Aedes albopictus in China. Application of insecticides is one of the main mosquito control methods; however, this approach can fail due to the knockdown resistance (kdr) gene mutation that causes decreased sensitivity to insecticides in Ae. albopictus. The kdr mutation patterns among different regions in China differ significantly. However, the underlying mechanism and factors that influence kdr mutation remain unclear. To explore the potential influence of genetic background on the development of insecticide resistance in Ae. albopictus, we analyzed the genetic structure of Ae. albopictus populations in China and its correlation with major kdr mutations.Mosquito control is needed to prevent dengue fever, which is mainly spread by Ae. albopictus from 17 sites in 11 provinces  across China from 2016 to 2021 and extracted the genomic DNA from individual adult mosquitoes. We selected eight microsatellite loci for genotyping, and based on microsatellite scores, we estimated intraspecific genetic diversity, population structure, and effective population size. The association between the intrapopulation genetic variation and F1534 mutation rate was evaluated by the Pearson correlation coefficient.We collected Ae. albopictus are highly polymorphic. The northern populations tended to belong to gene pool I , the eastern populations tended to belong to pool III , and the southern populations tended to belong to three different gene pools. Moreover, we observed that the greater the fixation index (FST), the lower the wild-type frequency of F1534 of VSGC.Based on variation analysis of the microsatellite loci of 453 mosquitoes representing 17 populations throughout China, more than 90% of the variation occurred within individuals, whereas only about 9% of the variation occurred among populations, indicating that field populations of Ae. albopictus populations in China was low. These populations were divided into three gene pools, in which the northern and eastern pools are relatively homogeneous, while the southern gene pool is heterogeneous. The possible correlation between its genetic variations and kdr mutations is also noteworthy.The degree of genetic differentiation among The online version contains supplementary material available at 10.1186/s40249-023-01096-x. Aedes albopictus is widely distributed in China, where it can be found from Liaoning Province in the north to Hainan Province in the south, an area that includes tropical, subtropical, and temperate climate zones  and ΔK across multiple runs [Ne) was estimated using NEESTIMATOR 2.1 [The relationships among populations were assessed using Principal Coordinate Analysis in GenAlEx 6.5 . Bayesiaple runs  as impleple runs . Finallyple runs  and DISTple runs  were useATOR 2.1  based onFST value, representing the intrapopulation genetic variation, was estimated by GENEPOP V. 4.2[kdr mutation in Ae. albopictus in China, and it is significantly associated with the pyrethroid resistance phenotype [ FST within 14 Ae. albopictus populations were evaluated by Pearson correlation analysis. The linear regression was implemented using the “ggpmisc” and “ggplot2” package of R 4.2.2 .The OP V. 4.2. Mutant henotype . The frehenotype , which sThe scores obtained from eight microsatellite loci in 453 mosquitoes collected from 17 localities across China, displayed different levels of polymorphism in terms of the number of alleles, which ranged from 7 AealbB52) to 27  alleles. The number of alleles per locus in each population is provided in Additional file  to 27 . Private alleles were detected in all populations, except for the SH, JZHZ, GDST, and SXYC populations, with the highest proportion of private alleles found in HNSS (np/n = 0.31). Analysis of the variability distribution in and among the populations (AMOVA) showed that most variation (91%) occurred within individuals, whereas only about 9% of the total variation was detected among populations, indicating that the field populations of Ae. albopictus are highly polymorphic.These eight loci were considerably variable across the 17 populations (Table n = 10). Therefore, a total of 443 individuals in 16 populations were analyzed for population genetic diversity. Pairwise comparisons indicate FST values were significantly different from zero, with the lowest value (0.012) found between SH with JZHZ, while the highest value (0.167) was between GDST with STWS (Table FST) and geographical distance, which was confirmed by an R2 value of 0.029 (P = 0.785). All FST values within the population were greater than 0.1, except for that of GDST (0.087), with the range level from 0.271 (HNSS) to 0.582 (SH)  were calculated. The FST values between cluster I and clusters II and III were 0.110 and 0.114, respectively, while that between clusters II and III was 0.027. The FST values within the three clusters were 0.124, 0.048, and 0.080, respectively.Results of Bayesian analysis implemented in STRUCTURE show that the proportion of individuals sharing coancestry in the 16 populations was not consistent with their geographic proximity. The natural logarithm of the likelihood of the data, i.e., ln (P(X/K)), increased from The coancestry of the populations in southern China were heterogeneous: i.e., populations in Hainan were tightly related to cluster II (green)  and cluster III (blue) (HNHK 57.7%), while populations in Guangdong were mainly related to cluster III (blue) (GDST 0.928) and cluster I (red) . Populations in the eastern part of China were weakly related to cluster III , and populations in the northern part of China were mainly related to cluster I  , indicating no significant correlation. However, we did observe the following trend: the greater the genetic difference (FST), the lower the wild-type frequency of F1534 , and the population had the lowest FST (0.087). Meanwhile, the ZJHZ population had the highest (91.55%) F1534 mutant allele frequency, and its FST value was also large (0.516)  Table .Ae. albopictus has long been considered of secondary importance as a vector for dengue transmission. However, this species is the most important vector in China because it is widespread, while the distribution of the typical primary vector, Ae. aegypti, is limited. Moreover, recent dengue outbreaks in China have been caused by Ae. albopictus [bopictus . TherefoAe. albopictus collected from the different climate regions in China based on the 8 polymorphic microsatellite loci. Most (91%) of the genetic variation occurred within individuals, whereas only about 9% of the total variation was detected among populations. We found no relationship between genetic distance and geographical distance, and populations in the southern part of China tended to be more heterogeneous. Within a population, we observed that the greater the genetic difference (FST), the lower the wild-type frequency of F1534 of VSGC.In this study, we analyzed the population genetics of Ae. albopictus mosquitoes were collected from 17 sites in 11 provinces , encompassing the main distribution range of Ae. albopictus in China , which is similar to the levels of diversity in the Ae. albopictus population at the coastal areas in southern China (HO = 0.384–0.641) [HO = 0.467–0.627) [HO = 0.551–0.633) [HO = 0.22–0.28). In China, Ae. albopictus occurs in different climate zones, with populations undergoing marked seasonal variations in abundance, in which high densities are reached only during the summer months. The high levels of genetic diversity suggest that Ae. albopictus is able to maintain a relatively large effective population size, despite the seasonal changes in temperature.We observed high allelic diversity and heterozygosity in most of the populations (4–0.641) , in 17 p7–0.627) , in popu1–0.633) , and in FST value (0.087) in the GDST population, which carried no mutation in F1534 [Ae. albopictus. This population became abundant and because the time of colonization time may be recent or it may be derived from a single invasion, the genetic difference within the population is small, with 92.8% of the individuals belonging to cluster III. These attributes are consistent with the founder effect. Oral interviews revealed that almost no insecticides have been used in the GDST area, thus the alleles of F1534 are all wild-type. Similarly, HNSS was collected from a tropical island far from the Chinese mainland and with no source of fresh water. This population is likely to have been transported to the island by ships from Hainan Island, thus both the HNSS and HNSY populations belong to cluster II.Notably, we observed the minimum value 0.07 in the Ae. albopictus populations are divided into three clusters. Each cluster could be considered as a gene pool, the northern Chinese Ae. albopictus populations belong to gene pool I while the eastern populations belong to gene pool III. Meanwhile, the southern populations consist of three different gene pools. The genetic differences among the three gene pools are small. The FST values between cluster I and clusters II and III were 0.110 and 0.114, respectively, while that between clusters II and III was 0.027.The results of STRUCTURE analysis show that these Ae. albopictus is also known as the Asian tiger mosquito, and it has been described as one of the 100 worst invasive species in the world. The species originated from South and East Asia, and it has spread throughout the world mostly since the second half of the twentieth century. It is now found on every continent except Antarctica [Ae. albopictus include disparate environments such as tropical South America, Africa, and the most temperate areas of Northern America and Europe. In China, the species likely invaded the south first, colonizing the tropical and subtropical regions. From these regions, Ae. albopictus can colonize new areas and expand its population. In addition, Ae. albopictus populations from other places may have been able to invade many times; therefore, the genetic pools of the population in the southwest and south are relatively complex, as shown by STRUCTURE analysis.tarctica . Areas tAe. albopictus in China indicates that the southern population of Ae. albopictus was introduced into the eastern and northern (the temperate regions) regions through the movement of people and goods. This describes a chaotic propagule distribution mediated by human activity, and it resulted in a relatively simple gene pool. Thus, Ae. albopictus populations in eastern and northern China should be temperate-diapausing, and there was sufficient gene flow with tropical nondiapausing populations. In particular, the photoperiodical diapause, which has a demonstrated genetic basis, seems to be an important component of climatic adaptation that favors the invasive success of Ae. albopictus. Conversely, anthropogenic activities also create new breeding and trophic niches of adaptation in close proximity to human living sites, which impacts the relationship between mosquitoes and humans.Analysis of the likely migration route of kdr mutation and genetic structure. However, we did observe that low FST is associated with more wild-type, while high FST is associated with less wild-type. The mechanism of kdr mutation, which is closely related to insecticide resistance, is a very complex issue. This intrinsic interaction may be influenced by other important influencing factors, including climate variations, geographic distance, and the properties of insecticides used for mosquito control in different regions. Therefore, understanding the relationship between genetic structure and kdr mutations requires careful analysis, with consideration of case-specific factors. To clarify this issue, more samples need to be collected over a wider geographical area, ideally over multiple years, along with insecticide usage history. Perhaps in the future, more definite conclusions about the mechanism of kdr evolution in Ae. albopictus populations with different genetic backgrounds can be compared using a laboratory model under artificial insecticide pressure.Pearson correlation analysis found no correlation between Ae. albopictus in the future to obtain more population data that can be used to further explore the correlation between resistance mutations and genetic background.The main limitation of this study is the paucity of samples considering the large geographic area of China. Thus, the sampling areas should be expanded to obtain more detailed data. Therefore, we will continue to collect large numbers of samples of Ae. albopictus populations in China, and we found no relationship between genetic and geographical distance. These populations consist of three gene pools. The gene pools of the northern and eastern Chinese populations were relatively homogeneous, while the southern populations were heterogeneous. Greater genetic difference within a population tended to be associated with a higher mutation rate of knockdown resistance, which deserves further attention and exploration.We observed a very low degree of genetic divergence among Additional file 1: Figure S1. Bayesian cluster analysis using STUCTURE. Graphical representation of the data set for the most likely K, where each color corresponds to a suggested cluster and each individual is represented by a vertical bar. The X-axis corresponds to the population codes, while the Y-axis represents the probability of assignment of an individual to each cluster. Figure S2. The collection sites for Aedes albopictus in China. The composition of the three clusters in each site is represented by a pie chart, with red, green, and blue representing clusters I, II, and III, respectively. The populations in the dotted circle belong to different clusters. Figure S3. Scatter plot of the Pearson correlation between F1534 wild-type frequency and FST value within 14 Ae. albopictus populations. Table S1. Sampling information of Aedes albopictus populations in China. Table S2. Primer sequences of 8 microsatellite loci of Aedes albopictus. Table S3. Number of alleles per population at each microsatellite locus. Table S4. Average coefficient of ancestry obtained from a STRUCTURE run with K = 3 for 443 individuals of Aedes albopictus from 16 samples collected in China.


EPAS1 allele was found in plateau populations as early as in a 5100-year-old individual and showed a sharp increase over the past 2800 years.Using genome-wide data of 89 ancient individuals dated to 5100 to 100 years before the present (B.P.) from 29 sites across the Tibetan Plateau, we found plateau-specific ancestry across plateau populations, with substantial genetic structure indicating high differentiation before 2500 B.P. Northeastern plateau populations rapidly showed admixture associated with millet farmers by 4700 B.P. in the Gonghe Basin. High genetic similarity on the southern and southwestern plateau showed population expansion along the Yarlung Tsangpo River since 3400 years ago. Central and southeastern plateau populations revealed extensive genetic admixture within the plateau historically, with substantial ancestry related to that found in southern and southwestern plateau populations. Over the past ~700 years, substantial gene flow from lowland East Asia further shaped the genetic landscape of present-day plateau populations. The high-altitude adaptive  Ancient human DNA from the last 5,100 years provides insight into Tibetan ancestry and population dynamics on the plateau. The Tibetan Plateau, with elevations often exceeding 4000 m above sea level (masl), is one of the most inhospitable places ever inhabited by humans. Archaic humans known as Denisovans were present on the northeastern regions of the plateau by at least 160,000 years , which was conquered by the Tibetan Empire that spanned from ~1300 to 1100 years ago (629 to 842 CE)  for 97 ancient human specimens from 30 archaeological sites across the Tibetan Plateau with elevations ranging from 2776 to 5000 masl    < 0 . In an outgroup f3 analysis comparing to diverse lowland ancient Asian individuals, the oldest plateau individual sampled (Zongri5.1k) shared the highest genetic drift with other ancient individuals from the plateau, followed by ancient northern East Asians (fig. S17). These patterns support that early humans on the Tibetan Plateau dating from 5100 to 2500 years ago are closely related to ancient northern East Asians.To evaluate the genetic origins of Tibetan Plateau populations, we next focused on the earliest plateau individuals sampled to date , whose locations span the full range of Tibetan Plateau from the Himalayan arc on the west to the Gonghe Basin on the northeast . To detef4 analysis helps to establish that despite deeply diverged ancestry making phylogenetic analysis and qpAdm modeling difficult, there is substantial support for a closer genetic relationship between Early Ancient Tibetans than with 19,000- to 12,000-year-old northern and southern East Asians, i.e., most f4  > 0 (table S9A). Overall, we find that plateau-related ancestry was probably formed through the mixture of a major source related to ancient northern East Asians and a minor source from a “ghost” population not yet sampled  relative to Early Ancient Tibetans (table S8). Moreover, in a rotating qpAdm analysis allowing potential sources including the Lajia population, the previously published 3400-year-old Lubrak individuals from the southern plateau region, the 5100-year-old Zongri individual, and a diverse array of ancient northern East Asians, we found that most Early Ancient Tibetans can be described as solely or primarily ancestry related to the Lubrak or Zongri individuals (table S15), and most reject Lajia4k as a potential source (table S15). These patterns show that Early Ancient Tibetans carry a unique ancestry distinct from that observed in populations from the upper reaches of the Yellow River. We denote this as Tibetan ancestry.The 4200-year-old individuals from the Lajia archaeological site who lived in the Upper Yellow River Valley were close neighbors of the northeastern plateau region, and the Lajia population was thought to be important in the formation of plateau-related ancestry . In an fFST analysis  and outgroup f3 statistics , which suggests that the population structure occurred fairly soon after their separation from other northern East Asians. We did not observe a significant correlation between longitude and genetic relationships to lowland East Asians in Early Ancient Tibetans , which was observed in present-day Tibetans . In an ADMIXTURE  ~ 0 . These patterns suggest that while migration from nonlocal populations greatly affected populations who lived in the Gonghe Basin such as those who lived at the Zongri site, these external impacts did not reach the neighboring, higher-altitude region of the Tibetan Plateau in the Yushu prefecture by 2800 B.P.The other members of the northeast cluster derive from the Pukagongma site, located 300 km west of Zongri at a higher elevation region in the Yushu prefecture of the Tibetan Plateau. Unlike the younger Zongri individuals (4700 to 3900 B.P.), the 2800-year-old Pukagongma individuals from Yushu share a similar number of alleles to Neolithic northern East Asians, i.e., P > 0.06, chi-square test; table S16). These results show that they all harbor a homogenous southern plateau ancestry, which is probably underpinned by the spread of an ancestral population carrying southern ancestry who possibly traveled upstream from the east to the west along the river valley before 3400 B.P.The Yarlung Tsangpo River . The Yarf3 analysis, they shared the highest genetic similarity with 2600- to 2200-year-old individuals from the Shannan and Shigatse prefectures (fig. S22). In a proximal qpAdm analysis, they are a mixture of southern (37 to 47%) and southeastern (53 to 63%) plateau ancestries . In the Ngari prefecture on the western plateau, the oldest individuals date to 2300 B.P. and were excavated from the Piyangjiweng archaeological site. In a phylogenetic analysis, they form a clade with individuals belonging to the south-southwest cluster , consistent with a phylogenetic analysis supporting that they form a clade with local Early Ancient Tibetans  in a qpWave analysis. In a proximal qpAdm analysis, they carry 55 to 100% southern plateau ancestry related to Lubrak  > 0  share the highest genetic drift with preceding populations from the region (fig. S24), suggesting population continuity. However, a closer examination shows that fluctuations in genetic ancestry did occur during this period. First, on the southern Tibetan Plateau, 2200- to 2100-year-old individuals share more alleles with 1300- to 1200-year-old individuals than with 1100- to 700-year-old individuals, i.e., f4  > 0 . After 1600 B.P., however, all ancient individuals from the Nagqu prefecture, including Nagqu1.6k (n = 1), Nagqu1.4k (n = 3), and Nagqu1.1k (n = 1), share the most genetic drift with ancient individuals from the south-southwest cluster who mainly carries southern plateau ancestry (fig. S23). In an f4 analysis, the 1600- to 1400-year-old individuals from the Nagqu prefecture share more alleles with ancient individuals from the south-southwest cluster than those from the southeast-central cluster, i.e., f4  > 0 . In phylogenetic and f4 analyses, these individuals fall entirely within the diversity of Early Ancient Tibetans from the south-southwest cluster, rather than grouping with the southeast-central cluster  than with other ancient plateau individuals, i.e., f4  < 0 . In ADMIXTURE and admixture f3 analyses, the Piyangjiweng individuals can be described as a mixture of ancestry related to ancient plateau populations and Bronze Age Central Asians . Inl Asians . In a qpf4  > 0 . These observations suggest that they also carry ancestry found in ancient Central Asians. Today, the Sherpa from the southwestern Tibetan Plateau harbors more Central Asian–related ancestry (black component) than present-day Tibetans (fig. S14), and in f4 analyses, they also tend to share more alleles with Bronze Age Central Asians, i.e., f4  > 0 . These patterns show that populations carrying Central Asian–related ancestry have also contributed to humans in the southern and southwestern regions as early as 1500 B.P.Central Asian–related influences can also be observed in the southern and southwestern Tibetan Plateau. An individual who dates to 1520 to 1363 B.P. (Shigatse1.5k_1) from the Zhangcun archaeological site shows distinct genetic patterns than other 2200- to 700-year-old individuals sampled from this region. In a PCA, this individual is shifted toward the Indian population cline , and in EPAS1) that introgressed into modern humans from archaic humans known as Denisovans . The adaptive haplotype is detected in 11 of the 17 Zongri individuals (4800 to 3900 years old), with each having one copy of the adaptive haplotype (table S21), showing that the adaptive haplotype was at fairly high frequency during the early human occupation of the northeastern plateau.Present-day Tibetans harbor a unique haplotype of the endothelial Pas domain protein 1 gene  for the 2400- to 1900-year-old plateau individuals, and 0.59  for the more recent 1600- to 700-year-old plateau individuals, showing an increasing trend from 3000 to 700 B.P. Nevertheless, all of these estimated proportions are much lower than that observed in present-day Tibetans  from the same vast region (table S22) marking a substantial increase in the EPAS1 haplotype frequency over the past 3000 years and a sharp increase over the past 700 years. This pattern shows no relationship to population shifts on the Tibetan Plateau, where despite local shifts within the plateau and influences from nonplateau regions, there is high genetic continuity into the present day. In comparison to randomly chosen SNPs across the genome, the haplotype frequency increased significantly , suggesting that it was driven by positive selection on this locus rather than demographic events.In the vast region encompassing the central, southeastern, southern, and southwestern plateau, one copy of the adaptive haplotype is detected in 11 of 16 individuals dated to 3500 to 2500 B.P. . The estIn this study, we show that the unique ancestry in present-day plateau populations can be found in ancient individuals across the entire Tibetan Plateau, extending as far back as 5100 B.P. We found that this plateau ancestry was formed through a mixture of a minor source from a deeply diverged population and a major source related to ancient northern East Asians, consistent with previous observations in 3400- to 1200-year-old individuals from the Himalayan arc in Nepal. The minor source is attributable to a ghost population that is deeply diverged from available Asian lineages. Whether this deeply diverged source is related to Paleolithic populations on Tibetan Plateau remains to be determined by future ancient DNA studies. We also note that this must have occurred before 5100 B.P., as the oldest individual sampled to date from the plateau similarly shows this unique ancestral pattern. The major source contributing to the plateau ancestry is related to diverse 9500- to 4000-year-old populations from northern East Asia spanning the Yellow River region and Inner Mongolia. This period overlaps with the Early to Middle Neolithic in northern China, which was characterized by substantial human population growth , a southeast-central cluster spanning the Nagqu and Chamdo prefectures of Tibet Autonomous Region (southeastern plateau ancestry), and a south-southwest cluster spanning the Shigatse, Shannan, and Lhasa prefectures of Tibet Autonomous Region and the Himalayan arc of Nepal (southern plateau ancestry). This pattern shows that Tibetan ancestry is highly structured, with enough genetic differentiation to distinguish between ancient plateau individuals ranging from 5100 to 2500 B.P. This suggests distinct histories associated with each of these different clusters. Notably, the southern plateau ancestry has a surprisingly large geographic span along the Yarlung Tsangpo River, suggesting that the Yarlung Tsangpo River valley was an important corridor for prehistoric human migration on the Tibetan Plateau.In the past three millennia, the Tibetan Plateau was home to several powerful polities. Archaeological and historical studies indicate that an early complex society called the Zhangzhung likely formed around 2500 B.P. in what is now the Ngari prefecture  at the Institute of Vertebrate Paleontology and Paleoanthropology of the Chinese Academy of Sciences provided approval and oversight, allowing us to sample the genomes of the ancient humans included in this study.The sequenced ancient individuals from this study span across the Tibet Autonomous Region of China (Xizang), covering all seven prefecture-level divisions, i.e., Ngari, Shigatse, Lhasa, Shannan, Nagqu, Nyingchi, and Chamdo (fig. S1). Samples were also obtained from the Yushu and Hainan prefectures in the Qinghai province of China (fig. S1), which are both considered part of the Tibetan Plateau geographically and are mainly inhabited by Tibetans today. Throughout the study, we refer to the Tibetan Plateau by six subregions based on geographic locations . All the samples were prepared in a dedicated ancient DNA laboratory at the Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, in Beijing, China. We extracted ancient DNA using an optimized protocol  and genome-wide nuclear DNA by hybridizing the libraries with oligonucleotide probes . The mtDNA probes were developed to capture the complete mitochondrial genome (https://github.com/grenaud/leeHom) (All mtDNA libraries were sequenced using the Illumina MiSeq and HiSeq 2500 sequencing machines to generate 2 × 76–base pair (bp) paired-end reads, and all nuclear DNA libraries were sequenced using the Illumina HiSeq 2500 and HiSeq X sequencing machine to generate 2 × 100– and 2 × 150–bp paired-end reads. We trimmed adaptors and merged forward and reverse sequences into a single sequence using leeHom . Second, we used ContamMix  extraction were used for C14 dating. The dates were calibrated with OxCal v.4.4 using the IntCal20 . If the most derived Y-chromosome SNP upstream was a C → T or G → A substitution, a substitution susceptible to ancient DNA damage, then we required at least two derived SNPs to assign the Y-chromosome haplogroup. Otherwise, we assigned the individual to the upstream haplogroup. When a more detailed haplogroup assignment could not be determined, the individual was assigned to the ancestral haplogroup they best fit .The mitochondrial haplotypes were determined for each individual based on BAM files mapped to the rCRS (https://github.com/COMBINE-lab/maximum-likelihood-relatedness-estimation), a tool that infers kinship using genotype likelihoods. The method takes into account the uncertainty in genotype calling when sequence coverage is low, which is common for ancient DNA samples. lcMLkin estimates the probability a pair of individuals are identical by descent (IBD), and it outputs the estimated probability that two diploid individuals share zero (k ^_0), one (k ^_1) or two (k ^_2) alleles at a given IBD loci, using this to determine the kinship coefficient (π ^). The expected values for k0, k1, and k2 depend on the familial relationship [see table 1 of  (We used the smartpca program (version16000) in EIGENSOFT  errors, we ran ADMIXTURE with K varying from 2 to 14 (fig. S14), and for each K, we ran 10 replicates using random seeds (table S5). The replicate of each K with the lowest CV error was plotted.The unsupervised structure analysis was performed using ADMIXTURE (version 1.3.0)  from the EIGENSOFT package . The parameter inbreed: YES was used when the target populations were represented by pseudo-haploid ancient DNA. The software qpDstat (v712) was used to calculate the f4 statistics by turning on the “f4mode: YES” option. Autosomal SNPs from the 1240k dataset were used unless mentioned otherwise.The software qp3Pop (v412) from the ADMIXTOOLS package  where X1 and X2 are individuals from the same test site and Z are individuals from a reference panel. The reference panel consists of populations that are temporally or geographically relevant to the studied ancient Tibetan Plateau individuals, including Tianyuan, UstIshim, Onge, Gonur1_BA, Afanasievo, Shamanka_EN, AR19K, Yumin, Boshan, YR_MN, Lajia4k, Qihe3, and Atayal. To increase the power, we also added five representative, high-coverage individuals across the Tibetan Plateau to the panel, including C4783_C202 from Zongri, C1 from Chokhopani, C3430 from Luozhating, CSP142 from Redilong, and CSP136 from Pukagongma. Individuals causing rejection of f4 ~ 0 were isolated and assigned to a different genetic group. For the genetic groups established in this study, we assigned names in the format of “region + age.” We used the prefecture names as the “region” names for individuals except at the Zongri site, where we keep the “Zongri” notation. The “age” used in the notation is averaged from the calibrated ages of the carbon dates of all individuals in the genetic group and then rounded to the nearest hundred.Individuals from the same archaeological sites were clustered into the same genetic set except when ancestral heterogeneity was observed. Ancestral heterogeneity was tested using f4 statistic was calculated using the intersection of the four populations involved, maximizing the power of the tests without introducing bias (We used qpAdm (v1000) program from the ADMIXTOOLS v.6.0 package  including Mota, UstIshim, Tianyuan, Clovis, Ganj_Dareh_N, Yumin, Tanshishan, Lajia4k, Suila, and Zongri5.1k were used as the right population in qpAdm analysis. In a qpWave analysis, we found that this outgroup set could distinguish ancestries among the five source populations, as an f4rank of four was rejected at We used qpGraph from the ADMIXTOOLS package (TreeMix (version 1.13) (https://github.com/BenjaminPeter/admixfrog/) to infer the introgressed tracts from archaic sources in our 57 ancient samples with more than 333,000 SNPs (table S20). We assumed that these target individuals has ancestry from the following three sources: Two archaic sources are represented by the high-coverage archaic genomes from Neandertals (NEA)  is given by the following equation:GP  = B + B + B, r is the number of reads observed for the allele, and t is the total number of reads observed. The allele frequency (p) of EPAS1 was estimated using a maximum likelihood framework where the total number of reads across all 20 SNPs was used to calculate the allele frequency:ri, ti, and ε are equivalent to that found above and i refers to the ith individual.For the Individuals with contamination or low SNP counts were excluded from the analyses. For other functional SNPs, we similarly count the number of mapped reads at each SNP. We restricted the analyses to reads with a mapping quality of ≥30 and bases with a quality of ≥30.


Betula ermanii saplings transplanted from 11 populations including high-altitude edge and low-latitude edge population. We estimated the climatic position of the populations within the overall B. ermanii’s distribution, and the genetic composition and diversity using restriction-site associated DNA sequencing, and measured survival, growth rates and individual size of the saplings. The high-altitude edge population (APW) was located below the 95% significance interval for the mean annual temperature range, but did not show any distinctive genetic characteristics. In contrast, the low-latitude edge population (SHK) exhibited a high level of linkage disequilibrium, low genetic diversity, a distinct genetic composition from the other populations, and a high relatedness coefficient. Both APW and SHK saplings displayed lower survival rates, heights and diameters, while SHK saplings also exhibited lower growth rates than the other populations’ saplings. The low heights and diameters of APW saplings was likely the result of adaptive selection to harsh conditions, while the low survival and growth rates of SHK saplings was likely the result of genetic load. Our findings shed light on the mechanisms underlying the reduced growth performance of range-edge populations.The reduced growth performance of individuals from range edges is a common phenomenon in various taxa, and considered to be an evolutionary factor that limits the species’ range. However, most studies did not distinguish between two mechanisms that can lead to this reduction: genetic load and adaptive selection to harsh conditions. To address this lack of understanding, we investigated the climatic and genetic factors underlying the growth performance of  Even in the absence of geographic barriers, all species have finite geographic ranges, with several ecological and evolutionary factors contributing to a species’ range limit, often with complex interactions  and southern-most (low-latitude edge) population. In the high-altitude edge population, individual trees of B. ermanii are characterized by low tree height, bending and multiple-stemmed trunks, which are the result of adaptations to strong winds and heavy snow fall , Akkeshi (AKS), Hakkoda (HKD), Goyo-San (GYS), Choukai-San (CKS), Bandai-San (BDS), Mikuni-Touge (MKT), Alps-West (APW), Nougouhaku-San (NGH), Alps-South (APS), and Shakaga-Take (SHK) .In autumn 2019 or spring 2020, the containerized saplings were planted at eight established common garden sites across Japan: Nayoro (NYR), Sado high altitude (SDH), Tsukuba (TKB), Yatsugatake (YGT), Hiruzen (HRZ), Chiba (CBA), Shitara (STR) and Tano (TAN) Fig. , and theB. ermanii across Japan. Because of a data collection bias, the location data available online or in academic articles was not complete for the entire range of B. ermanii. Therefore, we predicted the overall B. ermanii habitat in Japan from climatic variables, then estimated the climatic positions of the populations within them.We estimated the climatic position of the 11 origin populations within the range of potential habitat of B. ermanii in Japan was predicted using the maximum entropy principal algorithm in MaxEnt  and annual precipitation (AP) of the 11 origin populations and the potential habitat range for B. ermanii is wind-pollinated species and produces tens of thousands of seeds per mature tree per year. Thus, we believe the family structure effect in each population is very small in our study.This study performed double-digest restriction site-associated DNA sequencing (ddRAD-seq) analyses on the saplings in the planting sites, not on the mother trees in each population. We chose this analysis procedure because we intended to analyze the relationships between the growth performance and its underlying molecular basis. Although such an investigation is now being undertaken as part of a separate study, here we conducted a preliminary investigation which provides necessary information on the growth performance analyses. In these cases, family structure may affect the results to some degree, however, we collected the seeds from 7 to 15 mother trees in each population and PstI and Sau3AI restriction enzymes , ligated with Y-shaped adaptors, and amplified using a polymerase chain reaction (PCR) with KAPA HiFi polymerase . After PCR amplification with adapter-specific primer pairs , an equal amount of DNA from each sample was mixed and size-selected with BluePippin 2% agarose gel . Library fragments between 450 bp and 600 bp were retrieved. The quality of the library was checked using KAPA library quantification kits on a LightCycler 480 Instrument . Finally, nucleotide sequence libraries were sequenced using a high-throughput Illumina Hi-Seq X Ten platform  to generate paired-end reads that were 150 bp long.First, at each common garden planting site, leaf samples from surviving saplings were collected in summer 2021. Total genomic DNA was extracted from the leaves following a modified 2×CTAB (cetyltrimethylammonium bromide) protocol . Pairwise r2 was calculated across all SNPs using the --geno-r2 function in VCFtools. In addition, the proportion of significant LD pairs  was calculated using the --geno-chisq function in VCFtools.As well as SNP filtering, estimation of population structure and genetic diversity were performed individually for each planting site. First, we calculated the coefficients of linkage disequilibrium (LD) using squared allele-frequency correlations  of each population using the program GenAlEx v.6.5  from Ritland  using the molecular co-ancestry method of Nomura  or 1  in spring 2020 and spring 2022. For each population, the survival rate was calculated by subtracting the number of saplings surviving between spring 2020 and spring 2022 from the number of surviving saplings in spring 2020. The height and stem diameter (at 10 cm above ground) of the surviving saplings was measured in autumn 2020 and 2021. The rates of height and diameter growth were then calculated as height and diameter increments relative to the initial measurements. These traits values were compared among the origin populations using a Tukey multiple comparison calculated by the emmeans package Lenth  in R 4.0He), nucleotide diversity (π), ρ mean, the relatedness coefficient (RI) from Ritland . As variables for the PCA, we used mean annual temperature (MAT), annual precipitation (AP), gene diversity  and nucleotide diversity (π) values were extremely low in the SHK population  mean p values than the other populations   in the 11 origin populations  and nucleotide diversity (π), high inter-population genetic differentiation based on the ρ statistic and the ADMIXTURE analysis, and high relatedness coefficient (RI) throughout the 8 planting sites  showed a large decline in growth compared to the other populations  populations originating from their warmer thermal range-edge had low tree height compared to the cooler thermal range-edge , saplings transplanted from three geographically marginal populations showed lower survival rates than saplings from four central populations  and populations without enough gene flow (low-latitude edge population: SHK). Previous common garden studies of tree species also have revealed poor performance of individuals transplanted from their range edge. For example, a southern population of Scots pine  and relatedness between individuals (RI)   Table , and didRE) Fig. . In addiion Fig.  appear tion Fig. . To comprs Table . HoweverPs Table . In manyPs Table . BecauseThe climate experienced by the APW population was also characterized by a shorter growing season compared with the other populations, estimated at 198 days compared to 219–269 days, and transplanted saplings from this population showed a later bud break . The growth of saplings with a lower photosynthetic rate in cooler temperatures and shorter growing seasons might have favored and selected for a lower height and diameter. Moreover, smaller trees may benefit from the relative facilitation of the microclimate, which includes a warmer air layer closer to the soil surface, particularly for trees embedded in low-alpine vegetation Körner .Cryptomeria japonica in snowy regions is genetically differentiated from other populations . Therefore, the SHK population has probably experienced an altitudinal range shift during the Quaternary climatic oscillations and might be experiencing an ongoing decline in population size.Of the 11 origin populations, the SHK population had the lowest ly Table . The SHKled Fig. . Based oSmall, isolated populations can ultimately lead to extirpation or even extinction because of the combined effects of genetic drift, inbreeding depression and inefficient purifying selection , consistent with the SHK population  exhibited adaptive selection for surviving harsh alpine conditions, resulting in lower heights and diameters compared with other populations. The low survival rates observed in the APW saplings suggest that traits associated with adaptation to harsh alpine conditions may also have a negative impact on survival. However, APW saplings exhibited moderate genetic diversity, and did not show a significant reduction in growth rate. This demonstrates the importance of genetic diversity in promoting resilience to marginal climate conditions.This study provides important insights into the effects of marginal climatic conditions and isolated small populations on the survival, growth and size of B. ermanii, SHK, exhibited low genetic diversity, high levels of genetic distinctness, high relatedness between individuals, and a significant reduction in survival rate, growth rate, height and diameter. This suggests that deleterious mutations and biparental inbreeding depression may have a large effect on the reduced fitness of saplings from small, isolated populations. From these results, saplings of the SHK population were vulnerable to environmental fluctuations. To provide conservation guidelines, further studies in the SHK population such as in-natura fitness of surviving trees, purging effects of genetic load and the comparison of genetic characteristics among other southern-most populations of B. ermanii that this study could not considered is deemed necessary.Transplanted saplings from the southern-most origin population of Supplementary MaterialSupplementary Tables 1-5


Leptidea sinapis) that differ considerably in their karyotype as a consequence of at least 27 chromosome fissions and fusions. The recombination data were compared to estimates of genetic diversity and measures of selection to assess the relationship between chromosomal rearrangements, crossing over, maintenance of genetic diversity and adaptation. Our data show that the recombination rate is influenced by both chromosome size and number, but that the difference in the number of crossovers between karyotypes is reduced as a consequence of a higher frequency of double crossovers in larger chromosomes. As expected from effects of selection on linked sites, we observed an overall positive association between recombination rate and genetic diversity in both populations. Our results also revealed a significant effect of chromosomal rearrangements on the rate of intergenic diversity change between populations, but limited effects on polymorphisms in coding sequence. We conclude that chromosomal rearrangements can have considerable effects on the recombination landscape and consequently influence both maintenance of genetic diversity and efficiency of selection in natural populations.Reshuffling of genetic variation occurs both by independent assortment of chromosomes and by homologous recombination. Such reshuffling can generate novel allele combinations and break linkage between advantageous and deleterious variants which increases both the potential and the efficacy of natural selection. Here we used high-density linkage maps to characterize global and regional recombination rate variation in two populations of the wood white butterfly ( Reshuffling genetic variation is fundamental for maintaining genetic diversity and creating novel allelic combinations. The two main processes involved are the independent assortment of chromosomes and homologous recombination. The number and size of chromosomes can influence the amount of pairwise reshuffling and local recombination patterns. However, studying this in natural populations is challenging. In this study, we used the wood white butterfly, which exhibits an extreme within-species karyotype difference. Extensive fusions and fissions have resulted in almost twice as many chromosomes in the southern populations compared to the northeast populations. This unique system allowed us to assess the relationship between karyotype differences, pairwise reshuffling, recombination rate variation and subsequent effects on diversity and linked selection. We found that a higher number of chromosomes results in higher number of crossovers, although the difference was less than expected due to multiple recombination events occurring on longer chromosomes. Both populations showed an association between recombination rate and genome-wide patterns of genetic diversity and efficacy of selection. We provide evidence that chromosomal rearrangements have considerable effects on the recombination landscape and thereby influence the maintenance of genetic diversity in populations. Genetic variation is a prerequisite for evolutionary change and mutation is the ultimate source of novel genetic variants. However, mutation only contributes to variation at individual genomic sites and establishment of new combinations of alleles across loci is dependent on reshuffling via independent segregation and recombination. Physical linkage can lead to a reduction in genetic diversity as a consequence of fixation or loss of haplotype blocks, either by random drift, or more rapidly by selection via hitchhiking and/or background selection ,2. RecomReshuffling of genetic variation occurs by two different mechanisms in diploid organisms. First, the independent assortment of homologous chromosomes to the gametes during meiosis (Mendel’s second law) results in reshuffling of loci located on different chromosomes . Hence, Leptidea sinapis), a species within the Eurasian genus Leptidea which have considerable interspecific karyotype variability compared to the majority of other lepidopterans which usually have a remarkably stable karyotype of 2n ~ 62 [L. sinapis, there is extreme intraspecific variation in karyotype; chromosome numbers range from 2n = 56–62 in the northern (Scandinavia) and eastern  parts to 2n = 106–110 in the south-western (Iberian peninsula) part of the distribution range [L. sinapis predominantly have been driven by recurrent fissions and fusions [Heliconius ssp. has shown that fused chromosomes have a reduced recombination rate and genetic diversity as compared to chromosomes not involved in rearrangements [In order to explore how karyotype differences affect the recombination rate in a natural system, we used the wood white butterfly  to compare the genome-wide probability of pairwise reshuffling between the two populations, and, iii) to quantify the effect of chromosomal rearrangements on maintenance/loss of genetic diversity and the efficacy of selection. We hypothesized that the Catalan population, with a more fragmented karyotype, would have a higher per base recombination rate and a higher total pairwise reshuffling rate, resulting in reduced linkage disequilibrium, higher neutral genetic diversity and increased efficacy in removal of slightly deleterious mutations.The aims of the study were; i) to characterize the recombination landscape in Swedish  and Catalan (2n = 106–110) populations of −03). As expected given the similar genome sizes but different karyotypes in the two populations, the recombination distance per individual chromosome was lower in the Catalan  than in the Swedish population . In the Catalan population, the per base recombination rate was lower on the Z-chromosomes than on the autosomes, but this difference was not significant . This effect can be a consequence of the relative difference in size between the Z-chromosomes and the autosomes in the two populations. The Z-chromosomes are virtually conserved between the two populations and hence constitute the largest chromosomes in the Catalan population, except for one large autosome. In the Swedish population on the other hand, the sizes of the Z-chromosomes are comparable to the majority of autosomes and the recombination rates of the two chromosome classes were also very similar .We used pedigree data from a previous study to estimate the recombination rate from linkage maps for the two populations. The total recombination distance was considerably longer for the Catalan  than for the Swedish  population , and, co−3) and the Catalan population  with a reduced crossover rate in the center and at the ends Figs  and S2. We proceeded by estimating the relative contribution of independent segregation and homologous recombination to the total reshuffling rate in the different populations. The major mechanism for genome-wide reshuffling in both populations was segregation rather than recombination . The malThe analysis revealed that the proportional difference in total reshuffling is less than 1% higher in the Catalan than in the Swedish population, despite the fact that the difference in chromosome numbers is almost two-fold and that females contribute more to the total reshuffling probability in the Catalan population . The dif−04–7.05*10−07), but we did not detect any difference between the populations in the ratio of heterozygosities at zero- (π0) and four-fold (π4) degenerate sites , were significantly associated with the recombination rate in both populations  and the Swedish population , suggesting that the overall recombination landscape has been stable enough to have a similar effect on diversity and efficacy of selection in both populations since they diverged  than windows with higher recombination rate , and cases where incomplete lineage sorting or reuse of chromosomal breakpoints have resulted in differences in karyotype between the two populations. This analysis unveiled a significant positive association between the change in recombination rate and intergenic π  that karyotype rearrangements affect both the genome-wide and the regional recombination rate, ii) that the number of chromosomes and their relative size are major determinants of the recombination rate, but that karyotype changes have very limited effects on the total pairwise reshuffling rate, and iii) that recombination rate changes associated with chromosome fissions and fusions have detectable effects on the neutral diversity at intergenic sites, but not on the efficacy of selection.Leptidea populations had global recombination rates in parity with genome-wide estimates in other Lepidoptera species, although direct comparison here is difficult since different methods have been applied [L. sinapis than in many other butterflies, since it has a comparatively large genome size (686 Mb) as a consequence of a considerable proliferation of transposable elements in intergenic sequences and introns [C. elegans [C. elegans can have multiple crossovers during meiosis [C. elegans. The increase in excess recombination (> 50 cM / chromosome) and the bimodal distribution of recombination events across large chromosomes in the Swedish population are hence consistent with a scenario where the chiasmata interference length in L. sinapis is longer than a typical chromosome in the Catalan population, but shorter than the largest chromosomes in the Swedish population. Notably, we also found that smaller chromosomes sometimes showed a lower average crossover rate than expected if crossovers are necessary for correct segregation during meiosis in males (i.e. < 50 cM / chromosome). This could potentially be a result of very closely located double crossovers or single crossovers occurring close to the chromosome ends. Such events are likely largely undetected with our marker density. Another explanation, perhaps less likely, could be that correct segregation in male meiosis might not require chiasma formation at all, as has been observed in the achiasmatic Lepidoptera females. Unpaired univalents have for example been observed during meiosis in crosses between chromosome races of L. sinapis [Our linkage map data revealed that the recombination rate was higher in the Catalan than in the Swedish population. This is expected given that chromosome size previously has been shown to be strongly associated with recombination rate in Lepidoptera ,32. We f applied ,34,37. H introns . The dif elegans , but rat elegans . It shou meiosis , indicat sinapis . AnotherWe also found that the recombination landscape along many of the larger chromosomes was bimodal, with a tendency towards lower recombination rate in the center. Similar patterns have been observed in other recombination analyses in Lepidoptera ,32,43, bL. sinapis populations. The probability of interchromosomal pairwise reshuffling was close to the maximum (50%) in both populations, which means that the difference in number of chromosome pairs between the Catalan and the Swedish L. sinapis  has a marginal effect on the difference in reshuffling rate  are multivoltine with 3–4 generations per year, while the populations at higher latitudes (e.g. Sweden) predominantly are univoltine [To get more information about how chromosomal rearrangements and recombination rate differences between populations and genomic regions are associated with genetic diversity, we estimated a set of population genetic summary statistics at different site categories. Our results show that there was a significantly higher overall genetic diversity in the Catalan population than in the Swedish population. Such a difference could obviously be a consequence of differences in demographic history or other evolutionary processes that affect the populations differently. First, we know that ivoltine . A shortivoltine . Second,ivoltine ,52. HencDrosophila [To further investigate the effects of recombination on patterns of genetic diversity we therefore focused on intra-genomic variation in each population separately. We found that the levels of genetic diversity were heterogeneous across chromosomes and there was no association between genetic diversity and chromosome size after excluding the Z-chromosomes from the analysis. The latter result could be a consequence of the dynamic ongoing karyotype rearrangements in this species, and fits well with linkage-disequilibrium-based recombination rate estimates in the Swedish population . The recosophila . This isosophila .L. sinapis mainly have involved fusions and/or fissions of comparatively large chromosome parts [The regional distributions of genetic diversity along the chromosomes were in line with the expectations , followime parts . The disme parts . It shoume parts  and thatLeptidea genus where e.g. L. sinapis has a karyotype including three Z-chromosomes and three W-chromosomes. Of the three Z-chromosomes, one is homologous to the inferred ancestral Z-chromosome (Z1) in Lepidoptera while the other two  seem to have been recruited as sex-chromosomes in the Leptidea genus specifically [Ne of a Z-chromosome should be approximately ¾ of any autosome, under the assumption of equal sex ratios. A reduced level of genetic diversity on the Z-chromosome has also been observed in the Heliconius melpomene and the Monarch butterfly, but not in the moth Manduca sexta [Ne of Z-chromosomes and autosomes might vary between species, or that other factors than mutation-drift balance drive relative levels of genetic diversity between chromosome classes. One factor that can contribute to discrepancies between expected and observed levels of genetic diversity on autosomes and sex-chromosomes based on differences in Ne, is the effective recombination rate [0 / π4-ratios as the autosomes, Z2 had a dramatically higher π0 / π4-ratio compared to the other chromosomes in both populations. The higher ratio was a consequence of an elevated π0, indicating reduced selective constraints on Z2 (or possibly a group of genes under strong positive selection). This result is somewhat puzzling since Z2 is the most structurally conserved of all chromosomes within Leptidea [Both cytogenetics , linkageifically . To undeca sexta ,56, suggion rate . Female ion rate . An inteLeptidea ,29 and t0 / π4-ratio with reduced efficacy of selection. Since the efficacy of selection is dependent on Ne, the frequency of (slightly) deleterious mutations is expected to segregate at higher rates in regions of low recombination [0 / π4-ratio in both populations, an observation supporting that physical linkage between sites under selection reduces the efficacy of selection to remove slightly deleterious variants [Ne in the Swedish population). In addition, there was no detectable effect of the variance in recombination rate in rearranged chromosomes on the ratio of π0 / π4. This indicates that the average recombination rate is high enough to efficiently remove slightly deleterious mutations in both populations, consistent with the observations that selection efficacy can be maintained at a stable level even at relatively low recombination frequencies [0 / π4-ratio approached the asymptote when the recombination rate was > 2 cM / Mb. Hence, an increase in the recombination rate above that level does not seem to affect the efficacy of selection in this system. It is also possible that the relatively short divergence time between these populations [The efficacy of selection translates to both removal of deleterious mutations and fixation rate of adaptive mutations. Hence, under the assumption that most novel mutations are neutral or slightly deleterious, i.e. the nearly neutral theory , we expebination . Our datvariants . Howeverquencies . In the ulations , in comb0, π4). This is likely a consequence of temporal differences in the effects of reduced linkage disequilibrium on genetic diversity at sites under direct and indirect selection pressure.We found a positive association between the variation in intergenic diversity change and the variation in recombination rate change across ancestry blocks, with recombination rate explaining > 12% of the variation in intergenic diversity in rearranged chromosomes. A positive association between recombination rate and genetic diversity has been observed in many different taxa  and our L. sinapis with extreme difference in karyotype structure and chromosome count. We show that karyotype evolution directly affects the global recombination rate, but that recombination has a limited effect on total pairwise reshuffling, partly as a consequence of multiple crossovers in larger chromosomes. A key observation from our data was a significant difference in neutral genetic diversity (but not on the efficacy of selection) in regions where chromosomal rearrangements have affected the recombination rate. Finally, we validate the importance of recombination rate variation driving differences in maintenance of nucleotide diversity between populations at an early stage of divergence.We investigated the difference in recombination rate between two populations of L. sinapis to develop RAD-seq based linkage maps for two populations that represent the most extreme karyotype variants in the species. Details about family structure, RADseq data generation and preprocessing are available in [mem [sort and filtered based on quality (samtools view -q 10), and reads with multiple mapping locations were removed with a custom script [mpileup [-q) 10 and minimum base quality (-Q) 10 was used to identify variants, which were converted to genotype likelihoods using Pileup2Likelihoods in LepMap3 with default settings [minCoverage = 3) and < 30% of the individuals allowed to have lower coverage than minimum coverage (numLowerCoverage = 0.3). Only markers mapping to chromosome-sized scaffolds in each respective population were retained for downstream analysis. LepMap3 [ParentCall2 using default settings, except for ZLimit which was set to 2 to identify markers segregating as sex chromosomes. Non-informative markers were removed with removeNonInformative = 1. Markers showing segregation distortion, minimum allele frequency below 0.05 or absence in more than 50% of the individuals were excluded with Filtering2 . In addition, only markers represented in at least four families in each population were retained (familyInformativeLimit = 4). As a first mapping step, markers were assigned to linkage groups with SeparateChromosomes2 using female informative markers only (informativeMask = 2). The optimal LOD-score threshold was empirically estimated to 11 for the Swedish and 7 for the Catalan population. Markers that were informative in either males or both sexes were subsequently added with JoinSingles2All with the same LOD-score thresholds. Linkage groups covering several scaffolds were split based on information from the physical assembly before ordering the markers along the chromosomes with the module Ordermarkers2. Only male informative markers (informativeMask = 1) were used for the ordering since females are achiasmatic (recombination2 = 0). We set a penalty for diverting from the order in the genome assembly with likelihood chain  and improveOrder = 0. The ordered linkage maps were visually inspected in R and uninformative markers (extending the map ends) were removed. After manual removal of uninformative markers, OrderMarkers2 was run again with the same settings (see above) and the final genetic distances were calculated using Kosambi’s map function to account for multiple recombination events along the chromosome.We used Swedish  and Catalan  full-sib families of lable in . To get lable in . The fil in [mem  with defm script . Mappingm script  and only[mpileup  with optsettings ; minimum LepMap3  was usedRegional recombination rates were estimated by dividing the genetic distance with the physical interval between each marker pair (i.e. cM / Mb). Weighted means for 2 Mb windows, per chromosome and per population were obtained by weighting the recombination rate at each interval by the proportion of the physical distance. Interpopulation comparisons of the weighted mean recombination rates were performed with Wilcoxon Signed rank test and potential associations between recombination rate and chromosome length were assessed with Spearman’s rank correlation coefficient, as implemented in cor.test in R . To comp2. The total pairwise reshuffling was calculated as the sum of the interchromosomal and intrachromosomal contribution [The probability of total pairwise reshuffling is the probability that two random loci are originating from different parental alleles during gamete formation. The total pairwise reshuffling has two components; the interchromosomal component describing the effects of independent assortment and the intrachromosomal component describing the effect of crossing over. The heterogeneity in the spatial distribution of crossovers is also corrected for. The probability of interchromosomal pairwise reshuffling was estimated by the probability of two loci being located on different chromosomes with independent assortment of the chromosomes during meiosis )2))*0.5 . The int)2))*0.5 . The sumribution .https://github.com/FelixKrueger/TrimGalore), a wrapper for cutadapt 3.1 [mem [filter 1.16–1 [mask-file $REPEAT_GFF,—soft-filter "REPEAT", and -i FMT/DP > 5 & FMT/DP < 25’—set-GTs.Previously available resequencing data from 10 Swedish and 10 Catalan individuals were used for population genetic analysis . Positions with Phred-score < 33, sequencing adapters and seven bases from the 3’-ends of all reads were trimmed using TrimGalore 0.6.1  and genomic features  were extracted using BEDTools 2.29.2 complement [0 / π4 as a proxy for selection, reflecting the efficacy in removal of deleterious mutations, since most protein changing mutations are either deleterious or neutral according to the nearly neutral theory [Nucleotide diversity (π) for intronic, intergenic, 0-fold and 4-fold sites were estimated in 50 kb windows with the software pixy 1.2.5.beta . Coordinmplement . Potentimplement . We usedl theory .Leptidea reali), or where incomplete lineage sorting or reuse of breakpoints have resulted in a fission in one population or a fusion in the other. To investigate if observed changes in recombination rate between populations have resulted in a corresponding change in π, we compared the ratio of the Catalan and the Swedish π with the observed recombination rate ratio between the populations within each ancestral chromosomal unit. Both the recombination rate and the π estimates are random variables, so we applied a model II linear regression using ordinary least squares (OLS) and a permutation test with 1,000 permutations to determine the significance of the slopes, as implemented in the R-package lmodel2 [Potential associations between the recombination rate and π were assessed by binning data into 2 Mb genomic windows for each population separately. Population genetic summary statistics were also estimated in ancestry blocks , i.e. previously identified homologous regions of chromosomes in which no rearrangements have occurred between the populations . We incl lmodel2 .S1 FigL. sinapis. Genetic distances are given on the y-axes (cM) and physical positions along each respective chromosome on the x-axes (Mb).Marey maps for the Catalan  and the Swedish  population of (PDF)Click here for additional data file.S2 FigRegional recombination rates  as estimated in 2 Mb windows using local regression (LOESS) along the chromosomes in the Catalan and the Swedish population, respectively. Physical position is given on the x-axis (Mb).(PDF)Click here for additional data file.S3 Fig0 / π4) per chromosome (y-axis) and chromosomal length (x-axis) for each diversity class. Spearman’ rank correlation coefficient including Z-chromosomes (purple) and without Z-chromosomes (black).Population specific pairwise differences (π and bottom panel π(PDF)Click here for additional data file.S4 Fig0 / π4) as a function of the recombination rate in each population. Diversity was estimated in 2 Mb windows. Lines represent local regressions, statistics represent results from a model II linear regression, and permuted p-values for the OLS slope are given in black font. The statistics for windows with a recombination rate 2.0 cM / Mb or below and > 2.0 cM / Mb are given in blue and grey font, respectively.a) Nucleotide diversity (π) as a function of recombination rate in each population estimated in 2Mb windows in zero-fold, four-fold degenerate sites, intergenic and intronic sites for each population. b) The ratio of polymorphisms at 0-fold and 4-fold degenerate sites (π(PDF)Click here for additional data file.S5 Figπ) and b) genetic diversity at 0-fold to 4-fold degenerate sites (Ratioπ0/π4), as a function of the ratio between Catalan and Swedish recombination rate for each ancestral chromosomal unit with known fusion/fission history. The line represents the slope from model II linear regression, with the estimated R2 and permuted p-value.The ratio between Catalan and Swedish estimates of a) diversity (Ratio(PDF)Click here for additional data file.S1 TableMean weighted recombination rate (Rec rate) per chromosome in the Swedish (Swe) and Catalan (Cat) populations. Chromosome length in Mega base-pair (Chr length Mb), genetic map length (cM) and number of markers (Mkr no) per chromosome were also included.(PDF)Click here for additional data file.


Pectinophora gossypiella) is one of the world’s most destructive pests of cotton. This invasive lepidopteran occurs in nearly all cotton-growing countries. Its presence in the Ord Valley of North West Australia poses a potential threat to the expanding cotton industry there. To assess this threat and better understand population structure of pink bollworm, we analysed genomic data from individuals collected in the field from North West Australia, India, and Pakistan, as well as from four laboratory colonies that originated in the United States. We identified single nucleotide polymorphisms (SNPs) using a reduced-representation, genotyping-by-sequencing technique (DArTseq). The final filtered dataset included 6355 SNPs and 88 individual genomes that clustered into five groups: Australia, India-Pakistan, and three groups from the United States. We also analysed sequences from Genbank for mitochondrial DNA (mtDNA) locus cytochrome c oxidase I (COI) for pink bollworm from six countries. We found low genetic diversity within populations and high differentiation between populations from different continents. The high genetic differentiation between Australia and the other populations and colonies sampled in this study reduces concerns about gene flow to North West Australia, particularly from populations in India and Pakistan that have evolved resistance to transgenic insecticidal cotton. We attribute the observed population structure to pink bollworm’s narrow host plant range and limited dispersal between continents.The pink bollworm ( Pink bollworm (Pectinophora gossypiella) is a major pest of cotton that has colonised more than 150 countries worldwide, including much of tropical America, Africa, Asia, and Australasia  has been grown in Australia since 1996 and has been effective against pink bollworm in the United States and China14. However, pink bollworm populations in India and Pakistan have evolved resistance to Bt cotton, with serious practical consequences15. In principle, connectivity among populations could result in the introduction of Bt-resistant pink bollworm into North West Australia. Thus, a greater understanding of pink bollworm population structure could be useful for assessing the threat to cotton in Australia, as well for improving surveillance and management elsewhere.In Australia, pink bollworm was first reported on cotton in Queensland in 192417. Two of these studies evaluated only the mitochondrial DNA (mtDNA) cytochrome c oxidase subunit I (COI) locus in samples from India17, two focused primarily on China and used two mtDNA loci 16 or 13 microsatellites5 respectively, and one was based on a piggyBac-like transposon insertion and its flanking sequences7. Of these, only the latter included populations from Australia. The conclusions from these studies were mixed, including apparently conflicting results from mtDNA and microsatellite DNA analysis of the same set of populations16. Population structure of pink bollworm has not been explored previously using genome-wide data that includes Australian samples and a large number of genetic markers. Such analysis may provide increased accuracy and resolution to address these discrepancies.Five previous studies of population structure in pink bollworm have analysed genetic variation based on DNA sequences from one to 13 loci18 to produce genome-wide data in the form of 6355 single nucleotide polymorphisms (SNPs) identified from 88 individual pink bollworm from seven wild populations in Australia, India, and Pakistan; and four laboratory colonies in the U.S. We used these data to assess the population structure of pink bollworm, with a particular focus on the Australian population. We also analysed data from Genbank for mtDNA COI for pink bollworm from six countries. Our results show minimal gene flow between Australia and the other populations that were sampled in this study, which reduces concerns about the threat of introductions of Bt-resistant pink bollworm from India and Pakistan.Here, we aimed to understand pink bollworm population structure by using a genotyping-by-sequencing technique (DArTseq)Pectinophora gossypiella; Entosol  Pty Ltd., NSW Australia). Fourth instar larvae were collected from fields of Bt cotton producing Cry1Ac + Cry2Ab (Bt toxins) in 2010 from the states of Telangana, Maharashtra, Karnataka, and Andhra Pradesh in India19, and from fields of Bt cotton producing Cry1Ac in 2016–2017 from the provinces of Punjab and Sindh in Pakistan.We analysed the genomes of 88 pink bollworm from Australia, India, Pakistan, and the U.S. Table . Male mo20. Both APHIS-S colonies were obtained from the United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) laboratory in Phoenix, Arizona, where they had been maintained for over 40 years without exposure to Bt toxins or other insecticides22. Subsets of the APHIS-S colony were provided to the USDA Agricultural Research Service in Maricopa, Arizona in 2006 (APHIS-S_2) and 2018 (APHIS-S_1). Bt4R and Bt4-R2 are laboratory-selected resistant colonies: Bt4R was derived from the Bt-susceptible Western Cotton Research Laboratory (WCRL) colony from 2007 to 2008 by laboratory selection for Cry1Ac resistance23. Bt4-R2 was obtained from Bt4R in 2010 and has 28-fold resistance to Cry1Ac and > 10,000-fold resistance to Cry2Ab24. Larvae from all four colonies were reared in the laboratory on wheat germ diet25 at 26 °C and a photoperiod of 14 h light:10 h dark.For pink bollworm from the U.S., we used individuals from four laboratory colonies in Arizona: APHIS-S_1, APHIS-S_2, Bt4R, and Bt4-R2, where this pest was declared eradicated in 201826. Short-read sequences were then processed following the DArTseq bioinformatic pipeline to yield genotyping data in the form of SNPs26.We sent 90 pink bollworm samples to Diversity Arrays Technology  for DNA extraction, quantification, and genotyping , call rate (threshold: 0.95), and minor allele frequency (threshold: 0.02). The final filtered dataset contained 88 individual genotypes, 6355 SNP markers, and 1.41% missing data.We received genotyping outputs in the DarT ‘2 row’ format, where alleles are scored “0” for homozygous reference state, “1” for heterozygous, and “2” for homozygous alternate (or SNP) state31 to calculate observed (Ho) and expected (He) heterozygosity, as well as inbreeding coefficients  for each population from the filtered dataset.In R, we used ‘hierfstat’ version 0.5.1132. We randomly permuted the AMOVA output 1000 times to test if populations and colonies were significantly different using the function ‘randtest’ from the package ‘ade4’ version 1.7.1933. We then estimated pairwise genetic differentiation (FST) between populations and colonies using the function ‘genet.dist’ and method ‘WC84’ from the ‘hierfstat’ package, version 0.5.1135.We used R to analyse genetic variation between and within populations by performing an Analysis of Molecular Variance (AMOVA) with the ‘poppr’ package version 2.9.3Pectinophora scutigera; see Fig. We conducted a principal component analysis based on Euclidean genetic distances, using the function ‘glPCA’ implemented in the ‘adegenet’ package in R. We performed two analyses, one with only the 11 populations and colonies of pink bollworm, and another that also included an ‘outgroup’ population of spotted pink bollworm . A maximum likelihood (ML) phylogeny was constructed in Iqtree version 1.2.139 using the best-fit substitution model automatically selected by the software, with 10,000 bootstrap iterations to assess clade support. The resulting output was read into R using ‘ape’ version 5.6.240 for data visualisation.To examine relationships among samples in a phylogenetic context, we converted our SNP data to variant call format (VCF) using ‘radiator’ version 1.2.241. The nucleotide diversity of each population was then calculated using the ‘nuc.div’ function in R from the ‘pegas’ package version 1.142. COI sequences were converted to haplotypes using the same package, and the function ‘haploNet’ was used to construct a haplotype network.Finally, to further investigate genetic diversity and population structure of global pink bollworm populations, we obtained 31 mitochondrial COI sequences in FASTA format from GenBank Table  for popu9 = 4.87; P < 0.001). Mean Ho was significantly higher for the field populations from India and Pakistan (0.20) than for Australia (0.14) . Ho was not correlated with the number of field sites sampled per population across the seven field populations  nor the number of individuals sampled per population across all 11 populations and colonies .Observed heterozygosity (Ho) was low within each of the 11 populations and colonies studied  was higher for field populations (0.13) than laboratory colonies (0.03)   was significantly lower than expected heterozygosity (He) (mean = 0.18)  analysis of the filtered dataset (6355 SNPs) indicated an optimal K value of five clusters Fig. . We founWe identified 13 haplotypes from 31 COI sequences from GenBank, obtained from pink bollworm individuals in six countries: Australia , India (n = 12), Pakistan n = 5), the U.S. (n = 3), Israel (n = 4), and Kenya (n = 4). Nucleotide diversity was 0.002 in India (10 haplotypes), 0.0007 in Israel (two haplotypes), and 0 in the other four countries (one haplotype each)  was higher in the field populations. This was surprising as laboratory colonies are expected to show greater FIS because their smaller population sizes increase mating between related individuals47. However, a meta-analysis of self-incompatible plant species also found that FIS was positively correlated with population size48, which is similar to our findings. Estimates of diversity and FIS using SNP data may be influenced by missing data, rare alleles, sample size, and population structure49. However, in our study Ho was not correlated with the number of individuals nor the number of field sites sampled per population, and our SNP matrix had < 1.5% missing data. As noted by Schmidt et al.,49 more work is needed to evaluate the implications of differences between observed and expected heterozygosity50.Our results showed that genetic diversity based on heterozygosity at 6355 SNP markers and nucleotide diversity at mtDNA COI was low within all populations and colonies. As expected, heterozygosity was significantly lower within laboratory colonies from the U.S. compared to within field populations from Australia, India, and Pakistan. However, putative inbreeding 54. In such studies, the newly available reference genome for pink bollworm55 and wider geographic sampling could yield insights facilitating better monitoring and management of this cosmopolitan pest.Although pink bollworm is present in over 150 countries worldwide Fig. ”. Thus, Supplementary Information.


Phascolarctos cinereus) are a globally recognized species that captured the hearts and minds of the world during the 2019/2020 Australian megafires. In 2022, koalas were listed as ‘Endangered’ in Queensland, New South Wales, and the Australian Capital Territory. Populations have declined because of various threats such as land clearing, habitat fragmentation, and disease, all of which are exacerbated by climate change. Here, we present the Koala Genome Survey, an open data resource that was developed after the Australian megafires. A systematic review conducted in 2020 demonstrated that our understanding of genomic diversity within koala populations was scant, with only a handful of SNP studies conducted. Interrogating data showed that only 6 of 49 New South Wales areas of regional koala significance had meaningful genome-wide data, with only 7 locations in Queensland with SNP data and 4 locations in Victoria. In 2021, we launched the Koala Genome Survey to generate resequenced genomes across the Australian east coast. We have publicly released 430 koala genomes  on the Amazon Web Services Open Data platform to accelerate research that can inform current and future conservation planning.Genome sequencing is a powerful tool that can inform the management of threatened species. Koalas ( The field of conservation genomics is rapidly developing , with geThe year 2020 saw a massive change in the way many view the natural world and how science can assist in our understanding of it. From catastrophic megafires in Australia that saw 12.6 million acres burned  and an eBox 1The human genome project  led to aPhascolarctos cinereus) is an iconic Australian species. This specialist folivore only eats certain types of feed trees, making their ongoing management problematic in a changing world [2 levels directly impacting the nutritional quality of leaves [The koala  had experienced significant losses in ecological communities, with losses between 26% and 50% of their original extent [The number of koalas in Australia is currently unknown. Although an expert elicitation on koala numbers, published in 2016, estimated there to be 329,000  koalas in Australia, with numbers declining by 53%, 26%, 14%, and 3%, in Queensland, New South Wales, Victoria, and South Australia, respectively, over the past three generations  . One of l extent . In addil extent . Long-term management of koalas requires an understanding of the demographic and genetic status of different populations, gene flow between populations, and current threats. Although there are many factors contributing to the management of these fragmented populations, an understanding of genetic diversity is needed to determine the capacity of a population to be able to survive long-term in this fragmented landscape or if active management interventions, such as translocation and/or captive breeding, are required. During previous periods of climate instability, koalas retracted into refugia ,47, but A systemic review was undertaken in August 2020 by querying Web of Science (WoS) and Scopus using the “Topic” (WoS) and “Title” (Scopus) functions. The keywords used were ‘koala’ AND ‘genet*’ and ‘koala’ AND ‘genom*’ to obtain all genetic and genomic papers for koalas. This resulted in 551 papers that included 191 duplicates between the two search engines. Removing the duplicates left 360 individual papers. Each paper was attributed to 15 groupings , of whicPopulation genetics papers (1996–2020) predominantly used microsatellites N = 17; . UnfortuFuture koala conservation management needs to address issues surrounding habitat loss and fragmentation, disease, climate change, dog attacks, and vehicle collisions via expanding and restoring habitat, vaccine development, and protecting climate refugia. If koala populations continue to decline, they will become subject to small population pressures of genetic drift, inbreeding, and loss of adaptive potential to respond to emerging threats to survival, which can ultimately lead to local extinction. Unfortunately, there is limited genetic knowledge for many koala populations across the species’ range. To make conservation planning decisions, we need to understand if populations have unique genetic variants, their level of inbreeding and relatedness, their disease status, their adaptive potential, and their effective population size. Genomics is a useful tool to address these questions and for informing conservation planning ,89. LookSalvelinus alpinus; [Caretta caretta; [Chlamydia infection is influenced by genetic variation within MHC Class II DMA and DMB genes and CD8-a genes [Chlamydia disease in koala populations [Chlamydia infection [A suitable conservation policy can only be implemented in the presence of sound science . As is talpinus; ) and logcaretta; ). In a s-a genes , while Mulations . A recennfection . This lenfection .Our solution to the current conundrum of genetic data deficiencies for koala conservation planning was to generate a publicly available resource of whole-genome resequencing data . We aimhttps://awgg-lab.github.io/australasiangenomes/species/Phascolarctos_cinereus.html). A total of 430 genomes were released in September 2021 (N = 116), March 2022 (N = 144), and October 2022 (N = 170), under an open-access licensing agreement (see webpage). Metadata for each sample sequenced and released, includes sampling location, date, sex, estimated age (if known), name and contact details of sample provider, and permits that samples were collected. Other researchers who are interested in using the data and require more metadata are encouraged to contact the sample providers to facilitate research engagement and potential collaboration.Initial sequencing yielded poor results, likely due to DNA quality and library pooling. As a result, DNA repair using an FFPE DNA repair protocol  improved DNA quality. Sequencing was undertaken at the Ramaciotti Centre for Genomics  on an Illumina NovaSeq 6000, using a TruSeq DNA PCR free library prep kit . Forty-eight samples were pooled across one lane of an S4 200 cycle flowcell. Coverage was assessed after the first sequencing run, and pooling was adjusted accordingly to meet the 30X coverage goal. A total of 430  samples were sequenced across 48 wild locations C and twoChlamydia, genetic variants associated with climate conditions and habitat types, taste receptor variation and feed tree preferences, and more.This open data resource will now be used by teams of researchers across the globe to investigate key genetic questions pertaining to koala management, including population differentiation, signatures of selection, populations at extinction risk, genetic basis of diseases such as koala retrovirus and Strigops habroptilus; [Notiomystis cincta; [Orcinus orca; [Galemys pyrenaicus; [Koalas are one of the most iconic globally recognized species. Even before the catastrophic megafires, many koala populations across the northern part of their range were declining due to a range of threats that will continue to be exacerbated by a warming climate. It has been predicted that Australia will continue to experience more drying and significant droughts. By creating an open data genomic resource across eastern Australian koala populations, we have generated an asset that will inform current conservation planning and be a future resource to assess whether conservation actions improve/maintain/lose genetic variation across the species’ range over time. The power of genomic data is fully realized, and with ever-declining sequencing costs, the opportunity to apply this technology to threatened species is increasing, as seen by whole-genome resources for kākāpō , hihi , killerus orca; ), and Pyenaicus; ). By gen