Hmong–Mien (HM) -speaking populations, widely distributed in South China, the north of Thailand, Laos, and Vietnam, have experienced different settlement environments, dietary habits, and pathogenic exposure. However, their specific biological adaptation remained largely uncharacterized, which is important in the population evolutionary genetics and Trans-Omics for regional Precision Medicine. Besides, the origin and genetic diversity of HM people and their phylogenetic relationship with surrounding modern and ancient populations are also unknown. Here, we reported genome-wide SNPs in 52 representative Miao people and combined them with 144 HM people from 13 geographically representative populations to characterize the full genetic admixture and adaptive landscape of HM speakers. We found that obvious genetic substructures existed in geographically different HM populations; one localized in the HM clines, and others possessed affinity with Han Chinese. We also identified one new ancestral lineage specifically existed in HM people, which spatially distributed from Sichuan and Guizhou in the north to Thailand in the south. The sharing patterns of the newly identified homogenous ancestry component combined the estimated admixture times via the decay of linkage disequilibrium and haplotype sharing in GLOBETROTTER suggested that the modern HM-speaking populations originated from Southwest China and migrated southward in the historic period, which is consistent with the reconstructed phenomena of linguistic and archeological documents. Additionally, we identified specific adaptive signatures associated with several important human nervous system biological functions. Our pilot work emphasized the importance of anthropologically informed sampling and deeply genetic structure reconstruction via whole-genome sequencing in the next step in the deep Chinese Population Genomic Diversity Project (CPGDP), especially in the regions with rich ethnolinguistic diversity. The Yungui Plateau and surrounding regions are the most ethnolinguistically diverse regions of China with a population size of approximately 0.205 billion (2020 census), which is the home to many ethnic groups, including the major population of Han Chinese and minorities of Hmong–Mien (HM), Tai–Kadai (TK), and Tibeto-Burman (TB). This region is a mountainous and rugged area, consisting of Sichuan, Chongqing, Guizhou, Yunnan and most parts of Tibet Autonomous Region, which is characterized by the Sichuan Basin in the northeast, the karstic Yunnan–Guizhou Plateau in the east, and the Hengduan Mountains in the west, and the majority of the region is drained by the Yangtze River. Historical records documented that portions of Southwest China were incorporated as unequivocal parts of greater China since at least the end of the third century BCE , and thiDuring the past decade, paleogenomic studies have transformed our knowledge of the population history of East Asians . A recenCurrently, HM groups mainly dwell in South China   and VietThe Miao people are the largest of the HM-speaking populations and the fourth largest of the 55 ethnic minorities in China. The Miao are a group of linguistically related people mainly living in mountainous areas of South China. Xuyong is a county in the southeastern of Sichuan province, which borders Guizhou to the south and Yunnan to the west. Here, we generated new genome-wide data of the 52 northernmost HM-speaking Miao individuals from Xuyong, Sichuan, and co-analyzed newly generated data with publicly available genome-wide data of present-day and ancient East Eurasians leveraging shared alleles and haplotypes. We first aimed to 1) study the structure of genetic variations of Sichuan Miao people and explore the genetic relationship between Sichuan Miao and other geographically different HM-speaking people, such as Miao, She, Gejia, Dongjia, Hmong, Dao, and Xijia from China and Southeast Asia. 2) We then explored the genetic relationship between Miao people and other ethnolinguistically different East Asians and published spatiotemporally different East Asians based on the sharing alleles in the descriptive and qualitative analyses. 3) Based on the sharing alleles and haplotypes, we additionally reconstructed the demographic history of Miao people in the context of the modern geographically close ancestral source candidates and genetically related ancient surrogate populations. 4) Based on the cross-population signatures of natural selection and enrichment analysis, we finally explored the genetic adaptative history of the Chinese Miao people.p values of the Hardy–Weinberg exact test (--hwe 10−6). We used the King software to estimate the degrees of kinship among 52 individuals and remove the close relatives within the three generations  using the mergeit software. Besides, we also merged our new dataset with modern population data from China and Southeast Asia and ancient population data from Guangxi, Fujian, and other regions of East Asia . The Oragene DN salivary collection tube was used to collect salivary samples. This study was approved via the Ethical Board of North Sichuan Medical College and followed the rules of the Helsinki Declaration. Informed consent was obtained from each participating volunteer. To keep a high representative of our included samples, the included subjects should be indigenous people and lived in the sample collection place for at least three generations. We genotyped 717,227 SNPs using the Infinium Global Screening Array (GSA) version 2 in the Miao people following the default protocols, which included 661,133 autosomal SNPs and the remaining 56,096 SNPs localized in X-/Y-chromosome and mitochondrial DNA. We used PLINK (version v1.90)  to filteerations . We finaast Asia  and finaast Asia . In the ast Asia .We performed principal component analysis (PCA) in three population sets focused on a different scale of genetic diversity. Smartpca package in EIGENSOFT software  was usedWe performed model-based admixture analysis using the maximum likelihood clustering in ADMIXTURE (version 1.3.0) software  to estimWe used PLINK (version v1.90) to calculate the pairwise Fst genetic distance between studied Sichuan Miao (SCM) and other modern and ancient references and also estimated the allele frequency distribution of included populations in the TreeMix analyses. Both modern and ancient populations were used to construct the maximum-likelihood–based phylogenetic relationship with population splits and migration events using TreeMix v.1.13 .f3-statistics in the form of f3 , which was calculated using qp3Pop (version 435) package in the ADMIXTOOLS software .We assessed the potentially existed admixture signatures in SCM via the admixture-software . The tarp-values of the rank tests of all possible population pairs among HM-speaking populations and other geographically close modern and ancient reference populations using qpWave in the ADMIXTOOLS package  in the ADMIXTOOLS package . One of We used the R package of ADMIXTOOLS 2  to exploWe estimated the decay of linkage disequilibrium in SCM using all possible population pairs of modern East Asians as surrogate populations in ALDER 1.0 . Two addWe used SHAPEIT software (Segmented HAPlotype Estimation & Imputation Tool) to phase our dense SNP data with the default parameters (--burn 10 --prune 10 --main 30) . PairwisWe ran ChromoPainterv2 software  to paintWe identified the fine-scale population substructure using fineSTRUCTURE (version 4.0) . Perl scWe ran the R program of GLOBETROTTER  to furthWe calculated the integrated haplotype score (iHS) and cross-population extended haplotype homogeneity (XPEHH) using the R package of REHH . Here, bThe online tool of Metascape  was usedWe genotyped 52 genome-wide SNP data in three SCM populations  and found that five samples possessed close sibship with other samples. After removing relatives, we merged our data with the human origin dataset in AADR (merged HO dataset) to explore the genetic diversity of SCM and their genetic relationship with modern and ancient Eurasian populations. East-Asian-scale PCA results showed three genetic clines , which iFocused on the genetic diversity of ST and TK people in China and all studied and reference HM populations, we used a panel of 65 populations and identified three primary directions in the first two dimensions represented by ST, HM, and Hainan Hui people , SCM shared much ancestry with ancient northern East Asians, southern Fujian, and modern East Asian ancestry. Compared with SCM, prehistoric Guangxi populations shared much Neolithic to Iron Age Fujian and Guangxi ancestries. We also tested the genetic relationship between SCM and YRB farmers using asymmetric-f4-statistics and found YRB millet farmers shared more alleles with SCM people than with early Asians and southern Fujian and Fujian ancient populations. As expected, SCM harbored many HM-related alleles or ancient Fujian and Guangxi ancestries compared with millet farmers. Generally, formal test results demonstrated that SCM possessed the strongest genetic affinity with ∼500-year-old Guangxi GaoHuaHua people and additionally obtained genetic influx from northern East Asians recently.Focused on the deeper temporal population dynamics, we next tested the genetic relationship between SCM and ∼1500-year-old BaBanQinCen using the same strategies . Positivf-statistics. We further conducted two-way qpAdm models with two Guangxi ancient populations as the southern surrogates and four northern ancient populations from YRB and Amur River as the northern ancestral sources to estimate the ancestral composition of SCM and their ethnically and geographically close populations  based on the deep whole-genome sequencing on anthropologically informed sampling populations, we have genotyped the array-based genome-wide SNP data in 29 ethnolinguistically different populations. We reconstructed phylogenetic relationships between three studied SCM populations and 26 other Chinese populations from ST, Altaic, AA, and HM . We idenWe finally reconstructed the deep population admixture history of HM-speaking populations using the qpGraph model with population splits and admixture events. We used the ancestral lineage of Mbuti in Africa, Loschbour in western Eurasia, Onge in South Asia, and Tianyuan in East Asia as the basal deep early continental lineages. We used Baojianshan in the early Neolithic period and GaoHuaHua in the historic time from Guangxi and Qihe in the early Neolithic in Fujian as southern East Asian lineages and used Neolithic YRB millet farmers as the northern East Asian lineages. In our first best-fitted model , we addeWe obtained high-resolution uniparental maternal and paternal lineages in SCM . We idenneuroblastoma breakpoint family member 9/10 (NBPF 9/10) locus, reflecting well-known signals associated with susceptibility of the neuroblastoma. We further identified a strong selection signal implicating polypeptide N-acetylgalactosaminyltransferase 13 (GALNT13) and potassium voltage-gated channel subfamily J member 3 (KCNJ3) located in chromosome 3. The former one is expressed in all neuroblastoma cells and encodes a glycosyltransferase enzyme responsible for the synthesis of O-glycan. The latter one encodes G proteins in the potassium channel and is associated with susceptibility candidates for schizophrenia (abhydrolase domain containing 10 (ABHD10), RNA-binding motif single-stranded interacting protein 3 (RBMS3), RBMS3 antisense RNA 3 (RBMS3-AS3), and transgelin 3 (TAGLN3). ABHD10 is one of the important members of the AB hydrolase superfamily and is associated with enzymes for deglucuronidation of mycophenolic acid acyl-glucuronide (via binding poly(A) and poly(U) oligoribonucleotides and controls Prx1 expression and indirectly collagen synthesis (myotubularin-related protein 7 (MTMR7), which was localized at and associated with the susceptibility of Creutzfeldt–Jakob risk. Three top genes were identified in chromosome 9, which included contactin-associated protein-like 3B (CNTNAP3B), phosphoglucomutase 5 pseudogene 2 (PGM5P2), and SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 2 (SMARCA2). SMARCA2 encodes the protein-controlled coactivator participating in transcriptional activation and vitamin D-coupled transcription regulation. Genetic evidence has shown the association between its genetic polymorphisms and the susceptibility of schizophrenia  situates in chromosome 10, and ADAM12 encodes trans-membrane metalloproteinase, which can secrete glycoproteins that are involved in cell–cell interaction, fertilization, and muscle development. We also identified natural selection signatures in cytochrome P450 family 2 subfamily A member 6 (CYP2A6), isthmin 1 (ISM1), and cytochrome P450 family 2 subfamily D member 6 (CYP2D6).Genetic studies have identified many biologically adaptive genes or pathways in ethnolinguistically diverse populations. Evolutionary adaptative mutations could be accumulated and generated as longer extended haplotype homozygosity with their increase of allele frequency of the initial mutations. We scanned for candidates of the positive selections using iHS and XPEHH in SCM. We first calculated XPEHH values for Miao using northern Han as a reference population and identified obvious candidates in chromosomes 1-3, 9, 20, and 22 . Chromosophrenia . We alsocuronide . RBMS3 eynthesis . It alsoynthesis . TAGLN3 ynthesis . Chromosophrenia , Nicolaiophrenia , and lunophrenia . ADAM mep-values of XPEHH scores larger than 5 and normalized iHS scores larger than 3.3 were used in the enrichment analysis via the Metascape. Overlapping loci observed among three gene candidate lists showed the more common gene candidates inferred from XPEHH and less overlapping loci between XPEHH-based loci and iHS-based loci , response to stimulus (GO:0050896), cellular process (GO:0009987), regulation of biological process (GO:0050789), biological adhesion (GO:0022610), and developmental process (GO:0032502). The results from the iHS also showed other top-level gene ontology biological processes, which included immune system process (GO:0002376), biological regulation (GO:0065007), positive regulation of biological process (GO:0048518), behavior (GO:0007610), signaling (GO:0023052), multicellular organismal process (GO:0032501), locomotion (GO:0040011), negative regulation of biological process (GO:0048519) and localization (GO:0051179), the detailed enriched terms, pathways, and processes enrichment analysis and their networks of top twenty clusters showed in do not reveal the previously reported naturally selected loci-associated pigmentation, alcohol metabolism, and other common adaptive signals  of East Asians , we made enrichment analysis based on three sets of identified natural-selection genes. Loci with sed loci . A heatmpathways  showed tt Asians .via lower-density genetic markers  and higher-density array-based genome-wide SNPs and whole-genome sequencing data, which advanced our understating of the origin, diversification, migration, admixture, and adaptation of Chinese populations , SCM shared much derived ancestry with northern East Asians, suggesting that the persistent southward gene flow from northern East Asians influenced the modern genetic profile of HM people. Based on the admixture times dated via GLOBETROTTER and ALDER, complex population migration and admixture events occurred in the historic and prehistoric proto-HM people. Spatiotemporal analysis between modern HM people and their genetic evolutionary relationship with surrounding modern ethnolinguistically diverse populations as well as the genetic relationship between ancient Yellow River millet farmers and Fujian and Guangxi ancient populations suggested that HM people originated from the crossroad region of Sichuan and Guizhou provinces. Modern HM people may have remained the most representative ancestry of ancient Daxi and Shijiahe people in the middle Yangtze River basin, which needed to be validated directly via ancient genomes in this region.To comprehensively provide a complete picture of the genetic diversity of China and make comprehensive sampling and sequencing strategies in the next whole-genome sequencing projects, it is necessary to explore the basal pattern genetic background using the small sample size and array genotyping technology. As our part of the initial pilot work in the CPGDP based on anthropologically informed sampling, we reported genome-wide SNP data of 55 SCM samples from three geographically diverse populations. Our analysis reveals the key features of the landscape of southwestern HM lineage, including the identified unique HM cline in East-Asian-scale PCA and population stratification in regional-scale-PCA, the observed dominant specific ancestry in geographically distant HM people, the estimated strong genetic affinity among HM people Ethnically different populations undergoing historical differences in the pathogen exposure may remain as different patterns of the allele frequency spectrum and extended haplotype homozygosity under natural selection processes. We identified different natural selection candidates  associated with several different biological functions  in Miao people compared with other East Asians. Denisovan archaic high-altitude adaptive introgression signals were observed in Tibetans (EPAS1 and EGLN1), which is not observed in HM people with obvious natural selection signatures . More DeTaken together, we provided genome-wide SNP data from SCM and directly evidenced their genetic affinity with the southmost Thailand and Vietnam Hmong and ancient 500-year-old Guangxi GaoHuaHua people. We identified HM-specific ancestry components spatially distributed ranged from the middle Yangtze River basin to Southeast Asia and temporally distributed at least since 500 years ago. These results provided direct evidence that supported a model in which HM-speaking populations originated from the ancient Baiyue in the middle Yangtze River basin and experienced a recent southward migration from Sichuan and Guizhou to Vietnam and Thailand. Additionally, unique patterns of naturally -selected signatures in SCM have identified many candidate genes associated with important neural system biological processes and pathways, which do not support the possibility of recent large-scale admixture occurring between HM people and surrounding Han Chinese. If these phenomena occurred, genetic changes can produce shifts in the allele frequency spectrum of pre-existing mutations and trend to show a consistent pattern of the selected signals.


The Italian Peninsula, a natural pier across the Mediterranean Sea, witnessed intricate population events since the very beginning of the human occupation in Europe. In the last few years, an increasing number of modern and ancient genomes from the area have been published by the international research community. This genomic perspective started unveiling the relevance of Italy to understand the post-Last Glacial Maximum (LGM) re-peopling of Europe, the earlier phase of the Neolithic westward migrations, and its linking role between Eastern and Western Mediterranean areas after the Iron Age. However, many open questions are still waiting for more data to be addressed in full. With this review, we summarize the current knowledge emerging from the available ancient Italian individuals and, by re-analysing them all at once, we try to shed light on the avenues future research in the area should cover. In particular, open questions concern (1) the fate of pre-Villabruna Europeans and to what extent their genomic components were absorbed by the post-LGM hunter-gatherers; (2) the role of Sicily and Sardinia before LGM; (3) to what degree the documented genetic structure within the Early Neolithic settlers can be described as two separate migrations; (4) what are the population events behind the marked presence of an Iranian Neolithic-like component in Bronze Age and Iron Age Italian and Southern European samples.The online version contains supplementary material available at 10.1007/s00439-021-02328-6. An increasingly detailed portrait of ancient Europe has been emerging in the last few years thanks to the joint analyses of archaeological findings, and the pattern of modern and ancient genetic variations elucidating past cultural transitions. The coarse edges of the European mosaic have been traced by three major contributions arriving in different periods and from different homelands: the first hunter-gatherers on the European ground, the Neolithic farmers from Anatolia, and the Bronze Age herders from the Steppe , modern humans have lived there   involving dramatic changes in the behavior, culture, and ecology of human populations, culminating in the domestication of plants and animals, and the almost ubiquitous adoption of pottery  showed that this separation is possibly due to different amounts of ancestry related to WHG. It may be possible that the populations spreading through Central and Mediterranean routes mixed differently with residing hunter-gatherer populations, with the former witnessing a higher number of admixture events, in line with some archaeological evidence suggesting a sparser mesolithic hunter-gatherer population in Southern Italy, although new archaeological surveys are needed  tentatively suggest  was a period of major cultural changes, indeed, with the introduction of new metalworking techniques and breakthrough innovations, such as the wheel, the chariot and the ox-drawn plough, the rising of the first cities and the intensification of commercial networks whose routes for selling metal goods connected people from far and wide  and ended with the Roman conquest Peroni . This peThe most particular case is represented by the Sardinians: a strong Anatolian Neolithic footprint is visible in the Iron Age samples due to the persistent isolation in previous times and despite the documented contacts with Mycenaeans and Phoenicians , those Greek-speaking communities are believed to have lived in Southern Italy probably since the Magna Graecia times.As Sardinia, also Sicily was an important Punic trading post, especially on the western coast. Despite the paucity of samples from this area, the presence of Northern African ancestry on the island could be tentatively reconducted to the Iron Age (or at least Antiquity), because, although absent in previous time layers, it’s present in modern Sicilians . The Etruscan civilization and, specifically its earliest phase called Villanovan culture, dates back to the ninth century BCE and ended with the slow and gradual Roman assimilation from the sixth century BCE. However, where the Etruscans came from is still uncertain. Both ancient and modern historians have different opinions on the matter and, while some researchers believe in an autochthonous process of formation from the previous Villanovan society, others claim an Anatolian origin  on the banks of the Tiber river. Central Italy and Rome inhabitants of the first millennium BCE exhibited high genetic variability, in particular, they showed a relevant Steppe-related ancestry, an increase in the Iranian-Neolithic component, respect to previous times, and the appearance of the first Northern African signatures on the Peninsula , economic stagnation, and invasions from non-Roman populations Harper . The EmpThe split of the Empire also had repercussions on the Italian genetic composition. Antonio and colleagues observed a shift from the Eastern Mediterranean to continental Europe in Central Italian samples , which probably resulted from the reduction of the East–West mobility, the demographic drop, and the arrival of people from Central and Eastern Europe. Besides the Romans, the Longobards were another relevant population for the Italian cultural landscape during the Late Antiquity. Samples from the Longobard cemetery of Collegno  show genetic affinities to Bronze Age people from Central and Eastern Europe  and the software PLINK 1.9  and 60 . We kept the Mbuti individuals from Congo [HGDP, ” (“Assessment” column) and we removed those coming from Uzbekistan, Kazakhstan, Algeria, Morocco, Tunisia, Libya, as well as some populations from Russia. We excluded SNPs on sexual chromosomes and those monomorphic or with more than 5% missing data in the modern samples using PLINK.To further refine the backbone of the modern-day European genetic variability, without mixing different genotyping techniques, we selected the modern samples flagged as “convertf.We extracted the bulk of variants built on modern samples from each dataset and we merged them with PLINK. Finally, we excluded the ancient samples with more than 5000 missing SNPs and we converted the dataset to the EIGENSTRAT format using Our final dataset contained 2144 ancient and 1091 modern samples .We assigned each sample to a geographic macro-area among “Northern Europe”, “Western Europe”, “Eastern Europe”, “Central Europe”, “Continental Italy”, “Peninsular Italy”, “Sicily”, “Sardinia”, “Balkans”, “Middle East” and “Northern Africa” and to a period among “Hunter-gatherers”, “Neolithic”, “Bronze Age”, “Iron Age”, and “Antiquity” based on the available cultural information . Given the complete lack of ancient data from Corsica, we do not discuss the genetics of Corsica in this review, even though its geographic proximity to Sardinia and the Italian mainland suggests that many of the demographic changes of Italy would be shared with the island.We additionally distinguished, among the hunter-gatherers, the Pre- and Post-Villabruna samples using 18kya as a threshold.We computed the derived allele frequencies for the twelve genetic variants, which have been found under selection among hunter-gatherers, early farmers, and steppe ancestry , in a way that the higher is the f3, the closer is the relatedness between the two populations.To explore the genetic affinities among ancient samples, we performed a series of Outgroup f3, after removing samples flagged as outliers or with family relationships with other individuals (Supplementary Table 4).We grouped the non-Italian ancient samples into higher cultural labels, which are shown in the column “Higher.Label” of Supplementary Table 1 and we performed the Outgroup f3 results by subtracting the f3 values from 1 and we performed a classical (metric) multi-dimensional scaling (MDS) with default options from such distance matrices using the function cmdscale in the stats package of R [version 4.0.4 Supplementary Table 1 (XLSX 235 KB)Supplementary Table 2 (XLSX 98 KB)Supplementary Table 3 (XLSX 23 KB)Supplementary Table 4 (XLSX 1982 KB)Below is the link to the electronic supplementary material.


Palaemonetes sinensis) is an important species widely distributed throughout China, which is ecologically relevant and possesses ornamental and economic value. These organisms have experienced a sharp decline in population due to overfishing. Therefore interest in P. sinensis aquaculture has risen in an effort to alleviate fishing pressure on wild populations. Therefore, we investigated the genetic diversity and variation of P. sinensis to verify the accuracy of previous research results, as well as to assess the risk of diversity decline in wild populations and provide data for artificial breeding.Chinese grass shrimp  values and dendrograms were constructed with the MEGA 11 software package.Hd) and nucleotide diversity (π) indices ranged from 0.244 ± 0.083 to 0.790 ± 0.048 and from 0.0004 ± 0.0001 to 0.0028 ± 0.0006, respectively. Haplotype network analyses identified haplotype Hap_1 as a potential maternal ancestral haplotype for the studied populations. AMOVA results indicated that genetic variations mainly occurred within populations (73.07%). Moreover, according to the maximum variation among groups (FCT), analysis of molecular variance using the optimal two-group scheme indicated that the maximum variation occurred among groups (53.36%). Neutrality and mismatch distribution tests suggested that P. sinensis underwent a recent population expansion. Consistent with the SAMOVA analysis and haplotype network analyses, the Ds and FST between the population pairs indicated that the JN population was distinctive from the others.Our study obtained sequences from 325 individuals, and 41 haplotypes were identified among the populations. The haplotype diversity (P. sinensis populations, and our findings elucidated highly significant differences within populations. The JN population was differentiated from the other six populations, as a result of long-term geographical separation. Overall, the present study provided a valuable basis for the management of genetic resources and a better understanding of the ecology and evolution of this species.Our study conducted a comprehensive characterization of seven wild The online version contains supplementary material available at 10.1186/s12862-021-01893-8. Palaemonetes sinensis) is an important species belonging to the Palaemonidae family, which is widely distributed throughout China , M.  = 0.94) , and P. Hd = 0.79 in Southsampling .P. sinensis populations showed high haplotype diversity (Hd = 0.627) but low nucleotide diversity (π = 0.0004) in this study. The remarkably lower nucleotide diversity (π < 0.005) may be due to the commercial overexploitation of wild stocks, as P. sinensis is a prevalent specialty food and is also sought-after as a pet in this region. During sample collection, we found that some fishermen would engage in illegal fishing during the closed season, and this illegal fishing would lead to a sharp decline in the population size, resulting in a decrease in genetic diversity. Overharvesting can drive the decline of genetic diversity [Overall, the iversity , 27, 34,y Hd = 0.27 but loP. sinensis, which accounted for 73.07% of the total genetic variation, was much higher than that among populations. Moreover, according to the maximum variation among groups (FCT) determined by SAMOVA, analysis of molecular variance using the optimal two-group scheme (JN and the other populations) indicated that the highest variations were found among groups (53.36%). Similarly, both the pairwise Ds and FST values revealed that the JN population was genetically different from the other populations. According to the criterion made by Thorp [Ds values between pairs of P. sinensis populations ranged between 0.0007 and 0.0034, indicated that they were closely populations (Ds < 0.2). In contrast, the pairwise FST values among the other six populations were low or moderate [FST values of the six populations indicated that they might share the same ancestors, and the haplotype analysis results also supported this conclusion. This was likely because the samples in the present study were collected from two geographical areas. Specifically, the JN population belonged to the Huaihe River Drainage in East China, whereas the other six populations inhabit the Liaohe River Basin and Related River Drainage in Northeast China. Therefore, the divergence between the JN population and the other six populations is due to the long-term geographical separation of their ancestors, which is consistent with our previous study using microsatellite markers [Based on the outcome of AMOVA, the genetic differentiation within populations of by Thorp , the pais Ds < 0.. In cont markers . Neverths Ds < 0.. In conts Ds < 0.. In contD and Fu’s Fs) and mismatch distribution analysis were performed to understand the demographic dynamics of seven P. sinensis populations. Both mismatch distribution analysis and significant negative values of neutrality tests indicated a pattern of recent population expansion in most populations. The insignificant values of PSSD and PRag (P > 0.05) did not reject the hypothesis of the population growth in all seven populations. Moreover, the extremely significant negative values in the majority of Fu’s Fs statistics in populations DL, PJ, SL, SY, and JN also confirmed the expansions of these five populations despite the negative but non-significant Fu’s Fs value in population AS and SH.Neutrality tests (Tajima’π < 0.005) and higher haplotype diversity (Hd > 0.5)  were identified in the DL, PJ, AS and JN populations, indicating that they may have recently experienced a bottleneck or founder effect produced by minority populations [The demographic history was reflected in the genetic indices of this species, which showed low nucleotide diversity (5) Table . As descion size . Additioulations . The genulations , 17.P. sinensis populations was assessed based on partial sequences of the COI gene. The results of AMOVA showed highly significant differences within populations. Forty-one haplotypes were detected among the populations, and the haplotype network indicated that haplotype Hap_1 might be the maternal ancestral haplotype for the studied P. sinensis populations. Neutrality and mismatch distribution tests suggested that P. sinensis underwent recent population expansion. The result of SAMOVA analysis, haplotype network analysis, Ds, and FST among pairs of populations suggested that the JN population was distinctive from the other six populations, which was due to long-term geographic separation. Overall, our results provide important insights for the development of genetic resource management strategies and enable a better understanding of the ecology and evolution of this species. Lastly, we propose several strategies for future P. sinensis genetic germplasm protection and aquaculture. First, illegal fishing should be strictly limited during the closed fishing season to avoid reductions in P. sinensis population size. Second, the DL, PJ, AS and JN populations were not suitable for further selective breeding as a base population, which highlights the need for in situ conservation efforts. Moreover, populations with low genetic diversity can be recovered by releasing shrimp from adjacent populations, and unnecessary cross-basin introduction should be avoided to preserve the local genetic resources.The genetic variability of seven Additional file 1: Table S1. Distribution of the COI haplotypes of P. sinensis.


Lupinus polyphyllus Lindl. and Heracleum sosnowskyi Manden. from Russia and Ukraine, and whether it may be associated with the strategy of their further expansion.Aggressive-invasive species often interact with native ones, thus considerably changing the biological communities, with ecological, economic, and even social effects. It is a challenge to evaluate the direction and the rate of microevolution in native and introduced populations. One of the ways to do this is to estimate the genetic diversity. An introduction often imposes a reduction in population size , which has the potential to reduce genetic diversity. However, after a lag, the genetic diversity can be restored due to repeated invasions (multiply introductions), hybridization between individuals from two different subspecies or species in the invaded ranges, as well as during rapid genetic changes under selection pressures in the novel environment. The purpose of this study was to determine the level of genetic diversity in successful invasive species Lupinus polyphyllus Lindl. and Heracleum sosnowskyi Manden. from Russia and Ukraine, were investigated. The success in naturalization of both species is associated with human activities, since they have been used in agriculture and floriculture and both have qualities such as environmental tolerance, high fertility and phenotypic plasticity. The purpose of this study was to determine the level of genetic diversity of both species. For Heracleum sosnowskyi Manden., genetic diversity was compared in invasive and native populations. For Lupinus polyphyllus Lindl., the genetic diversity was compared in variety, feral and invasive populations. A genetic diversity was formulated using RAPD, ISSR and REMAP. For Heracleum sosnowskyi Manden., the average genetic diversity within the invasive population was similar (0.432), but slightly less (0.502) than within the native Caucasian population. This may suggest the successful naturalization of invaders and almost complete reconstruction of their genetic diversity. For Lupinus polyphyllus Lindl., the genetic diversity for the invasive population was the highest, with an average of 0.294, while for variety, it was the lowest, with an average of 0.194. The feral population had an intermediate place with an average of 0.248, which could suggest an increase of diversity in the process of naturalization.In our study, two aggressive-invasive species,  Lupinus polyphyllus Lindl. and Heracleum sosnowskyi Manden. From Russia and Ukraine, were studied.Invasive-alien species (IAS) can be considered the second most important threat to biodiversity, after habitat destruction . When inL. polyphyllus is an herbaceous biennial or short-lived perennial of the Fabaceae with a height of 0.8–1.5 m. The flowers (up to 80 pieces) are blue, or less often pink or white, and are grouped in a terminal erect inflorescence. The natural habitat of L. polyphyllus is western North America—Canada (British Columbia) and the United States . This species grows on riverbanks, meadows, roadsides, and the other disturbed habitats [L. polyphyllus to England, and soon lupins were cultivated in Europe as garden plants. As early as the 1840s, botanical gardens offered a variety of colour forms for this species. The distribution of lupins in Europe had increased by the beginning of the 20th century, as they were grown for soil amelioration and animal feed [L. polyphyllus and Bradyrhyzobium spp., which form nitrogen-fixing root nodules [L. polyphyllus became widespread, to the extent that it became listed in the “Central Russia Floral Black Book” [habitats . In 1826 nodules . In Germ nodules , while i nodules ,12. In L nodules . In Russck Book” . H. sosnowskyi, or hogweed, is a perennial monocarpic herbaceous plant of the Umbelliferae with a height of 3–4 m. The inflorescence is a large complex umbel (up to 50–80 cm in diameter) consisting of 30–75 rays. The flowers are white or, less often, pink, and each inflorescence has from 30 to 150 flowers. Thus, there can be more than 80,000 flowers on one plant. The natural range of hogweed is east of the Greater Caucasus, the east and southeast of Transcaucasia, and the northeast of Turkey. It grows in subalpine meadows, glades, and edges of beech and fir-beech forests in the middle and upper forest belt [H. sosnowskyi has been cultivated throughout the former USSR as a highly productive and stable silage plant. The active cultivation of H. sosnowskyi was promoted because of its extraordinary productivity  and high protein (10–20%), sugar (10–31%), vitamins, and mineral elements [H. sosnowskyi cellular fluid has several pathogenic properties; it inhibits cell growth and leads to cell death, oppresses mitosis, induces chromosomal mutations and causes apoptosis [H. sosnowskyi acquire an unpleasant bitter taste, and then the phytoestrogens that are part of the plant induce infertility in animals. Consequently, when all these unpleasant side-effects became clear, the breeding of H. sosnowskyi was stopped. By the early 1980s, using hogweed for silage was mostly abandoned; however, the plant turned out to be very tenacious, and feral populations developed quickly. By 1990–2000, hogweed became a serious problem in many ranges of the European part of Russia. The spread of H. sosnowskyi was facilitated by the abandonment of once cultivated lands that occurred with the decline of agriculture following the collapse of the USSR. The consequence then was that hogweed began to spread especially actively. Thus, H. sosnowskyi is called “an indicator of mismanagement” [H. sosnowskyi increased in its growth area by 10% annually, with its invaded ranges including the countries of the Baltic region , Belarus, Ukraine, and Russia. In Russia, hogweed has spread widely in the European part of Russia, penetrating the Urals, the south of Western Siberia, and the Far East. It grows on the outskirts of roads, fields and forest belts, wastelands, forests, and floodplain meadows [H. sosnowskyi began in the 2000s, with one of the first projects being the Giant Alien Project (2002–2005), dedicated to three giant hogweeds . More than 40 scientists from seven countries participated, proposing, methods to combat all three species [est belt . Since 1elements . Selectipoptosis . The milotein 10–%, sugar otein 10–%, sugar  species . L. polyphyllus and H. sosnowskyi by means of RAPD, ISSR, and REMAP. The genetic diversity of invasive populations of H. sosnowskyi was compared with its native population. The genetic diversity of invasive population of L. polyphyllus was compared with its variety and feral population, as examples at the beginning of the invasive process. Upon introduction of a new species in an invaded range, the level of genetic diversity within populations is often reduced . HoweverH. sosnowskyi samples 2SO4 and 25 mM MgCl2)  and 30 ng of DNA. The reaction mixture was overlayed by 30 µL of mineral oil. Following optimization, the amplification conditions were: denaturation at 94 °C for 2 min, 5 cycles as follows: denaturation at 94 °C for 20 s, annealing at t °C for 10 c, elongation at 72 °C for 10 c; 35 cycles as follows: denaturation at 94 °C for 5 c, annealing at t °C for 5 c, elongation at 72 °C for 5 c; 1 cycle as follows: elongation for 2 min at 72 °C; t °C was 37 °C for RAPD primers, 37–55 °C for ISSR primers and 50–60 °C for REMAP primers. Amplifications were done in a thermocycler MC2 . To control the purity of reagents, water was added to the reaction mixture instead of DNA (reactions C1- and C-). The experiments were done in triplicate.Twenty-five microlitres of the reaction mixture contained 0.25 µM primer , 0.2 mM each dNTP, 2U of Taq DNA polymerase, standard 10× PCR buffer  was used to measure the sizes of DNA fragments.To quantitatively estimate the genetic diversity, the data are presented as binary trait matrices, where the presence or absence of the same size PCR bands were assigned values of 1 or 0, respectively. The value of 1 was assigned only to bands of high intensity consistently detected in all experiments. To estimate the diversity, the binary data for each population were summed, and the most common values were used to build the matrices. Gd(xy) = 1 − 2Nxy/(Nx + Ny), where Nx is the number of fragments present in profile x, but absent in profile y; Ny is the number of fragments present in profile y, but absent in profile x; Nxy is the number of fragments present in both profiles [The binary trait matrices were used to derive the difference matrices, with the Nei and Li genetic diversity (Gd) calculated as follows: profiles .The matrices obtained were used to build phylogenetic trees. The trees were built by the Unweighted Pair Group Method with Arithmetic Mean (UPGMA)  using TrAll primers yielded PCR bands amplified from plant genomic DNA. A total of 39 RAPD and 17 ISSR primers were used. Depending on the primer, the number of amplified fragments varied from 3 to 16, with their sizes varying from 200 to 2000 bp; the optimal annealing temperature was determined experimentally for each primer. The optimal primer pairs were designed for REMAP. Five REMAP pairs gave sharp and consistent profiles for lupin and seven REMAP pairs for hogweed .L. polyphyllus. Other primers amplified fragments specific for groups of plants (RAPD: OPA-2–large (about 2000 bp) fragments from the DNA of most plants of the ‘Minaret’ variety; a 400 bp fragment specific for Smolensk region plants; QR-5–1450; and 1500 bp fragments from the ‘Minaret’ variety; REMAP: MS7+Wis–900 bp from the feral population at MBG). Such fragments may be of interest as markers for those populations . These fragments may represent nucleotide sequences specific to ulations .The genetic diversity between populations and the ‘Minaret’ variety varied from 0.196 to 0.341 and was 0.260, on average . Additionally, the genetic diversity within invasive and feral populations and the ‘Minaret’ variety was calculated. To evaluate the genetic diversity within invasive population, the populations from Moscow, Kaluga, Smolensk, and Kostroma regions were pooled (34 samples). The amplified fragment profiles from 34 plant samples were presented as binary matrices and used to determine genetic diversity values . DependiPhylogenetic trees were built by using individual methods  and all methods combined .Regardless of the method used, the ‘Minaret’ variety was considerably different from the other populations. RAPD and REMAP clustered together populations from the Moscow region and the feral population at MBG a,c. HoweThe phylogenetic tree of the invasive population had four clusters that almost coincided with the geographic origins of Moscow, Kaluga, Smolensk, and Kostroma populations .The genetic diversity values within the feral MBG population (21 samples) and the ‘Minaret’ variety (29 samples) were calculated similarly . Interestingly, with primers MS6 + Thv19, a 440 bp fragment was detected, which is characteristic of plants growing in the centre of Zhytomir but absent in plants of the industrial zone of Zhytomyr .The populations from industrial zones and the centre of Zhitomir were pooled (33 samples), and the genetic diversity between the populations of hogweed was significant, varying from 0.438 to 0.614, and was 0.539, on average . The greatest genetic diversity was between populations from Moscow and Caucasus (0.614). This could be explained by the significant remoteness of the Moscow population and its evolution in the process of adaptation to a new habitat. The least genetic diversity was found between the Zhitomir and Peskovka populations (0.438). This could be explained by anthropogenic dispersal from one region to another. This is hardly a possible result of gene flow, since the distance between the Zhitomir and Peskovka is about 100 km . The genPhylogenetic trees were built using individual methods  and all methods combined .Regardless of the method used, the Zhytomyr and Peskovka populations clustered together with high bootstrap values, indicating their close relationship . HogweedL. polyphyllus. All techniques gave reproducible results with an adequate number of polymorphic and monomorphic fragments necessary for estimating the genetic diversity both within and between populations. As there are few investigations of L. polyphyllus by means of PCR markers, especially where genetic diversity was calculated [Lupinus.RAPD, ISSR, and REMAP techniques were used to study the genetic diversity of various populations of lculated ,36, we cL. albus L. Wolko et al. [L. albus, seven cultivars of L. angustifolius L., and twelve cultivars of L. luteus. Gilbert et al. [L. albus accessions from a collection of lupin germplasm. Yorgancilar et al. [L. albus, L. angustifolius, and L. luteus). Using ISSR, Artyukhova [L. angustifolius, L. hiteus, and L. albus. Clements et al. [L. angustifolius genotypes using ISSR markers. Similarly, Guilengue et al. [L. mutabilis using ISSR. Khapilina et al. [L. angustifolius.Qiu et al.  used shoo et al.  identifit et al.  used ISSr et al.  successftyukhova  developes et al.  investige et al.  analyseda et al.  succeedeThe reliability of ISSR, REMAP and, especially, RAPD is supported by the results obtained. Firstly, three phylogenetic trees of the analysed populations built upon RAPD, ISSR, and REMAP data showed the ‘Minaret’ variety is considerably divergent from the other populations (presumably due to its independent origin) . SecondlL. polypyllus in Lithuania with RAPD and found that Nei’s average genetic distance between populations was 0.148 ± 0.021, which was also less than the average genetic distance between individuals in the forest (0.290 ± 0.062) and field populations (0.229 ± 0.037). The authors observed significant genetic differentiation among populations in genetically heterogeneous seed stock, low gene flow between populations, and, possibly, in local adaptations [L. polypyllus populations in Finland using 13 polymorphic microsatellite loci. The authors found that the genetic variation among populations was 0.05; it was significantly higher (0.25) within populations. Pairwise FST values among populations ranged from 0.02 to 0.25 and the global FST value was 0.19, suggesting moderate levels of genetic differentiation. The authors suggested that it was the result of human-mediated dispersal with multiple introductions from different sources rather than the natural spread of L. polyphyllus from a single or few sources in Finland [L. albus populations from two zones of Ethiopia and found that the genetic diversity was 0.223 for Merawi, 0.198 for Addis Kidam, 0.189 for Sekela, and 0.167 for Wembera: larger genetic diversity was found within rather than between populations. The authors supposed that high genetic diversity between populations might be the result of moderate gene flow and within the populations could be caused by the presence of preferential or diverse adaptive genes [L. albus using RAPD and ISSR, where the Nei genetic similarity index ranged from 0.74 to 0.88 (diversity from 0.12 to 0.26) [The genetic diversity for the invasive population was the highest, and varied from 0.045 to 0.411 , with anptations . Li et. ptations  investig Finland . Oumer eve genes , who estto 0.26) .In our study, the high level of genetic diversity within populations  was most likely associated with genetically heterogeneous seed stock and cross-pollination. The lower level of genetic diversity between populations was hardly a possible result of gene flow, since the distance between the Smolensk and Kostroma regions was about 700 km , but morL. pilosus using RAPD and found its genetic similarity to range from 0.02 to 0.450 (diversity 0.550–0.980). The low similarity could have been due to a high, long diverging process in non-coding regions [L. albus, L. angustifolius, L. cosentinii, and L. luteus) by means of AFLP and ISSR markers. Similarity values were 0.70–0.82 (diversity 0.18–0.30) among accessions of L. albus, 0.24–0.61 (diversity 0.39–0.76) among accessions of L. cosentinii, and 0.68–0.88 (diversity 0.12–0.32) among accessions of L. luteus [Lupinus spp. The intraspecific polymorphisms were revealed by AFLPs with similarity values of 0.908–0.955 (diversity 0.045–0.092) among L. albus accessions, 0.913–0.954 (0.046–0.087) in L. angustifolius, 0.920–0.988 (0.012–0.080) in L. hispanicus, 0.867–0.940 (0.060–0.033) in L. luteus, and 0.957–1.000 (0.043–0.000) in L. mutabilis [There are studies where the values of genetic diversity within species are much higher than ours. Al Rawashdeh et al.  studied  regions . There a regions  revealed. luteus . There a. luteus  used RAPutabilis .L. polypyllus due to the lack of samples. Thus, it is unclear whether the invasive population has recovered after invasion and adaptation, or whether its genetic diversity is increasing. As far as we are aware, there are no published data on the genetic diversity or similarity of the native population of L. polypyllus.Unfortunately, it was not possible to compare the genetic diversity of the invasive population in our study with the native population of In our research, in addition to the invasive and feral populations, the ‘Minaret’ variety was analysed. That this variety is not the source of the feral population is confirmed by all the dendrograms , with 0.The genetic diversity of the feral population (MBG) varied from 0.130 to 0.363 , with anL. polyphyllus from the introduced populations were larger in size, they flowered less frequently and with fewer flowering shoots than plants from the native populations. Tkacheva [L. polyphyllus as an invasive species could be explained by the increased competitive ability (EICA) hypothesis, which proposes that a larger size is selected due to intense intraspecific competition or reduced herbivore pressure in a new area [Tkacheva  comparedTkacheva  also stunew area .L. polyphyllus invasion.The intermediate level of genetic diversity in the lupin feral population and high level in the invasive population discovered in our research, together with the other data revealed for the same populations  suggestL. polyphyllus, there are few published investigations of H. sosnowskyi by means of PCR markers, especially where the values of genetic diversity have been calculated [Heracleum spp. A study of H. sosnowskyi was performed previously using RAPD [Heracleum using RAPD and ITS analysis, while noting its high genetic and morphological heterogeneity. Unfortunately, as with lculated . Therefoing RAPD ,55. Strying RAPD  analysedH. mantegazzianum from eight populations in Finland using microsatellite markers. The authors found that the range of pairwise FST values between populations was high (0.052–0.797), and the mean FST among all populations was 0.498, showing a significant difference. The authors observed differentiation between populations, resulting from multiple introductions combined with limited gene flow [We compared published data with our study, where the genetic diversity among the populations varied from 0.438 to 0.614 with an average of 0.539 . Niinikoene flow .L. polyphyllus with 2n = 48 [Heracleum spp. with 2n = 22 [It is difficult to say why the average genetic diversity for lupin (0.260) was two times lower than for hogweed (0.539). Both species are diploid,  2n = 48  and Hera 2n = 22 . It is pH. sosnowskyi to self-pollinate. Walker et al. [H. mantegazzianum populations in northeast England using four nuclear microsatellite and one plastid marker. Similar to our research, the genetic differentiation between populations from different river catchments was higher (FST mean pairwise 0.28 ± 0.1) than that within populations from the same area (FST mean pairwise 0.11 ± 0.08). The overall FST mean pairwise was 0.24 ± 0.13. The authors suggested that the high genetic differentiation between populations may indicate a large initial founder population or multiple introductions. Within population, seeds of this species are dispersed in water, resulting in a relatively small dispersal area and the relatively small dispersal range of likely pollinators [Unlike lupin, the genetic diversity among hogweed populations was greater (0.539)  than thar et al.  investiglinators .Heracleum species using AFLPs. The genetic similarity (Pairwise Dice’s similarity coefficient) in H. sosnowskyi among all populations was 0.891, on average (diversity 0.109); within Europe, it was 0.901, on average (diversity 0.099), and within the native range, it was 0.908 (diversity 0.092). The authors noted that more within-taxon variation was detected in the invaded range (Europe) than in the region of the native distribution, probably driven by hybridisation and inbreeding. The results also indicate that multiple introductions, rapid evolution and drift are likely to have occurred [H. mantegazzianum populations from the western Swiss Alps and 11 Caucasian populations using eight nuclear microsatellite loci together with plastid DNA markers and sequences. FST mean pairwise genetic differentiation for nuclear microsatellite loci between invasive populations varied from 0.180 to 0.396 with an average of 0.315 and between native populations from 0.092 to 0.202 with an average of 0.162. The authors indicated that the high population differentiation observed in the invasive range compared to the native range could have been generated by sequential founder events. The species may have been introduced multiple times, possibly from disparate source populations [H. persicum with 27 microsatellite markers and estimated that FST mean pairwise genetic differentiation (averaged over the population) was lowest between England and Sweden (0.267 ± 0.006), and highest between Norway and Denmark (0.552 ± 0.005). The mean FST was lower in the native (0.25) compared to the introduced range (0.30), but the difference was marginal and nonsignificant [The genetic diversity within invasive populations varied from 0.398 to 0.457 with an average of 0.432 ; diversioccurred . Henry eoccurred  investigulations . Rijal eAnalysing other data such as the percentage of polymorphic loci, observed and expected heterozygosity, allelic richness and inbreeding coefficient, Henry et al.  and RijaH. sosnowskyi was widely introduced at the state level. The Institute of Biology of the Komi Republic received H. sosnowskyi seeds from many botanical gardens and from native ranges . In the decade 1940–1950, accumulation of seeds and cultivation of this plant was of great importance in five main centres: Murmansk, Moscow, and Leningrad regions, the Republic of Komi, and Kabardino-Balkaria. For example, 140 hectares of H. sosnowskyi were planted in the Rzhevsky region, and 7600 kg of its seeds were sent to 60 addresses [According to the literature, invasive populations often have lower genetic diversity compared to the native population due to bottlenecking, genetic drift during colonisation, and founder effects . Howeverddresses . H. Sosnowskyi. The sizes of all parts of invasive plants of H. sosnowsky are larger than plants of native populations: the height of the stem (1–1.5 m in the Caucasus and 2–4 m in Europe) and the size of the seeds . In addition, Caucasian plants grow in forests and meadows, whereas plants of the invaded range are photophilous and mainly found in open habitats [A rapid local adaptation and evolution in invasive ranges is characteristic of habitats .H. sibiricum and H. spondyllium and other introduced hogweeds [H. sosnowskyi invasion.A high level of genetic diversity in hogweed invasive populations, similar to the native Caucasion population, was found in our research, suggesting reconstruction of their genetic diversity. This high level of genetic diversity, together with data suggesting the increase of some biomorphological traits, the ability to occupy new biotopes , and to hogweeds  reportedL. polyphyllus and H. sosnowskyi have been used in agriculture . Many snce 1922 . Where vocenoses ,68. Thus


Setosphaeria turcica (Luttrell) Leonard and Suggs is one of the main biotic constraints on sweet corn (Zea mays L.) yield and quality in Fujian Province, China. Currently, however, there is comparatively little information available regarding the distribution of mating types, population genetics, and reproductive strategies of this pathogen in Fujian. In this study, we investigated the distribution of mating types and population genetics of 117 isolates of S. turcica collected from seven of the main sweet corn-growing regions in Fujian Province, based on multiple polymerase chain reaction analyses using two mating type-specific primer pairs and 11 inter-simple sequence repeat markers. Furthermore, we examined the mode of reproduction of Fujian S. turcica populations. Both MAT1-1 and MAT1-2 mating types were detected throughout all seven sampling locations. The majority of MAT1-2 isolates were detected from Dongyou, Jian’ou, Pingnan, Songxi, and Longyan, whereas a large proportion of the detected MAT1-1 isolates were among those collected from Dongfeng and Nanjing. Furthermore, we detected five shared multi-locus haplotypes among S. turcica isolates from Dongyou, Jian’ou, Pingnan, Nanjing, and Songxi, whereas no shared haplotypes were observed between the Dongfeng (or Longyan) population and these five populations. Pairwise comparisons of the indices ΦPT and Nm, and population structure and principal coordinate analyses indicated genetic differentiation between both the regional and the mating type populations of S. turcica in Fujian. The skewed mating type ratio associated with low a haplotypic diversity and evident linkage disequilibrium reveals a mixed reproductive strategy for S. turcica populations in Fujian Province. The findings of this study advance our current understanding of the genetic diversity, population structure, and reproductive strategies of S. turcica populations infecting sweet corn in Fujian Province, and will potentially contribute to further resistance breeding efforts.The northern corn leaf blight (NCLB) pathogen  Setosphaeria turcica (Luttrell) Leonard and Suggs) , the causal agent of northern corn leaf blight (NCLB), is a biotic constrain factor, which worldwide is responsible for substantial reductions in the yield and quality of corn (Zea mays L.), a vital basic human food source. Moreover, more than half of the global production is used as an industrial raw material and as a source of livestock fodder  gene locus that can occur in two alternative allelic forms, designated MAT1-1 and MAT1-2  exhibit high within population genetic similarities, but show evident genetic differentiation from S. turcica populations in southwestern China . However, although natural populations of S. turcica in north and southwest China tend to be well characterized with respect to mating type distribution, genetic diversity, population structure, and mode of reproduction  and incubated in darkness at 25°C for 3–5 days , Dongfeng , Jian’ou , and Songxi  regions, which are located in north Fujian; Pingnan , Nanjing , and Longyan  regions located in east, south and west Fujian, respectively. The straight-line distance between any two adjacent regions is at least 15 km . Three l3–5 days . Thereaf3–5 days . Finally3–5 days . In addiS. turcica isolates on PDA at 25°C under darkness for 7 days, with each isolate being grown on two 90-mm Petri dishes. For each isolate, we selected a Petri dish showing good mycelial growth for the extraction of DNA using a mini-preparation approach. Mycelia were gently scraped from each Petri dish and placed into individual 2.0-mL microfuge tubes, to which was added an 800-μL volume of extraction buffer  and an equal volume of phenol/chloroform/isoamyl alcohol (25:24:1). The tube contents were then lightly homogenized and incubated in a 180 rpm shaking incubator at 37°C for 1–1.5 h. Following centrifugation at 12000 rpm for 20 min, the liquid supernatant (approximately 600 μL) from each tube was transferred to a fresh 1.5-mL tube, to which an equal volume of ice-cold isopropanol was added and allowed to precipitate at −20°C for more than 30 min. The resulting DNA preparations were washed one to two times with 70% ice-cold ethanol and resuspended with 50 μL TE buffer . The concentration of each DNA sample was measured using a NanoDrop 2000 spectrophotometer  and adjusted to 100 ng⋅μL–1 using TE buffer. The DNA samples were then stored in a freezer at −20°C until used for analysis.For the purposes of DNA extractions, we cultured individual TM Thermal Cycler  with initial denaturation at 94°C for 5 min, followed by 35 cycles at 94°C for 45 s, 57.2°C for 45 s, and 72°C for 60 s, and a final cycle at 72°C for 10 min. The amplicons were analyzed by 1.0% agarose gel electrophoresis  and stained with ethidium bromide . Each isolate was independently amplified at least three times, and the sizes of amplicons were estimated using a 2000-bp DNA marker . The ratios between MAT1-1 and MAT1-2 from each geographical population and combined isolates were subjected to a Pearson Chi-square test using DPS software v7.05  to evaluate divergence from the expected ratio of 1:1 at the P < 0.05 level. Chi-square tests were not performed on populations derived from single leaves owing to the limited population size.The mating type of isolates was determined based on multiple PCR analyses using two mating type-specific primer pairs (StMAT01-2 and StMAT02-3), the sequences of which are as follows: MAT1-1 , with a specific amplicon size of 816 bp; and MAT1-2 , with a specific amplicon size of 132 bp . PCR wasTM Thermal Cycler  with initial denaturation at 94°C for 5 min, followed by 35 cycles at 94°C for 45 s, 48–56°C for 45 s, and 72°C for 1.5 min, and a final cycle at 72°C for 10 min. The products were separated on 1.5% agarose gels containing ethidium bromide  and visualized using a Gel DocTM XR + Imaging System . DNA fingerprints across the entire population for each ISSR primer on agarose gels were scanned using Image Lab software v5.2  and transformed as 0 and 1  matrixes.Eleven highly polymorphic, sensitive, and repeatable ISSR primers, which were optimized from the ISSR primer database  as described previously , were usS. turcica populations from Fujian Province were grouped according to geographical origins and mating types. Genetic diversity parameters, including the percentage of polymorphic loci (PL), observed number of alleles (Na), Shannon’s information index (I), Nei’s genetic distance, and Nei’s genetic identity, were automatically calculated based on bootstrap analysis with 1,000 replicates using POPGENE v1.32 software (M), number of private loci (NP), and Nei’s unbiased gene diversity (HU) were calculated using GenALEx v6.5 based on 999 permutations (HS) was calculated as HS = (–Σ(Piln(Pi)))/ln(n), where Pi is the frequency of the ith haplotype in one population and n is the population size (F) was calculated using the formula CF = 1–[(NM)/(subpopulation size)]. All the aforementioned diversity parameters were calculated based on the full data of the products amplified using the 11 ISSR markers.For the purposes of genetic analysis, software . The numutations . Haplotyion size . The cloPT; a measure of population genetic differentiation that is analogous to GST or FST) for ISSR markers among regional populations, we calculated the pairwise matrix of ΦPT values with corresponding P values and gene flow (Nm) among populations of S. turcica from the seven sweet corn-growing regions in Fujian, using the full datasets in GenALEx 6.5 , using GenAlEx 6.5 with 999 permutations to identify those coordinates for grouping isolates from the seven locations  was carried out based on the full datasets in GenAlEx 6.5 using Nei’s genetic distance with 999 permutations . Indepenocations .S. turcica isolates and mating types. Population genetic structure analysis was performed using STRUCTURE v2.3.4, a clustering program based on a Bayesian model  and P < 0.05 level.To determine the mode of reproduction in izations . For botS. turcica isolates obtained from seven locations covering the main sweet corn production regions in Fujian Province based on multiple PCR analyses using two mating type-specific primer pairs, among which, 33.3% (39 isolates) were identified as MAT1-1 and 66.7% (78 isolates) identified as MAT1-2  were characterized as MAT1-2  to nine (at marker UBC856)  in the observed number of alleles, Nei’s gene diversity, and Shannon’s information index among the regional S. turcica populations  . Of the  UBC856) . In addist twice . Furtherulations . Among tulations . The cloulations . Moreoveulations .With respect to populations of the two mating type, we identified a total 54 and 68 polymorphic loci  from MAT1-1 and MAT1-2 populations, respectively , and amoS. turcica from DY, JO, PN, NJ, SX, and LY were found to show a high genetic similarity based on the values obtained for Nei’s genetic distance and genetic identity, which were generally smaller than 0.1 and larger than 0.9, respectively (the only exceptions being between populations LY and JO)  . However regions .S. turcica populations from PN and NJ  and DY and JO   . Howeverm values .PT ranged from 0.065 (Nm = 7.149) to 0.316 (Nm = 1.082) on the basis of an analysis of regional populations and was 0.018 (Nm = 27.559) on the basis of an analysis of combined isolates (PT > 0.15) was detected between the two mating type populations in SX, PN, and NJ, whereas we detected low genetic differentiation (ΦPT < 0.15) between these two populations from DY, JO, DF, and LY, respectively  and ΦPT = 0.012 (P = 0.105), respectively  for both the regional and the mating type populations was two . Therefoc groups . These rite side . The firectively . In contectively . These rectively , indicatP > 0.05). For all seven regional populations, there were significant differences in two indices of linkage disequilibrium  between isolates from NJ and those from PN. This evidence for widespread gene drift between the NJ and PN populations of S. turcica also tends to be indicative ongoing frequent contact between the two populations, although as previously mentioned, alternative explanations are also possible.For most of the isolates , therebyisolates , particuPT) among the Fujian regional populations ranged from 0.066 to 0.492   . Previouh Africa , whereash Africa , b obser sorghum . Our finS. turcica populations infecting sweet corn in Fujian Province. The values we obtained for the haplotypic diversity of S. turcica isolates (0.20–0.41) differ from those of the high-haplotypic diversity populations from tropical climates characterized by frequent sexual reproduction  datasets, we detected significant differences in linkage disequilibrium (IA and−P < 0.05), and thus obtained no convincing evidence in support of the occurrence of random mating in Fujian S. turcica populations. Collectively, the low haplotypic diversities, skewed mating type distribution, and conspicuous linkage disequilibrium (IA and−S. turcica populations distributed throughout Fujian, which is consistent with finding reported by S. turcica isolates along with those of a further 48 single leaf isolates based on multiple PCR analyses using two mating type-specific primer pairs, we failed to detect any S. turcica isolate with both MAT1-1 and MAT1-2 mating types in these populations. Moreover, our observation of uneven MAT1-1 and MAT1-2 distributions in all examined geographical populations is inconsistent with the hypothesis of frequency-dependent selection (In the present study, we used several approaches to investigate the potential reproductive strategies adopted by oduction . Using belection , therebyS. turcica populations sampled from seven locations in Fujian Province. Our results revealing a skewed mating type distribution, low haplotypic diversities, and significant linkage disequilibrium are indicative of a mixed reproductive strategy among the S. turcica populations. Further studies are necessary to clarify the influence of corn varieties on the selection of this pathogen, given the important role played by host resistance in pathogen population selection (S. turcica or their distribution in Fujian Province.In conclusion, we detected genetic differentiation among election . FurtherThe original contributions presented in the study are included in the article/YD, XY, and ZG: design the experiment and further review and editing. YD, LG, and CL: methodology and technology and investigation and data analysis. YD, LG, CL, XL, and XY: samples and isolates collection. YD: writing the draft manuscript. YD and XY: project supporting. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Comparison with control populations evolved on standard food revealed repeatable genomic differentiation between the two set of population, involving >3,000 candidate SNPs forming >100 independently evolving clusters. The candidate genomic regions were enriched in genes implicated in hormone, carbohydrate, and lipid metabolism, including some with known effects on fitness-related life-history traits. Rather than being close to fixation, a substantial fraction of candidate SNPs segregated at intermediate allele frequencies in all malnutrition-adapted populations. This, together with patterns of among-population variation in allele frequencies and estimates of Tajima’s D, suggests that the poor diet results in balancing selection on some genomic regions. Our candidate genes for tolerance to larval malnutrition showed a high overlap with genes previously implicated in acute starvation resistance. However, adaptation to larval malnutrition in our study was associated with reduced tolerance to acute adult starvation. Thus, rather than reflecting synergy, the shared genomic architecture appears to mediate an evolutionary trade-off between tolerances to these two forms of nutritional stress.Periods of nutrient shortage impose strong selection on animal populations. Experimental studies of genetic adaptation to nutrient shortage largely focus on resistance to acute starvation at adult stage; it is not clear how conclusions drawn from these studies extrapolate to other forms of nutritional stress. We studied the genomic signature of adaptation to chronic juvenile malnutrition in six populations  The availability and quality of organic nutrients is a key ecological factor that determines the survival and fitness of animals. Natural selection imposed by periods of nutrient shortage is therefore thought to have shaped manifold aspects of animal physiology, life history and behavior . ResultiDrosophila. Selection for greater starvation resistance of adult fruit flies results in longer development time, greater longevity, increased lipid storage and larger body size  do not have the option of arresting growth and development. Thus, they must not only survive, but also develop and grow with whatever nutrients they manage to acquire. Waiting out until better times by relying on energy reserves, which is a major target of selection for acute starvation resistance, is not a viable option under chronic juvenile malnutrition. Finally, in insects and many other animals with distinct larval stages, energy metabolism and the general physiology of the juvenile and adult organism are likely to be substantially different, as the two stages are optimized for different functions. Adaptation to chronic juvenile undernutrition is therefore expected to involve at least partly different mechanisms than resistance to acute starvation. Consistent with this, although adult Drosophila selected for starvation resistance have larger body weight with higher lipid stores . The poor diet imposes a strong nutritional stress: larvae from nonadapted populations suffer high mortality and the survivors take twice as long to develop until pupation on the poor diet than on standard diet; yet, the adults still emerge at half of the normal size . These results indicate that 150 generations of evolution on the poor versus standard diet resulted in a replicable differentiation of gene pools, with adaptation to the poor diet being to a large degree parallel at the genomic level across the replicate Selected populations.We sequenced pools of 400 females from each of the six Selected populations (evolved on poor diet) and six Control populations (maintained on standard diet). We identified 976,247 high confidence single-nucleotide polymorphic sites (SNPs) that satisfied stringent SNP calling criteria. The SNPs were spread across the five major chromosomal arms , with a few on the dot chromosome 4. Principal component analysis on allele frequencies of all these SNPs clearly separated the Selected from the Control populations, mainly along the first principal component axis, which accounted for 19% of the SNP variation (Ne) of Selected populations relative to Controls by increasing variance in reproductive success . Although these estimates did tend to be smaller for Selected than Control populations, the difference was small: 120 versus 144 (P = 0.15) for the two main autosomes (chromosomes 2 and 3), 65 versus 74 for the X chromosome . Interestingly, eN estimates for the X chromosome are about half of those of autosomes; the theoretical expectation is that eN of the X should be ¾ of eN of autosomes (π and Watterson’s θ) were likewise similar between the evolutionary regimes  in all populations  without significant differences among the regimes . This is consistent with either an initial loss of genetic variation when setting up the experimental populations or with reductions of effective population size in response to the evolutionary regime both in the Selected and the Control populations. Variation in Tajima’s D among genomic regions .For each replicate population, we further estimated the population genetic statistic Tajima’s  at play . Negativions see A may indTo identify candidate SNPs underlying differentiation between the Selected and Control populations we employed a statistical approach based on combining Fisher’s exact tests (FET) and generalized linear mixed models (GLMM). The GLMM emphasizes parallel changes in all replicate populations whereas the FET-based approach emphasizes the difference in mean allele frequencies and can detect cases where an allele becomes fixed in most replicate populations but lost by drift in one. The false discovery rate (FDR) was estimated based on permutations following wr. The imprint of linkage disequilibrium is visible as a large excess of wr > 0.8 between candidate SNPs on the same chromosomal arm, relative to the distribution of wr between pairs of SNPs on different chromosomes  distance between them was <200 kb and 2) any SNP within 200 kb left of the midpoint between them was correlated with any SNP within 200 kb right of the midpoint with wr > 0.8. While necessarily arbitrary, this criterion was effective in grouping most highly correlated SNPs within the same cluster, with correlations between clusters on the same chromosomal arm having a similar distribution to correlation of SNPs on different chromosomes .To address this prediction, we calculated the standard deviation of population allele frequencies at each candidate SNP for each regime separately. We then compared these standard deviations between Selected and Control populations, binned by the respective regime means. Candidate SNPs with mean frequency in Selected populations in the 0.25–0.75 range were generally characterized by lower among-population variance than candidate SNPs satisfying the corresponding criterion in Control populations . To formeN estimates of the former. Thus, the lower among-population variation in allele frequency in the Selected than Control populations at SNPs that are far from fixation is specific to candidate SNPs. It should be noted that the statistical tests used to detect the candidate SNPs, as well as the above comparison of variation, are both symmetric with respect to evolutionary regimes and the identity of the alleles. Therefore, this difference in the pattern of variance of candidate SNPs suggests that diet-specific selection acts to maintain polymorphism to a greater degree in the Selected than in the Control populations.No such difference in among-population variation was observed for noncandidate SNPs B, for whIn order to see if candidate SNPs showing these two distinct allele frequency patterns show different functional annotations, we operationally defined two subsets of candidates. “High frequency” candidates were defined as those with frequencies of the “selected” allele ≥ 0.75 in all Selected populations, or in five Selected populations and being lost from the sixth population  in 72 genes were annotated as nonsynonymous coding  online, ecdysone oxidase (Eo), with three missense candidate SNPs in its coding region, and several of its paralogs  are likewise associated with candidate SNPs. Also associated with candidate SNPs are key members of insulin receptor signaling, including foxo, a major transcription factor mediating transcriptomic response to reduced insulin signaling  and their mobilization for catabolism  with two missense SNPs, and salivary gland proteins involved in adhesion of pupae to surfaces, again with one glue gene (Sgs4) with missense SNPs and one (chc) previously found to have a lower expression in Selected larvae . The statistical significance of top GO term enrichment was markedly higher in this separate analysis of “high frequency” and “mid-frequency” types than when all candidate SNP were analyzed jointly (table 1). This suggests that these two categories of candidate SNPs not only show different allele frequency patterns, but are also functionally different.Interestingly, when testing for enrichment separately in the “high frequency” and “mid-frequency” sets of candidate genes defined in the preceding section, we found no overlap in significantly enriched GO terms. As in the analysis of all candidate SNPs, we found that the “high frequency” candidates were enriched in genes involved in hormonal metabolic processes, lipid metabolic processes and Insulin/Insulin-like signaling, whereas the “mid-frequency” candidates were mainly associated with sugar/amino-sugar transferases , these candidates include numerous genes involved in insulin signaling and lipid metabolism already mentioned in the previous section: foxo, Ilp6, dock, Acsl, Lpin, Lsd-2, dob, pdgy; all are downregulated in the Selected populations except for dock—which is a negative regulator of insulin signaling. It also includes fiz, a putative ecdysone oxidase with natural variants that affect larval growth . Almost all genes in this set formed a single interconnected network (P = 0.026). We explore some specific links between genes with candidate SNPs and genes that differ in expression in the Discussion.To explore potential functional links between genomic and expression candidates, and to see to what degree the different candidate genes interact physically, we constructed networks of candidate genes based on the existence of known interactions between them (based on the DroID database ). The co network  online,  network , and miR network . Even thIn the Introduction, we hypothesized that adaptation to chronic juvenile malnutrition would rely on different mechanisms than resistance to acute adult starvation—not only is a different life stage concerned but also the type of adaptation that can be expected differ. To address this hypothesis, we assessed the overlap between our candidate genes and candidate genes identified in two experimental evolution studies of adult starvation resistance . We founfig. 5F1,15.0 = 74.5, P < 0.0001). This held for both sexes . The difference was greater in magnitude in flies raised on the poor diet ; the Selected flies died ∼10 h (14%) earlier than Controls when raised on the standard diet  and ∼20 h (27%) earlier when raised on the poor diet .The overlap of candidate genes between our study and those of fig. 5These results suggest that experimental evolution of improved tolerance to larval malnutrition traded off with poorer resistance to adult starvation. This may suggest that the overlap of candidate genes for adaptation to larval malnutrition versus adult starvation reflects antagonistic phenotypic changes favored by selection driven by these two types of nutritional stress.Our results imply that experimental adaptation to an extremely nutrient-poor larval diet was highly polygenic. We identified >3,000 candidate SNPs, spread across the genome and annotated to >700 genes, that were significantly divergent in allele frequency between the poor diet-adapted Selected populations and the Controls evolved on a standard diet. Many of those candidate SNPs formed linked clusters with highly correlated allele frequencies, as observed in other evolve and resequencing studies . This inEo and its paralogs, pdgy, Lsd-2 and dob). The center of this cluster corresponds to a region that has been subject to a selective sweep during the out-of-Africa expansion of D. melanogaster and contains multiple genetic variants unique to temperate populations . Such a pattern could be generated under at least three evolutionary scenarios.First, these SNPs might represent incomplete selective sweeps in the Selected populations. If so, the fact that they have not reached fixation after 150 generations would imply weak selection and/or low initial frequencies of the “selected” allele.Second, initially neutral alleles might have segregated at intermediate frequencies in the base population and later increased under directional selection in the Control populations whereas remaining neutrally polymorphic in the Selected populations. Although the Control populations were maintained on the same diet as the base population, we cannot rule out that the base population had not yet completely exhausted additive variation for fitness on that diet. Furthermore, to synchronize the generations of Control and Selected populations despite a difference in developmental time on the two diets, the Control populations reproduced at a slightly older age than Selected populations (∼9 vs. 6 days since eclosion on average) . AlthougD  observed in the Selected populations at the corresponding genomic regions, this provides indirect support for some role of balancing selection in adaptation to the poor diet.Third, stable intermediate allele frequencies may be maintained by balancing selection. Evidence for balancing selection during experimental evolution is relatively scarce e.g.,  and the Jon66Cii, which is also overexpressed in the guts of Selected larvae; [One aspect of the evolutionary adaptation of the Selected populations to the poor diet is an improved ability to extract and assimilate nutrients , overalSREBP) is likewise downregulated while its key activator (SCAP) carries a nonsynonymous candidate SNP. These genomic and transcriptomic results are consistent with the finding that the Selected larvae assimilate less carbohydrates and accumulate less triglycerides than Controls  and/or strongly reduced expression. The reaction catalyzed by ecdysone oxidases inactivates ecdysone; thus, lower ecdysone oxidase activity might lead to a faster rise in ecdysone. This finding dovetails with the lower critical weight for metamorphosis initiation and shorter development (independent of diet) evolved by the Selected larvae .Beat family  used in that study  was above those on which not only our Selected populations, but even our Controls evolved (respectively 3.2 and 12.5 g/l). The difference in the nutritional conditions used in the two studies is also reflected in the egg-to-adult developmental time, which is 11 days on average on the poorest diet used by A major question we asked concerned the relationship between adaptation to chronic larval malnutrition and resistance of adult flies to starvation. As argued in the Introduction, the two main strategies mediating starvation resistance—accumulation of energy reserves and minimizing energy demands while food-deprived —are unliHowever, despite being more tolerant to larval nutrient shortage , our SelIt has been hypothesized that resistance to different types of environmental stresses may be mediated in large part by the same physiological mechanisms and be positively genetically correlated . Our resD. melanogaster adults collected close to Basel/Switzerland in 1999. Four populations derived from these founders were maintained under standard laboratory conditions at census sizes of >200 individuals for >120 generations, allowing them to adapt to the lab conditions. Before the start of the experimental evolution, the four populations were mixed and bred for seven generations at a census size of >1000 individuals to allow recombination to break up large haplotypes in the artificial populations. The evolution experiment was initiated in 2004: six Selected and six Control populations were derived from this base population and maintained ever since at constant laboratory conditions  and controlled larval densities  on poor and standard larval diet, respectively. The standard diet consists of 15 g agar, 30 g sucrose, 60 g glucose, 12.5 g dry yeast, 50 g cornmeal, 0.5 g MgSO4, 0.5 g CaCl2, 30 ml ethanol, 6 ml propionic acid, and 1 g nipagin per 1 l water; the poor diet contains a quarter of the amounts of sugars, yeast, and cornmeal of the standard medium. Under both regimes, adults were transferred to standard diet with supplemental yeast to facilitate oviposition at every generation; the nutritional restriction was thus limited to the larval stage  with modifications . DNA of cutadapt  and common commensals and pathogens, such as Saccharomyces cerevisiae (GCF_000146045.2), Wolbachia pipientis (NC_002978.6), Pseudomonas entomophila (NC_008027.1), Commensalibacter intestine (NZ_AGFR00000000.1), Acetobacter pomorum (NZ_AEUP00000000.1), Gluconobacter morbifer (NZ_AGQV00000000.1), Providencia burhodogranariea (NZ_AKKL00000000.1), Providencia alcalifaciens (NZ_AKKM01000049.1), Providencia rettgeri (NZ_AJSB00000000.1), Enterococcus faecalis (NC_004668.1), Lactobacillus brevis (NC_008497.1), and Lactobacillus plantarum (NC_004567.2) using bwa mem  and re-aligned sequences flanking indels with GATK  miniAll sequenced libraries were of high quality with average raw PHRED-scaled base qualities exceeding 28 in all samples. Our mapping pipeline with stringent quality filtering resulted in similar coverages across all major chromosomal arms and yielded sample-wise average read depths ranging between 30× and 46× see  online.t-test.We performed a principal component analysis in SAS v. 9.4 on allele frequencies of 976,247 SNPs which fulfilled the coverage threshold criteria in all twelve replicate samples; because allele frequencies are already measured on the same scale, this was done on the covariance rather than correlation matrix. To test for separation between the evolutionary regimes we compared their scores on the principal component axis with Welch’s π, Watterson’s θ and Tajima’s D focusing on SNPs located on the six major chromosomal arms . Prior to calculations, we homogenized all sequencing data to an even 30-fold coverage to control for the coverage sensitivity of Watterson’s θ and Tajima’s D. Sites with coverages >30-fold were randomly subsampled without replacement. In contrast, we thirty times randomly sampled reads with replacement for sites where sequencing depths were below the target coverage  of the Selected and Control populations based on among-populations variance in allele frequencies at putatively neutral SNPs. Under drift, the expected variance in allele frequencies at a SNP locus among replicate populations as estimated from Pool-seq data is p0 is the initial allele frequency, t = 150 is the number of generations since the populations were established from a common base, and C is a correction factor to account for variance due to genetic drift and sequencing  and C and solving the equation yields a SNP-specific estimate of eN. Following Jónás et al. (2016), for each population j we first calculated jC = 1/2S + 1/jR + 1/(jSR) where jR is the number of reads used to estimate the allele frequency  of population j, and S is the number of individuals included in the pooled sample . C for a given SNP was then calculated as the arithmetic mean of jC across populations (C is thus a function of harmonic mean of read depth across populations). Because sequence data from the initial base population were not available, the initial frequency p0 for each SNP was estimated as the average frequency of this SNP across all 12 populations at time t We estimated the effective population sizes  as our final estimates. The significance of differences in eN estimates between regimes was assessed by randomization. We generated 100 data sets by dividing the 12 populations at random in two groups each consisting of three Control and three Selected populations. We then estimated eN for the two groups for each of these randomized data and used the distribution of pairwise differences of these estimates as a null distribution to which we compared the difference of eN estimates between Selected and Control populations.We applied the above approach to SNPs that were in short introns (<60 bp length), and thus thought to be neutral . Of thosd upward . For eacAlthough some recent evolve and resequence studies took advantage of temporal trajectories of alleles from the beginning of experimental evolution, such data are not available for our populations, limiting us to the analysis based on the comparison of allele frequencies between sets of populations exposed to different regimes. Because the expected distribution of SNP allele frequencies under a combination of selection and drift does not correspond to the assumptions of any classic statistical tests, a range of heuristic statistical approaches has been used to identify putative candidate SNPs in this type of data. Some studies employed approaches that emphasize the consistency of allele frequency differentiation among replicate populations, such as the “diffstat” statistics  or GLMM R for each SNP separately. 2) Independently, we fitted SNP-wise GLMM with a binomial error structure to allele counts without subsampling, which detect SNPs that might have only moderately diverged in frequency between the regimes, but in a way that is consistent among the replicate populations. We used the glmer function of the R package lme4 (iy = Regime + Population(Regime) +i?, where iy is the allele frequency of the ith SNP, Regime (control vs. selected) is a fixed factor, Population is a random factor for replicate populations nested within Regime (to account for overdispersion) and i? denotes a binomial error term. We tested for significant effects of the evolutionary regime by comparing the full model to a reduced model without the fixed effect by means of likelihood ratio tests using the R function anova.We employed a two-pronged approach to detect adaptive alleles showing either of the above patterns. 1) We used SNP-wise FET, which targeted SNPs that were on average strongly differentiated between the regime groups, but not necessarily consistent across replicate populations . To equaage lme4  to calcuP-values  corresponding to a given raw P-value P was then estimated as As has been noted previously, FET applied to pooled data results in a large excess of small P-values , and eve uniform , that isP-values were different between the two tests, we performed this procedure separately for the FET and GLMM. Sites with q ≤ 0.05 in at least one of the two tests were considered significant. Prior to all subsequent analyses, we further filtered the resulting candidate SNP data set by removing those where average allele frequency difference between Selected and Control populations was smaller than an arbitrary threshold of 0.3. This removed 101 SNPs detected by GLMM, that had very uniform allele frequencies across replicate populations, not expected after 150 generations at a rather small population size. Even if these SNPs were under selection, the small difference in mean allele frequency despite 150 generations of selection would imply a very small selection coefficient.Because the null distributions of raw To assess the extent of linkage disequilibrium resulting in nonindependent evolution of linked SNPs we calculated within-regime correlations between allele frequencies at different candidate SNPs. To this end, we first expressed allele frequencies as deviations from the regime means , thus removing the component of covariance which is due to evolution under the same selection regime. Then we calculated pairwise correlations of these frequency deviations between all pairs of SNPs on the same chromosomal arm across all 12 populations. The fact that allele frequencies at two neighboring SNPs have highly correlated residuals is most parsimoniously explained by a combination of linkage disequilibrium and drift (including founder effect). These correlations were plotted as a heat map.Drosophila recombination rate calculator . Then, we converted genomic positions to match the Drosophila reference v.6 coordinates and calculated approximate pairwise recombination probabilities by summing site-specific recombination rate estimates between candidate SNPs on the same chromosomal arm.Complementary to the analyses of pairwise correlations of allele frequencies, we investigated and similarly visualized pairwise recombination rates among candidates. We therefore downloaded average recombination rate estimates in 100-kb windows  from theBased on the Ensembl genome annotation (v. BDGP6.82) we used SNPeff v.4.2;  with staGowinda (Gowinda allows to control for a spurious overrepresentation of GO terms composed of long genes, since these have a higher probability to contain false-positive candidate SNPs compared with short genes.Using Gowinda , which plimma voom (R package SuperExactTest (P-value using the number of interactions of the true candidate set as our threshold. Using cytoscape (We further tested for functional links between our list of genomic candidates and previously published transcriptomic data from the same experimental populations . We thermma voom  analysesxactTest , which exactTest  to identytoscape , we thenR package SuperExactTest as described above.At last, we tested to which extent the complete list of candidate genes, as well as separately the “mid-frequency” and “high frequency” subsets, are shared with the studies of We measured starvation resistance in flies of the Selected and Control populations raised as larvae on the standard or the poor diet. This took place after 219 generations of selection, followed by three generations on the standard diet to remove the effect of parental nutritional environment . Flies oMolecular Biology and Evolution online.msab061_Supplementary_DataClick here for additional data file.