There are only six isolated living giant panda populations, and a comprehensive understanding of their genetic health status is crucial for the conservation of this vulnerable species. Liangshan Mountains is one of the main distribution areas of living giant pandas and is outside the newly established Giant panda national park. In this study, 971 giant panda fecal samples were collected in the heartland of Liangshan Mountains . Microsatellite markers and mitochondrial D‐loop sequences were used to estimate population size and genetic diversity. We identified 92 individuals  from the three reserves. Our results showed that: (1) genetic diversity of three giant panda populations was moderate; (2) several loci deviated significantly from the Hardy–Weinberg equilibrium and almost all these deviated loci showed significant heterozygote deficiencies and inbreeding; (3) three giant panda populations have substantial genetic differentiation with the most differentiation between MB and the two other populations; and (4) a large amount of giant panda feces outside the three reserves were found, implying the existence of protection gap. These results indicated that under stochastic events, the giant panda populations in Liangshan Mountains are at risk of genetic decline or extinction and urgent need of human management. This study revealed that high attention should be paid to the protection of these giant panda populations outside the Giant panda national park, to ensure their survival in their distribution areas. Although China's recent efforts have greatly increased the number and distribution of the wild giant pandas, the species is only distributed in six isolated mountains, namely, Qinling Mountains, Minshan Mountains, Qionglai Mountains, Liangshan Mountains, Daxiangling Mountains and Xiaoxiangling Mountains  using GPS units and the distribution of samples was mapped as shown in Figure 2.2Fecal DNA was extracted using the kit (Biobase Upure DNA stool kit) and nucleic acid purifier (Thermo KingFisher). Fecal samples collected in the field were soaked in anhydrous ethanol and frozen at −20°C. DNA extraction was undertaken according to the manufacturer's instructions. The mitochondrial control region was amplified by PCR for those samples where DNA was successfully extracted, using primers from Zhang et al. .2 (25 mmol/L), 0.8 μl BSA (1 mg/ml), 0.8 μl Ptp primer (15 pmol/L), 0.8 μl BEDH primer (15 pmol/L), 5 μl template DNA (50 ng/μl), and 1.6 μl ddH2O. Amplifications were performed using the following PCR procedure: an initial denaturation step for 5 min at 94°C, followed by 40 cycles of 94°C for 50 s, 55°C annealing for 45 s, 72°C elongation for 50 s and a final elongation for 10 min at 72°C. Finally, samples were stored at 4°C.PCR amplifications were performed in a 20 μl reaction volume containing 10 μl 2xTaq PCR Pre Mix (+dye), 1 μl MgCl2.32 (25 mmol/L), 0.8 μl BSA (1 mg/ml), 0.8 μlF‐primer (15 pmol/L), 0.8 μl R‐primer (15 pmol/L), 5 μl template DNA (50 ng/μl), and 1.6 μl ddH2O. Amplifications were performed using the following PCR procedure: an initial denaturation step for 5 min at 94°C, followed by 40 cycles of 94°C for 50 s, annealing temperature . Allele calling was performed using Gene Mapper v4.0.2.4To minimize the genotyping error in the final individual identification results, we utilized the software Micro‐Checker  tested was from 1 to 6, and 10 independent runs were carried out for each. The lengths of Markov Chain Monte Carlo (MCMC) iterations and burn‐in were set at 1,000,000 and 100,000, respectively. The true K is selected using the maximal value of the log likelihood [Ln Pr(X/K)] of the posterior probability of the data for a given K . We successfully extracted DNA from 731 fecal samples  successfully completed PCR amplification and genotyping for seven microsatellite loci in this study. Analyses determined that a combination of six loci was most effective for individual identification for the three populations, calculating PID (sib) 0.00911 and 0.00964 Table . AccordiIndividual identification using microsatellites determined that the number of giant pandas in the three reserves was 27 (MB), 22 (MG) and 43 (HZG). Two of the reserves' individual numbers were higher than the fourth survey (18 (MB), 22 (MG) and 29 (HZG)). In addition, two individuals in the HZG were immigrants from MG.3.3Seven microsatellite markers were successfully amplified from the three populations. A total of ninety‐six alleles were detected at seven loci in 92 individuals from the three populations. The number of common alleles in the three populations was 13, but the 13 common alleles in each population were different. We identified five unique alleles in each of Mabian and Meigu populations. Similarly, these two populations had the same number of rare alleles  (9). The numbers of unique and rare alleles in Heizhugou population were 15 and 13, respectively. These rare alleles are at risk of being lost due to inbreeding or genetic drift.p < 0.01), while three in Meigu population and two in Heizhugou population  of the three populations was 0.632 (MB), 0.598 (MG), and 0.466 (HZG), the average expected heterozygosity (He) was 0.577 (MB), 0.502 (MG), and 0.555 (HZG), and the polymorphic information content (PIC) was 0.514 (MB), 0.441 (MG), and 0.508 (HZG), respectively  showed that there was a significant genetic differentiation between the three giant panda populations, with Fst ranging from 0.0756 to 0.1588 . Identification of larger populations in MB and HZG may have two causes, not necessarily mutually exclusive. The first being that different methods were used for our study and the fourth national survey. The fourth national survey predominantly used the “Distance‐Bamboo Stem Fragments Method” adopted in the third national survey (1999–2002)     , Meigu , and Heizhugou  populations were lower than the diversity of Wolong wild giant panda population  and Shaanxi captive population  Huang, , but higp < 0.01), while three deviated in the Meigu population and two deviated in the Heizhuguo population. Almost all loci that deviated from the Hardy–Weinberg equilibrium showed significant heterozygote deficiencies and significant inbreeding. Inbreeding may be the main cause of deviations from the Hardy–Weinberg equilibrium. Our results showed that the three giant panda populations, especially Mabian, are genetic disequilibrium and there is the risk of further loss of genetic diversity.Serious genetic imbalance may lead to the loss of genetic diversity and population decline . Further support for observed clustering was the high genetic differentiation and low gene flow of Mabian population  with the other two populations. The gene flow in Heizhugou and Meigu, which were close to each other, reached the maximum (3.05907). The genetic clustering also confirms that the geographical clusters were likely indications of higher panda activity and not an effect of sampling method. However, Hu et al.  found thThe genetic and geographical clustering of the three populations suggests that there is a barrier preventing genetic exchange between the two areas. Feng  found thConclusively, the level of genetic diversity of three giant panda populations was moderate, while the genetic diversity of Mabian giant pandas was the lowest. The existence of genetic isolation, a high number of rare alleles, inbreeding and significant deviations from the Hardy–Weinberg equilibrium indicated that these three populations were genetically unstable, and inbreeding may further result in the loss of genetic resources Wang, .4.3Liangshan Mountains is one of the main distribution areas of living giant pandas and belongs to the southernmost distribution of giant pandas. Mabian, Meigu and Heizhugou reserves are located in the heartland of Liangshan Mountains, and are also the core distribution areas of giant pandas in the Liangshan Mountains. The effective protection of the three giant panda populations is crucial for the conservation of all Giant pandas in Liangshan Mountains. The results of our have shown that the three giant panda populations are at risk of decline or extinction given stochastic events, especially the Mabian population. It is therefore urgent to improve each population's genetic status by increasing genetic resources. We recommend two strategies for improving the genetic status of three populations. Firstly, improve genetic diversity of three populations by the introduction of genetically distinct individuals. The China Conservation and Research Center for the Giant Panda and the Chengdu Research Base of the Giant Panda have the largest captive breeding populations of giant pandas in China. These captive populations are genetically stable and distantly related to populations from the Liangshan Mountains (Shan et al., Our second recommendation for improving the genetic status of the three populations is to increase connectivity and genetic exchange between the two geographically and genetically distinct panda groups. Although significant genetic differentiation between the two groups exists, no significant difference in behavior and morphology has been found. Similarly, there was no evidence that the Mabian population was subject to different geographical or climatic conditions and thus no unique or local adaptation. Therefore, there should be no genetic, behavioral or morphological impediment to breeding and risk of intraspecific hybridization (Frankham, Although Wei et al.  concludeIn conclusion, giant panda populations in Liangshan Mountains had moderate genetic diversity, with a high number of rare alleles, significant heterozygote deficiencies and inbreeding. Three populations clustered into two geographically and genetically distinct groupings, with the Mabian population being separated from the other two by a large tract of unsuitable habitat. The giant panda population in Liangshan Mountains is genetically unstable and at risk of decline or extinction given stochastic events. It is therefore recommended that connectivity between populations be re‐established by improving habitat quality and continuity, and genetic health be enhanced by the introduction of captive‐bred distantly related individuals. These changes could be incorporated into the updated conservation plans for the Liangshan Mountains. Our study revealed that high attention should be paid to the protection of these giant panda populations outside the Giant panda national park, to ensure their survival in their distribution areas, and can serve as a reference for the genetic management of Giant panda populations in other distribution areas and some key conservation species in China and world.The authors declare no competing interests.Figures S1‐S3Click here for additional data file.


Trifolium pratense) in a medium-size city in Central Europe to test the cityscape effect on within- and among-population genetic diversity. We used eight inter-simple sequence repeat markers to examine the genetic structure of 16 populations, each represented by eight individuals. The isolation by resistance was analysed using a least cost patch approach, focusing on gene flow via pollinators. We found great variation among T. pratense populations, with no discernible geographic pattern in genetic diversity. We linked the diversity to the long history of the city and high stochasticity of land use changes that occurred with city development. In particular, we did not find that the Odra River (ca. 100 m wide) was a strong barrier to gene transfer. However, notable isolation was present due to resistance and distance, indicating that the populations are threatened by genetic drift. Therefore, gene movement between populations should be increased by appropriate management of urban green areas. We also found that small urban grassland (UG) patches with small populations can still hold rare alleles which significantly contribute to the overall genetic variation of T. pratense in the city.Urban grasslands provide numerous ecosystem services, and their maintenance should be based on naturally regenerating plant populations. However, the urban environment is challenging for preserving viable populations, mostly because of their high fragmentation and small size, which can lead to genetic drift. We examined red clover ( T. pratense is not considered to be a valuable plant for urban grasslands  is an insect-pollinated crop native to southeastern Europe and Asia Minor. It is a perennial plant belonging to the Fabaceae family, and as a legume it has the ability to fix atmospheric nitrogen through symbiosis with the bacteria Rhizobium leguminosarum biovar trifolii  and honeybees (Apis mellifera L.)  , and varfera L.) . Bumbleblla tube . Short-tlla tube . Honeybella tube . Howeverlla tube . Honeybella tube .T. pratense provides a good nectar source for pollinators, and it has recently been suggested as a desirable component of seed mixtures for pollinators (Trifolium species such as T. incarnatum and T. resupinatum (information from City Greenery Office of Wrocław). Therefore, we assume that most of the T. pratense populations in the city were not intentionally introduced but exist as a remnant of previous land use or were established in the course of spontaneous succession. Nonetheless, we cannot exclude accidental introduction by contaminated seed mixtures.Long-range seed dispersal is related to endozoochory by cattle and wild ungulates, while short-range dispersal is related to ants (myrmecochory) . T. pratlinators ; howeverlinators . Its prelinators . In WrocTrifolium pratense plants belonging to 16 populations  were collected from Wrocław city  tests were applied using ARLEQUIN 3.5.1  softwarePrincipal coordinates analysis (PcoA) was performed on a genetic distance matrix generated from the haploid data. The GeneAlex program was used for this purpose, and the genetic distance was calculated following T. pratense individuals, and then correlating the matrices using the Mantel test. Since the relation was not linear, we applied the Mantel test based on Spearman rank correlation, using the ‘vegan’ package in the R environment (The urban landscape matrix was quantified using the Topographic Objects Database (BDOT10k), which shows the land cover in Poland. The database generally corresponds to a map scale of 1:10,000, and it is regularly updated by local state services. The database is the most comprehensive recent data set regarding land cover, including urban green areas, available in Poland . The effironment . The isoironment  using thironment . The cosironment , based oironment . Parametironment . Informaironment , and we ironment . Howeverironment , especiaironment  as well The effects of the urbanization gradient and the grassland patch size and connectivity on the genetic diversity of populations were tested by correlating the Nei’s genetic diversity with the above-mentioned landscape-scale grassland patch characteristics. As a proxy for measuring urbanisation, we used the percentage of impervious surface , and folT. pratense populations. The results of the genetic polymorphism analysis for populations are presented in The ISSR primers amplified 112 loci across 128 individuals. Analysis of the data indicated that 85 of the 112 loci were polymorphic, which reflects the high allelic diversity of the studied The results of Bayesian clustering with Evanno’s delta K estimation suggest that the optimal number of clusters is 9 . The spaFst = 0.55  and high differentiation among populations within groups . It corresponds with relatively low values of Fct (0.5 for two groups and 13.6 for nine clusters) and high Fsc values   .The effect of differentiation between populations results in clear separation of some populations in PCoA ordination space . Howeverr = 0.12, p = 0.001, r = 0.17, p = 0.05 The spatial analysis showed that the populations are significantly isolated by distance . Population diversity was also higher within cluster on the opposite banks of the Odra River, than between them.We observed high genetic differentiation between populations, which we assume was caused by long-term fragmentation and stochasticity in historical development and land-use system changes in the urban landscape. The city of Wrocław, like other Central European cities, has a rather long and complex history of spatial development that included absorbing and transforming the surrounding rural areas, as well as undertaking reconstruction after war-related devastation . These fBombus pascuorum and B. terrestris, important pollinators of Trifolium pratense, are 124 m and 395 m, respectively (Primula elatior pollen transportation by insects showed that most transferring events occur over short distances (80% within 15–115 m), with a maximum distance of about 650 m. In the absence of stepping-stone habitat patches, plant populations were separated at a distance of 250 m, while the presence of small stepping-stones increased the interpopulation pollen transfer up to 600 m , which is produced as a potent antiherbivore defense , which can cause differences in the estimation of genetic characteristics in populations.The high diversity within, as well between studied populations is usually observed in  species . Consequ., 2004; ), partly •Trifolium pratense populations within the city, with no discernible geographic pattern of genetic diversity. This finding is likely due to the long history of the city and the high stochasticity of land-use changes along with city development. In particular, we showed that the Odra River, with a width of ca. 100 m, is not a strong barrier for gene transfer in red clover.Great variation exists within and between  •Strong isolation occurs through resistance and distance, which means that the populations are threatened by genetic drift. •Since naturally regenerating plants are critical to sustaining both the ecological functions and the ecosystem services of urban green areas, gene movement between populations should be increased by appropriate management of urban green areas. •Trifolium pratense in a city. This observation supports the importance of maintaining and preserving UG patches as much as possible, irrespective of their size.Small UG patches, with small populations, can still hold rare alleles which significantly contribute to the overall genetic variation of 10.7717/peerj.15927/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.15927/supp-2Supplemental Information 2Click here for additional data file.


New findings demonstrate two phases of population expansion occurring 22,000–26,000 and 2500–3800 years ago, coinciding with the Last Glacial Maximum, and Neolithic demographic transition, respectively. This research significantly enhances our understanding of the maternal genetic history of Thailand and MSEA, emphasizing the influence of South Asian admixture. Moreover, it underscores the critical role of prior information, such as mutation rates, within the Bayesian framework for accurate estimation of coalescence times and inferring demographic history.The evolutionary dynamics of mitochondrial DNA within the Thai population were comprehensively explored with a specific focus on the influence of South Asian admixture. A total of 166 samples were collected through randomized sampling, ensuring a diverse representation. Our findings unveil substantial genetic and haplogroup diversity within the Thai population. We have identified 164 haplotypes categorized into 97 haplogroups, with a notable inclusion of 20 novel haplogroups. The distribution of haplogroups exhibited variations across different populations and countries. The central Thai population displayed a high diversity of haplogroups from both the M and N clades. Maternal lineage affinities were discerned between several Mainland Southeast Asia (MSEA) and South Asian populations, implying ancestral genetic connections and a substantial influence of South Asian women in establishing these relationships.  Numerous archaeological sites, dating back to 10,000–45,000 years ago, have been well documented throughout the area4. During the late Pleistocene to Holocene periods, the Hoabinhian cultural system was widespread among hunter-gatherer groups spanning from southern China to Sumatra5. However, only two ancient DNA samples have been recovered from Pha Faen, Laos (7888 ± 40 years before the present (YBP)) and Gua Cha, Malaysia (4319 ± 64 YBP)6 and modeled as a deeply diverged East Eurasian lineage. Furthermore, the genetic data revealed a relationship with modern Andamanese hunter-gatherers such as Önge and MSEA Negritos, including Jehai7. The Neolithic population exhibits an admixed genetic profile between a deeply diverged east Eurasian lineage of hunter-gatherers and East Asian of southern Chinese agriculturalists, which is like modern-day Austroasiatic-speaking populations such as Mlabri7. The Bronze Age in MSEA emerged in the late second millennium BCE, with the earliest evidence of bronze metallurgy dating to around 1100 BCE, reflecting continued cultural interaction with southern China8. The genetic study of Bronze Age individuals from Northern Vietnam found substantial genetic turnover suggesting an additional wave of migration from Southern China during the Bronze and Iron Ages7. In the late first millennium BCE, the region was incorporated into the Maritime Silk Road through connections between regional networks linking Southeast Asia, China, South Asia, and beyond. The presence of walled urban settlements which are associated with centralized policies and can be classified as incipient states such as Angkor Borei, Oc Eo, and Khao Sam Kaeo, dating back to the fourth century BCE, indicates the existence of well-established and dynamic local and regional networks that facilitated integration9. The cultural interaction between South Asian and local MSEA populations created synergies that influenced early state formation in MSEA9.Mainland Southeast Asia (MSEA) has a rich and complex history. Archaeological evidence indicates that anatomically modern humans inhabited the region for at least 50,000 years10. Kutanan et al.11 initially analyzed 1234 complete mtDNA sequences from 51 Kra-Dai and Austroasiatic groups in Thailand and Laos, revealing high genetic heterogeneity across the region with 212 haplogroups. Significant genetic differentiation existed among samples from the same ethnolinguistic group. Notably, Kra-Dai groups exhibited more genetic homogeneity than Austroasiatic groups, which displayed a higher proportion of basal mtDNA lineages. In a subsequent study, Kutanan et al.12 analyzed an additional 560 complete mtDNA sequences from 22 distinct populations, leading to the identification of 62 new haplogroups. Researchers also found West Eurasian-associated haplogroups in some populations in MSEA, which could be a signal of South Asian admixture12. Previous genome-wide studies had documented the presence of South Asian genetic admixture in MSEA populations particularly those with strong cultural influences from India including the Bamar, Cham, Khmer, Malay, Mon, and Thai16. However, it is absent in present-day hunter-gatherer groups or relatively isolated groups from the highlands of northern Thailand13.Due to prehistoric and continuous human habitation, the region exhibits significant ethnolinguistic and genetic diversity. Specifically, Thailand, located in the center of MSEA, is home to approximately 68.61 million people who speak 73 recognized languages from five linguistic families: Kra-Dai, Austroasiatic, Sino-Tibetan, Austronesian, and Hmong-MienRecent archaeological discoveries and investigations into South Asian admixture in MSEA populations have highlighted the present-day Thai population’s complex mtDNA evolutionary history and diversity. This study aims to expand our knowledge of Thailand’s mtDNA landscape by comprehensively analyzing an additional 166 whole mtDNA sequences from the randomized samples of self-identified Thais, regardless of their ethnic backgrounds. This approach will minimize potential biases associated with targeting specific groups, providing a more comprehensive overview of the genetic diversity of the present-day Thai population. By examining the data in conjunction with previous studies and considering South Asian admixture, this study will achieve a more comprehensive understanding of the complex evolutionary history of mtDNA in Thailand  while the previous studies were 5.82 (1234 samples/212 haplogroups)11, and 3.22 (560 samples/174 haplogroups)12 respectively. A higher ratio implies that a larger number of samples is used to identify a smaller number of haplogroups, indicating lower genetic diversity or greater relatedness among the samples. The haplogroups observed belong to macrohaplogroup M , R9 , B , and N , respectively. The most frequent haplogroups are B5a1a , F1f. , and F1a1a1 . We employed HaploGrep2 to generate DOT graphs represented in Fig. 18. The phylogenetic tree generated by Bayesian Evolutionary Analysis Sampling Trees (BEAST) reveals the distinctions among individual samples in B5a1, F1a, and F1f. clades . It was noted that all samples collected from the northern region of Thailand were assigned to the R clade, while most of the samples from the southern region fell under the M clade  were grouped by Thai government administrative region. Samples are from Bangkok and Central , Northeast , South , East , North , and West    and 0.17% (P > 0.01), respectively12. These results suggest that the language family may be a better indicator of the genetic structure of Thai/Lao populations than geography. However, we caution that language family and geographic classification may be poor indicators of genetic structure due to the high levels of variation within populations and the low levels of variation between populations. Furthermore, a Mantel test for correlations between genetic and geographic distances indicated no correlation  supporting the limited impact of geography on the genetic structure of populations in Thailand.Analysis of Molecular Variance (AMOVA) revealed that only 0.4% of the total genetic variation was attributable to differences between populations classified by geographic regions. However, the observed genetic differentiation  was not statistically significant at the 0.05 significance level  was performed, and the result is shown in Fig. To examine the relationship of our data to other populations, we initially generated a nonmetric multidimensional scaling (NMDS) plot at the global population level Fig. a. Africa20. Thailand has been found to show a notable prevalence of haplogroup D in all studies  over time. In Fig. Ne underwent a substantial and remarkable increase. In particular, the Ne value exhibited a more than 20-fold surge, highlighting a significant population expansion event. After the initial expansion, Ne remained stable until approximately 3800 YBP, after which another growth phase was observed. This subsequent growth phase persisted from approximately 3800 YBP to 2500 YBP, during which Ne experienced a notable fourfold increase. Interestingly, from 2000 YBP to the present, the Ne exhibited a stable trend. These findings suggest a complex demographic history characterized by different population dynamics. The initial population expansion, followed by a period of stability, and the subsequent phase of growth indicate intricate patterns of population growth and contraction. The stable population size observed in recent history suggests a relative equilibrium in the population dynamics.The Bayesian skyline plot (BSP) is a valuable tool for visualizing the demographic history of populations based on changes in effective population size  approximately 7.2% using f-statistic-based Admixtool2 with autosomal SNPs and estimated the admixture date around 4 CE during the Angkor period using haplotype-based Globetrotter. However, a subsequent study found significant South Asian admixture (ca. 40–50%) in a protohistoric individual from the Vat Komnou cemetery at the Angkor Borei site in Cambodia. Radiocarbon dating places this individual in the early period of Funan (1 CE), suggesting the possibility of South Asian migration to MSEA and intermarriage with local populations before or during the early stage of state formation14. This study provides additional evidence from mtDNA that contemporary Cambodian populations carry genetic contributions from South Asian ancestry. Moreover, the result suggests a substantial contribution of South Asian women to the genetic makeup of present-day Cambodian populations.The primary objective of this study was to enhance our understanding of the evolutionary patterns of mtDNA in the Thai population and its correlations with neighboring countries, while also taking into consideration the impact of South Asian admixture. We observed a significant cluster that includes the haplogroups B5a1, F1a, f1f., and M7b1a1 Fig. . Notablyons Fig. b suggestons Fig. b. The dions Fig. b and hapons Fig. b highlig22. Machold et al.23 found that the Y-haplogroups R1a-M420 and R2-M479, associated with west Eurasia, occurred at low frequencies in the Ede population  and in the Giarai population (3.7% and 3.7%). Changmai et al.13 using qpAdm, qpGraph, and SOURCEFIND methods, consistently inferred the presence of South Asian ancestry in the Ede and Giarai populations . The comprehensive genetic evidence derived from Y-haplogroups, and autosomal DNA admixture strongly supports the relationship between the Ede, Giarai, and South Asian populations23. The NMDS plot  positions Ede and Giarai relatively closer to South Asian populations compared to other Vietnamese groups. However, the gap between them remains wider than seen in Cambodian and Myanmar populations. Therefore, our findings suggest that while Ede and Giarai show some maternal lineage affinity with South Asians, mtDNA evidence alone is insufficient to decisively establish a conclusion about the contribution of South Asian women to their genetic makeup.The Ede and Giarai are two distinct populations residing in Vietnam, all of which have Austronesian language roots due to cultural diffusion11, we have taken an additional step by visualizing and representing the genetic association between Mon and South Asian populations in a phylogenetic tree. Interestingly, Mon and Punjabi individuals in Lahore, Pakistan, share several haplogroups in the M clade, including M, M30, M45, M4a, and M5a, suggesting a common ancestral population. Mon also shares some haplogroups in the M clade, such as M40a1, M45a, and M5c1, with the Indian Telugu population in the UK. The Telugu-speaking people primarily reside in the states of Andhra Pradesh and Telangana in South India, while Punjabi-speaking people are predominantly found in the Punjab region spanning across India and Pakistan. It is surprising to note that Mon harbors haplogroups from both the South and North Indian subcontinents. The qpAdm analysis infers approximately 12% South Asian ancestry within the Mon population13. Additionally, the Mon group exhibits minor occurrences of West Eurasia-associated haplogroups, including J (5%) and R (16%)24. Furthermore, we observed that Mon shares specific haplogroups within the D clade , with the Wanchoo people , as well as with the Gallong people 25. This observation aligns with the findings of Changmai et al.13 and Kutanan et al.15, who reported the presence of additional ancestry from a Tibetan-related source in the Mon population. They hypothesized that this Tibetan-related ancestry in Mon might have been acquired through interactions with Sino-Tibetan speaking populations in Myanmar. Based on the aforementioned observations, we have performed additional analysis to further investigate the relationship between the Mon population and Sino-Tibetan speaking populations applying f4-statistics in the form of f4 and f4. The result of f4-statistics , which indicates that the Tibeto-Burman groups share more genetic drift with Mons than with other Thailand groups. In this study, we also found that Sino-Tibetan shared 12.5% mtDNA haplogroups  with Mon. This proportion is similar to the autosomal SNPs that Mon and Sino-Tibetan showed an admixture proportion of around 10% when inferred using SOURCEFIND13.Building upon the data obtained from the study conducted by Kutanan et al.11. The discrepancy in timing can be attributed to the variation in mitochondrial mutation rates and mtDNA partition schemes applied in the respective studies. The mutation rate of mtDNA is not constant but fluctuates over time, influenced by factors such as population size and dynamics. In growing populations, there is a higher number of lineages, leading to an increased accumulation of mutations per lineage. These lineages also persist for longer periods, resulting in an accelerated evolutionary rate that surpasses the mutation rate. Conversely, in declining populations, the evolutionary rate decreases.Our study identified two phases of population expansion in Thailand: an initial phase of around 22,000–26,000 YBP and a second phase of around 3800 YBP. The initial phase contrasts with a previous study that proposed an expansion of around 55,000 YBP while the second phase has not been reported in the previous study−8 substitutions per site per year based on a recent study26. However, there is no consensus on the best partitioning scheme and mutation rates to use in coalescent dating and BSP analyses. To assess the sensitivity of our results to the choice of partitioning scheme and mutation rate, we performed two additional analyses. The first analysis used a single model with a single mutation rate of 2.285 × 10−8 site−1 year−1, which was previously applied by Maier et al.27. This analysis showed that the timeframes shifted to around 48,000 YBP for the initial expansion and 7500 YBP for the second expansion , the flooding of the Sunda shelf (marine transgression)33 likely caused a contraction of inhabitable areas towards inland regions, stabilizing population expansion35. During LGM, several new haplogroups emerged, providing additional evidence for population expansion. Notable among them are the Southeast Asian lineage M91 , the Northeast Indian haplogroup M49e , M49e1 , and Indochina R9b 37. We also observed a second phase of population expansion occurring between 3800 and 2500 YBP, which had not been previously documented in studies on mtDNA in the Thai population. Moreover, the sudden increases in effective population size might indicate the arrival of new groups or gene flow from neighboring regions. Gignoux et al.38 demonstrated a global Neolithic expansion through mitochondrial lineage analysis. They also indicated that the population size of Southeast Asia began to expand around 4700 years ago (CI 3000–5700 years ago), which aligns with our findings. This period corresponds to the Neolithic demographic transition, characterized by the adoption of agriculture by prehistoric societies, resulting in increased food production and decreased mobility compared to foraging practices. In Southeast Asia, this transition took place between 2500 and 1500 BCE and brought about significant changes in the biological, linguistic, and cultural evolution of the region39. Around 6000–4000 BCE, rice (Oryza sativa japonica) was fully domesticated in the mid-lower Yangtze River valley of central China40. Subsequently, rice and millet farmers from the Yangtze and Yellow River regions migrated southward through different routes, reaching Baiyangcun in Yunnan around 2650 BCE41. By 2200–2000 BCE, they had reached coastal Vietnam and Thailand, and by 1700–1500 BCE, they had expanded to the interior Khorat Plateau, as reported by Higham et al.42. Archaeological excavations have yielded artifacts such as domestic rice, pottery styles, and tools, demonstrating a connection to southern China. Notable examples include Khok Phanom Di (ca. 2000–1500 BCE), Ban Chiang, and Non Nok (ca. 1100–1000 BCE) as well as Tha Kae and Khok Charoen (ca. 2000/1800–1100 BCE)43. In addition to archaeological findings, the studies on autosomal DNA have provided supporting evidence for a subsequent increase in the effective population size. Neolithic populations in MSEA were a mixture of deeply diverged East Eurasian lineages and East Asian agriculturalists who migrated from South China approximately 4000 years ago7 and the genetic structure of Bronze Age individuals from Northern Vietnam (ca. 2000 years ago) suggests another migration wave from southern China to MSEA7.The initial population expansion phase Fig.  correspo48 while the maximum genetic diversity theory is an alternative framework. Therefore, we approach our conclusions with caution.Our study provides insights into the mtDNA) landscape of the present-day Thai population and its connections with neighboring regions. However, some findings are based on the neutral theory of molecular evolution such as BSP, which has been challenged by many authorsf4-statistics indicate a Tibeto-Burman genetic element in Thailand's Mon population. Our findings indicate two population expansion phases, aligning with the LGM and Neolithic transition. Importantly, this work enhances our understanding of the mtDNA landscape of Thailand. Moreover, it also emphasizes the critical role of prior knowledge in the Bayesian framework for accurate inference of demographic history.In summary, our study investigated mtDNA evolution within the Thai population, focusing on South Asian admixture. We identified 20 novel haplogroups from 166 randomly selected individuals. Haplogroup distribution was found to vary across populations and countries. Maternal lineage affinities connect MSEA and South Asian populations, highlighting ancestral genetic association and the significant influence of South Asian women. Notably, This study was conducted to investigate the mtDNA landscape of the Thai population in strict adherence to ethical guidelines and principles. All experimental protocols received approval from the Institutional Review Board (IRB) of the Faculty of Medicine, Chulalongkorn University (IRB No. 817/64). The need for informed consent was waived by the IRB of the Faculty of Medicine, Chulalongkorn University under 45CFR46.101(b), as all 166 blood samples were taken from medicolegal autopsies authorized by the Faculty of Medicine, Chulalongkorn University (Certificate of Exemption No. 049/2021). The Thai ethnicity of the deceased individuals was recorded according to their identity cards, which were provided by their relatives.The sampling frame for this study was all deceased individuals who underwent a medicolegal autopsy at the Faculty of Medicine, Chulalongkorn University in 2019 and 2020. We employed a randomized sampling approach to minimize bias. The samples were randomly selected from the available medicolegal autopsy cases. This approach allowed us to collect samples without any specific bias toward gender, geographic location, or ethnolinguistic background. By doing so, we aimed to minimize the associated bias and the risk of selecting samples that might not accurately represent the overall population. We were able to collect 166 samples from the sampling frame. The sample consisted of 143 males and 23 females, with an age range of 3–87 years . We acknowledged that gender imbalance in our sample could influence the representation of certain maternal lineages in our study.DNA extraction using the QIAamp DNA Blood Mini Kit (Qiagen). All dsDNA was quantified using NanoDrop™ 2000 spectrophotometers (Thermo Scientific) and diluted to 0.1 ng/µl. A long-length amplification was performed with two sets of specific primer pairs  using the LA PCR Kit (TaKaRa) following the manufacturer’s protocol. The PCR products were qualified and quantified by Agarose gel electrophoresis and NanoDrop™ 2000 spectrophotometers, respectively. These PCR products for each sample were diluted to 0.2 ng/µl and pooled in 1 well with 10 µl for each PCR product.DNA libraries were prepared using Nextera XT DNA Library Preparation Kit for the first 90 samples and Nextera DNA Flex Library Preparation Kit (Illumina) for another 78 samples with 1 ng of DNA input. Library preparations were performed following the manufacturer’s protocols with a bead-based normalization method using 1% spiked-in PhiX control v3 (Illumina). Pool DNA libraries were sequenced by MiseqFGx (Illumina) using Miseq Reagent Nano Kit v2 (300 cycles) with a 2 × 150 bps read length.50 with HaploGrep251 following the established criteria of EMPOP.The sequencing data were aligned with the revised Cambridge Reference Sequence , mapping and determining variants using mtDNA Variant Processor and mtDNA Variant Analyzer v1.0.0 (Illumina). MtDNA haplotypes were assigned to haplogroups based on PhyloTree Build 1752, including geodist, vegan53, Ape54, Adegenet55, and Ade456. The results were visualized using the ggplot257 package. We acknowledge that our results may be influenced by ethnolinguistic background, gender ratio imbalance, and sample size bias. We are aware that some regions have few samples, and our interpretations of findings from these regions should be approached with caution, as they may not accurately represent the regional population. Findings from these regions should be considered preliminary and ideally validated through larger-scale or targeted group research to ensure the reliability and validity of the results.To investigate the mtDNA landscape of the Thai population, we grouped samples into six administrative regions and plotted their geographic locations to determine data distribution Fig. . We obse18, and data from previous studies59, which all detail can be found in Supplementary Table dist.genpop” from the Adegenet55 package and then applying the “metaMDS” function from the Vegan53 package in R. The results were visualized using the ggplot2 package. The application of NMDS allowed the identification of patterns and differences among these populations, further contributing to our understanding of the genetic diversity in this region.To provide a comprehensive population representation, a dataset of 6533 complete mtDNA sequences was compiled, incorporating 166 newly generated data from our study along with 2504 samples from the 1000 Genome Project Phase 3 as provided by Weissensteinersmartpca60 version 18,140 from the EIGENSOFT package (https://github.com/DReichLab/EIG). The computed principal components (PC) for populations from East Asia, Europe, South Asia, and Thailand were analyzed and visualized using the ggplot2 package in R.PCA was performed to observe a cluster of samples following to mitochondrial sequencing variant using HaploGrep2 based on PhyloTree Build 17. The “lineage” function generated a DOT file that was visualized with Graphviz and adapted in Inkscape. We complemented the HaploGrep2 tree with Bayesian inference using BEAST61 to ensure consistent topologies. Multiple sequence alignment of sequences to rCRS using Geneious Prime. All data conversion, alignment, and file formatting steps were performed in Geneious Prime. Software jModel test 2.1.762 was used to select the most suitable model during the creation of the input file for BEAST using BEAUTi v1.863. We applied the HKY + Γ model to our data. Exponential priors were employed to specify the prior distributions for the gamma shape parameters in the analysis. Additionally, lognormal priors were utilized to define prior distributions for the kappa parameters of the HKY model. These choices were made based on the previous study27 to incorporate prior information and assumptions regarding the parameter values. The strict clock model was tested in our analysis, utilizing a lognormal prior distribution for the mutation rate (μ). Numerous mutation rates have been published  mutations site−1 year−1 for the oldest period  to 4.33 × 10–8 (95% CI 3.90–4.82 × 10–8) for the most recent period26. We reasoned that this mutation rate is appropriate for our study because we study present-day populations. We also performed two additional analyses to assess the sensitivity of our results to the choice of partitioning scheme and mutation rate. The first analysis used a single model with a single mutation rate of 2.285 × 10−8 per site per year, which is an average of mtDNA mutation rates reported in the literature72 and applied by Maier et al.27. The second analysis partitioned mtDNA into coding and non-coding regions and applied mutation rates of 1.708 × 10−8 and 9.883 × 10−8 respectively. It is the same approach applied by Kutanan et al.12 in the studies of populations in Thailand. The results of these additional analyses are presented in Supplementary Table The phylogenetic tree was generated directly with 66. Therefore, to achieve the calibration of the phylogenetic tree, 20 ancient samples with associated tip ages measured in calibrated years before the present  were assigned based on dates available from previous studies79. By incorporating such reliable temporal information, we were able to enhance the accuracy and reliability of the inferred evolutionary relationships within the studied mtDNA lineages. We performed 4 × 107 MCMC steps by sampling every 4 × 103 steps. Tracer80 was used to visualize and diagnose MCMC output and to assess a stationary distribution of posterior samples, within and between MCMC chains. We used several tools from the BEAST package to analyze and visualize the posterior tree samples. First, we used LogCombiner to combine the posterior tree samples generated from the MCMC analysis. This step allowed us to merge the individual trees into a single file for further analysis. Next, we summarized the combined tree samples using TreeAnnotator, another component of the BEAST package. TreeAnnotator enabled us to derive the maximum clade credibility (MCC) tree, which represents a summary tree that captures the most credible evolutionary relationships among the sampled sequences. To visualize and interpret the results, we utilized iTOL; therefore, we were able to depict and analyze the MCC tree, facilitating the interpretation of the evolutionary relationships and providing a visual representation of the inferred phylogenetic patterns.In BEAST, tip date calibration refers to the process of assigning dates or age estimates to the tips  of the phylogenetic tree based on known or estimated dates of those samples. BEAST used Bayesian inference to estimate the posterior distribution of parameters, including divergence times, substitution rates, and tree topology, given the input data and prior knowledge Tip date calibration has been reporting no significant influence on the reconstructed topology but has a notable effect on substitution rate and divergence time estimatesNe over time based on the number of coalescent events. It is important to note that the inferred population demography obtained from the BSP analysis primarily reflects the dynamics of the samples included in the analysis. Therefore, to focus specifically on the population dynamics of Thailand, the 20 ancient samples that originated from regions outside of Thailand were excluded from the analysis. To ensure an accurate estimation, the BSP was conducted using a substantial number of MCMC chains, totaling 40,000,000. A burn-in period of 4,000,000 generations was implemented and excluded from the analysis to allow the chains to reach a state of convergence. By reconstructing the population's demographic history through time, we can gain valuable insights into the fluctuations in Ne and better understand the underlying processes that have influenced the population dynamics in Thailand.In this study, we employed a Bayesian Skyline analysis, which utilizes a Markov Chain Monte Carlo (MCMC) approach, to estimate F4-statistics analysis was performed using the Diploid genome-wide SNP. The dataset was derived from the Affymetrix Human Origins SNP "array and merged with previously published ancient and present-day population data. Detailed information regarding the SNP data can be found in the original article13. To calculate the f4-statistics, we employed the “qpdstat” function from the R package “ADMIXTOOLS2”81 with default settings (https://uqrmaie1.github.io/admixtools/index.html). We focused on two specific population combinations for our analysis: f4 and f4. To visualize the results, we generated plots in Supplementary Figs. ggplot2 package in R.Supplementary Information.Supplementary Tables.


Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities  could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations. Genome-wide association studies (GWAS) have spurred advances in breeding high-yield and stress-resistant crops ,2, uncovBox 1Y = Xβ + g + e, where Y is a vector of phenotype values across individuals, X is each individual's genotype at the focal SNP, β is the phenotypic effect of that SNP, g is the collective polygenic effect of other SNPs, and e is residual error .Bo,6.Bo4,6]Here, we argue that by mapping in individuals derived from a narrow geographic locality , more efficient discovery of significant associations is possible due to a reduction in sources of complexity that confound or complicate GWAS. First, we introduce these sources of complexity and explain how they are affected by the geographic composition of the mapping panel . Second,Of the factors that confound GWAS, the most infamous is population structure. This arises when a population has sub-groups between which mating is restricted, often due to geographic barriers or distance, causing allele frequencies to vary spatially. Population structure can lead to false-positive GWAS associations because it can lead to correlations between genotypes at causal and non-causal loci, or between causal genotypes and environmental variables that affect the phenotype of interest ,8. AlthoThe confounding effects of geographic population structure could be mitigated by choosing a local mapping population. When sampling individuals locally, dispersal distances are larger relative to the sampling range, so geographic variation in allele frequencies tends to be smaller. Thus, the confounding effects of geographic population structure should be weakened.A major determinant of power in GWAS is the number of loci contributing to a trait with a given amount of additive genetic variation (heritability) . As the Geography contributes to genetic heterogeneity in two key ways. First, many alleles have geographically restricted distributions , causingArabidopsis thaliana [Notably, one recent study in thaliana  investigNot only are most traits affected by many loci, but many causal loci harbor more than two functionally distinct haplotypes, termed allelic heterogeneity ,16. AlleSampling from a local population is expected to produce less allelic heterogeneity than sampling broadly, as both the exclusion of some geographically restricted alleles and the overall reduction in genetic diversity would lead to fewer alleles per locus. Our illustration of sampling spatially structured causal variants  depicts Recent analyses suggest allelic heterogeneity is common in plants and humans, and could likely be found at most casual loci given sufficient power ,20,24. AGlobally, most alleles are rare  . Power tLocal mapping panels may lessen the impact of each of these factors. First, both simulations and empirical studies have shown that the SFS is shifted toward intermediate frequency variants in local populations, lessening the proportion of variants that are rare , 31, 32 Epistasis refers to differences in a causal variant's effect across genetic backgrounds. GWAS is less powerful when a variant's effect is weakened or reversed in some genetic backgrounds, since standard GWAS models are formulated to detect average additive effects ,34. TheoPopulation structure across multiple causal loci can produce different genotypic combinations in different geographic regions, especially for large effect, locally adaptive loci . TheArabidopsis population from Toulon-sur-Arroux, France (TOU-A) provides an excellent example of local genetic and phenotypic diversity. Whole-genome sequencing was conducted for 195 accessions collected over a 350 m transect [Arabidopsis populations suggest the substantial diversity observed in TOU-A population is unlikely to be unique. Local German populations harbor multiple functionally distinct alleles at a regulator of defense (ACD6), beyond what could be discerned from GWAS of a broader population [RPS5) segregates at intermediate frequency in most populations globally [Although local mapping panels are uncommon in plants, an transect . GWAS untransect ,44, recotransect ,44, and transect —in addittransect ,45. Disspulation . Similarglobally .A comparison  of GWAS In humans, one major use of local mapping panels has been the study of “population isolates” or “founder populations” . Such poA particularly well-studied local mapping panel is composed of individuals from the Mediterranean island of Sardinia. GWAS in these samples has revealed a number of novel and compelling loci , 50, 51.TNFSF13B gene which encodes BAFF protein, 3) the length of the TNFSF13B transcript, 4) the number of B cells in the blood. Of note, the MS risk-increasing allele increases B cell levels by truncating the UTR of the transcript and thus removing a microRNA binding site that mediates decay of the transcript and in turn reduces BAFF levels. This mechanism suggests the autoimmunity disorder risk is increased from an overabundance of B cells. Further, the MS risk-increasing allele appears to have been selectively favored based on the haplotype diversity patterns and levels of differentiation with the mainland. While the mechanism of selection is unknown, one might predict that the MS riskincreasing allele provided an adaptive response to past (or ongoing) pathogen exposure by elevating B cell counts.One fascinating example  is the dA more general lesson here is that local mapping populations may reveal novel loci due to past-acting selective pressures that elevated the frequencies of the alleles. This is perhaps especially true for populations where local adaptation is facilitated by periods of isolation where selective pressures at other locations are moderated.Box 2Synthetic mapping populations, derived from crosses between a pair or small panel of parents, are a common way to avoid complications that arise in genetically diverse, geographically broad populations. They can eliminate rare variants and geographic population structure, and by including only a subset of the genetic variation in the broader population, they can reduce genetic and allelic heterogeneity . HoweverLocal populations fall in between simplified, synthetic populations and diverse, global populations – offering a balance between the advantages and drawbacks of each. The complicating factors from the previous section are reduced, but not eliminated. And they are already present in nature; most of the labor to “construct” them has already been completed.Alt-text: Box 2In many ways, local populations offer some of the advantages of both diverse natural populations and simpler synthetic populations from experimental crosses, while suffering less from some of the drawbacks of each . NeverthThe utility of local populations may also depend on how selection acting on a trait varies geographically. If selection is sufficiently strong to nearly fix locally adaptive variants, GWAS within local populations will have little power to detect their effects. Meanwhile, mapping populations spanning a broader geographic range (over which these loci are polymorphic) may not only succeed in mapping the effects of these loci on traits and fitness, but also enable population genomic analyses that uncover signatures of local adaptation ,40. TherWhen GWAS are being used as a preliminary step in producing polygenic risk assessment or trait prediction, broad mapping panels will yield higher predictive performance on average across populations . In contFinally, local populations may harbor elevated linkage disequilibrium due to younger coalescence time among individuals , and thiOverall, local and geographically broad mapping panels are complementary, as they typically reveal different loci associated with a trait , and chAlthough most efforts toward mapping phenotypic variation with GWAS have focused on regional or species-wide mapping populations, studies of local populations have been promising. In plants, they've revealed substantial genetic variation that shapes growth, phenology, defense, and immunity—likely maintained, in part, by spatially and temporally heterogeneous selective pressures. Importantly, genetic mapping of these traits has revealed loci that were missed in broader GWAS populations.Empirical studies of hierarchically constructed mapping panels—with similar numbers of individuals (and thus mapping power) at local, regional, and species-wide scales—are needed to more robustly test if and when GWAS recovers more associated loci locally. Studies of simulated populations will also be invaluable, especially for understanding the causes of such patterns and the extent to which they are context dependent. The contributions of the complicating factors highlighted in this review, their interactions , and theA deeper understanding of these patterns and mechanisms is already within reach owing to the powerful computational, statistical, high-throughput sequencing and phenotyping resources available. As these studies unfold, they will inform optimal approaches for understanding the genetic architecture and maintenance of trait variation in nature.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Strongyloides ratti is a common parasitic nematode of wild rats and we have investigated its population genetics using single-worm, whole-genome sequencing. We find that S. ratti populations in the UK consist of mixtures of mainly asexual lineages that are widely dispersed across a host population. These parasite lineages are likely very old and may have originated in Asia from where rats originated. Genes that underly the parasitic phase of the parasite’s life cycle are hyperdiverse compared with the rest of the genome, and this may allow the parasites to maximise their fitness in a diverse host population. These patterns of parasitic nematode population genetics have not been found before and may also apply to Strongyloides spp. that infect people, which will affect how we should approach their control.Nematodes are important parasites of people and animals, and in natural ecosystems they are a major ecological force.  Nematodes are important parasites of people and animals. Here, the authors study the population structure of a nematode parasite of wild rats finding that it consists of mixtures of mainly asexual lineages widely dispersed across the host population. In natural ecosystems, parasitic nematodes are highly abundant and a major force affecting host populations3, so understanding parasites’ biology is critical in understanding wider ecological patterns and processes. A study of the population genetics of parasitic nematodes can give important insights into their biology and patterns of transmission in host populations. This has been studied extensively in nematodes parasitizing livestock, for example, finding that they exist with very large effective population sizes, showing limited population genetic substructure, likely due to the high rate of livestock movement6. UK S. ratti populations appear to show a similar population structure7, and so in the present study our focus was on the high-resolution analysis of S. ratti populations. The population genetics of parasitic nematodes infecting humans has also been investigated to understand their host range and zoonotic potential9. In contrast, there has only been limited study of the population genetics and genomics of nematodes infecting natural, unmanaged species10.Parasitic nematodes are important parasites of humans, livestock and other animals. In humans, parasitic nematodes are responsible for four of the twenty World Health Organization-defined Neglected Tropical DiseasesStrongyloides spp. are a genus of parasitic nematodes with two species—S. stercoralis and S. fuelleborni—infecting some 100–600 million people worldwide12. S. ratti is a common parasite of rats, Rattus norvegicus7. In the Strongyloides spp. life cycle, hosts are infected by parasitic female worms only, which reproduce parthenogenetically13, producing eggs that pass out of the host in its faeces. Outside of the hosts, larvae can develop directly into infective larvae that then infect new hosts. Alternatively, and facultatively, larvae outside of the host can develop into a single generation of free-living adult males and females that reproduce sexually14, with their progeny then also developing into infective larvae to infect new hosts. Genetically, this means that Strongyloides reproduces by obligatory mitotic parthenogenesis inside the host and by facultative sexual reproduction outside of the host. The choice between asexual, direct development and sexual, indirect development is affected by environmental conditions (particularly the host immune response and the temperature outside of the host), but genotypes also differ in their propensity for these two developmental routes16. The extent of sexual vs. asexual reproduction in wild Strongyloides is not well known, though it appears to be rare in UK S. ratti7, perhaps more common in S. ratti from Japan15. This is in contrast to the development of the standard lab line of S. ratti17, though inadvertent selection in the laboratory may have selected for greater sexual development. In S. stercoralis, a parasite of people, development is predominantly asexual18.Strongyloides’ mode of reproduction is very likely to affect its population genetics. If reproduction is exclusively by mitotic parthenogenesis, then the only source of genetic variation is mutation, and such a population would consist of an assemblage of different genetic lineages. In this scenario, the mutation rate of a species is key in determining the extent of variation within a population. The occurrence of some sexual reproduction would allow genetic lineages of parasites to recombine, though the extent to which this will happen depends on the frequency with which sexual reproduction occurs. A three-locus study of S. ratti in the UK found that it consists of one interbreeding population, likely mainly reproducing by direct, asexual reproduction7. Populations of Strongyloides may change their balance of asexual and sexual reproduction during their history. A situation that we particularly consider here is that ancestral populations of S. ratti may have had more frequent sexual reproduction but then moved to principally asexual reproduction. Under this scenario, contemporary populations would have population genetic patterns consistent with asexual reproduction but also with some vestiges of ancestral sexual reproduction, such as some genotypes appearing to have a recombined ancestry.S. ratti has a compact 43 Mbp genome consisting of 2 autosomes and an X chromosome, and its genome assembly is among the most contiguous for any nematode. This facilitates the population genomic analysis of wild S. ratti. Further, genomic, transcriptomic and proteomic analyses have identified genes that are putatively critical to Strongyloides’ parasitic lifestyle. These were characterised in two ways: genes whose expression was significantly greater in the parasitic female stage compared with the free-living female stage; and genomic clusters of genes belonging to one of three families , which are gene families that have expanded as Strongyloides evolved to become parasites19.20. The genes and gene products underlying parasite local adaption are not well known. In Strongyloides, the genes that have been shown to be central to its parasitism are at the interface between the parasite and its host and therefore may play a role in such adaptation. In such a scenario, these genes could be under different selection pressures compared with the rest of the genome and so may have population genetic patterns that differ from the rest of the genome.In many, but not all, host-parasite systems, parasites can locally adapt to their host population, which enhances the fitness of those parasite genotypesS. ratti in a wild rat population, describing how parasite genotypes are distributed among individual hosts. We find that these S. ratti populations exist as a mixture of asexual lineages and that these lineages are likely very long-lived. Comparison of these lineages with historical, geographically dispersed samples shows that some of these lineages may be very widely spatially dispersed. We find that genes and gene clusters critical to the parasitic phase of the S. ratti life cycle are genetically hyperdiverse compared with the rest of the genome. This hyperdiversity may contribute to S. ratti’s local adaption to its hosts within the context of it existing as long-lived asexual lineages.Here we report the whole-genome, fine-scale population genomics of S. ratti infective larvae from 114 pellets (from a sample of 308). The proportion of infected faecal pellets significantly differed among the three sites  ; for SNPs not in HWE, 77% had an excess of heterozygotes. Most SNPs (84%) had negative FIS (inbreeding coefficient) values, consistent with heterozygote excess , suggesting genetic clustering among the worms at some level . Thus, we conclude that individual rats contain genetically diverse parasites , meaning that the genetic clustering of parasites is not at the level of the rat.We analysed the pattern of parasite population genetic variation (1) within and among rat hosts and (2) within and among sampling sites, finding evidence of some differentiation of the parasites among the sample sites. The pairwise relatedness (Φ) among the 90 parasites was non-normal  , and indeed zero between parasites at the two English sample sites  than parasites from different sample sites (Φ = 0.206)  in one of three clades (clades 1–3) Fig. . The strAnalysis of linkage disequilibrium (LD) shows that there are large-scale blocks of LD and that it decays slowly over large genomic distances (of tens of thousands of bases), especially on the autosomes of clade 3 parasites . Individual rats contained parasites from multiple clades; specifically, 11 rats contained parasites from two clades, and 3 rats contained parasites from three clades. Principal Component Analysis produced similar results, showing the clustering of parasites within clades and that these clades were dispersed across the three sampling sites  no sexually reproductive stages were observed, (2) populations were more heterozygous than expected, (3) there is extensive LD, (4) there is terminal divergence of genotypes within clades and differences among genotypes of the clades  we conclude that these parasites are asexually reproducing and have been asexually reproducing for very many generations, but that rare, or historical, recombination occurs in these populations.ST within and among clades 1–3 is consistent with S. ratti being structured into sympatric, genetically distinct clades, with the three clades dispersed across the three sampling sites. Specifically, Φ was high within clades, especially clades 1 and 3 , but lower between clades; FST among the clades was 0.3 and similar (0.22–0.35) between clades , identifying 58 haplotypes among the 90 parasites. There was a strong, positive correlation between the pairwise similarities of mitochondrial and nuclear genomes  and between minimum spanning maps of mitochondrial haplotypes and neighbour-joining trees of nuclear genomes .The population genetic structure of SHUA), which does not assume HWE22, shows that there is moderate genetic differentiation among rats from the three sites  with the assumption of complete asexual reproduction of (1) clade 3 and (2) clades 1 and 3. Assuming neutrality and the C. elegans mutation rate of 2.7 × 10−9 per site per generation24, we calculate that there were an average of 18,401  and 322,011  generations since the LCA of clade 3 and of clades 1 and 3, respectively. To convert these generations to years, we assumed, conservatively, that there are two S. ratti generations per year in the wild, noting that S. ratti parasitic females have a maximum lifespan of a year when infecting immunodeficient laboratory rats25. On this basis, the LCA of clade 3 existed approximately 9000 years ago  and the LCA of clades 1 and 3 approximately 161,000 years ago, which is the Middle Pleistocene (now renamed the Chibanian). Given the dates, it is a reasonable scenario that the LCA of these S. ratti clades evolved outside of the UK and arrived in the UK with their hosts. R. norvegicus is thought to have arrived in mainland Europe from Asia about 1800 years ago (c. 200 CE) and into the British Isles from c.1750 CE27, where the black rat (R. rattus) had likely been since c. 1100 CE. Clades 1 and 3 may well have emerged in Asia, where R. norvegicus originated, and the LCA of clade 3 perhaps in mainland Europe.We sought to understand the age of the S. ratti clades by examining whole-genome sequences of 10 S. ratti isofemale lines derived from rats sampled from the UK and Japan between 1989 and 2012  occurs very rarely, if at all, in these populations. Given the history of rats and their parasites, we suggest that ancestral S. ratti populations may have reproduced sexually but that they have since become largely or wholly asexual. Therefore the parasite lineages we observe in the UK are likely very long-lived—over thousands and tens of thousands of years—and widely distributed across the global host population. We observe no obvious geographical localisation of the different parasite clades, which might be expected with geographically-based host local adaptation. Indeed, given the likely asexual reproduction of these populations, it would therefore appear that S. ratti cannot genetically adapt to its local host populations except through mutational processes.In summary, these results show that S. ratti uses in the parasitic phase of its life cycle. We focussed on two sets of genes: (1) “parasitism genes”, which are genes whose expression is at least one log2-fold greater in the parasitic female morph compared with the free-living female morph, and (2) “expansion clusters”, which are genomic regions containing four or more genes encoding members of either astacin-like metallopeptidase, CAP domain-containing protein, or acetylcholinesterase families, which previous analyses have identified as having expanded with the evolution of parasitism in Strongyloides19.We next investigated the diversity of genes that S. ratti is not evenly distributed across the S. ratti genome  of genes in these regions encode astacin-like metallopeptidase and CAP domain-containing proteins, which is higher than their representation across the whole genome .Furthermore, two classes of genes—those encoding astacin-like metallopeptidases and CAP domain-containing proteins, both of which are associated with parasitism in We compared the SNP density among the hundred most parasitic and most free-living genes, though we excluded six of the parasitism genes due to concerns about their underlying sequence assembly, leaving 94 parasitic genes . Following this, we also found evidence of high genetic diversity in S. ratti’s parasitism. Parasites can locally adapt to their hosts to maximise parasite fitness, and we hypothesised that the evolution of the expansion clusters, independent of the rest of the genome, could be a means by which host local adaptation occurs, where the expansion clusters evolve differently from the rest of the genome. To investigate this, we created neighbour-joining trees based on individual expansion clusters to see if they strongly diverged from the whole genome-based trees. However, in these expansion cluster-specific trees, the whole genome-defined clades 1 and 3 were still strongly evident , with very little evidence of structuring across three host populations. The population genetic data are consistent with these parasites existing as wholly, or very largely, asexually reproducing parasite populations. By estimating the age of the last common ancestor of these clades, we find that these parasite lineages are very long-lived, existing over some thousands to tens of thousands of years. This result does raise the question of whether these different lineages should be considered as different species or subspecies. Considering the age of these lineages with the history of the spread of the brown rat from Asia into mainland Europe and then to the British Isles, a plausible scenario is that the S. ratti lineages originated in Asia, where these populations perhaps had more frequent sexual reproduction, and that they then spread to the UK, and likely the world. It is notable that the neighbour-joining dendrograms show Japanese-derived S. ratti isofemale lines among the UK-derived genotypes. More extensive global sampling of S. ratti genetic diversity is now warranted.For the facultatively sexual parasite of rats S. ratti contains an obligatory asexual parthenogenetic stage13, as well as a facultative sexual stage14. We observed no sexual stages during our work, suggesting that sexual reproduction is very rare, or even absent, in these UK populations, consistent with previous observations15. The population genetics of the populations that we studied is also consistent with the absence of sexual reproduction. Other lines of S. ratti  show some development of free-living males and females, suggesting that some sexual reproduction may occur in wild S. ratti populations beyond the UK15. Even low levels of sexual reproduction can be effective in the spread of alleles in a population, especially with strong selection28.This is a pattern of population genetic variation that has not, as far as we are aware, been observed in a parasitic nematode before. The life cycle of 29.In contrast to the parasites’ population genetic patterns, the host rat populations did show some evidence of population genetic differentiation among the different sample sites, consistent with restricted movement of rats between the sites. Studies of the population genetics of rats in cities have also shown evidence of limited dispersal of rats and population genetic differentiation among rats in different city regionsDracunculus medinensis) in human and dog hosts during sustained control efforts in human populations30. There is considerable interest in understanding the host range of S. stercoralis that infects people; specifically, Strongyloides in dogs has been considered to be a source of human infection31. If the population genetic structure we have observed with S. ratti—a mixture of asexually maintained clones widely distributed across host populations—also pertains to S. stercoralis, then there is likely to be considerable complexity in understanding the population genetics and host range of S. stercoralis genotypes. Where a species exits as a collection of asexually maintained lineages, then each lineage could diverge genetically, which raises the possibility that S. stercoralis could exist as a mixture of lineages, each with different host ranges, for example, some able only to infect people, some only able to infect dogs and some intermediate and indeed there evidence  of this in S. stercoralis in Cambodia31. Current approaches to studying the host range of S. stercoralis have commonly only used single or a few loci, which cannot resolve more complex patterns of population genetic variation. Whole-genome analysis (following whole-genome amplification) of S. stercoralis from people in Japan and Myanmar found differences between parasites from these two sources32. If a population genetic structure similar to S. ratti’s also occurs in Strongyloides spp. infecting people and livestock, then there will be consequences for the evolution of anthelminthic resistance. In the absence of sexual reproduction, anthelmintic resistance could evolve separately in different Strongyloides lineages but would not be passed among them. The effect of this might be to slow the development of anthelminthic resistance in Strongyloides populations, compared with obligatory sexually reproducing nematodes, though with Strongyloides sustained anthelminthic use would, of course, select against anthelminthic sensitive genotypes.Understanding a parasite’s population genetic structure can be important in understanding the host range of a parasite, which is of applied interest for parasites of humans. For example, population genetic analysis has been used to understand the possibly changing host range of Guinea worm , with evidence suggesting that diversity in these regions is maintained by balancing selection33. In the C. tropicalis genome, genetic variation is also distributed heterogeneously across its genome, for example, with some 140 high genetic diversity classified regions extending for more than 30 kb34. While there is a superficial similarity between the patterns of genomically concentrated genetic diversity in two Caenorhabditis species and in S. ratti, the mechanisms generating these patterns might be different. Notably, in S. ratti, we did not find any evidence of diversifying selection in the expansion cluster genes. What these two Caenorhabditis studies and the present study do demonstrate is that detailed, whole-genome analysis of wild individuals is uncovering hitherto unexpected patterns of genomic diversity that are likely to exist in other taxa too, and which need to be investigated.Genomic analyses of 35. If such a phenomenon was occurring in S. ratti, then it may be manifest as geographical clustering of parasite genotypes per se or geographical clustering of parasitism gene genotypes and/or expansion cluster genotypes. However, the dispersion of S. ratti genotypes and of the genetic diversity in parasitism genes and expansion clusters that we have observed does not show any suggestive signatures of such local adaptation to hosts.Parasites have commonly been found to locally adapt to their host populationsCaenorhabditis spp. chemoreceptor-coding gene families36; this idea has also been applied to hookworm parasites37. It is clear that further research is needed to understand the evolution of large gene families, as well as the full biological significance of high levels of genetic diversity in genes underlying S. ratti’s parasitism.Alternatively, mindful that the products of the parasitism genes and genes in expansion clusters interface with the host, some variants of these genes may give a parasite a fitness advantage when infecting certain host genotypes, compared with parasites with other variants. Each individual parasite’s suite of these genes may therefore represent a combination of different variants that have been selected for as these parasite lineages have, over their history, infected a range of host genotypes. In this scenario, these parasites are not locally adapted to their host genotypes per se but rather have available a set of gene variants that are appropriate for a wide range of already-encountered host genotypes. This idea—the so-called grey pawn hypothesis—that the aggregate effect of a large number of diverse genes is selected so as to cover a large phenotypic space has previously been suggested in trying to understand the very large number of S. ratti is a non-destructive method of sampling parasite genetic diversity, and the hope must be that these approaches are now expanded to other parasitic nematodes. Such analyses will likely uncover different patterns of population genetic variation in other species, and understanding this will more fully illuminate the interactions between parasites and hosts and so underpin a better understanding of the rich ecology of parasites. If Strongyloides infecting people has a similar population genetic structure, then this may have consequences for understanding Strongyloides’ host range and zoonotic potential, as well as for the evolution of anthelmintic resistance.Our whole-genome, single-worm analysis of wild S. ratti infective third-stage larvae, which were washed twice in distilled water, once in 1% w/v SDS, and then twice more in distilled water, before being stored at −80°C. We genotyped rat faecal pellets at 9 dinucleotide repeat microsatellite loci 41.We sampled at three sites in the southern UK—Avonmouth (AV), Cardiff (CA) and Long Ashton (LA)  fell on a nucleotide covered by at least 1000 reads , (2) had a mean mapping quality of at least 20, and (3) had a QUAL score of at least 50. Among the 90 S. ratti genome sequences, nucleotides that were identical among all samples (but different from the ED321 reference genome) were removed. We sequenced to an average coverage of 96% of nucleotides (range 75.8–99.3%) and an average read depth of 68 .We used BCFtools19.We noticed that the mean read depth on the X chromosome was 67.9% of the mean read depth on the two autosomes. We concluded that this was due to the GC content because (1) there was a significant correlation between read depth and GC content  was calculated considering only bi-allelic SNPs. Φ relatedness values45 of each pair of larvae were calculated using VCFtools and we used t-tests to compare Φ values. We also measured the differentiation among sites using the fixation index, FST.Basic genetic diversity and population genetic statistics were calculated using VCFtools version 0.1.1246 and visualised these in FigTree Version 1.4.3. Clades within the neighbour-joining trees were identified by eye. Fisher’s exact tests, performed in R, were used to determine whether there were significant differences in the frequencies of these clades among sampling sites or sampling seasons.We generated neighbour-joining dendrograms using TASSEL 5.047 using strategy FF-NST-1 for fast alignment, and maximum likelihood tree estimation performed using RaxML version 8.1.1548, using the general time reversible gamma model of substitution rate heterogeneity, and rapid bootstrapping with 100 replicates. We generated separate maximum likelihood trees for chromosome 1, the first 80 Mb of chromosome 2, the remainder of chromosome 2 and the two largest contigs of the X chromosome.We constructed maximum likelihood trees of the 90 parasites, producing consensus fasta sequences for each individual, but where an individual was heterozygous the reference allele was applied, with sequences aligned with MAFFT version 749 using only loci with a minor allele frequency greater than 0.05. We investigated the admixture among the 90 parasite genotypes using ADMIXTURE version 1.3.050. Due to computational constraints, for the 90 parasites, SNP data were first thinned so that no two SNPs were within 500 bp of each other, leaving a dataset of 35,559 SNPs. ADMIXTURE was run separately for k values 2-15.We conducted Principal Component Analysis using the R package pcadapt version 4.1.052, where we used 100 burn-in iterations to generate an initial estimate of haplotype frequency, and a further 100 iterations were used to estimate the genotype phase for each SNP in each sample. Phasing is influenced by the effective population size (Ne), which is not known for S. ratti, but we estimated this as 50,000; otherwise, default Beagle parameters were used. We also undertook phasing using Shapeit version 2-r90053, where we used 100 burn-in iterations, 100 phasing iterations, and an estimated Ne of 50,000. For both, we used a window size of 0.5 Mb to estimate haplotypes. Only bi-allelic loci were used in Shapeit, but tri-allelic loci were also included in Beagle. We report the results from phasing using Beagle; Shapeit gave similar results.We measured linkage disequilibrium (LD) among the 90 samples for the two autosomes and the two largest X chromosome scaffolds. We initially phased the genotype data into haplotypes using Beagle version 5.02, calculated for each pair. To measure LD across the whole genome, we further thinned the phased data so that no two SNPs were within 500 bp of each other when the analysis was repeated as above, except that this time each SNP was compared to every other SNP in the entire genome. We also repeated these analyses for subsets of parasites within the clades that we identified.To reduce computational time during linkage decay analysis, phased VCF files were thinned so that no two remaining loci were within 100 bp of one another. To perform linkage decay analysis, VCFTools was used to compare each SNP to each other SNP within a 50 kb window of it, with Pearson’s coefficient of correlation, r55. Haplotype maps were generated in PopART version 1.756 using the minimum spanning network method57, and maximum likelihood trees based on unique haplotypes were generated with RaxML version 8.1.1548, using the general time reversible gamma model of substitution rate heterogeneity and rapid bootstrapping with 100 replicates was applied. We calculated the proportion of SNPs shared among all pairs or worms and compared this to the nuclear Φ relatedness using a Mantel test.We also analysed the mitochondrial genomes  and used Analysis of Molecular Variance (AMOVA), which was conducted in GenAlEx version 6.5S. ratti  pairwise relatedness function to detect pellets with identical multilocus genotypes, which we took to have come from the same individual rat. We calculated Ritland and Lynch pairwise relatedness58, where each individual was compared with each other individual, and doubled these values to give a possible range of −1 to 1, from which we calculated the mean within-sample-site and mean among-sample-site relatedness. We determined the log-likelihood of the rat originating from each sampling site using GenAlEx to assign pellet genotypes to each sample site by comparing the multilocus genotype of each rat with the allele frequencies of each of the sampling sites (excluding the rat currently being investigated).We only used data for faecal pellets that were successfully genotyped at 6 or more loci, resulting in 132 genotyped faecal pellets. Locus D12Rat42 was excluded from further population genetic analyses due to the low number of rats successfully genotyped at this locus. We used GenAlEx’s (version 6.5SHUA) to quantify the differences in allele frequencies among sampling sites and to estimate the number of effective migrants per generation. SHUA measures and is valid despite deviations from HWE within subpopulations59. SHUA ranges from 0 (indicating unhindered gene flow) to 1 (indicating a complete lack of gene flow).We used Shannon’s mutual information index , moles , and squirrels (Sciurus carolinensis). With one exception, none of these microsatellite loci successfully amplified from these species (the exception was locus D12Rat42 which did amplify R. rattus DNA), confirming that all successful genotypes were from R. norvegicus. As positive controls, we used primer pairs (1) Scv1 previously used to amplify Sciurus sp. DNA60, which successfully amplified our Sciurus sp. DNA, and weakly amplified R. norvegicus DNA, and (2) RodActin previously used to amplify R. rattus DNA61, which successfully amplified our R. rattus and R. norvegicus DNA.We ensured  that none of the faecal pellets that we had genotyped were from species other than 19. We excluded parasitism genes if they were part of an expansion cluster (below) and the underlying genome assembly was poor. We calculated 95% binomial confidence intervals for percentages from www.sample-size.net.We followed previous work that identified “parasitism genes”19. As controls, we used “flanking regions”, which we define as the genomic region directly adjacent to the expansion cluster that is the same size as the cluster itself. Each expansion cluster has two flanking regions.We define an “expansion cluster” as a genomic region containing four or more genes coding for members of one of three protein families  where there is not more than one other gene between any two genes of those families. This definition differs somewhat from and is more conservative than that used by ref. 19, and from this, we initially identified 15 expansion clusters. Because clusters 10 and 11 were very close to each other, cluster 10’s right flanking region was shortened to end where expansion cluster 11 began, and expansion cluster 11 was considered to not have a left flanking region. Similarly, to avoid overlap of cluster 11’s right flanking region and cluster 12’s left flanking region, we shortened cluster 12’s left flanking region to the start of cluster 11’s right flanking region. Across all expansion clusters there were 135 genes in total, of which 126 belonged to one of the three gene families: 46 encoding CAP domain-containing proteins, 70 encoding astacin-like metallopeptidases, and 10 encoding acetylcholinesterases, representing 51.7%, 38% and 33.3% of CAP domain-containing proteins, astacin-like metallopeptidase and acetylcholinesterase encoding genes, respectively, in the genome as a whole. Flanking regions collectively contained 216 protein-coding genes, the products of which had varying predicted functional descriptions.Lists of genes belonging to these three gene families were collated from ref. 62. The repetitiveness of the sequence was examined via Dotplots with the software package Dotter63. Gene annotation schematics were retrieved from Ensembl’s ‘Region in Detail’ tool64, accessed via WormBase Parasite65 release 12 (https://parasite.wormbase.org/index.html) and added to the graphics produced by Gap5. Regions with poor mapping quality, unusually large distances between mate pairs and the occurrence of mate pairs facing opposite directions are suggestive of high rates of sequence misalignment. Peaks in read depth and fragment depth above background levels were evidence that multiple copies of a repetitive sequence were collapsed in the reference assemblies. Where expansion cluster genes or genes in flanking regions fell in poorly resolved reference assembly areas, these genes were excluded from further analysis.Mindful that these expansion cluster regions were repetitive in nature, we checked their original reference genome assembly by realigning the sequencing reads originally used to build the reference assembly back to the reference, available at NCBI, BioProject code PRJEB2398, and then assessed the quality of these alignments using Gap5Using this approach, we excluded expansion clusters 4, 11 and 13, and their flanking regions entirely and other genes within various clusters, resulting in 196 genes remaining, of which 61 were in expansion clusters and 135 were in flanking regions (Supplementary Data Further information on research design is available in the Supplementary InformationReporting SummarySupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Description of Additional Supplementary Files