae. albopictus has great potential for rapid population growth86, uncertainty regarding the duration of the bottleneck was taken into account. Therefore, we assumed a bottleneck period with a uniform prior distribution bounded between 0 and 50 generations. During colonization, the number of founder individuals for each colonization event was described as NF and its size was drawn from a uniform distribution bounded between 1 and 100 individuals. If necessary, the rate of admixture between two populations was drawn in a uniform distribution and set from 0.001 to 0.999. For the genetic parameters, the microsatellite Generalized Stepwise Mutation model was considered, and a mean microsatellite mutation rate across loci was set between 10−5 and 10−3. The parameter of geometric distribution was specified with a uniform prior distribution bounded between 0.1–0.3. For each locus, the above considered mutation parameters were allowed to vary using Gamma distributions. Finally, the possibility of single nucleotide insertion/deletion mutations were considered with a mean frequency of 10−8–10−5 87. This index helps to discriminate populations that have experienced recent losses in genetic variability from those that have been stable for a long time. One million simulated datasets were generated for each scenario. Competing scenarios were compared by calculating their posterior probabilities using a polychotomous logistic regression on the 1% of simulated data closest to the observed data88. Confidence in scenario choice was evaluated by computing type I and type II errors89. Once the most likely scenario was identified for each analysis, the posterior distributions of genetic and demographic parameters were estimated. This was achieved by computing a local linear regression on the 1% of the simulated data closest to our observed dataset, after applying a logit transformation to the parameters value. The goodness-of-fit of the estimation procedure was also evaluated by performing a model checking computation by generating 1000 pseudo-observed data sets with parameter values drawn from the posterior distribution given the most likely scenario32.The genetic variation within and among the populations was summarized using sets of statistics conventionally used in ABC analyses: the mean number of alleles per locus, the mean expected heterozygosity, the mean allelic size variance, the overall pairwise 37. The infectious status of head and saliva samples was determined by titration assays90. The blood meal titres, viral strains, days post-infection (dpi) as well as DE and TE data are summarized in Supplementary Table Vector competence for CHIKV was assessed on adults derived from field-collected eggs from the Americas, La Réunion Island, France, North Italy and Congo as described above Table . Batches38. All these ECSA strains were split into two different genotype groups according to the amino acid substitution at 226 position of E1 envelope protein: the group named ‘ECSA’ that contains the substitution E1-226A (Alanine) and the group named ‘ECSA E1-226V’ that contains a Valine at the same position. This mutation allows an increased transmission by more than 50-fold in Ae. albopictus51. The strain from Asian genotype used, CHIKV_NC, was isolated in New Caledonia in 2011.Six CHIKV strains isolated from human cases were used in infection assays. Five strains belonged to the ECSA genotype: CHIKV_0621 (Accession number AM258992) and CHIKV_05115 (Accession number AM258990) isolated in La Réunion, CHIKV_1909 (Accession number FR846305) from Southeast France, CHIKV_DRC and CHIKV_Congo from Congo91. For TE, only mosquitoes that had disseminated CHIKV infections were considered. The Odds Ratio (OR) has been considered as a measure of the risk of CHIKV dissemination and transmission. The main factor of interest was the degree of ancestry among populations (K-ancestry) and two approaches were used. First, each geographical population is defined by its major K-ancestry (Table K-ancestry components were considered but dichotomised as above or below the threshold of 0.25; this threshold was deliberately chosen so that some populations could be characterized by a mixture of several K-ancestors. For both dissemination and transmission, factors that presented P-values < 0.05 in univariate analysis were introduced in multivariate models. Two multivariate models were considered using either the major K-ancestry or the binary K-ancestry factors.The effect of viral genotype, dpi, and population ancestry on DE and TE were investigated using logistic regression models. In case of complete prediction of the outcome in one category, a penalized likelihood estimation was consideredry Table . Second,The details regarding the statistical analyses, the tests, software used, sample sizes and number of replicates are described in the relevant sections of the “Methods”.Further information on research design is available in the Supplementary InformationSupplementary Data 1Description of Additional Supplementary FilesReporting SummaryPeer Review File


Gyrodactylus konovalovi is an ectoparasite on the Amur minnow (Rhynchocypris lagowskii) that is widely distributed in the cold fresh waters of East Asia. In the present study, the phylogeography and demographic history of G. konovalovi and the distribution of its host in the Qinling Mountains are examined. A total of 79 individual parasites was sequenced for a 528 bp region of the mitochondrial NADH dehydrogenase subunit 5 (ND5) gene, and 25 haplotypes were obtained. The substitution rate (dN/dS) was 0.068 and indicated purifying selection. Haplotype diversity (h) and nucleotide diversity (π) varied widely in the Qinling Mountains. Phylogenetic trees based on Bayesian inference (BI), maximum likelihood (ML), and maximum parsimony (MP) methods and network analysis revealed that all haplotypes were consistently well‐supported in three different lineages, indicating a significant geographic distribution pattern. There was a significant positive correlation between genetic differentiation (Fst) and geographic distance. The results of mismatch distribution, neutrality test and Bayesian skyline plot analyses showed that whole populations underwent population contraction during the Pleistocene. Based on the molecular clock calibration, the most common ancestor was estimated to have emerged in the middle Pleistocene. Our study suggests for the first time that a clearly phylogeography of G. konovalovi was shaped by geological events and climate fluctuations, such as orogenesis, drainage capture changes, and vicariance, during the Pleistocene in the Qinling Mountains. Gyrodactylus konovalovi is an ectoparasite on East Asia minnow Rhynchocypris lagowskii that is widely distributed in cold freshwater. In the study, the phylogeographic pattern and demographic history of G. konovalovi along with the distribution of its host in the Qinling Mountains have been examined. PhylogeGyrodactylus von Nordmann, 1832; Paragyrodactylus Gvosdev et Martechov, 1953; Laminiscus Palsson et Beverley‐Burton, 1983) and 60 species of Gyrodactylidae Beneden et Hesse, 1864 were identified from freshwater fishes in China, including Cypriniformes, Gadiformes, Gasterosteiformes, Mugiliformes, Perciformes, Salmoniformes, and Siluriformes , which led to secondary infections and, ultimately, mortality  from a fishpond in central China  hosts the most diverse fauna of Gyrodactylus described on any fish species in the Palearctic region, including 17 species, whereas the Amur minnow R. lagowskii hosts three species of Gyrodactylus worldwide  gene from 79 individuals from eleven populations and its host distribution in the Qinling Mountains in central China, assessed the population genetic differentiation and demographic history and tested how the geological events or climate oscillation during the Pleistocene may have affected the current phylogeographic pattern of this parasite.In this study, we first examined the phylogeography and demographic history of the 22.1https://www.iucnredlist.org). None of the species (fish or parasite) sampled is endangered or protected in China  following the manufacturer's protocol. The ND5 gene sequences of the 79 individuals of G. konovalovi from the eleven populations were amplified by polymerase chain reaction (PCR) amplification using a newly specific primers designed based on the congeneric species Gyrodactylus brachymystacis and Gyrodactylus parvae  and the reverse primer gkND5‐R (5'‐ATTTGTAGATGATAGCAAG‐3') were used to amplify a segment of 528 bp. Each PCR amplification was performed in a total volume of 25 µl, containing 3.0 mM MgCl2, 10 mM Tris‐HCl (pH 8.3), 50 mM KCl, 0.25 mM of each dNTP, 1.25 U rTaq polymerase , 0.4 μM of each primer, 45 ng gDNA, tapped with Milli‐Q water. The following cycling conditions were applied: initial denaturation for 1 min at 93°C followed by 35 cycles of denaturation for 10 s at 92°C, annealing for 1.5 min at 50°C and extension for 2 min at 60°C with a final extension for 6 min at 72°C. All PCR fragments were initially purified with a PCR purification kit , subsequently subjected to electrophoresis in a 1% agarose gel and finally sequenced with PCR forward primer with an ABI Prism® 3,730 automated sequencer .Total genomic DNA of 2.42.4.1n) and synonymous (s) substitution rate (dN/dS) were calculated in MEGA v6.06.A total of 79 ND5 gene sequences were visually inspected and manually edited using BioEdit v7.0.9.0 Hall,  and thenH), haplotype diversity (h), and nucleotide diversity (π) were calculated in DnaSP v5.10.1 , maximum likelihood (ML), and maximum parsimony (MP) methods. For the BI and ML analyses, the TrN + I + G model was the most appropriate model of nucleotide substitution. The congeneric species 2.4.3Fst pairwise comparisons. The molecular variance was partitioned among groups (Fct), among populations within groups (Fsc), and within populations (Fst) in Arlequin v3.5.1.2  of the sample localities were analyzed to test for isolation by distance (IBD)  based on the haplotype sequences showed congruent topologies and were all divided into three lineages  and genetic diversity (π) across the samples were 0.955 ± 0.007 and 0.04984 ± 0.00458, respectively, indicating high level of genetic diversity. The h was highest in the YP population, whereas π was highest in the TTG population. The h and π were lowest in the XYB population, except for three populations , and the relationship between genetic diversity and the population latitude was uncorrelated  and G. truttae (0.1% to 0.5%) and approximating G. teuchis (0.1% to 11%) and G. corydori (0.1% to 6.2%)  against nonsynonymous substitutions in accordance with results from other gyrodactylids based on COI and ND5 genes, such as G. thymalli, G. salaris, G. teuchis, and G. arcuatus  was 0.068, indicating strong purifying selection with genes of relatively low nucleotide diversity  was very high within populations (Table Fst) and geographic distance   and geographic distance fit the IBD model in accordance with other studies  indicated highly significant genetic differentiation among groups assigned to three lineages 75.09%), among populations 13.33%) and within populations (11.57%). The genetic differentiation metric .33% and .09%, amo) Figure , suggest) Figure . Thus, b) Figure  and resu) Figure . The hos4.4G. corydori, G. teuchis, G. arcuatus, and G. gondae (Bueno‐Silva et al., G. thymall from the Glomma River in Norway was estimated as very recent several thousand years during the Holocene after the LGM (Pettersen et al., R. oxycephalus was affected by geological events and Pliocene climate fluctuations (Yu et al., A strict molecular clock was estimated for the haplotypes Figure . The tim4.5D and Fu's Fs values was positive and not significant (Table The neutrality test between Tajima's nt Table . Howevernt Table . The neunt Table . Therefont Table .M. gonialosae, G. arcuatus, G. gondae, and G. corydori (Bueno‐Silva et al., G. konovalovi underwent a sharp contraction from 0.25 Mya to 0.01 Mya during the late Pleistocene glacial epoch, which corresponds to the end of Lushan glaciation in accordance with F. taihangnica in the Qinling Mountains (Huang, G. konovalovi in the Qinling Mountains.Most of demographic analyses indicated that the expansion of lineage A and lineage C began simultaneously, 0.01 Mya after the LMG during the late Pleistocene, which corresponds to the end of Dali glaciation in the Qinling Mountains in accordance with findings in s Huang, . The vals Huang, . A slighs Huang, . Ancient5G. konovalovi in the Qinling Mountains. After the LGM, most lineages have experienced expansions of population size and range. The findings provide evidence that Pleistocene climatic changes profoundly affect this species distribution pattern and demographic history in the Qinling Mountains. Further studies employing extensive sampling across larger ranges and multiple molecular markers might provide more insight into the phylogeography and demographic history of G. konovalovi across the Qinling Mountains and test coevolutionary relationships between G. konovalovi and its host R. lagowskii at the population level and the magnifying glass hypothesis.The results of this study showed that the climate fluctuation and tectonic changes led to formed vicariance and drainage capture during the Pleistocene, which promoted lineages divergence and influenced the phylogeographic structure of None Declared.You P designed the research; Chen T, Chen J, Tang L, Chen X, Yan J, and You P performed the research; Chen T analyzed the data; Chen T and You P wrote the paper.


The intricacies of human ancestry are buried deep within our DNA. For years, scientists have been working to piece together a vast picture of our genetic lineage. The purpose of this study was to further reveal this global picture of human genetic relatedness using identical-by-descent (IBD) genomic fragments. We processed over 65 million very rare single nucleotide polymorphic (SNP) alleles and detected over 17 million shared IBD fragments, including very short IBD fragments that allowed us to trace common ancestors back to 200,000 years ago. We also determined nine geographical regions representing nine unique genetic components for mankind: East and West Africa, Northern Europe, Arctica, East Asia, Oceania, South Asia, Middle East, and South America. The levels of admixture in every studied population could be assigned to one of these regions and long-term neighboring populations are strikingly similar, despite any political, religious, and cultural differences. Additionally, we observed the topmost admixture to be in central Eurasia. The entire picture of relatedness of all the studied populations presents itself in the form of shared number/size of IBDs, providing novel insights into geographical admixtures and genetic contributions that shaped human ancestry into what it is today.We performed an exhaustive pairwise comparison of whole-genome sequences of 3120 individuals, representing 232 populations from all continents and seven prehistoric people including archaic and modern humans. In order to reveal an intricate picture of worldwide human genetic relatedness, 65 million very rare single nucleotide polymorphic (SNP) alleles have been bioinformatically processed. The number and size of shared identical-by-descent (IBD) genomic fragments for every pair of 3127 individuals have been revealed. Over 17 million shared IBD fragments have been described. Our approach allowed detection of very short IBD fragments (<20 kb) that trace common ancestors who lived up to 200,000 years ago. We detected nine distinct geographical regions within which individuals had strong genetic relatedness, but with negligible relatedness between the populations of these regions. The regions, comprising nine unique genetic components for mankind, are the following: East and West Africa, Northern Europe, Arctica, East Asia, Oceania, South Asia, Middle East, and South America. The level of admixture in every studied population has been apportioned among these nine genetic components. Genetically, long-term neighboring populations are strikingly similar to each other in spite of any political, religious, and cultural differences. The topmost admixture has been observed at the center of Eurasia. These admixed populations  have roughly equal genetic contributions from the Middle East, Europe, China, and India, with additional significant traces from Africa and Arctic. The entire picture of relatedness of all the studied populations unfolds and presents itself in the form of shared number/size of IBDs. The global picture of human genetic relatedness still includes many controversial aspects. This controversy stems mainly from the widespread dissemination of human populations across the world due to various migrations, trading, and conquest events that occurred multiple times over thousands of years. In addition, the intricate nature of DNA inheritance, such as extensive meiotic recombination, obscures our genetic studies, hindering development of complete genetic histories. Nevertheless, certain methods may allow us to overcome these technical impediments. Every human has two parents, four grandparents, eight great-grandparents, and so on, doubling the direct number of ancestors every generation. Following this pattern, a person should have over a thousand direct genealogical ancestors at the tenth generation, over a million at the 20th generation, over a billion at the 30th, and over a trillion at the 40th. However, the entire population size of our 40th generation ancestors, who lived around one thousand years ago, is well below a billion. Clearly many of those 1000-year-old genealogical ancestors had multiple genealogical pathways of relatedness to every person within the current population, as well as to all other people from the same region. On another note, over 99.9% of direct genealogical 40th-generation ancestors for a given individual have not passed on any fragments of their own genomic DNA to this person. This is because those chromosomal fragments were randomly lost somewhere along the 40th generation genealogy tree [The exploration of genetic relatedness is based on the identification of shared IBD fragments between groups of people. IBDs are identified using various statistical algorithms that analyze the patterns of single nucleotide polymorphisms (SNPs). Most of these algorithms are mathematically sophisticated and operate with abstract statistical values and parameters that do not have obvious biological correlates. For example, genetic relatedness may be measured by Multiple Sequentially Markovian Coalescent (MSMC) statistics ,4, putatThere have been several successful, large-scale projects for sequencing multiple human genomes, with inference of population relatedness. One of the first pivotal projects was the 1000 Genomes Project, which characterized 2500 people from 26 populations . AfterwaThe main goal of the study was to reveal a global picture of genetic relatedness and the evolution of mankind. Combining SNP from several milestone whole-genome sequencing datasets, that present a vast spectrum of populations across all continents as well as archaic and ancient people, became an important task. One of the major challenges we faced was associated with the differing qualities of sequencing within different datasets. Besides significant variations in sequencing coverage, various scientific groups used different computational parameters for SNP characterization, and this resulted in large variations in the number of alternative SNP alleles in the individual genomes. We demonstrate here that all these technical predicaments are solvable. We present an extensive dataset of 17 million shared IBD chromosomal fragments, which resulted from pairwise comparison of 3127 individual genomes from 232 populations and seven prehistoric people, all obtained from public datasets. A crucial aspect of our project is the comparison between a broad spectrum of human populations from all major groups around the world and prehistoric people. Each were assessed with the same rules and statistics. Our earliest detected relatedness event in pre-human history was the admixture of Neanderthals with African populations that occurred approximately 200,000 years ago, potentially even earlier. We also observed admixture of African and Oceania populations around 125,000 years ago. The results should be of great interest to anthropologists, historians, linguists, evolutionists, and those interested in the origins and migrations of humans.N individuals from population A have been compared to M individuals from population B. We analyzed all the possible N× M pairs that contained one individual belonging to A and one belonging to B, and from this analysis, we calculated the average number of shared IBD fragments per A/B pair of individuals. This dataset of shared IBD fragments between populations is presented in a 243 × 243 supplementary table  have been severely exposed to inbreeding and as well as founder effects. A majority of individuals within these populations are relatives of one another. This intra-populational relatedness is manifested in the highest numbers of shared IBD fragments within the same population. These numbers are often higher than 100 IBDs per random pair. We observed the highest number of shared IBD segments within the same population in Oceania . We also saw similar numbers in small populations from South America , Arctica (222 Eskimos), and Africa . Due to the effect of inbreeding, the number of shared IBD fragments between these small populations and neighboring populations may be high as well. For example, the Bedouin intra-population number of shared IBDs is very high (176), while the intra-populational IBD sharing number of its much larger neighbor, Iran, is only 12. This is a large difference; however, the number of shared IBDs between Bedouins and Iranians is 20 . This effect is due to elevated inbreeding among Bedouins, which makes a fraction of very rare SNP alleles much more frequent among Bedouins .There is a strong distinction between the characterization of very rare SNP alleles in ancient genomes versus modern genomes, especially when taking into account that Neanderthal lineages separated from modern humans more than 500,000 years ago. Due to this extended period of separation, Neanderthal groups should have their own pool of very rare SNP alleles, which must differ significantly from the pool of rare alleles in modern people. However, because there are only a handful of Neanderthal/Denisovan genomes currently available for genotyping, it is impossible at this time to deduce any very rare alleles from these ancient people. Therefore, we considered all the alleles of ancient people that matched the very rare SNP alleles found in modern peoples as “very rare SNP alleles” of ancient people. Thus, when evaluating this paper, a reader should keep in mind that many of these alleles might have actually been frequent alleles among Neanderthal/Denisovan genomes. When a small group of Neanderthal/Denisovans admixed with much larger populations of the ancestors of modern peoples thousands years ago , Chagirskaya (Cha_XXX), and Vindija (Vin_XXX)) have strikingly similar patterns of relatedness to modern populations, despite these prehistoric people having lived ~30,000 years apart from each other in the area from southern Europe to Western Eurasia and Altai  on modern Europeans than the Stuttgart (Lbk_XXX) genome  is shown in Comparisons of neighboring populations result in the detection of large,  shared IBD fragments. In contrast, comparisons between populations from different continents or between archaic and modern peoples produce much shorter IBDs. These short IBDs pose significant problems for genetic interpretation. For example, when we compared archaic peoples with African populations, we observed the median size for shared IBDs to be 22 Kb long. This short size contrasts with the even shorter IBD size estimated by Povysil and Hochreiter, who conducted an earlier analysis on the same groups . Their IIn summary, IBD fragment characterization has a natural threshold at around 20 Kb in length. At this threshold, we encounter a significant dilemma: Either losing a significant proportion of short IBDs, or including a large assemblage of false positive hits. The foundation for this dilemma is arguably trivial. The shortest DNA fragments do not host enough polymorphic sites for their undisputable identification. As just noted, we made our criteria highly stringent to greatly reduce the amount of false positive data. Inescapably, it is likely that we missed a substantial number of very short IBD fragments. However, given the choice between these two, a high threshold for rare alleles is more likely to yield misleading results than is the exclusion of the very shortest IBDs.L = 1/(2 × g × r), where L is the average length of IBD fragment, r is the meiotic recombination rate, and g is the number of generations since LCA [Another problem that requires consideration is the chronological placement of the Last Common Ancestor (LCA) based on IBD lengths. The theoretical formula for the conversion of average length of shared IBD fragments into the rough date for their LCA is: ince LCA ,27. The ince LCA . Moreoveince LCA .Our results concerning the relatedness of modern people generally align with previous evolutionary studies based on the same datasets of the 1000 Genomes, Simons SGDP, and Estonian EGDP projects, which were processed and obtained through different methods in the original publications ,7,8. A mRegarding Europeans, the strongest genetic distinction in their population occurs along the North-South axis. All European Mediterranean countries  have around the same level of relatedness to the Middle East and North Europe  stands out from the others by its unique pattern of genetic relatedness. Specifically, the Roma population has the highest genetic relatedness (44%) to the SAS natives, which is, on average, eight times higher than in other Europeans. The Roma people came to Europe from India, and astonishingly, they have almost fully preserved their genetic identity despite producing dozens of generations since their separation from their original founder population. The Hungarian ethnic group represents the opposite situation. Hungarians are the only people in Central Europe who speak a language belonging to the Uralic language group instead of Indo-European, like all other peoples of this region. It is well-known that the ancestors of the Hungarians initially lived in the steppe regions of the Ural region, and their closest relatives were the ancestors of the modern Khanty and Mansi ,33. In tAs for American populations, the smaller ethnic groups, recently characterized in the Simons SGDP and Estonian EGDP projects in South America, did not show significant levels of relatedness (<2%) to either Europeans or African populations. In contrast, American populations, first characterized within the 1000 Genomes Project, all have predominant relatedness to Europe, Middle East, and Africa. Interestingly, the Americans from the 1000 Genomes Project demonstrated almost the same level of relatedness to Europeans and to Middle Easterners . It happThe populations of the Arctic (ARC) are strikingly distinct from their much larger and more populated neighbors. We were surprised to see only 1% of relatedness between both Koryaks and Chukchi populations to the EAS group. In addition, many ARC populations have less than 1% of relatedness to Europeans. At the same time, the strong genetic contribution of the Arctic group (up to 50%) has been detected in several ethnic groups in Siberia as well as in small populations from Northern China . This hiWe acknowledge that the most common approach for studying African populations is accomplished through comparing populations from the North and South African regions ,37. We sOceania populations represent an interesting case because they separated from the mainland around 40,000 years ago according to our estimations . The excWe tried to reveal genetic traces of Medieval global migrations across Eurasia associated with the expansion of the Genghis Khan empire, which was created in 1206 and eventually spread from China to just outside of Central Europe. Russia was one of the regions occupied by Mongols for about three centuries. Tatars, the presumable descendants of Mongols, and many of their neighboring populations demonstrate surprisingly small relatedness (3–5%) to East Asia. For the modern Mongolian population, the EAS people are the dominant relatedness component 60%, see . Among RAs we explained in the The most recent admixture of Neanderthals/Denisovans was observed in Oceanian populations and occurred ~55,000 years ago. Following this admixture was the admixing of Europeans and a large spectrum of Asian populations about 65 to 75 thousand years ago. While admixture of archaic people with Africans occurred much earlier . Additionally, African–Neanderthal shared IBD fragments are among the shortest (~22 Kb median). The detection of such small IBD fragments requires special analysis, and this is why the Neanderthal–African relatedness has not been detected in several publications. In fact, given the strict criteria we used (discussed above), this is near the lower size limit of what we could reliably detect in our analysis.As for genomes of ancient individuals who lived around 7000–9000 years ago in the vicinity of Luxemburg (Los_XXX) and Stuttgart (Lbk_XXX), we see the progenies of the former spread primarily within Northern Europe, while the latter spread in the Mediterranean region and then the Middle East as well see .Analyzing clusters of very rare SNP alleles to accurately detect numerous shared IBD fragments within diverse, whole-genome sequencing datasets is an instrumental method in piecing together a cohesive global picture of human genetic relatedness. This approach allows for the detection of very short IBD fragments , which may be over 200,000 years old and difficult to harness due to their shorter size. Resolution of such small IBD fragments enables the detection of genetic relatedness between various remote populations from different continents. We were able to uncover human populations with the highest and lowest admixtures, as well as trace the inheritance of IBD fragments from seven archaic and ancient peoples to modern genomes all over the globe. Relative relatedness of every studied population to nine distinct human genetics regions has been calculated. Further unravelling the peculiarities in the origin and evolution of human populations boils down to simplicity, explicitness, and the opportunity for continued in-depth analysis of individual IBD fragments.ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502); (2) the Simons Genome Diversity Project included 279 genomes from 142 populations [https://www.ncbi.nlm.nih.gov/sra accession number SRP053199). To obtain SNPs, we processed fastq files with a custom pipeline. It consisted of the following steps: Estimating the quality of sequencing data with fastqc v0.11.8 [All of the main principles, protocols, and programs used in the creation of the rare SNP alleles database have been described in detail in Fedorova et al. . Here weulations ; (3) theulations   and anatomically modern humans leaved 45,000 (Ustishim) and 7000–8000 (Stuttgart and Loschbour) years ago . The SNPp-value < 10−13) of any possible sequencing errors to nearly zero, which may mimic real IBD fragments and create false positive IBDs [Shared IBD segments between a pair of individuals were identified as clusters of at least five very rare neighboring SNP alleles that are common to both individuals. As previously demonstrated, the threshold of five neighboring very rare SNP alleles reduces the chances  [We created special identifiers for the individuals, populations, and geographic regions under analysis. Population identifiers were constructed using two groupings of three characters (triplets) that were concatenated together by an underscore symbol. The first triplet is related to the population name, while the second refers to the geographic region of the respective population. For example, Rus_EUR is the identifier for the Russian population from Europe, while Rus_CAS represents the Rushan-Vanch population from Tajikistan . We considered 12 geographical regions: AFE—East Africa; AFW—West Africa; AMR—America; CAS—Central Asia; CAU—Caucasus; EUR—Europe; ARC—Arctic region; EAS—East Asia; SAS—South Asia (within the Hindustan Peninsula); OCE—Oceania and Australia; MDE—Middle East, and SIB (Siberia). In our classification SAS is represented by populations from India, Pakistan, Bangladesh, and Myanmar, while Indonesia and Philippines were assigned to the OCE region followed by Simons SGDP project publication . The geoABI is the number of IBD fragments per pair of individuals from populations A and B;AN is the normalization coefficient for population A from the HeatMapTable (last row);BN is the normalization coefficient for population B from the HeatMapTable (last row);ABJ is the normalized ABI value.All of the results presented in this paper were obtained through computer processing of vrGVdb_1K_S_E_V_A7_all_v3. In the initial stage of this processing, we obtained individual-specific databases of very rare SNP alleles using the updated program vrGVindividualDBS_A2.pl as described in Fedorova et al. 2016. During the second stage, we obtained “dat3” and “dat4” files for each individual using an updated version of the program, RVC_v3_A2.pl. This version of the program was automatically launched for each individual by the TotalStat_vrGVs_v4_A2.pl script. In the third stage, we obtained AN × BN) in the Formula (1), without taking the square root first, caused a systematic over-normalization with the opposite effect.In this formula we used the square root of Ban_AFR or YiX_EAS) and populations names/regions. The first column represents the number of analyzed genomes in the population. These numbers may be followed by letters ‘s’, ‘e’, ‘v’, or ‘se’, etc., which stand for the databases their respective individuals came from (Simons SGDP—(s), Estonian EGDP—(e), Siberian Project—(v), or SGDP + EGDP (se)). For example, for the identifier is 10se, or 1v. If there is no letter, the corresponding population came entirely from the 1000 Genomes Project.The first two rows and three columns in rankingATLAS2_v9.pl, and the data from the rankingATLAS2_v9.pl.The fourth stage of our computations is unique to this research and was absent in Fedorova et al. 2016. In this stage, we created http://bpg.utoledo.edu/~afedorov/lab/ATLAS2.html) in a package that includes an Instruction Manual and Protocols and also as All of the described Perl programs are available on our website (L = 1/(2 × g × r), where L is the median length of IBD fragments, r is the meiotic recombination rate, and g is the number of generations [L = 1/(g × r), which lacks a factor of two in the denominator [C) into the two formulas, resulting in the following variations: L = C/(2 × g × r) and L = C/(g × r) respectively. This calibration constant we established experimentally taking into account the known time period between ancestral men in our study (Los_XXX and Lbk_XXX who lived about 8000 years ago REFS). When considering that, on average, r = 0.0118 Mb−1, the median IBD length between these two ancestral people and Europeans is 585 KB, and the fact that Los_XXX and Lbk_XXX were separated from modern people by approximately 320 generations (8000 years ago), we calculated that C ≈ 2. Therefore, to convert IBD median lengths (L = 2/(2 × g × r) for modern people and L = 2/(g × r) for ancestral vs. modern genomes. These calculated ages of IBD separation are presented in the To calculate the time period for the last common ancestor, we examined shared IBD fragments based on their lengths using the theoretical formula erations ,27. In cominator . In orde lengths  into theThe multidimensional scaling (MDS) was made with the cmdscale function from the stats R package . The inp


Once present in the entirety of Europe, mouflon (wild sheep) became extinct due to intense hunting, but remnant populations survived and became feral on the Mediterranean islands of Corsica and Sardinia. Although now protected by regional laws, Sardinian mouflon is threatened by crossbreeding with domestic sheep causing genetic hybridisation. The spread of domestic genes can be detrimental for wild populations as it dilutes the genetic features that characterise them. This work aimed to identify diagnostic tools that could be applied to monitor the level of hybridisation between mouflon and domestic sheep. Tens of thousands of genetic markers known as single nucleotide polymorphisms (SNPs) were screened and we identified the smallest number of SNPs necessary to discriminate between pure mouflon and sheep. We produced four SNP panels of different sizes which were able to assess the hybridisation level of a mouflon and we verified that the SNP panels efficacy is independent of the domestic sheep breed involved in the hybrid. The implementation of these results into actual diagnostic tools will help the conservation of this unique and irreplaceable mouflon population, and the methodology applied can easily be transferred to other case studies of interest. Ovis aries musimon) populations. Although conservation policies, including reintroduction plans, have been enforced to preserve Sardinian mouflon, crossbreeding with domestic sheep has been documented. We identified panels of single nucleotide polymorphisms (SNPs) that could act as ancestry informative markers able to assess admixture in feral x domestic sheep hybrids. The medium-density SNP array genotyping data of Sardinian mouflon and domestic sheep (O. aries aries) showing pure ancestry were used as references. We applied a two-step selection algorithm to this data consisting of preselection via Principal Component Analysis followed by a supervised machine learning classification method based on random forest to develop SNP panels of various sizes. We generated ancestry informative marker (AIM) panels and tested their ability to assess admixture in mouflon x domestic sheep hybrids both in simulated and real populations of known ancestry proportions. All the AIM panels recorded high correlations with the ancestry proportion computed using the full medium-density SNP array. The AIM panels proposed here may be used by conservation practitioners as diagnostic tools to exclude hybrids from reintroduction plans and improve conservation strategies for mouflon populations. Hybridisation of wild populations with their domestic counterparts can lead to the loss of wildtype genetic integrity, outbreeding depression, and loss of adaptive features. The Mediterranean island of Sardinia hosts one of the last extant autochthonous European mouflon ( Ovis aries musimon) present on the Mediterranean islands of Corsica and Sardinia [Ovis aries) have been associated to a second wave of domestication which occurred ~5,000 years later [Genetic hybridisation among related species is increasingly studied due to its role in the adaptation, evolution, and diversification of species ,2,3,4,5.Sardinia ,13. The rs later . The Eurrs later . Sardinirs later ,13,15. Trs later  and evidrs later ,16. Impors later . Such anrs later . Hence, rs later . Howeverrs later .Small sets, or panels, of ancestry informative markers (AIMs) have been developed and used to infer population genetic parameters in several species. AIM panels have been used to estimate biogeographical ancestry and structure in human populations ,20,21,22x domestic sheep crosses when tested both on real and simulated data. Our results provide a fundamental conservation tool for the affordable identification of hybrid mouflon x domestic sheep and for the quantification of their admixture level.In this work, we applied supervised machine learning approaches on mid-density DNA array data and identified AIMs able to correctly discriminate mouflon We collected publicly available genotype data (~50k SNPs) from 23 non-admixed feral Sardinian mouflon (MSar) and 23 Sarda sheep (SAR) , and froNon-autosomal variants, markers with missing data (> 0), minor allele frequency (MAF) ≤ 0.1, and markers significantly out of Hardy–Weinberg equilibrium (HWE ≤0.001) were excluded from the subsequent analyses. Pruning was performed using PLINK v1.9 .To identify the markers from the medium-density SNP array genotyping data for MSar and SAR able to detect admixture between mouflon and domestic sheep, we applied a two-fold approach which included (1) a preselection step aimed at removing the least significant markers, and (2) supervised machine learning classification approaches to identify the most informative markers among those left after preselection.The dataset was first reduced based on Principal Component Analysis (PCA) results. PCA is a widely used dimensionality reduction technique that generates new uncorrelated variables  sorted according to the variance they explain. The contribution of each SNP to every PC is expressed as a loading score. A PCA analysis of the reference breeds was performed using R v3.4.3 . The firSNPs which passed preselection were submitted to supervised machine learning classification, as implemented in Boruta v6.0.0 , in ordeWith the aim to identify AIM panels which forcibly intercepted all autosomes, we applied the iteration algorithm to each autosome separately . Feature selection was performed for each autosome and SNPs listed as important were assembled in a panel named CH1. Lastly, to further reduce the number of significant SNPs, a last panel (CH2) was generated by selecting from CH1 the three most discriminant SNPs per each autosome.https://github.com/barbatom/Hybridiser). Given two parental populations, Hybridiser computes the allele frequency at each locus, and then generates hybrid genotypes at each locus by selecting an allele from each of the parental populations with probability equal to the parental allele frequency. A dataset of 270 simulated hybrids (HYBs) was generated pooling 90 MSar x SAR first-generation crosses (F1) and the reciprocal backcrosses F1 x MSar (BC1M), and F1 x SAR (BC1S), each comprising 90 individuals. The accuracy of each of the five AIM panels (three genome-wide and two chromosome-wide) to assess ancestry levels, or levels of admixture, was tested using genotype data from both simulated and real hybrid populations (MxS). To test the AIM panels with simulated hybrids, we developed Hybridiser v0.1, an R script able to simulate F1 hybrid individuals (available at 2r).Admixture v1.3.0  was usedp-value for the correlation obtained using the GW3 panel was calculated according to Davison and Hinkley [p = (1 + x)/(1 + n), where x is the number of random sets that produced an 2r score greater than or equal to that calculated using GW3 and n is the total number of random set tested . The coefficient of determination values obtained using the 5,000 random SNP sets were standardised by z-scores. An empirical  Hinkley  as p =  90 F1 offspring between MSar and the test domestic breed, ii) 90 backcrosses between F1 and the test domestic breed and iii) 90 individuals obtained as backcross between F1 and MSar. The AIM panels were tested on simulated hybrid populations of MSar with MER, LAC, and TEX, respectively. Coefficients of determination were computed between the supervised admixture results obtained using AIM panels (selected markers) and the full medium-density genotype array (full set of markers). Finally, we evaluated the ability of the five AIM panels to detect mouflon hybrids with domestic sheep breeds other than Sarda. For each mouflon Pruning for missing data, MAF, and HWE left 33,481 non-rare, neutral SNPs. PCA was performed on the two reference populations (MSar and SAR) to evaluate how many PCs contributed in discriminating mouflon and domestic sheep. As expected, PC1 , split mouflon and domestic sheep as two distinct clusters . PC2 7.. ConsequThe PCA-based preselection step identified 1,279 SNPs that contributed the most to discriminate between mouflon and domestic sheep reference populations GW1; . This fiPCA analyses were used to visually compare the performance of the full set of SNPs and the five AIM panels applied to simulated (HYB) and real hybrid (MxS) populations. The PCA of HYB using the full set of SNPs discriminated the parental populations (SAR and MSar) at opposite sides of the graph and positioned the hybrid populations according to their ancestry proportions, with F1 at the centre of the plot and the two backcrosses BC1S and BC1M closer to SAR and MSar, respectively . PCA of PCA performed on HYB using the AIMs showed clustering comparable with the PCA results obtained using the full set of SNPs . Each AIA quantitative assessment of the ancestry proportions in HYB was obtained through supervised Admixture using MSar and SAR as reference populations. HYB showed ancestry proportions coherent with the expected values of each offspring group ; mean an2r ≥ 0.960), with GW1 and CH2 scoring the highest and lowest, respectively  against the null hypothesis that the same results could be obtained using any equal sized set of SNPs chosen at random. The null hypothesis was rejected with high significance (2r > 0.92 (AIM panels were further tested to detect admixture between mouflon (MSar) and three commercial sheep breeds . Coefficients of determination calculated between the ancestry percentage obtained using the AIM panels and full set of SNP ancestry resulted in 2 > 0.92 .Hybridisation between wild species and their domestic counterpart may lead to genetic homogenization and loss of local adaptation . SardiniIn this work we implemented a supervised machine-learning-based classification approach to identify highly discriminant markers which can be included in rapid and low-cost diagnostic assays with the aim to support mouflon conservation . The accWe assessed the performance of the panels in identifying crosses between domestic sheep and feral mouflon using both simulated and real data. As expected, using the AIMs on simulated data performed moderately better than on real admixed mouflon samples. Real admixed populations present a more complex genetic make-up, influenced by demography, selection, inbreeding and introgression events occurred during centuries. Conversely, the simulated individuals were generated from the same reference populations used to select the best AIMs. In addition, the mating system applied in simulations generates simplified admixture patterns with respect to those occurring in real populations. However, the use of simulated hybrid genotypes may aid in AIM panels testing to overcome the lack of real admixed samples. When available, real hybrids samples can guarantee the accuracy of the AIMs panels.The detection of hybrids is a fundamental task in biodiversity conservation, and genetics provides an unmatched tool to identify even cryptic levels of hybridization. Applying a machine learning-based approach, we identified reduced SNP panels which proved effective in identifying domestic sheep introgression in Sardinian mouflon. The AIM panels we propose can be applied for large-scale assessment of the Sardinian mouflon population, and aid in the conservation of this unique genetic resource. Lastly, the method we present can be easily applied to other wildlife and domestic species conservation to develop accurate and affordable tools for hybrid identification.


Phytophthora infestans is a widespread destructive plant pathogen that causes economic losses worldwide to potato production. In this study, we sequenced four mitochondrial DNA gene sequences of 101 P. infestans isolates from five potato-growing regions in China to investigate the population structure and dispersal pattern of this pathogen. The concatenated mtDNA sequences in the populations showed high haplotype diversity, but low nucleotide diversity. Although there was a degree of spatial structure, our phylogeographic analyses support frequent gene flow between populations and the direction of gene flow, primarily from north to south, corresponds to the route of seed potato transportation, suggesting a role of human activities in the dispersal of P. infestans in China. Cryphonectria parasitica, are spatially structured and quarantine procedures can be effective in preventing the invasion of plant diseases caused by the pathogens , are the most frequently used genetic markers  and one mitochondrial (Cox1) gene, and revealed the low genetic diversity and low frequency of heterozygotes in P. infestans.annually . Knowled markers  and havenfestans . Howevernfestans . Mitochonfestans . CardenaSolanum tuberosum L.) is the fourth most important staple food crop worldwide. China is the world’s largest producer of potatoes, producing ∼99.21 million tonnes on ∼5.76 million ha in 20171. With government support and shifts in dietary habits, potato production in China is expected to increase substantially in the coming decades , P. infestans in Northern China. Subsequently, a population analysis of P. infestans inferred from mtDNA haplotypes and restriction fragment length polymorphism (RFLP) data suggested multiple migrations of this pathogen into China , NADH dehydrogenase subunit 9 (nad9), NADH dehydrogenase subunit 4 (nad4), and ATP synthase subunit alpha (atp1) genes from 101 P. infestans isolates collected in China. The objectives of this study were to: (i) characterize the genetic structure of P. infestans in China; (ii) investigate the geographic and host-origin effects on the population structure; and (iii) determine gene flow among populations, particularly in terms of the source–sink dynamics of the populations.This study analyzed the concatenated mtDNA sequences of the partial P. infestans infected potato (Solanum tuberosum) and tomato (S. lycopersicum) leaves, stems and tubers exhibiting typical late blight symptoms were collected from 34 sampling sites in Fujian, Hebei, Beijing, Heilongjiang, Inner Mongolia, and Jiangsu, China (n = 36), Hebei and Beijing , Heilongjiang , Jiangsu , and Inner Mongolia . The pure culture of each isolate was preserved on antibiotic-free rye agar slants covered with mineral oil  at 15°C in the dark and transferred to new slants at 6-month intervals. P. infestans genomic DNA was extracted using a modified cetyl trimethylammounium bromide (CTAB) method, as described previously , and 1 μL of template cDNA. The PCR program comprised an initial denaturation at 95°C for 2 min, followed by 30 cycles of 90°C for 20 s, 57°C for 25 s  and deposited in GenBank under accession numbers nslatorX . A recennfestans . To testnfestans . No recoπ) and haplotype diversity (Hd), using DnaSP 5.10  was classified as low (<0.05), moderate (0.05–0.15), large (0.15–0.25); or great (> 0.25) .Genetic differentiation among populations was evaluated using aSP 5.10 . The levsing FST . We calcquin 3.5 . Default(> 0.25) . We alsoulations . This me package  implemenP. infestans, we conducted analysis of molecular variance (AMOVA) using Arlequin 3.5 . To determine the potential geographic and host-origin effects on P. infestans populations, the isolates were divided into five populations based on geographic origin, as described above. They were also divided into two populations based on host origin, i.e., S. tuberosum  and S. lycopersicum . Pairwise population differentiation was also estimated based on GST using the PopGenome 2.16 R package  runs were performed to estimate the posterior distributions of the parameters, with samples taken every 10,000 steps over 50,000,000 steps. After discarding the first 10% of the samples as burn-in, all parameters showed sufficient sampling, as confirmed by Tracer 1.7 , nad9 (348 bp), nad4 (396 bp), and atp1 (489 bp). No indels were detected in our dataset. In the entire mtDNA, 141 polymorphic sites were identified, representing 7.16% of all sites analyzed, including 100 single variable sites and 41 parsimony-informative sites. The transition/transversion ratio (R) was 0.702, indicating a strong transversion bias during the mitochondrial evolution of P. infestans.We obtained 1,968 bp of mitochondrial genes in P. infestans isolates studied, with a haplotype diversity of 0.920  was observed in the FuJ population and the lowest (0.00153 ± 0.00033) was found in the HLJ population. Greater variation in the concatenated mtDNA sequences was observed in P. infestans isolates isolated from potato compared with those isolated from tomato, when the isolates were categorized according to host origin , accounting for nearly 8.66% of the total variation in P. infestans .The results of sliding-window analysis of the pairwise FST values were lower than 0.33 .Among the five geographic populations of MC jumps . Comparam tomato . This micox1 gene, which was used for DNA barcoding to identify plants and fungi (eEF-1α gene of P. infestans (atp1 gene is a rotary enzyme that exploits the proton gradient across the inner membrane, generated by the electron transport chain, to synthesize ATP (P. infestans populations between different geographic regions or host species; therefore, it could be an ideal genetic marker for population genetic analysis.Current studies usually use the relatively short, reasonably variable gene fragments flanked by highly conserved sequences as mtDNA markers, such as the nd fungi . For intnfestans . The atpsize ATP , howeverP. infestans in China (P. infestans dispersal is associated with human-mediated activities. In China, the majority of potatoes in the winter-cropping and central double-cropping zones in southern China are intended for export, and not for planting, while seed potatoes are supplied from the northern single-cropping zone. Further studies to understand how human-mediated activities influence the migration of P. infestans should be informative.Our phylogeographic analysis revealed multiple north-to-south dispersal events in in China . This acP. infestans. The results showed high haplotype diversity, but low nucleotide diversity, of P. infestans in China. The diversification patterns are likely shaped by gene flow. We also found multiple north-to-south migration pathways of P. infestans in China, suggesting that the movement of seed potato tubers through the country has played a major role in the dispersal of P. infestans in China. This information is potentially valuable for the development of more effective strategies to control this pathogen. Further analyses of larger datasets with P. infestans outside of China will lead to a more comprehensive view of the evolutionary history of this pathogen.In conclusion, this study examined the population genetic structure and phylogeography of MN458052 to MN45815 for cox1, MN458254 to MN458354 for nad9, MN458153 to MN458253 for nad4, and MN457951 to MN458051 for atp1.The datasets generated for this study can be found in the GenBank database under accession numbers QC and FG conceived and designed the experiments and analyzed the data. CC, FG, BL, and QW performed the experiments. QC and FG wrote the manuscript. All the authors reviewed 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.