New Zealand has the fourth largest feral horse population (Kaimanawa and Far North horses) in the world. The Kaimanawas (KHs) are feral horses descended from various domestic horse breeds released into the Kaimanawa ranges in the 19th and 20th centuries. Over time, the population size has fluctuated dramatically due to hunting, large-scale farming and forestry. Currently, the herd is managed by an annual round-up, limiting the number to 300 horses to protect the rare and unique native flora in this region. Here, we examined 96 KHs to investigate their genetic similarity with respect to other domestic horse breeds, using uniparental markers . Our results indicate that although six maternal and six paternal lineages contributed to the KH gene pool, the current population is dominated by few ancestral lineages, and possibly represents two KH sub-populations. We show that three horse breeds, namely Welsh ponies, Thoroughbred and Arabian horses had a major influence in the genetic-makeup of the extant KH population. Moreover, our results suggest that mitochondrial genetic diversity in KHs is closer to the Sable Island horses, and less than other feral horse populations around the world. Our current findings, combined with ongoing research will provide insight into the KH population-specific genetic variations and level of inbreeding. This will advance equine genomic research and improve the management strategies to conserve these treasured New Zealand horses.New Zealand has the fourth largest feral horse population in the world. The Kaimanawas (KHs) are feral horses descended from various domestic horse breeds released into the Kaimanawa ranges in the 19th and 20th centuries. Over time, the population size has fluctuated dramatically due to hunting, large-scale farming and forestry. Currently, the herd is managed by an annual round-up, limiting the number to 300 individuals to protect the native ecosystem. Here, we genotyped 96 KHs for uniparental markers  and assessed their genetic similarity with respect to other domestic horses. We show that at least six maternal and six paternal lineages contributed unequally to the KH gene pool, and today’s KH population possibly represents two sub-populations. Our results indicate that three horse breeds, namely Welsh ponies, Thoroughbreds and Arabian horses had a major influence in the genetic-makeup of the extant KH population. We show that mitochondrial genetic diversity in KHs (π = 0.00687 ± 0.00355) is closer to that of the Sable Island horses (π = 0.0034 ± 0.00301), and less than other feral horse populations around the world. Our current findings, combined with ongoing genomic research, will provide insight into the population-specific genetic variation and inbreeding among KHs. This will largely advance equine research and improve the management of future breeding programs of these treasured New Zealand horse. Equus caballus) is dramatically eroding and no truly wild horses exist in the world [Feral horse populations are found throughout the world . They exhe world ,3. Althohe world ), there he world , and thehe world ,7. In thhe world , genetiche world , and genhe world ,10,11,12Currently, with an estimated number of 2,500 horses across the country, New Zealand’s feral horse population (Kaimanawa horses (KHs) and Far North horses) is the fourth largest in the world, after Australia , United States of America  and Canada  . HistoriThe majority of mustered horses are offered to the public for rehoming, depending on their good health, suitability of the new home, and a taming plan having been put in place. To avoid further breeding in the domestic setting, and to facilitate the taming process, all young stallions and colts get castrated. Recently (May 2022), and after decades of research, immune-contraception was applied to 150 female mustered horses as an alternative and complementary option to rehoming for controlling the population growth . This chThe KH’s uncertain origin beyond the historical reports raises questions regarding their phylogenetic relationship with other horse breeds, genetic diversity, long-term genetic health and viability. The first and only genetic study on KHs was conducted in the 1990s [i) examine the number of mtDNA haplogroups/types (HGs/Ts) in KHs as an indirect indication of the population’s current genetic diversity, and (ii) determine Y-chr HG/T and compare them to the other domestic breeds to understand the origin and influence of different patrilines in the KH gene pool.Since the late 1990′s, no significant genetic studies have been conducted in KHs. Here, we infer for the first time the current maternal and paternal genetic diversity of KHs, by means of uniparental markers (mtDNA and Y-chromosome (Y-chr)). Previous studies on domestic horses have shown high levels of genetic diversity and lack of phylogeographic structure in mtDNA ,20,21. AIn addition, pictorial records obtained from the largest archival photos of the Kaimanawa horses, suggest the existence of two KH herds within the muster area . The greWe collected hair follicles from a total of 96 KHs , mustered during the period of 1993–2019 from different zones in the Kaimanawa ranges and rehomed by private owners or sampled for research purposes . Among wGenomic DNA was extracted from hair follicles of 96 KHs, following a magnetic bead-based DNA extraction protocol by the technical personnel at Neogen. The individuals were genotyped with the Neogen GeneSeek Genomic Profiler (GGP) Equine v4. array (75K), following the manufacturer’s instruction. This array contains ~71K SNP markers evenly distributed throughout the equine genome . The raw data files from Illumina’s GenomeStudio Final Report were converted to plink input files , using a custom python script. Following , genotypi) SNPs that were missing in >5% of individuals; (ii) SNPs with minor allele frequency < 5%; (iii) SNPs with Hardy–Weinberg equilibrium deviation (--hwe) with p-value < 1 × 10−5 (as recommend in [iv) SNPs that were in linkage disequilibrium ; and (v) individuals with >5% missing data.Prior to relatedness estimation, the following SNPs and individuals were excluded using PLINK v1.90 : (i) SNPmmend in ,25); (ivAs pedigree information (first-degree relatives) was only available for a few samples, we performed the identity-by-descent (IBD) analysis to evaluate the relatedness of the individuals. We calculated the genome sharing value (pi-hat) based on autosomal SNPs, using PLINK v1.90 . The fii) individuals with missing data > 5%; and (ii) SNPs with missing data in more than one individual. We further excluded: (iii) maternally related individuals (pi-hat ≥ 0.25 & same mtDNA HT) (Equus asinus: NC_001788.1)  . Our fintDNA HT) . Moreove01788.1) , by extrd), nucleotide diversity (π) and theta estimator based on the segregating sites (θW) were calculated by DnaSP v6.12.03 [The genetic diversity indices including the total number of haplotypes (h), total number of polymorphic (segregating) sites (S), average number of nucleotide differences (k), haplotype diversity , by applying uniform substitution weights and without activating the external rooting option  tree using MEGA v.10.2.5  with theg option . The useg option .To compare the genetic diversity in KHs with the other feral horse populations, we calculated the diversity indices  and consn = 43). Out of 170 variants, 121 determine the HTs in the so-called “crown” group (detected in the majority of modern domestic breeds), while 49 define non-crown HTs (detected in Asian and some northern European breeds) [To investigate the paternal ancestry and evaluate the Y-chr HT diversity in the KH population, 170 genetic variants, which were previously described as “male-specific Y-chr (MSY) variants” by , were ge breeds) ,34. Five breeds)  (Tables  breeds) . The pos breeds) . Informai) individuals with missing data > 10%; (ii) variants with heterozygous calls in males; and (iii) variants with missing data > 5%. The remaining missing calls were imputed by introducing the allelic states observed in the samples belonging to the same HG, following the method described in [iv) paternally related individuals (pi-hat ≥ 0.25 & same Y-chr HT)  . Our finhttps://www.fluxus-engineering.com, accessed on 6 October 2022), using uniform substitution weights and without activating the external rooting option  reported in  Table S. A ML phg option . As mentST (Fixation Index) [To evaluate the possibility of population structure within the sampled KHs, we performed nuclear (157 Y variants) Fn Index)  pairwisen Index)  with 100Based on the IBD evaluation using autosomal SNPs, we show that ~53% of the individuals in the KH population have one or more second degree or closer relatives (pi-hat ≥ 0.25) in the sampled population. We excluded 22 maternally and 6 paternally related individuals from the mtDNA and Y-chr analysis, respectively .d) = 0.518 ± 0.043  versus complete mtDNA sequences (π = 0.0049 ± 0.0002) indicate that in the face of missing data, the nucleotide diversity can be an overestimation of the true value as invariant sites are missing in SNPs genotype data and hence omitted from the calculations .d = 0.11 ± 0.051) was comparable with the estimates obtained for the Sable Island (Hd = 0.095 ± 0.084) and Grand Turk (Hd = 0.182 ± 0.144) feral horse populations, while lower than other feral horse populations such as Assateague, Fluoride Cracker, Mustangs, Saint-Pierre et Miquleon and Theodore National Park. Moreover, our data show that even though the value of nucleotide diversity in KHs (π = 0.00687 ± 0.00355) is lower than any other feral horse populations, it is still closer to the Sable Island (π = 0.0034 ± 0.00301) population (d = 0.782 ± 0.093) and nucleotide diversity (π = 0.12597 ± 0.01716) based on D-loop analysis. In addition, we observed slightly lower haplotype diversity and nucleotide diversity in a combined feral horse dataset , compared to the domestic ones   and compared the value with the overlapping data from seven feral horse populations . The mtDpulation . Among t0.00586) .Our analysis on a combined dataset (157 SNPs) of male KHs (n = 37), and published Y-chr dataset  retrieved from  Table S, indicatThe HT Ad-b is represented by more than half of our samples (Ad-b: 20/37), and it has been reported in breeds other than Arabians and Thoroughbreds, such as Connemara pony (Ireland) and Kladruber (Baroque type breed from Czech Republic). The HT Am is represented by only one KH sample (Am: 1/37), and it has been previously detected in Barb (North Africa) and Marchador (Brazil) horses. Among the three typical Arabian HTs  commonly observed in occidental and local  Arabian lines, we only observed the Ao-aD2 in our KH samples (Ao-aD2: 6/37) .Tb-dm here , and the Godolphin Barb (sub-HG Tb-oB3b1) . Within -dm here .While the Darley Arabian line was represented by only two KHs, the Godolphin Barb line is more common (HT Tb-oB3b1: 8/37). We have not detected any signature of the sire line lineage related to Byerley Turk (sub-HG Tb-oB1), which has previously been reported in Thoroughbreds, Akhal Teke/Turkomans, and in a small set of Arabian horses with unknown ancestry .ST pairwise genetic distance based on the Y-variants in KHs, indicate a significant  and moderate structure within the male KH population harboring ancestral maternal G and P lineages. In addition, our results indicate that all grey KHs sampled in the Kaimanawa range (7/7) harbor maternal HG P, while chestnut horses (22/30) are predominantly found in horses with maternal HG G , while the majority of KHs with maternal HG P have Arabian paternal ancestry (Ao-aD2: 7/13) . FST painal HG G .Although we observed the contribution of at least six ancestral maternal lineages in the KH gene pool, the majority of samples contributed to only two major HGs (G and P). These HGs are frequently observed in the Middle East and European horse breeds . The HG d = 0.518 ± 0.043; π = 0.025 ± 0.002) is lower than the corresponding values obtained for the global modern horse dataset . This can, in principle, be explained by a smaller proportion of ancestral mitochondrial diversity in KH population, or by an effect of genetic drift in a small, isolated population. In addition, SNPs genotype data have ascertainment bias towards known variants in certain horse breeds. Therefore, any rare variants private to the KHs remain undetected. Although these unrepresented SNPs are more likely to be rare, they can be common in such a small, isolated population. In the absence of the complete mtDNA data, that bias leads to underestimating the diversity indices within the small, isolated KH population.Mitochondrial haplotype and nucleotide diversity in KHs  to accuMoreover, we attempted to compare the mitochondrial genetic diversity in KHs with other feral horses based on the overlapping partial D-loop region. However, it is worth noting that even though high levels of genetic diversity in horse mtDNA D-loop region (~350–650 bp) have been previously detected ,41,42,43We have observed an unequal contribution of at least six ancestral paternal lineages in the KH gene pool, dominated by three HTs  . RegardlBased on written reports by James Boyd in the mid 1990’s , one of Considering all British and Irish pony breeds share a very similar breeding and lineage history—originated from Celtic ponies and were refined with oriental, Iberian and English Thoroughbred stallions —we suggeAmong the three typical Arabians HGs/Ts  reported in , we obseIn addition to the Arabian and British pony breeds stated above, KHs show footprints of two of the three influential Thoroughbred foundation sires, namely the Darley Arabian (HG Tb-d) and the Godolphin Barb (sub-HG Tb-oB3b1) . This maThese diverse and unequal contributions of different paternal lines in KHs can be the result of founder events, selection and genetic drift in a small, isolated population. Moreover, it can be explained by the natural social structure in horses, which is remarkably uniform in feral horses around the world ,47,48. TWe predict that the rare mtDNA HGs in KHs, such as A and B, will be lost in a few generations as they are observed only in one 14-year- and 20-year-old female, respectively . In addiIt is worth noting that maternal HG L, and paternal Tb-dw—which are common worldwide—were both observed only once and as a combination in one 18-year-old male individual with tan coat color. Tan coat color is very rare in the KH population and has been observed only in this sample in our collection. To reduce the sampling bias, we have almost sampled 32% of the KH population mustered during the period of 1993–2019 from different zones in the Kaimanawa ranges. Considering the small census size  of the population and high level of relatedness (~50%), we can assume that our data exhibit a realistic picture of the maternal and paternal haplotype contributions in the KH population. Nevertheless, we cannot fully rule out the possibility of existing very rare and hence undetected haplotypes in the KH population.We observed that among male horses with maternal HG G, the majority have British and Thoroughbred paternal ancestry, while the majority of KHs with maternal HG P have Arabian paternal ancestry . This coWhether this ancestral parental combination is only a reflection of the past founder groups, or today’s KH population splits into two genetically isolated sub-populations, would have a major impact on effective population size and genetic viability of the population. In the case of the later, it would not be a one herd of 300 horses, but rather an equal split of each approximately 150, which has to be carefully considered in management strategies to control the population size. Obtaining whole genomic data, and geographic information of sampled KHs would be the crucial future step to accurately evaluate the population structure of today’s KHs and to conduct informed management decisions.In summary, our data indicate that although a diverse genetic ancestry contributed to the KH gene pool, today’s population is dominated by few maternal and paternal lineages. Therefore, to ensure the long-term viability of a genetically healthy population, minimizing the loss of the current genetic diversity in KHs must be a priority. This has already been initiated by returning the KHs with more rare and interesting colors  into the range, and excluding such mares from the immune-contraception intervention . In addiWhether KHs harbor rare and population-specific (private) functional variations as a result of adaptation to their natural environment, can only be confirmed or rejected with more comprehensive genome-wide data. As previously shown , whole g


Manis pentadactyla). However, recently reported activities of Chinese pangolins in several sites of Guangdong province in China indicate a promising sign for the recovery of this threatened species. Here, we re-sequence genomes of 15 individuals and perform comprehensive population genomics analyses with previously published 22 individuals. These Chinese pangolins are found to be divided into three distinct populations. Multiple lines of evidence indicate the existence of a newly discovered population (CPA) comprises entirely of individuals from Guangdong province. The other two populations (CPB and CPC) have previously been documented. The genetic differentiation of the CPA and CPC is extremely large (FST = 0.541), which is larger than many subspecies-level differentiations. Even for the closer CPA and CPB, their differentiation (FST = 0.101) is still comparable with the population-level differentiation of many endangered species. Further analysis reveals that the CPA and CPB populations separate 2.5–4.0 thousand years ago (kya), and on the other hand, CPA and CPC diverge around 25–40 kya. The CPA population harbors more runs of homozygosity (ROHs) than the CPB and CPC populations, indicating that inbreeding is more prevalent in the CPA population. Although the CPC population has less mutational load than CPA and CPB populations, we predict that several Loss of Function (LoF) mutations will be translocated into the CPA or CPB populations by using the CPC as a donor population for genetic rescue. Our findings imply that the conservation of Chinese pangolins is challenging, and implementing genetic rescue among the three groups should be done with extreme caution.Poaching and trafficking have a substantial negative impact on the population growth and range expansion of the Chinese pangolin ( Whole-genome resequencing of Chinese pangolins reveals a new population CPA that is genetically distinct from and harbor more homozygosity than CPB and CPC populations, indicating prevalence in inbreeding and implying challenges in conservation. Establishing gene flow between populations is an effective method to improve the fitness of isolated small populations by increasing genetic diversity and decreasing inbreeding, which is also known as the genetic rescue5. For example, gene rescue substantially improved the fitness of small inbred populations in African lions (Panthera leo)6, mountain pygmy possum (Burramys parvus)4 and Florida panther (Puma concolor coryi)7. However, the making of suitable strategy for genetic rescue highly depends on comprehensive investigation of the genetic background of these small populations, including the genetic diversity, population differentiation, mutational load, gene flow, local adaptation, the extent of inbreeding and outbreeding, etc9.Small populations particularly tend to be crashed due to loss of genetic diversity, the accumulation of deleterious mutations, and changes in genetic make-up resulting from genetic drift and increased inbreeding12. However, limitations of these molecular markers are also obvious, including the limited information to explain local adaptation, inbreeding/outbreeding depression and adaptive variations13. Bias introduced into parameter estimations due to the limited genetic information is also needed to be emphasized for the used of neutral markers15, and the evaluation of genetic diversity of the giant panda (Ailuropoda melanoleuca) is a good example for this issue16. Fortunately, all the above-mentioned limitations could be much improved at the era of whole genome sequencing. With the rapid development of sequencing technology and the plummeting cost of re-sequencing, the conservation genetics is in transition to conservation genomics17. Recently, several genomic-based studies on investigation of endangered animals were reported, explaining many long-standing questions, such as the purging of deleterious mutations in Bengal tigers (Panthera tigris tigris)18, the local adaptation of giant panda19, the detailed inbreeding depression in kākāpō (Strigops habroptilus)20, which are unthinkable without application of genome-wide markers.Molecular markers have ever been widely used in conservation genetics to guide the protection and conservation of threatened species, with the most promising markers of microsatellite and mitochondrial DNA22. They play important roles in ecosystems, including predators of social insects, creators of burrows, hosts of endo- and ectoparasites, and also prey for other predators25. Humans, on the other hand, have extensively killed and exploited pangolins as a luxury delicacy and used their scales in traditional medicines28. Because of this overexploitation, all the eight extant Pholidota species have undergone severe population decline29. Of all eight pangolin species, the Chinese pangolin (Manis pentadactyla) is one of the most threatened species with a ~94% decline in its whole population between the 1960s and 1990s, largely due to the extensively illegal trade in Asia31. It has been classified as a critically endangered species in the IUCN Red List of Threatened Species and listed in ‘Appendix I’ of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES)32. This species was once distributed in the vast areas of the southern Yangtze River in China and some northern areas of Southeast Asia34. However, new burrows have not been seen for more than 20 years in some areas of the Dawuling Natural Reserve and Luofushan Natural Reserve in Guangdong province36. Moreover, the Chinese pangolin might have been extirpated from several areas, including Jiangsu, Shanghai, Henan, etc38.Pangolins belong to the placental mammal order of Pholidota, representing one of the most unusual orders of mammals due to their overlapping epidermal scales, myrmecophagous diet, lack of teeth, as well as their extraordinarily elongated tongue40. Recently, Hu et al.41 reported the population identities of illegally traded individuals, revealed population fluctuations, and an increase in inbreeding and mutation load in Chinese pangolin populations, providing valuable information for the conservation of this species. Further, great progress has been made towards the conservation of Chinese pangolin, with several Chinese pangolins being photographed by infrared cameras in the wild in at least six cities of Guangdong province since 202042, which was a promising sign for the rescue and recovery of this species. Although, Hu et al.’s study included samples from Taiwan43, Yunnan province, and some unknown geographical locations, more samples from Guangdong province are needed for conjoint analysis to reveal more comprehensive genomic make-ups of this critically endangered species.A large majority of research on pangolins is restricted to ecological study or genetic study but with DNA markers limited at mitochondrial DNA and microsatellite fragmentThe planning of genetic rescue necessitates a thorough assessment of genetic background from a variety of perspectives, which is the prime objective of our research to aid in the continued conservation of Chinese pangolins. In this study, we intensively investigated the genomic characteristics of Chinese pangolins from Guangdong province. Together with previously published genomes, we revealed the population structure of the Chinese pangolins, and extensively explored the genetic diversity, population differentiation, mutational load, gene flow and local adaptations of each population. Our study provides a valuable resource and insights for the future conservation and conservation of this endangered species.P = 7.5 × 10−11 ~ 1.1 × 10−12)  and the other one (CPB: n = 18) consisting of individuals from Guangdong (n = 4), Taiwan (n = 1) and Yunnan (n = 13). CPC population (n = 8) was made up of 8 individuals who might have originated from Myanmar41.We first constructed phylogenetic trees with nuclear genomes and mitochondrial gene sequences and confirmed that these Guangdong individuals are Chinese pangolins  value of 0.541. The FST between the closer CPA and CPB populations was still larger than 0.1  as the Y (the outgroup). A, B and X were from all possible combinations of the CPA, CPB and CPC populations. Interestingly, when the X population was set to be the CPC population, we were unable to detect any significantly unbalanced derived allele sharing, supporting the large differentiation between CPC and other two populations  analysis showed no large segments (>1 Mb) among populations. For medium-size (100 kb < IBD < 1 Mb) segments, we identified 0.81 Mb shared between CPA and CPB, 0 Mb shared between CPA and CPC, and 4.44 Mb shared between CPB and CPC  estimation showed that the separation between CPA and CPB was 2.5 to 4.0 thousand years ago (kya), which was much more recent than that between CPC and CPA (25–40 kya), and CPC and CPB (25–40 kya) Fig. . Conside44. The average heterozygosity (He) among all the individuals was 0.18% calculated by all SNPs. This value decreased to 0.15% when low-frequency alleles were excluded  < 5%). To better compare our results to the previous report, we also calculated the average He (0.11%) using SNPs with AF > 20%  ranging from 100.0 kb to 3155.8 kb in the whole Chinese pangolin population. Of these ROHs, 448.58 Mb (FROH = 18.69%) and 21.57 Mb (FROH = 0.90%) were assigned to medium-size (100 kb–1 Mb) and long ROH ( > 1 Mb) , 451.63 Mb (FROH = 18.82%), and 413.52 Mb (FROH = 17.23%), respectively. The long-size ROHs in CPA, CPB, and CPC population was 29.54 Mb (FROH = 1.23%), 22.28 Mb (FROH = 0.93%), and 9.11 Mb (FROH = 0.38%), respectively.By using the same method from Hu et al.Mb) Fig. . The medP = 1.7 × 10−10) and CPB  populations. However, there was no significant difference in the ratio of deleterious nsSNPs to total nsSNPs across all populations . However, the CPC population had a significantly higher ratio of LoF variants to total nsSNPs than CPA and CPB populations  across all three populations  for LoF mutation, missense mutation, and deleterious missense mutations. We found that this ratio of LoF mutation, missense mutation, and deleterious missense mutations in the 36 Chinese pangolins was 0.505, 0.482 and 0.432, respectively Fig. . The ratWe further evaluated the number of potentially introduced LoF mutations to the receiving population from the donor population. We found that CPB individuals would introduce 120–238 LoF mutations and CPC individuals would introduce 336–381 LoF mutations when CPA was regarded as the receiving population. The minimum LoF mutations (103–135) would be introduced to the CPB population from the CPA population. However, this number rise to 393 to 555 by introducing CPA and CPB individuals to the CPC population Fig. .P = 3.8 × 10−13), visual perception  and protein digestion and absorption  (Supplementary Tables P < 0.01)” was found to be significantly enriched in all three populations. In addition, the Insulin signaling pathway , cellular carbohydrate metabolic process  and response to fructose  were found in the CPB population. In particular, we did not find enriched gametogenesis-related pathways, such as male gamete generation, in the CPC population.We then analyzed the 2229, 1044, 1574 and 962 genes affected by the LoF mutations in the all 36 individuals, CPA, CPB and CPC populations. In general, significantly enriched Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicated that these genes were associated with male gamete generation , 3154 (CPB) and 1878 (CPC) SNPs were identified under strong positive selection with iHS scores in the top 0.1%  were mentioned to be possibly from Guangdong province, the detailed population structure is hard to be further described due to the ambiguous sampling site and small sample sizence Fig. . Negligince Fig. .FST = 0.541) population suggested a potential subspecies-level divergence when compared with other animals. For example, the Sichuan and Qinling giant panda subspecies was defined in 2005 by the skull and molar size45, and their genomic differentiation is much less than the CPA and CPC populations (FST = 0.140)16. The differentiation of tiger subspecies could be dating back to 110 kya46, however, the genomic differentiations among tiger subspecies are still much lower than these two Chinese pangolin populations, with the highest FST value between Amur tiger and Sumatran tigers is of 0.31847. This was not surprising, because the CPC population was significantly distinct from CPA and CPB from the perspective of genetics  populations with the average FST value of 0.10448, the two largest isolated populations (Minshan and Qionglai populations) of the giant pandas16, and even comparable to that between African and Asian humans (FST = 0.120)49 which are known to be separated before ~ 55 kya50. Taken together, we infer that the first and newly discovered CPA was a distinct Chinese pangolin population, and suggested an extra conservation unit that is parallel with CPB and CPC population. We believe the future large-scale population genomic analysis and corresponding ecological and morphological studies will further enrich and strengthen this claim.Interestingly, the genomic differentiation between CPA and CPB was moderate with a 52. The extremely cold weather and the possibly continuous impact on the prey of Chinese pangolins during this period could be one of the reasons for population separation, considering that southern China was also severely influenced by the LGP53. However, climate changes may not be the main reason for population separation55. Human activity is always considered an important factor for shaping the evolution of animals56. The human expansion in East Asia occurred as early as 40 kya57, and frequent migration, admixture and replacement also occurred at the original distributed area of the Chinese pangolins41. Interestingly, the Han Chinese population began to expand and separated from the European human population at ∼30 kya59. Moreover, the key period in human history was the onset of the Holocene since the development of more favorable climate conditions60. We concluded that human activity was most likely responsible for the separation of the CPA and CPB populations.We inferred the separation between CPA and CPB was much more recent than that between CPC and CPA/CPB Fig. , which iHe among the three populations are similar , but lower than some other critically endangered species that are suffering from long-term population decline as the Chinese pangolin, such as Northern white rhinoceros (Ceratotherium simum cottoni) (0.110%) and Western lowland gorilla (Gorilla gorilla gorilla) (0.144%)61. Such a low level of genetic diversity suggested that Chinese pangolins likely had a relatively low adaptive potential62. The inbreeding often contributes a lot to the decrease of genetic diversity for endangered species, such as the ROH segments of highly inbred grey wolves (Canis lupus) population ranging from 2,695 bp to 95.8 Mb63 and the modern Malay Peninsula population of Sumatran rhinoceros (Dicerorhinus sumatrensis) estimating 30% of the genomes contain longer ROH segments (≥ 2 Mb)8. However, ROHs in the Chinese pangolins are found to be much shorter than the above-mentioned endangered species. We infer that the low genetic diversity in the Chinese pangolin may not from the extensively recent inbreeding due to the absence of large size ROHs64, but the long-term isolation without frequent gene flow between subpopulations could be an alternate explanation. Here we cannot exclude the bias introduced from the reference genome, because the accurate evaluation of ROH highly depends on the contiguity of the reference genome65, but no long-read assembled high-quality reference genomes are available now.Regarding the genetic status of Chinese pangolin in this study, we observed that genome-wide 67. When focusing on Guangdong populations, the ratio of deleterious mutations in CPA and CPB populations was higher than CPC population. LoF mutations enriched in pathways related to male gamete generation in the CPA and CPB populations, but not CPC population, further indicated more genetic burdens in Guangdong populations, considering the impaired gamete quality may reduce reproductive success and further affect species fitness and survival68. Together with the overall distribution of deleterious mutations, we speculated that the CPC population has higher fitness than both CPA and CPB populations.The decrease of fitness in a small population can accelerate by the accumulation of deleterious mutationsP < 0.01) in all the three populations, revealing a potentially weak ability of digestion and absorption of protein in the whole Chinese pangolin population. In the CPB population, we also found enriched KEGG pathways related to carbohydrates metabolism, indicating an inferior performance in the energy supply. Considering that pangolins enjoy high protein, high fat, high calorie food70, the possibly weakened ability to use the protein and carbohydrates may further lower the fitness of the Chinese pangolins.LoF mutations were also found significantly enriched in the protein digestion and absorption pathway . We cannot conclude that it is 100% not suitable to implement genetic rescue among these Chinese pangolin populations, however, genetic rescue between CPA and CPB should be safer from the perspective of outbreeding depression. The conclusion will be more convincing only if the exact effects of these LoF mutations on genes could be parsed in future works.In addition, we predicted an average of 309 LoF mutations could be introduced into other populations as new deleterious alleles when conducted translocations among CPA, CPB and CPC populations Fig. . The num74, hence more reliable evidence is still needed to be put forward in further studies. Furthermore, as a distinct population separated from the CPB and CPC, the newly discovered CPA Chinese pangolin population in our study should be given high priority in future conservation works of the Chinese pangolin.We should keep in mind that genetic rescue is a complicated processApprovals of all necessary research ethics and permits were granted by the Institutional Review Board of BGI BGI-IRB E21056). 15 Chinese pangolin samples were collected from the Guangzhou Wildlife Rescue Center, Guangdong province, China for whole genome sequencing. All samples were taken from rescue individuals dead of natural causes or confiscated individuals from forestry police, and all individuals were identified as Chinese pangolins through distinct morphological characteristics. In addition, sequencing data of 22 Chinese pangolin individuals  for paired-end (PE) DNA library construction with the insert size of ~350 bp following the manufacturer’s instructions of BGISEQ platform . DNA libraries were then subjected to the DNBSEQ-T1 sequencer for sequencing.Genomic DNA was isolated using standard phenol/chloroform-isoamylalcohol extractionM. pentadactyla reference genome  and M. javanica 41 by using the Burrows-Wheeler Aligner (BWA) with the mem algorithm76 (version: 0.7.10–r789) using the default parameters. Sorting, reordering and reads deduplication were performed by the Picard tools (http://picard.sourceforge.net) (version: 2.1.1). HaplotypeCaller implanted in the Genome Analysis Toolkit 77 was used for raw variants calling. Hard filtering was performed on the raw SNP variant set with parameters of “QUAL < 30.0 ||  QD < 2.0 ||  FS > 60.0 ||  MQ < 40.0 ||  MQRankSum < −12.5 ||  ReadPosRankSum < −8.0”. The final high-quality SNP set was used for further genetic analyses. Lastly, SNPs in scaffolds with length smaller than 100 kb were excluded.Sequencing reads were mapped to 78 (version: 2015-12-14) with the genome of M. pentadactyla reference genome (YNU_ManPten_2.0). We sorted out the basic information of the resequencing data of each population  and grouped SNPs in different categories by results from ANNOVAR, including exotic, nonsynonymous, synonymous, UTR, intronic, intergenic, splicing, and non-coding RNA (ncRNA). In addition, we draw the distribution of MAF for all individuals. We prepared three SNP sets by controlling the filtration on the allele frequency for downstream analysis, including: 1) SNPs without any filtration of allele frequency; 2) SNPs with the allele frequency larger than 5%; 3) SNPs with the allele frequency larger than 20% following the method of Hu et al.41.SNPs were annotated by the software ANNOVAR79 (version: 1.90b3.38) was used to produce a pruned subset of SNPs by Linkage disequilibrium (LD) values, resulting in a set of 171,405 SNPs. This SNP set was then converted into PHYLIP-format file by using an in-house Python script to construct the Maximum-Likelihood (ML) phylogenetic tree with 1000 replications by the PhyML80 (version: 20151018) with all 37 samples. The Malayan pangolin (YNU_ManJav_2.0) was used as an outgroup.To avoid closely-linked sites, PLINKb (Cyt b) and cytochrome c oxidase subunit 1 (CO1) sequences from Felis catus: NC_001700.1 and 8 species of Manis , 37 Chinese pangolin individuals and 72 Malayan pangolin individuals. ML trees with 1000 replications based on mitochondrial gene sequences were constructed using Molecular Evolutionary Genetics Analysis  with the Kimura’s two-parameter model81.For species identification, the Chinese pangolin genome (YNU_ManPten_2.0) and Malayan pangolin genome (YNU_ManJav_2.0) was regarded as the reference genome for 109 pangolins , respectively. We then constructed two ML phylogenetic trees according to the above methods. We also extracted the cytochrome 82  software was used for PCA inference. The population genetic structure was then inferred by using the same dataset as PCA analysis with the ADMIXTURE83 (version: 1.3) program. We predefined the number of genetic clusters (K) from 1 to 10 and ran the cross-validation error (CV) procedure to explore the best K, using the default parameters and settings.All SNPs without pruning and AF filtering were used as the initial input data for PCA and ADMIXTURE analysis. The Genome-wide Complex Trait AnalysisFST84 to estimate the population differentiation. All bi-allelic SNPs were used for the calculation of genome-wide FST between each pair of the populations using the VCFtools (version: 0.1.13) software85.We used Weir and Cockerham’s 86 (version: 5.1). The EIGENSTRAT format input data was generated by CONVERTF program in the ADMIXTOOLS.In order to explore whether one population was admixed by the other two populations, we performed the F3 test with combinations of CPA, CPB and CPC using the ‘qp3Pop’ in the ADMIXTOOLS87 using the “-informative” command of the POPSTATS88. The four populations were set to be , ), and the A, B and X were among each of CPA, CPB, and CPC. The Y was Malayan pangolin which was the outgroup. We screened significant D values using the Z-score (|Z | > 3) based on a block jackknife procedure.To examine the excess of shared derived alleles between different populations of Chinese pangolins, we applied the classic ABBA-BABA test (D statistics)89 (version: 16May19. ad5) with analysis parameters (length = 0.1 and lod = 3.0). We compared different lengths of IBD (IBD > 1 Mb or 100 kb < IBD < 1 Mb) and their percentage on the genome among populations to evaluate the degree of gene flow.The sharing of identity by descent (IBD) between individuals was calculated by the RefinedIBD90 (version: 1.13) to investigate the phylogenetic relationship in the presence of admixture events among populations. TreeMix was run with the parameters -bootstrap 5000 -global and the migration event -m (from 0 to10).We constructed maximum-likelihood population trees using TreeMix59 (version: 2.1.2) to infer the separation among the three populations. MSMC2 was performed for four independent replications with two samples randomly selected from each population. Genotype phasing was using the Beagle91 (version: 5.0) software with default parameters before the MSMC2 inference. Parameters for MSMC2 calculations were as follow: -skipAmbiguous -I 0-4, 0-5, 0-6, 0-7, 1-4, 1-5, 1-6, 1-7, 2-4, 2-5, 2-6, 2-7, 3-4, 3-5, 3-6, 3-7 -i 20 -t 6 -p ‘10*1 + 15*2’. To further validate the results inferred by the MSMC2, we performed the SMC++ 92 (version: 1.5.2) to infer the split time of the two populations, because the SMC++ software does not depend on phasing data, which can avoid calculation bias introduced by switch errors during phasing analysis. The mutation rate and generation interval of the M. pentadactyla we used here was 1.47×10−843 per site per generation and one year93.We first used MSMC2He) and nucleotide diversity (π)94. ROH was identified by PLINK following the method described by Dobrynin et al.95. Long ROH is often the indicator of recent inbreeding that occurred several decades ago. According to the formula reported by Kardos et al. (Generations = 100/2 * ROH length)63, we only counted ROHs that were larger than 100 kb in this study. If ROHs were longer than 1 Mb, we assumed that these ROHs were generated by more recent inbreeding (< 50 years). The genetic diversity, ROH and mutation load of Taiwan individuals have been studied in the study by Hu et al.41. Therefore, Taiwan individuals were excluded from this part and mutational load analysis.VCFtools was used to estimate whole-genome genetic diversity, including heterozygosity 97. Here, nsSNPs with GS ≥ 150 were defined as deleterious mutations98. We used “-aamatrixfile grantham matrix” parameter in the package ANNOVAR to print out GS for nonsynonymous variants. We counted the number of deleterious nsSNPs and the ratio of deleterious nsSNPs to total nsSNPs. Moreover, we selected derived mutations in coding regions of each pangolin individual for annotation by SnpEff99 (version: 4.3). LoF variants here we used included splice_donor_variant, splice_acceptor_variant and stop_gained. Numbers of LoF variants and ratios of LoF variants to total nsSNPs in Chinese pangolin populations were counted. Missense mutations were represented by missense_variant. The ratio of homozygous (two per site) to (homozygous (two per site) plus heterozygous sites (one per site)) for all LoF, missense and deleterious variants were calculated for estimating the level of mutational load44.We used the genotypes of the same alleles in the 100. The GO terms and KEGG pathways with an enrichment factor > 2 and a multi-test adjusted P-value < 0.05 were considered to be significantly enriched. P-values and multi-test adjusted P-values were transformed with log base 10.GO and KEGG functional enrichment of genes affected by LoF mutations was performed using Metascape website 101 (version: 1.3) to identify genomic signatures of positive selection in the CPA, CPB and CPC populations. The iHS calculations were performed independently in each population. As the genetic distance between adjacent SNPs was needed for the calculation, a chromosome segment of 1 Mb was straightly converted as 1 centiMorgan (cM).We computed the iHS101: a) regions of consecutive 50 SNPs; b) regions of 100 kb with 50 kb step size; c) 5 kb flanking regions away from candidate sites. We then calculated the sum of the iHS scores (siHS score) of all candidate sites in each genome region, and candidate regions were selected with the top 10% siHS score. The intersection of candidate genes obtained by three region-selected methods were used for the next analysis101. For the candidate genes, GO and KEGG functional enrichment was performed using Metascape website100. The GO terms and KEGG pathways with an enrichment factor > 2 and a multi-test adjusted P-value < 0.05 were regarded as significantly enriched.For the identification of candidate genes, SNPs within the top 0.1% iHS scores were assigned as candidate sites. Based on candidate sites, we then used three methods to screen candidate regions described as Voight et al.t-tests were performed in R102 (version: 4.0.2). P-value less than 0.05 was considered to be significant. All statistics was done using available packages and reproducibility can be accomplished using parameters we mentioned in Methods.To test the significant difference of ROH and mutation load between different populations, two-sided Welch two-sample Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


CYP2D6, encoding an enzyme involved in the metabolism of about 25% of commonly prescribed drugs. In a directed search of the 1000 Genomes Phase III variation data, 86 single nucleotide polymorphisms (SNPs) in the CYP2D6 gene were extracted from the genotypes of 2504 individuals from 26 populations, and then used to reconstruct haplotypes. Analyses were performed using Haploview, Phase, and Arlequin softwares. Haplotype and nucleotide diversity were high in all populations, but highest in populations of African ancestry. Pairwise FST showed significant results for eleven SNPs, six of which were characteristic of African populations, while four SNPs were most common in East Asian populations. A principal component analysis of CYP2D6 haplotypes showed that African populations form one cluster, Asian populations form another cluster with East and South Asian populations separated, while European populations form the third cluster. Linkage disequilibrium showed that all African populations have three or more haplotype blocks within the CYP2D6 gene, while other world populations have one, except for Chinese Dai and Punjabi in Pakistan populations, which have two.A significant portion of the variability in complex features, such as drug response, is likely caused by human genetic diversity. One of the highly polymorphic pharmacogenes is  CYP2D6 gene was cloned and sequenced [CYP2D7 gene, and a CYP2D8 pseudogene. The CYP2D6 gene contains nine exons and is highly polymorphic.Human CYP2D6 protein was purified in 1984 [equenced , and it CYP2D6 by proposing a haplotype-based star (*) nomenclature system [CYP2D6 star alleles have been described and are available on the Pharmacogene Variation (PharmVar) Consortium website [In 1996, a group of international experts in pharmacogenetics decided to systematize allelic variants of the e system . Since t website ,6.CYP2D6 affect the metabolizing activity of the CYP2D6 enzyme. Its activity can vary from complete absence to increased activity, and can be grouped into four different drug-metabolism phenotypes: (1) poor metabolizer ; (2) intermediate metabolizer ; (3) extensive metabolizer ; and (4) ultra-rapid metabolizer  [CYP2D6 gene have also been studied as a risk factor for a number of diseases: Parkinson’s disease [Genetic variations of the  allele) ,8,9,10.  allele) ,12, but  allele) ,17,18,19 disease ,21,22, s disease ,23, Alzh disease ,25, as w disease ,27.CYP2D6 genetic diversity, according to Fuselli (2010), are a result of the selective pressure of new or more potent CYP2D6 substrates that emerged as food choices, particularly at the start of the Neolithic transition, in response to worsening nutritional conditions and higher disease burdens [Although CYP2D6′s role in the metabolism of naturally occurring xenobiotics has not been researched extensively, it is well-known that this enzyme has a very high affinity for alkaloids . Therefo burdens .CYP genes that metabolize exogenous compounds have far higher frequencies of SNPs that vary greatly between populations [More genetic variation can be seen in genes encoding detoxification enzymes, which shows that exposure to various substrates also aided in the evolution of genetic variants. Detoxification enzymes really exhibit signs of positive selection, such as modifications of the amino acid sequence that increase substrate selectivity ,32. Compulations .CYP2D6 gene (such as the presence or absence of a particular substrate or a variable concentration of substrates) [It has not yet been possible to pinpoint the selective factor that causes diet-related patterns of evolution in the strates) .CYP2D6 variation, most focus on the pharmacological consequences of different variants. The goal of this paper was to identify which single nucleotide polymorphisms (SNPs) and haplotypes in the CYP2D6 gene determine the genetic specificity of 26 world populations, and to test intra- and inter-group differences in continental groups defined by ancestry. Furthermore, we investigated the role of population differentiation in the definition of the CYP2D6 star alleles.Although there are numerous papers on the world distribution of CYP2D6 is located on the reverse strand of chromosome 22:42,126,499–42,130,865 (GRCh38). Using Data Slicer, a tool implemented on the Ensembl website [The investigated pharmacogene  website , data on website . The dat2 measurement of LD between SNPs defining the ends of haplotype blocks, both implemented in Haploview software. The CYP2D6 haplotypes were inferred using Phase ver. 2.1 [Allele frequencies and the Hardy–Weinberg equilibrium were calculated separately for each population using VCFtools . VCF filver. 2.1 ,41. Haplver. 2.1 .Indices of intrapopulation molecular diversity  and AMOVA, the statistical significance of which was assessed by generating 100,000 random samples, were calculated using Arlequin 3.5 software .CYP2D6 gene, we investigated 86 SNPs within the gene region , whose minor allele frequencies (MAF) were higher than 0.1%. The allele frequencies of studied polymorphic sites in 26 world populations from the 1000 Genomes database are shown in In order to capture most of the variability in the Several diversity indices were calculated in order to see the intrapopulation variation, and the findings are displayed in African populations had the highest number of polymorphic sites: the most poly-morphisms (55) were found in the population of African ancestry in the Southwest USA, and the least (46) in the Mende population in Sierra Leone. The lowest number of polymorphic sites was found in East Asian populations ranging from 20 in the Japanese population to 30 in the Han Chinese population in Beijing and in the Kinh population in Vietnam. The highest number of haplotypes were found in populations of African ancestry, ranging from 37 in the Gambian population to 31 haplotypes in the Yoruba population from Nigeria. The lowest number of haplotypes of all 26 investigated populations was found in the population of Japan (11), followed by the population of Peru (16) and the British population from England and Scotland (17). Interestingly, although considered a genetically isolated population, Finns did not have the lowest number of haplotypes.Overall, haplotype diversity and nucleotide diversity were high in all populations, and highest in African populations where haplotype diversity ranged from 0.908 to 0.936. The lowest haplotype diversity was observed in East Asian populations ranging from 0.607 (Vietnam) to 0.717 (Japan). The results of the nucleotide diversity analysis are also very similar: the highest diversity was found in African populations, and the lowest in East Asian populations. According to diversity indices, the Japanese population has the lowest genetic variation.CT = 0.077), while the interpopulation variation was 9% (FST = 0.091). When we examined each continental group separately, we discovered that East Asian populations showed the greatest differentiation (FST = 0.031), while European populations showed the smallest (FST = 0.002).In order to calculate the level of population differentiation, we performed AMOVA analyses. Populations were joined in five continental groups based on shared common ancestry. Approximately 8% of the variation was due to between-group differences  contribute to the inter-population differentiation higher than 10%, with FST values ranging from 0.103 to 0.366  (To compare world populations based on is (PCA) . Its resCYP2D6 gene, while other world populations have one haplotype block, except Chinese Dai and Punjabi in Pakistan populations, which have two. A large block of the Chinese Dai population (in the range rs1135840–rs28371702) has an r2 of 0.888 and a large block of the Punjabi population in Pakistan (in the range rs16947–rs1080995) has an r2 of 0.976, while both small blocks in these two populations have an r2 below 0.8. In all African populations but Yoruba from Nigeria, we observed the CYP2D6 gene haplotype block ranging from rs1081000 to rs1080995 (r2 > 0.8). All African populations except the African ancestry in the southwest USA population share the same haplotype block in the range rs1135840–rs27371730 , and ASW is the only African population to have all its blocks with r2 greater than 0.8. The Yoruba in Ibadan (Nigeria) population has a total of four haplotype blocks, but only one block is in complete LD (in the range rs75203276–rs61736512). The same haplotype block occurs in the Mende in Sierra Leone population (r2 of 0.935) and the African Caribbean in Barbados population (r2 of 1.0). In addition to the block in the range rs1081000–1065850, the Luhya in Kenya population has another one that is in high LD . The Gambian in Western Division-Mandinka population’s second haplotype block in high LD is the one in range rs569421388–rs76327133, with an r2 of 0.868.Linkage disequilibrium (LD) was calculated and visualized using Haploview 4.2 software, which constructs blocks based on D’ values. All African populations have three or more haplotype blocks within the 2 values substantially below 0.8. The same haplotype block was also detected in three East Asian populations (Japanese and two Han Chinese populations), in three South Asian populations , and in two American populations (Colombian and Puerto Rican), also with r2 below 0.8. The only remaining East Asian population, Kinh from Vietnam, has a haplotype block bit shorter than the one previously mentioned, ranging from rs28371730 to rs1065852. In contrast, the population of Gujarati Indians in the USA, the last remaining South Asian population, also has a shorter block ranging from rs1135840 to rs1080995, with both r2 below 0.8. The latter block, again with r2 < 0.8, was also found in the Mexican Ancestry in Los Angeles and Peruvian populations.The haplotype block found In the European continental group, ranging from rs1135840 to rs1065852, in all five populations has rCYP2D6 gene were analysed in 26 world populations from the 1000 Genomes database [CYP2D6 haplotype diversity on population differentiation.Genetic polymorphisms are responsible for a substantial proportion of inter-individual and inter-ethnic heterogeneity in drug response . A numbedatabase , in ordeST (0.07) indicates a moderate overall genetic differentiation level. However, when continental population groups were examined separately, European populations showed more than 10 times lower interpopulation differentiaton than East Asian populations, which showed the highest. Similar results were found in a study by Jay and colleagues (2013), which showed that the lowest genetic variation of the ADME genes was in Europe, followed by Asia, Africa, and the Americas. In general, the difference between two European populations separated by 1000 km is far less than in other world populations [ST values among human populations [The estimated Fulations . Geograpulations .ST values greater than 10%. After calculating the MAFs of these 11 SNPs, we observed a clear clustering of SNPs in relation to the studied populations: four SNPs were present in all world populations, but the most frequent in East Asian populations , six SNPs were almost exclusively found in African populations , while rs16947 was found in all world populations.Locus-by-locus AMOVA revealed 11 SNPs with FCYP2D6 gene characteristic of East Asian populations occur in Vietnamese and three Chinese populations with a frequency in the range of 60–68%, and the Japanese population in the range of 36–39%. The finding that the Japanese population has frequencies that deviate from the rest of the group is not surprising since Japan regularly diverges due to their relative isolation throughout history. The rs1081003, a synonymous variant (Phe112Phe), showed the highest overall FST value of 0.37, and due to its substantially higher frequency in East Asian populations, it can be considered a typical East Asian variant. In addition, this SNP’s minor A allele was also found to be the major allele in some South East Asian populations [Four SNPs of the ulations . The PhaST value of 0.21, is also predominant in East Asian populations. In 2018, the association of a minor C allele with response to Tramadol was recorded in the ClinVar archive. The PharmVar defines that this SNP determines suballeles for a number of star alleles .SNP rs2004511, an intron variant with an overall FSNP rs1065852 in East Asian populations (except Japan) has frequencies of its A allele over 60%, making the G allele a minor allele in these populations. In addition to the four East Asian populations represented in the 1000 Genomes, its high frequency was also noticed in populations of Lisu  and Wa, CYP2D6*17 star allele occurs in at least 30% of Africans [SNPs characteristic for the African group of populations were almost completely absent in other populations. rs28371706, whose minor A allele causes a missense variant, is the core SNP for defining star alleles *17, *40, *58, *64, *82, *141, and *154. The Africans , and is The remaining five SNPs characteristic for the African populations have very similar MAF frequencies within each population. In PharmVar, SNPs rs80262685  and rs76327133  are associated with suballeles *2, *29, *146, *149, *155, *156, and *157, while the intron variant rs75203276 (C > T) is associated with suballeles *29, *155, *156, and *157. SNPs rs61736512 and rs59421388  define the core of several star alleles; both define alleles *29, *70, *149, *155, *156, and *157, while rs61736512 also defines allele *107, and rs59421388 defines allele *109. Those two variants are significantly associated with the decreased metabolism of debrisoquine, according to the PharmaGKB.The distribution of rs16947 is the most intriguing. If we exclude the population of African ancestry from the Southwest USA where MAF was 42%, its MAF ranged from 50–65% in African populations, to 13–17% in East Asian populations. Muaymbo et al. (2022) found that this SNP had a significantly lower MAF among Africans in Southern Africa (12%) compared to their counterparts in West (65%) and East 56%) Africa % Africa . In the SLC04C1 and NAT1), others  exhibit high levels of world population differentiation. On the other hand, the global diversity of some genes primarily reflects differentiation within a particular geographical region, such as Africa, Europe, or East Asia [The distribution of MAFs of the 11 SNP variants distinguishes African and East Asian populations from others. Haplotype-based PCA analyses showed separate clusters of African and East Asian populations, while South Asian, European and American populations were much less separated. Separation of African populations follows their genetic history and is visible in different genetic studies as well ,58. The ast Asia . The genast Asia ,64,65,66Population variation has been studied through patterns of haplotype blocks. African populations have the most diverse block pattern, while it is the most homogeneous in Europe, followed by the Americas, and South and East Asia. The investigated haplotype blocks, based on confidence intervals , encompaHaplotypes containing functional/associated variants are more likely to determine clinical drug metabolism phenotypes than a single independent SNP . Among tSistonen and colleagues demonstrated different distributions of the CYP2D6 slow  and null-function  alleles on different continents, probably caused by demographic events . DecreasCYP2D6 haplotypes, which is consistent with all known studies of genetic variability in humans. However, the greatest differentiation is found among East Asian populations due to extreme homogeneity of the Japanese population and the specific distribution of haplotypes among the Chinese population. The greatest continental homogeneity is found in Europe, followed by South Asia and the Americas.African populations showed the highest variability of the Locus-by-locus analyses revealed 11 SNP loci affecting inter-population differentiation, six of which are specific to African, four to East Asian populations, while one is present globally. Five of these SNPs  contribute to the known pharmacogenomic effects on clinical outcomes of drugs metabolized by CYP2D6.


Luronium natans, an aquatic macrophyte endemic to northwestern and western Europe. It is threatened across its whole distribution range due to human influences, in particular due to eutrophication and intensive fish farming. In spite of habitat protection populations continue to decline and re-introductions are one possibility to prevent the species’ extinction. Therefore, insights in genetic diversity and relatedness of source populations is warranted.Information provided by population genetic studies is often necessary to effectively protect endangered species. In general, such data is scarce for aquatic plants and this holds also for Großenhainer Pflege and Niederspree), complemented with numerous additional occurrences from Europe. In addition, we conducted experiments on plant growth to assess optimal conditions for ex-situ cultivation taking water temperature, water level and substrate into account. Thus, we performed Amplified Fragment-Length Polymorphism (AFLP) on two large populations in Saxony, Germany  implying that populations are not restricted to clonal growth only but reproduce also by open-pollinated flowers. Remarkably, the two geographically close Saxon populations were genetically distant to each other but subpopulations within a locality were completely intermingled. Concerning optimal cultivation conditions, longest roots were obtained at temperatures >14°C and saturated, but not submerging water levels. ex-situ cultivation.Thus, our findings advocate for a re-introduction scheme from nearby source populations and provide detailed information on successful  The loss of aquatic vegetation has been accelerating in the last four decades . OligotrLuronium natans (monotypic genus within Alismataceae) is a typical example of such an aquatic pioneer plant . Furthermore, Belgian populations were found to have a high degree of clonal reproduction , where the species is found in two pond areas approximately 90 km distant from each other: Niederspree (DE_Ni) and Großenhainer Pflege  were collected and dried in silica gel. To ensure that the harvested material came from one plant, only a single leaf per plant was harvested. In many cases plants occurred in rather dense patches, from which we sampled only one leaf to avoid collecting clonal plants.Both pond areas contain a number of distinct populations in the respective single ponds Table 1.Additionally, leaves from plants of two other localities in Germany , as well as several samples from the Czech Republic, Poland, Great Britain, and Norway were investigated Table 1.DNA was isolated from silica gel dried leaf material using innuPREP Plant DNA Kit  following the manufacturer’s instructions except for the elution of DNA, which was eluted in two steps: 1. with 70 µl HPLC grade water, 2. with 30 µl HPLC grade water. The quality of isolates was checked with agarose gel electrophoresis (1%) and DNA quantity was estimated using Qubit Fluorometer (Thermo Fisher Scientific). Extracted DNA was stored at -22°C until further processing.PstI and MseI. Restriction and ligation were performed in a single reaction for 10 hours at 37°C. For selective PCR, we followed L. natans (PstI-ACG/MseI-CAC), S8 (PstI-ACG/MseI-CCG), S11 (PstI-ACT/MseI-CTC), S12 (PstI-ACT/MseI-GCT). Samples were randomly distributed on 96-well plates to avoid position effects. Sixty-six of 253 samples were analyzed twice for quality control. In addition, five identical samples were repeated on each of the five 96-well plates, and negative controls for restriction/ligation, pre-selective PCR, and selective PCR were included. Automated detection of AFLP fragments was performed by the Senckenberg Biodiversity and Climate Research Center  with an ABI 3730 sequencer  using the LIZ-600 size standard (ABI Life Technologies).To investigate genetic diversity within Saxon populations and their relationships to other European populations, AFLP  was used. natans  and seleFragment scoring was processed as described in To estimate the genetic diversity within populations (for those with >5 samples) we calculated Expected Heterozygosity (He); Shannon’s Index of Diversity (I), and the Percentage of Polymorphic Loci (%P) with GenAIEx v. 6.5 . PopulatKleiner Tiergartenteich  and lowest diversity detected in the Norwegian population (NO: He = 0.234).Genetic diversity of Niederspree = DE_Ni) from the remaining samples along the first axis (Großenhainer Pflege (DE_Gr) were clustered, whereas samples from Northern and Southern Germany , Great Britain (UK), and Norway (NO) were found in a rather intermediate position. Note that samples obtained from ex situ collections of Botanical Gardens clustered according to their geographical origin (Niederspree (Eastern Saxony) were clearly separated along the first axis. We observed no genetic structure among the populations within both pond areas, respectively.The Principal Coordinate Analyses including all 151 samples across Europe separated mainly samples from Eastern Europe (Czech Republic = CZ and Poland =PL) and those from Eastern Saxony  were assigned to the green cluster, whereas populations from Niederspree were found in the same cluster containing most of the Czech (CZ), Polish (PL), and Norwegian (NO) samples. Admixture between both clusters was detected for a few samples from various ponds in Central Saxony and for plants originating from Norway (NO) and Wales (UK). Mantel test did not detect a significant correlation between geographic and genetic distances .Analyses of Molecular Variance Table 3 The best-fitting model for root length  included an interaction between temperature and water level 2. The sThe best-fitting model for leaf length  included an interaction between plant size at the onset of the experiment and water level. According to the best-fitting model 4, leaf L. natans populations in Poland are mainly represented by multigenerational clones reproducing primarily by fragmentation. However, he observed plants in water reservoirs with deeper water bodies, where cleistogamy occurs more often  in Europe  were reported within three species of the closely related genus n Europe . HoweverL. natans samples clustered mainly according to their geographic origin, whereby we detected three groups of samples: (1) Czech populations, (2) Saxon populations from Niederspree and (3) Saxon populations from the pond area Großenhainer Pflege, with remaining samples from Germany and Norway in between  as these areas are not connected by ditches but rather suggests zoochoric dispersal, e.g. by birds. Although, Luronium nuts lack adaptations to bird dispersal, more recent studies reported likely dispersals of 50 to 100 km  but due to the restricted availability of plant material our sampling was rather unbalanced.According to our hypothesis of a pronounced genetic structure between Saxon populations,  between Figure 2s Europe . Sampless Europe , it doeso 100 km . Here, bWielkopolska, Poland (Niederspree (DE_Ni) to Czech populations rather than to the more western Saxon samples from Großenhainer Pflege (DE_Gr) implies a geographic separation of L. natans between Eastern and Western Europe, whose border might cross Saxony.Remarkably the genetic similarity of Polish (PL) and Czech (CZ) samples Figure 2, Poland  remnantsLuronium plants developed longer roots under semi-terrestrial (saturated) conditions at higher temperature (Luronium’s Ellenberg Indicator Value for temperature (T6; Luronium has longer leaves than the semi-terrestrial form. In addition, plants which were larger at the onset also produced longer leaves, however, plant size at onset was an uncontrolled factor, and the plants were randomly selected according to size. Thus, the best results for obtaining robust plants for potential reintroduction are obtained under semi-terrestrial conditions and at higher temperatures, as a strong root system can facilitate active planting.Our experiment showed that perature Figure 4ture T6;  indicatiOur study revealed moderate levels of genetic diversities in Saxon populations suggesting that the observed decline in populations is not caused by lacking genetic diversity. We assume that populations in ponds rely strongly on regeneration by seeds emerged from outcrossing rather than by clonal reproduction or cleistogamous flowers. While we found a considerable genetic differentiation among Saxon populations our results showed no genetic differentiation within pond systems indicating sufficient genetic exchange between neighboring ponds probably mediated by birds or water. Thus, our results imply to use rather nearby populations from the same area as source for reintroductions. Our plant growth experiments demonstrated that possible reintroductions of plants are facilitated by optimal growing conditions in gardens since we obtained plants with longest roots when grown under saturated water conditions and higher temperatures, whereas the tested soil type seems to have less impact.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.CR, SW, BD, and FR planned the study, WM and BD collected the samples, WM did the laboratory work, WM, VH, and CR performed population genetic analyses, JA performed statistical analyses on plant growth, WM, JA, CR, and SW wrote the manuscript. All authors read, contributed and approved the final manuscript.


Genome-wide genotype data from 48 carefully selected population samples of Transylvania-living Szeklers and non-Szekler Hungarians were analyzed by comparative analysis. Our analyses involved contemporary Hungarians living in Hungary, other Europeans, and Eurasian samples counting 530 individuals altogether. The source of the Szekler samples was the commune of Korond, Transylvania. The analyzed non-Szekler Hungarian samples were collected from villages with a history dating back to the era of the Árpád Dynasty. Population structure by principal component analysis and ancestry analysis also revealed a great within-group similarity of the analyzed Szeklers and non-Szekler Transylvanian Hungarians. These groups also showed similar genetic patterns with each other. Haplotype analyses using identity-by-descent segment discovering tools showed that average pairwise identity-by-descent sharing is similar in the investigated populations, but the Korond Szekler samples had higher average sharing with the Hungarians from Hungary than non-Szekler Transylvanian Hungarians. Average sharing results showed that both groups are isolated compared to other Europeans, and pointed out that the non-Szekler Transylvanian Hungarian inhabitants of the investigated Árpád Age villages are more isolated than investigated Szeklers from Korond. This was confirmed by our autozygosity analysis as well. Identity-by-descent segment analyses and 4-population tests also confirmed that these Hungarian-speaking Transylvanian ethnic groups are strongly related to Hungarians living in Hungary. Transylvania is a historical region located in today’s Romania, which is bordered on the east and south by the Carpathian Mountains and the Apuseni Mountains on the west. From the 1st century BC until the 2nd century AD, it was the center of the Kingdom of Dacia. Transylvania was under the control of various tribes residing in the Carpathian basin between the 3rd and 9th centuries AD. Right before it went under Hungarian control, Transylvania was ruled by the Bulgars, a Slavic ethnic group of the Balkans. After Hungarians conquered the Carpathian basin, it became under partial Hungarian control in 1003 AD . The setThe Szeklers are a Hungarian-speaking, Catholic, Hungarian ethnic group. They declared themselves as a special cultural entity with special origin and history. The first written evidence of Szeklers originates from the 12th century, 1116 AD, and traditionally they derive themselves from the Huns . The terKorond is a Szekler commune comprising five villages with the administrative central village named Korond. It lies in Hargitha County, Transylvania, Romania, deep in the heart of the Székely Land, and its roots go back with the history of the Szeklers to the 12th century. Nowadays, the Szeklers are one of the largest minorities of Europe, from which the majority, 500–700,000 people are living in the Székely Land, Transylvania . Korond Here we analyzed 24 Szekler samples collected from Korond (in Romanian: Corund), and 24 samples from people identifying themselves as non-Szekler Hungarians living in Transylvanian villages with known history from the age of the Árpád Dynasty (1000–1301 AD). We used the high-density Illumina Infinium Global Screening Array v.3.0 platform to genotype the DNA samples collected from systematically selected individuals and extracted from whole blood samples. We conducted allele frequency correlation-based population stratification, ancestry estimation, and admixture analysis methods as well as haplotype-based ancestry source and homozygosity-by-descent analysis to lay down the foundations of the characterization of Transylvanian Hungarian ethnicities based on genome-wide autosomal marker data. The aim of this study was to assess the extent of isolation of these ethnic groups in order to characterize the population and also to determine the accuracy of our sampling strategy. We also investigated their relationship to contemporary Hungarians of Hungary.n = 5), Marosjára , and Magyaró .Samples from 24 Szekler individuals living in the Transylvanian village of Korond (Transylvania-living Szeklers—TLS) and another 24 samples obtained from Transylvanian villages (Transylvania-living Hungarians—TLH), having documented history since the age of the Árpád Dynasty (1000–1301 AD), were involved in this study. EDTA-anticoagulated whole blood samples were collected from the participants and served as the source of DNA. The samples were stored below −30°C until DNA isolation and genotyping. Sample collection was based on self-declaration, and was carried out by local community leaders and local pastors. The participants were unrelated, and both genders were represented approximately equally. Their age group was 40–80 years old. Samples were collected exclusively from those persons who could declare with high fidelity that they are Szeklers, or Transylvanian non-Szekler Hungarians. All subjects have documented records back to at least two generations, the parents and grandparents of each participant belong to the same ethnic group and were born in the same area as the respective study participant. The Szekler participants were from Korond, while the non-Szekler Hungarians derived from the Szászrégen  area, Maros (Mureş) county. Within a radius of 15 km, the following villages were the sources of the non-Szekler Hungarian samples: Disznajó , Fickópataka  were also removed from the data. After the preparation procedures, 665,073 SNPs remained in the dataset.A total of 48 samples of Hungarian-speaking ethnic groups from Transylvania were genotyped on the Illumina Infinium Global Screening Array platform . DNA isolation, genotyping, and preliminary quality control were carried out by a third-party service provider (Human Genomics Facility) on the University of Rotterdam in the Netherlands. Additional quality control and data preparation for population genetic analyses were performed domestically applying PLINK1.9 and 2.0 software packages . IlluminThe participants gave their written informed consent to participate in this study. They all got personal verbal information prior to their signed consent. All samples were anonymized. The study belongs to a series of investigations that—along with the consent form—were approved by the National Ethics Board (ETT TUKEB), and by Regional Ethics Committee of Pécs. The research was conducted according to the principles expressed in the Declaration of Helsinki.n = 29), Orcadians (n = 16), French Basques (n = 24), North Italians (n = 13), Sardinians (n = 28), Tuscans (n = 8), and Russians (n = 25). A population from the Caucasus, the Adygei (n = 17) was also used. South Asian populations were the Balochi (n = 23), Brahui (n = 25), Burusho (n = 25), Hazara (n = 16), Kalash (n = 23), Makrani (n = 23), Pashtun (n = 23), and Sindhi (n = 23). We used the Uyghurs (n = 10), the Han Chinese (n = 44), and the Yoruba samples (n = 19) from the HGDP dataset as outgroups. From the data of the Estonian Biocentre, we selected Hungarian samples (n = 20) representing the Hungarians living in today’s Hungary (we will refer to them simply as Hungarians from now on) and Romanians (n = 16) of contemporary Romania. German samples were also selected from the Estonian data. All the data were tested in preliminary population structure and ancestry analyses such as PCA and ADMIXTURE. German data were filtered according to preliminary PCA, since we discovered that some of the German samples are outliers possessing significant non-West European genetic ancestry. After filtering, 10 German samples remained.In addition to our data, we also used genome-wide autosomal marker data of populations from the Human Genome Diversity Project whole genomes to increase overlap (genotyped on Illumina 650Y platform) and also considered two datasets (genotyped on Illumina 610K platforms) obtained from the open genotype database of the Estonian Biocentre detailed in 2 threshold was set to 0.3, all other parameters were left at their default setting. After the pruning process, 80,056 SNPs were present in the data. We run SMARTPCA with its default settings. Sigma threshold of outlier samples was set to 6.0 and the option “altnormstyle” was set to enabled, which uses the normalization formula presented in st matrix containing the pairwise average allele frequency differentiation values between the analyzed populations. Therefore, Fst calculations were also carried out with the SMARTPCA algorithm.Population structure analysis was carried out with the SMARTPCA software, which is part of the EIGENSOFT 6.01 software package . In thesAncestry estimation was performed using ADMIXTURE 1.22 software package . We calcn = 578; 110,734 SNPs). Genotyping data in binary PLINK format was converted to VCF format using the PLINK/SEQ algorithm (DNA segments of interest were identified using Refined IBD 1.02 . Both idlgorithm . RefinedAverage pairwise IBD sharing was calculated from the output of refined IBD according to the method applied in ij is the length of the IBD segment shared between the individuals i and j from populations I and J, and n, m are the number of individuals in populations I and J.In the equation, IBDHBD segment counts were also calculated from the output data of the same refined IBD run. Length and number of shared IBD segments and HBD segments were investigated by calculating averages and statistics in the form of boxplots.Four-Population tests were applied to check if the relationship of investigated Hungarian-speaking groups, the Szeklers from Korond and Transylvanian Hungarians with Hungarians are well detectable. The purpose of this method was to test the assumed connection of TLH and TLS to Hungarians. It was carried out with the 4-population test algorithm of ADMIXTOOLS 4.1, which is implemented in this software package as D-statistics . In thisBoth PCA and ADMIXTURE analysis showed that the investigated Szeklers and Transylvanian Hungarians were relatively homogeneous and clustered tightly together and close to Hungarians and Romanians . These pThe investigated populations are clustered between South and East Europe with Hungarians and Romanians mirroring their actual geographical positions. Europe and the Caucasus are well separated from South Asian populations. Some of the South Asian ethnicities split into two groups depending on whether they have significant East Asian or African derived ancestry components. In this investigation, Han Chinese represented the Eastern component and Yoruba represented the African component, which are clearly separated from each other and from European and South Asian populations. On the contrary, Europeans do not show such a tendency. Han Chinese and Yoruba groups were not incorporated on the PCA graphs due to the large plotting distances, however, the complete PCA can be found in the st calculations also show that investigated populations are close to each other, with neighboring populations, and with Germans. This observed similarity with Germans might be formed through their admixture with the Transylvanian Saxon population and in the case of Hungarians with direct admixture with Swabian immigrants (st with geographically isolated groups like Orcadians and Sardinians and genetically isolated populations like French Basques. Fst values also reflect the geographical distances, since Adygei samples have a relatively high Fst with the four groups, the TLH, TLS, Hungarians, and Romanians. It can also be observed that TLH samples have the highest Fst with all European groups compared to TLS, Hungarians, and Romanians. Fst values of TLS is most similar to those of Romanians. Hungarians—along with TLH—tend to have lower Fst with West Europeans, Romanians have lower Fst with East and South Europeans. According to the average pairwise allele frequency differentiation values, it looks like that TLH is most similar to Hungarians in terms of Fst differences considering the various European populations. They share a same pattern and are just farther away from them genetically than Hungarians.Fmigrants . It can The cross-validation error of the ADMIXTURE analysis was the lowest at K = 5 hypothetical ancestral groups . The anaAccording to the average pairwise IBD sharing results, the relationship of TLS and TLH to Hungarians of contemporary Hungary is varied. TLS and TLH both show similarities to Hungarians in certain aspects. Our investigation showed that non-Szekler Transylvanian Hungarians have more similarity to Hungarians in the case of ancestry derived from West Europe. TLS, on the other hand, shares more similarities in South European-derived ancestry with Hungarians. Hungarians have a much more significant East European ancestry component than either TLS or TLH . These cAverage sharing results show that Hungarians are somewhat closer to TLS than TLH. We know that Hungarians have been admixed heavily with immigrating populations from the age of the Árpád Dynasty until today. This might also suggest that TLH is a somewhat more isolated population and is less affected by admixture with other Transylvanian ethnic groups. Examinations regarding the average length of IBD segments shared between pairs of individuals do not show any recognizable pattern. Analyzing the average number of IBD segments between pairs of individuals pointed out the similarity of Hungarians, TLS, and TLH. However, it also shows that populations in the Transylvanian region, including Romanians, are in general less admixed with West Europeans and also with Russians .According to the HBD segment analysis, as expected, French Basques, Sardinians, and Orcadians showed the highest extent of autozygosity indicating their strongly isolated nature. Szeklers and Transylvanian Hungarians show a higher autozygosity relative to most European populations, therefore, also higher than the autozigosity observed in Hungarians and Romanians , which cThe 4-population tests revealed that TLS and TLH are strongly related to contemporary Hungarians living in Hungary despite the many admixture events Hungarians went through during the centuries. The results were similar in all three cases applying the three distinct ethnic groups as outgroup populations. Z-scores showed significant negative values indicating that W,Z or X,Y groups are related to each other on the hypothetical unrooted phylogenetic tree . In our Population structure analysis by PCA and ancestry estimation with ADMIXTURE suggested that TLH and TLS groups are homogeneous, and they showed similar genetic ancestry. Placing these populations in a Eurasian perspective, they were shown to be a well-defined, tightly clustered group, suggesting no signs of recent admixture with populations from other regions considered in these tests.The DNA segment analyses using identity-by-descent segment discovering tools pointed out that average pairwise IBD sharing results are similar in TLS, TLH, Hungarians, and Romanians. However, TLS samples had a higher average sharing with the Hungarians. Average sharing results also showed that the investigated Szeklers from Korond might be somewhat less isolated than the inhabitants of Árpád Age villages. This was confirmed by our autozygosity analysis as well. Our IBD calculations pointed out that Romanians differ from Hungarians, TLH, and TLS in the number of detected IBD segments between pairs of individuals, suggesting that Hungarians, TLH, and TLS have a common history. Although this tendency is much more significant in the case of TLH, they also show similar tendencies than of Hungarians regarding the relationship with West Europeans. This has been also indicated earlier by the fixation index values. Our investigations of haplotype-based DNA segment analysis concluded that these two areas, the commune of Korond and the people of the surrounding villages of Szászrégen, are noticeably isolated compared to most European populations, which could have helped to retain the strong relationship with the people of contemporary Hungary even though the contemporary Hungarian population is admixed, showing strong sources of ancestry from both West and East Europe. The source of West European ancestry might be primarily the Germans through the Swabian immigration mentioned before.The 4-population tests also showed that these Hungarian-speaking ethnic groups, the Szeklers from Korond and Transylvanian Hungarians, are strongly related to Hungarians living in Hungary.Earlier genetic studies involving Hungarians and Hungarian-speaking ethnicities outside of contemporary Hungary are based on Y chromosomal and mitochondrial DNA haplogroup analyses and gene frequency analysis of blood groups and red cell enzymes coding nuclear loci . The resIn summary, we can conclude that Szeklers from the area of Korond and non-Szekler Transylvanian Hungarians are homogeneous, and more isolated than the continental population of Europe, especially in the case of the Western Europeans. Our analyses show that their genetic connection to Hungarians is still well detectable not only using methods based on allele frequency calculations, but also results of haplotype-based methods suggest this conclusion.Our study is the first genome-wide autosomal marker-based analysis of Szekler population samples, and of samples from Transylvania living non-Szekler Hungarian population living in settlements established in the age of the Árpád Dynasty. With our investigation, we described the genetic makeup of Szeklers, assessed their degree of isolation, and found that they can be separated from Transylvanian Hungarians using genome-wide data. We also described their connection with Hungarians, Romanians, and European populations. Subsequent research should aim at the study of their assumed Central Asian and East Asian Turkic ancestry.