Wheat is one of the major staple cereal food crops in India. However, most of the wheat-growing areas experience several biotic and abiotic stresses, resulting in poor quality grains and reduced yield. To ensure food security for the growing population in India, there is a compelling need to explore the untapped genetic diversity available in gene banks for the development of stress-resistant/tolerant cultivars. The improvement of any crop lies in exploring and harnessing the genetic diversity available in its genetic resources in the form of cultivated varieties, landraces, wild relatives, and related genera. A huge collection of wheat genetic resources is conserved in various gene banks across the globe. Molecular and phenotypic characterization followed by documentation of conserved genetic resources is a prerequisite for germplasm utilization in crop improvement. The National Genebank of India has an extensive and diverse collection of wheat germplasm, comprising Indian wheat landraces, primitive cultivars, breeding lines, and collection from other countries. The conserved germplasm can contribute immensely to the development of wheat cultivars with high levels of biotic and abiotic stress tolerance. Breeding wheat varieties that can give high yields under different stress environments has not made much headway due to high genotypes and environmental interaction, non-availability of truly resistant/tolerant germplasm, and non-availability of reliable markers linked with the QTL having a significant impact on resistance/tolerance. The development of new breeding technologies like genomic selection (GS), which takes into account the G × E interaction, will facilitate crop improvement through enhanced climate resilience, by combining biotic and abiotic stress resistance/tolerance and maximizing yield potential. In this review article, we have summarized different constraints being faced by Indian wheat-breeding programs, challenges in addressing biotic and abiotic stresses, and improving quality and nutrition. Efforts have been made to highlight the wealth of Indian wheat genetic resources available in our National Genebank and their evaluation for the identification of trait-specific germplasm. Promising genotypes to develop varieties of important targeted traits and the development of different genomics resources have also been highlighted. Out of the total entries of wheat in the Genesys system, 7,535 accessions are of Indian origin, which are held in the Australian Grains Genebank , USDA-ARS , CIMMYT , John Innes Institute, United Kingdom , NI Vavilov Institute , and ICARDA (407) , next only to the USDA Genebank  is the fifth-largest collection in the world (CIMMYT Genebank ranks first)  . A majorDA (407) . Of the DA (407) . Even beDA (407) . The NP4DA (407) . Based oGenebank . In the s first) . These iMost of the wheat-breeding programs across the world have relied more on limited sets of diverse genotypes. This has resulted in the narrowing of the genetic base of cultivated wheat and became a dominant production constraint. Therefore, an expansion in the genetic base of genotypes should be considered in the wheat-breeding program. This can be carried out in various ways: 1) use of plant genetic resources, including wild relatives and landraces; 2) germplasm-assisted breeding using advanced genomic tools; and 3) development of transgenic and use of modern techniques like gene editing. Several gene banks across the globe house a large number of diverse germplasm accessions of wheat. These germplasm accessions harbor many important genes not only for various biotic and abiotic stresses but also for nutritional qualities and yield traits. To harness the true potential of the vast wealth of accessions stored in the Indian National Genebank, the Department of Biotechnology (DBT), Govt. of India, has supported a mega project to dissect the available genetic resources for new trait discovery using genomics and phenomics approaches and their integration for improving climate resilience, productivity, and nutritional quality. In addition, it will also help in the identification of the novel QTL and the markers linked with these QTL for these traits. The presented reviews emphasized the dominant stresses limiting wheat production in India and its impact on global food security. The possible path of a second green revolution using preserved genetic resources in the Indian Genebank with prospects to make a necessary plan for the exigencies that may be arising due to various biotic and abiotic stresses, nutrient utilization, and sustainability was also discussed.Wheat is one of the key cereal crops not only in India but also in the world and is the primary grain consumed by humans around the world. It is a food source for around 35% of the world population, a major cereal crop, and the main contributor to the agricultural economy of India  and needFusarium head blight , powdery mildew, spot blotch, Karnal bunt, and d blight . Althougd blight . This isd blight . Moreoved blight . This isPuccinia graminis f. sp. tritici, a causative agent of stem rust, resulted in up to 100% yield loss  pathogen varies and may be up to 60% under severe conditions , nivalenol (NIV), and zearalenone (ZON). Yield losses occur due to shriveled grain, low test weight, and failure of seed formation. Mycotoxin accumulation is a major concern from an international trade perspective. Mycotoxins, especially DON and its acetylated forms (3-ADON and 15-ADON), make grain unsuited for food or feed upon accumulation  are associated with undesirable agronomic traits , is considered an economically important disease prevalent worldwide. This disease could result in as high as 70% yield losses under severe epidemic conditions (Sb1 on 7D (Sb2 on 5B (Sb3 on 3B (Spot blotch (SB), a destructive leaf disease of wheat caused by nditions . The disnditions . From thnditions . The resnditions . Complexnditions . Crucialnditions . Neverthb1 on 7D , Sb2 on b2 on 5B , and Sb3b3 on 3B . AccessiBlumeria graminis (DC) E.U. Speer f. sp. tritici Em. Marchal  is powdery mildew (PM) of wheat, a foliar disease of universal occurrence resulting in dreadful yield loss , and there is a need for additional sustainable approaches to increasing productivity on restricted land, which will prevent the detrition of biodiversity . ClimateAround nine million ha of wheat in the subtropical or tropical zone  are heatDrought is the second most serious abiotic stress limiting wheat production in different parts of the world and occurs with varying frequencies . DroughtAmong abiotic stresses, increased soil salinity and sodicity pose a challenge to agriculture. The high salt concentrations of the soil can be attributed to the poor land and water management practices as well as the lack of soil reclamation processes in many parts of the world. In India, approximately 8.6 mha of the cultivated land is affected by soil salinity. Furthermore, the areas under salinity are expanding each year due to low precipitation, mixing with the coastline, saline water irrigation, high surface evaporation, and poor cultural practices . It has 2O5), and potash (K2O) are the major and most salient nutrients required by the plants  and emmer (Triticum dicoccum) to be in the Mesopotamian crescent of the Near East at about 7500 BC  and from there, it spread to the Middle East, Asia, and North Africa, and ultimately Europe, America, and South Africa  is an official institute at the national level for the governance of plant genetic resources (PGR). Its headquarters is in New Delhi. The Indian NGB housed at ICAR-NBPGR has also been reported to store more than 31,000 wheat accessions, which include landraces, exotic lines, and indigenous collections . The majThese are of infinite value for agriculture, food, research materials, human resources development for sustainable agricultural growth, boosting the efficient use of genetic and genomic resources of cereals, pulses, and other orphan and ornamental crops, and allied research . In addiThese accessions might prove very useful in the development of high-yielding and climate-resilient wheat varieties if the gene/germplasm is deployed in adapted cultivars in a planned way. However, only a small proportion of this collection has been utilized in breeding programs to date, primarily due to a lack of information about the traits and associated genes/markers in this collection. The evaluation of huge gene bank collections for the targeted traits is a costly and labor-intensive task. Although recent efforts have been made to develop core sets based on agro-morphological traits, these may not accurately represent the original collection’s diversity because agro-morphological parameters are influenced by environmental conditions . PreciseGlobal warming is severely affecting weather patterns, reflecting extreme heat, drought, frequent frost, and snowfall in high altitudes . In the Dreb and Fehw3  and make selections of individuals or breeding lines before phenotyping . This apAegilops, Elymus, and Eremopyrum), distributed primarily in the western and north-western Himalayas, for use in future programs on climate resilience. It would also serve as a reference for identifying the required areas for exploration and collection of Triticum species based on the gaps identified in the gene bank holdings, especially for trait-specific and unique germplasm accessions.The outcome of the Indian wheat germplasm genomics initiative would be a comprehensive pipeline connecting germplasm evaluation and genomic information, which could be used to accelerate the utilization of indigenous wheat germplasm in the national breeding programs for the improvement of biotic, abiotic, and quality traits . The fol


The structure of genetic data was analyzed by Principal Component Analysis and Hierarchical Cluster analysis that revealed the existence of two main clusters (Clu1 and Clu2) characterized by samples with different geographical origin. The Solina genetic dataset was further merged and analyzed with a public genetic one provided by CIMMYT containing 25,963 genotypes from all over the world. The Solina accessions occupied a vast space, thus confirming a high heterogeneity of this landrace that, nevertheless, is considerably unique and placed quite far from other clusters. Clu1 and Clu2 divergence were clearly visible. Solina clusters were genetically closer to landraces from Turkey and the central fertile crescent than to the Italian genotypes present in the dataset. Selected commercial quality traits of accessions of the two Solina clusters were analyzed , and significant differences were found between clusters. The results of this investigation did not highlight any relationships of Solina with Italian genotypes, and confirmed its wide genetic diversity by permitting to identify two genetic groups with distinct origin and quality traits.Different Solina wheat accessions ( Solina is a historically cultivated landrace in Abruzzo  and strongly linked with the territory and culture of mountain rural areas. It is not clear from where and when the Solina came out. It is surely possible to affirm that Solina is sown at least since the sixteenth century when it is mentioned in some notary deeds of sale and purchase stipulated at the Lanciano Fair .From an agronomic point of view, Solina is very rustic, well suited to poor soils and those consisting of stone, very resistant to cold weather (it can resist for a long time covered by snow), and to the spring–summer drought, of limited productivity (on average 2 t/ha) but offering constant production; these traits favored its wide use on a territorial scale . DespiteIn the early twentieth century, Solina wheat has been extensively used in breeding programs carried out in Italy  but scieThe Green Revolution (1940–1960) allowed the doubling of wheat production, responding to the doubling of the world population in the last 40 years. The spread of highly productive varieties, together with the massive use of fertilizers and crop protection products, permitted a spectacular increase in agricultural production, in particular wheat and rice. At an environmental level, this intensive agriculture has been considered, in accordance with “the Borlaug hypothesis”, a tool to conserve the biodiversity of ecosystems, allowing land sparing in favor of natural environments . Green Revolution and, in general, modern breeding, are involved in the erosion of genetic diversity in wheat: the use of a limited number of parental genotypes in breeding reduced the genetic basis of this crop, causing a genetic bottleneck. The genetic heritage represented by many old cultivars and landraces was put aside in favor of a few High Yielding Varieties (HYV), which were disease- and lodging-resistant, highly responsive to input, and characterized by Distinctness, Uniformity and Stability (DUS). Since modern cultivars were implemented primarily under modern agronomic practices using high agricultural inputs, several landraces  remained protected and continuously cultivated in marginal areas, thanks also to their high adaptability in unfavorable growing conditions  .In this context, landrace seeds’ management became typically an in situ on-farm conservation conducted by local farmers themselves, and at the same time, allowed to avoid genetic erosion and to obtain new alleles, deriving from casual mutation or from other neighboring populations . GeneticIn order to unravel the genetic structure and breeding history lying behind landraces, a genome-wide approach is needed; thanks to modern high-throughput technologies, such as DarTseq (Diversity Arrays Technology sequence), researchers can efficiently genotype thousands of genomic loci in parallel with a relative low cost . RecentlIn this context, important is the work of Khan and co-workers  who studTriticum aestivum, L.) and increasing the number of accessions collected and SNPs analyzed. Solina accessions, in situ conserved by different farmers, were characterized for both intra- and inter-farmer accessions genetic diversity, and also evaluated in relation with some quality parameters.Nevertheless, the pioneering studies above reported presented two major limitations: the number of SNPs analyzed and the number of accessions collected per landrace (farmer populations). According to Negri and co-workers , “on-farMoreover, in order to identify any genetic relationships within a vast common wheat germplasm and therefore shed light on the Solina history, the Solina genetic dataset was merged with a public genetic one (CIMMYT) obtained by 25,963 different common wheat genotypes from all over the world and further analyzed. To the authors’ knowledge this is the first attempt to merge a landrace genotyping dataset with the international CIMMYT dataset to gain information on landrace genetic variability and relationships.Twenty-four accessions of Solina wheat were collected in several locations, representative of Solina cultivation area in the Abruzzo region . Figure The sampling was conducted in order to obtain the greatest number of accessions representative of the typical areal of Solina cultivation. The sites were distributed mainly in the province of L’Aquila (only two accessions were sampled in the province of Pescara); their average elevation was 755 ± 263 m above sea level and they were characterized by different types of pedo-climatic conditions, distinctives of the Abruzzo region.The accessions were sampled from grains harvested in the year 2018 in different farms in which Solina wheat was cultivated for at least ten years under organic management. In order to evaluate the genetic variability within accessions and among accessions, four single seeds and one seed bulk (50 seeds) were sampled per accession and named A, B, C, D, and E, respectively. Seeds of accessions 2 and 3  suppliedhttp://www.diversityarrays.com, accessed on 1 December 2021, Canberra, Australia): single seeds were pounded separately avoiding DNA cross-contamination, and contemporary 50 seeds for every accession were milled; 96 pounded Seeds and 24 milled Bulk (20 mg per accession) were carefully placed in two 96-well plates and shipped with silica gels to DArT laboratories for SNP analysis ); 45,055 SNP markers were obtained and used for further statistical analyses.The samples were pre-treated following the instructions provided by Diversity Arrays Technology (DArT) Pty. Ltd.  was rearranged as trinary values  as follows: (i) “0” when only the reference allele homozygote is present; (ii) “1” when both the alleles are present (heterozygosis); (iii) “2” when only the SNP allele homozygote is present; (iv) “null” when both the alleles are absent (absence of fragment with SNP in genomic representation); “null” were considered as missing.Subsequently, several quality SNP filters were applied to check uninformative or misinformative markers or genotypes: (i) exclusion of SNP markers and genotypes with more than 25% missing values; (ii) exclusion of SNP markers with less than 5% of Minor Allelic Frequency (MAF).Remaining missing values were imputed by using the marker means.https://data.cimmyt.org/ (accessed on 10 January 2020) and were downloaded and rearranged (see AlleleID and AlleleSequence as marker identificators) and 25,963 genotypes of cultivars and landraces of Triticum aestivum L. aestivum, which in combination with 96 single Seeds produced a unique matrix of 26,059 genotypes with trinary values. Since significant differences in heterozygosis have been seen between CIMMYT and Solina genotypes, stricter filters were used: (i) exclusion of SNP markers with more than 10% missing values; (ii) exclusion of genotypes with more than 20% missing values; (iii) exclusion of genotypes with more than 10% heterozygosis; (iv) exclusion of SNP markers with less than 5% of MAF.The study of , implemenged see  to be mePhenotyping of three commercial quality parameters was done for every accession in duplicate. The total nitrogen content (NC) was determined using the Kjeldahl method (AACC 46-12.01): the sample is digested in sulfuric acid, ammonia is distilled, and then the distillate is titrated with hydrochloric acid. A conversion factor of 5.7 was used for wheat. Test weight (TW) and thousand-kernel weight (TKW) were analyzed after removing all impurities. Moisture content was also analyzed (AACC 44-15.02).na.mean function (R package imputeTS). SNPs density plot were obtained thanks to CMplot function (R package CMplot). Principal Component Analysis (PCA) based on covariance matrix was computed by the prcomp function (R package stats), whereas Hierarchical Clustering Analysis (HCA) was performed using Euclidean distances and average-linkage agglomeration method implemented in the hclust function (R package stats). Moreover, PC1 scores (obtained with single seed dataset) were used in multiple linear regression (MLR) with geographic coordinates  as explanatory variables; thus β coefficients were calculated in order to compare the relative weights of the variables.Statistical analyses of genetic data were carried out using R 4.0.4 (R Core Team 2020) and several specific packages. Missing value replacement was done using Statistical analysis of phenotypic data was performed using XLSTAT 2021 software . Mixed nested ANOVA was carried out on phenotypic data from the 24 accessions of DArTseq analysis permitted to detect approximately 45 K SNPs for each of the 120 Solina samples investigated. Original Solina dataset was initially divided into Bulk (24 genotypes) and single Seed (96 genotypes) matrices, and were studied always separately. Bulk genotypes may be comparable to a genetic superimposition of 50 seeds, tested to evaluate a larger sample size, and so, the within-accession variability . After tprcomp function is not able to process missing values, they were substituted by marker means. In order to explore the genomic structure of Solina accessions, a PCA based on covariance matrix was computed only on single Seed dataset; since Despite the small, explained variance, the PC1 permitted to identify two main and distinct Solina clusters (Clu1 and Clu2), as highlighted by confidential ellipses in 2adj. value of 0.231 (p < 0.001), and significant β-coefficients (p < 0.05) were found with the following values: −0.31, 0.25, and 0.19. Clu2, characterized by positive scores in PC1, which tends to localize in the south-eastern area of Abruzzo region, that on average shows a higher altitude value.PC1 scores were further used in multiple linear regression (MLR) using latitude, longitude, and altitude  as explaTo better analyze the genetics of Solina accessions in an international and multi-varieties context, a public SNP database provided by CIMMYT  was impoThe filtered merged dataset is constituted of 3058 cultivars and 21,719 landraces; the most represented nations were Mexico , Iran (3695), and China (2198). Only 28 genotypes were Italian. To understand how the Solina accessions related with CIMMYT populations, PCA and HCA were computed. Since this dataset was larger than previous ones, a clearer 3D score plot was depicted and shown in p < 0.05), even though the nested farm effect resulted always in the main effect (p < 0.001). No significant differences among clusters were observed for protein content which showed an extremely high variability (from 9.66 to 15.48% d.b.).TWK, TW, and proteins were analyzed on the Solina sample set used for DArTseq analysis. p > 0.05). The effect of genetic cluster on TWK and TW was confirmed by the in situ experiment carried out by comparing two Solina accessions cultivated in two different farms over two years, and this experiment highlighted also a significant difference in the protein content of the two accessions . On the Clu1 was the cluster with the highest TWK and TW in both the sample set used for DArTseq analysis and the sample set obtained by the in situ experiment. In the latter experiment, Clu1 resulted in the genetic cluster with the highest value of protein content. This result reflects the highest score values of Clu1 along the PC3 calculated using the CIMMYT phenotypic dataset see .The current study aimed at obtaining valuable information on Solina wheat, a historically Italian landrace, adapted to the mountain environment of central Italy; as a matter of fact, characterizing genetic population structures is a fundamental step towards landrace protection, conservation and proper utilization in breeding program, agronomic management, and product transformation. The challenge was to draw out useful results from a consistent genetic diversity, typical of landraces , which mDArTseq analysis permitted to deeply investigate the Solina genome. As previously stated, wide genetic diversity is expected by local and historical landraces, and for this reason also, a bulk was considered in the analysis. The higher heterozygosis in Bulk dataset  was the Aegilops tauschii (2n = DD), the common wheat D-genome donor, and the relative genetic bottleneck [Even though the final datasets were significantly reduced, high-quality SNP markers have remained widely distributed abroad the three genomes, as shown in ttleneck . Since SNP datasets are matrices with a great imbalance in variables/cases ratio, they were analyzed through an unsupervised multivariate approach to reduce dimensionality and better visualize huge datasets. A PCA based on covariance matrix and HCA based on average-linkage agglomeration method permitted to identify clearly two genetic clusters (Clu1 and Clu2). This is clearly shown in the score plot in Recent works in this field demonstrated how geography was the main effect driving the genetic diversity ,24, evenSolina wheat is isolated from all the worldwide cultivars and landraces reported in the CIMMYT database so it could be considered as a distinct landrace but it shows a wide genetic variance, even stressing the results reported by Khan et al.  on a sinIn an attempt to clarify a possible historical origin of Solina clusters or to find an event that significantly affected its genetic diversity, an analysis of the first genetic outsiders was carried out and revealed that Solina is located neither near nor within the Italian genotypes cluster, and is very close to landraces from the central fertile crescent (mainly Turkish), which is the major center of wheat domestication.Triticum aestivum first arose in Turkey, in which earliest records dated 8600–7800 before present, have been found. However, it is even possible that several allopolyploid speciation processes occurred, still carrying several unanswered questions [It has been hypothesized that uestions . After sa landrace grown for a long period in the farming system concerned. As the environment changes annually and as the landrace becomes ‘contaminated’ with few genotypes of other landrace(s), or cultivar(s) it will continuously adapt itself”. On the contrary, “allochthonous landrace is an autochthonous landrace of a foreign region recently introduced into the region concerned. Depending on the number of generations of after growth and on the frequency of seed change, it may become an autochthonous landrace”. Starting from the facts that Solina is an historical and dynamic population, with distinct identity, very well adapted to the Abruzzo agroecological conditions, it can be categorized as an Italian autochthonous landrace that still maintains strong genetic relationships with the landraces of the major ancient center of diversification of common wheat.The genetic similarity found among Solina and Fertile Crescent germplasm arises the question if Solina can be considered an allochthonous or an autochthonous landrace . Zeven  defined Phenotypic variation within the Solina landrace was already observed by Bonvicini , who, seEven though it was not in the purpose of this work to verify if the two different genetic clusters identified in this study resulted from an adaptation of the two lines selected by Bonvicini , it was Clu1 that was collected in a wide area characterized by a mean altitude of 703 m showed higher TW, TKW, and protein contents than Clu2 that was collected in a restricted area characterized by a mean altitude of 883 m and located in the south-east of the sampling area. Differences in TW and TKW were observed in both the seeds set collected in 2018 and in the samples collected in the in situ experiments conducted in 2019 and 2020, whilst protein content resulted significantly different only in the in situ experiments. This result is due to the fact that the samples collected in 2018 were cultivated in different geographical areas , pedo-clThe results of this investigation highlighted the genetic relationships of Solina with Turkey and Middle East landraces, and confirmed its wide genetic diversity by permitting to identify two principal genetic groups with distinct origin and quality traits. These findings constitute a first step towards protection and conservation of Solina genetic heritage, but could also have implications for its agronomic management and product end-use. Further experiments  need to be carried out in order to study the origin of this genetic differentiation, probably due to adaptation phenomena caused by environment pressures. Despite its between accessions diversity, the Solina wheat remains genetically distant from all the worldwide cultivars and landraces reported in the CIMMYT database, thus it could be also considered as a unique genetic resource for breeding programs.


Over the past 50 years conservation genetics has developed a substantive toolbox to inform species management. One of the most long‐standing tools available to manage genetics—the pedigree—has been widely used to characterize diversity and maximize evolutionary potential in threatened populations. Now, with the ability to use high throughput sequencing to estimate relatedness, inbreeding, and genome‐wide functional diversity, some have asked whether it is warranted for conservation biologists to continue collecting and collating pedigrees for species management. In this perspective, we argue that pedigrees remain a relevant tool, and when combined with genomic data, create an invaluable resource for conservation genomic management. Genomic data can address pedigree pitfalls , and in return robust pedigrees allow for more nuanced research design, including well‐informed sampling strategies and quantitative analyses  to better inform genomic inquiry. We further contend that building and maintaining pedigrees provides an opportunity to strengthen trusted relationships among conservation researchers, practitioners, Indigenous Peoples, and Local Communities. This fiAs HTS continues to advance, there will no doubt be more new and exciting tools incorporated into conservation genetic inquiry. In addition to new methods enabled by advances in HTS, there remains one long‐standing tool within the conservation genetics toolbox that is often overlooked: the pedigree. Pedigrees, or documented ancestry of individuals in a population, have been recorded by humans for millennia. These family trees have long provided a proxy for understanding the transmission of traits from generation to generation and maintain diverse applications in agriculture  being a metric of the coefficient of relatedness , founder genome‐equivalents , and population‐level gene diversity  or intensively managed wild (in situ) or semi‐wild  —a species on the brink of extinction— with blight‐resistant Chinese chestnut (C. mollisima). In this instance, ancestry data from pedigrees and phenotypic data on blight resistance were used for crossing programs, based on the hypothesis that blight resistance had a genetic basis , researchers used pedigrees to predict the effects of selection and gene flow on how declining populations might evolve in a short time period  in Yellowstone National Park combined with phenotypes recorded for those individuals led to advances in understanding the heritability of behaviour and the genetic basis of mange in this iconic species  to elucidate dispersal and connectivity between populations, with implications for restoration and translocation efforts  demonstrated that full‐sibling and half‐sibling relationships were represented amongst founders  are equally unrelated. As many threatened populations have experienced significant declines before populations are pedigreed, founders from these populations may include more variance in kinship amongst one another than individuals randomly sampled from nonthreatened populations. For instance, a study of microsatellite‐based relatedness in kākāpō . While microsatellites are useful, high density single nucleotide polymorphisms  generated through high throughput sequencing approaches  often provide better resolution for estimating identity‐by‐descent, even when populations are inbred or relationships are more distant  and dama gazelle . Molecular data can also be used to validate uncertain and semi‐wild pedigrees. For example, microsatellite markers have recently been used to validate pedigrees for intensively‐monitored and critically endangered wild populations of kakī/black stilt  estimates of relatedness from can be used to complete and complement pedigrees. For relatively diverse wild populations, microsatellite‐based approaches can be informative , which can have different social and ecological consequences. Pedigrees also carry rich demographic data that may be inaccessible from molecular data, including sex for species without genetic sex determination , or the heterogametic sex to capture the alternative and often highly repetitive sex chromosome  was used to streamline the selection of individuals representative of founder diversity across many managed populations for downstream diversity comparisons. In a recent study in California condors, individuals with low inbreeding and kinship coefficients were selected using the pedigree, and were compared in terms of runs of homozygosity using WGS . For example, a pedigree can inform the choice of closely related family groups for genomic inquiry  are used as markers to identify and measure diversity  used pedigree‐based heritability to reveal low evolutionary potential in this threatened bird  using deep pedigree data and thousands of genome‐wide SNPs, which has provided an understanding of flycatcher genome architecture in comparison to other species , and kakī . Over the past 5 years, we have collaborated with conservation researchers to integrate pedigrees from long‐term records into digital studbook databases , validate pedigrees with genetic and genomic data , and inform genetic and genomic studies with our research collaborators using pedigree data .We  are conservation practitioners in Aotearoa New Zealand working with three critically endangered endemic bird species with pedigree data: kākāpō  has not only strengthened our pedigree tools and our confidence using them, but has strengthened our relationships. The pedigree acts as a common ground between conservation genetic research and practice, and has contributed towards trust that allows genetic and genomic research to inform management efforts for these critically endangered species. These trusted relationships have built a bridge for us to learn about aligned conservation projects in Aotearoa New Zealand and abroad, and incorporate new approaches for pedigree and species management in our own programs.In illustrating connections that link the present to the past, pedigrees are well‐aligned with both Indigenous and non‐Indigenous worldviews  and genetic researchers  who work in partnership to enhance the recovery of threatened species treasured by Māori .For Māori, whakapapa is a genealogical framework that describes the origins and relationships of all things, linking plants, animals, and people with the environment including the mountains, rivers, and winds, across time and space Roberts, . UltimatYCL is kaitiaki (guardian) for kākāriki karaka, or orange‐fronted parakeet, on behalf of Ngāi Tahu, a large tribe on the South Island of Aotearoa New Zealand. When deciding which individual to choose for the kākāriki karaka reference genome, SJG and TES provided a pedigree showing the whakapapa of all individuals available for sampling. Using this resource, YCL chose Maverick, a captive male bird. From the pedigree, she learned that his ancestors included individuals from the last three remnant populationson the mainland Box A and thaBeyond kākāriki karaka, based on our collective experience working with other threatened taonga (treasured) species in Aotearoa New Zealand, we are confident pedigrees will continue to help grow our relationships with mana whenua—as well as local communities—leading to improved conservation outcomes. We encourage those interested in building trusted relationships with Indigenous Peoples and Local Communities to seek guidance relevant to their local context and to engage in the extensive scholarship already available  for as many individuals as possible  to ensure you can inform inter‐individual relationships. In addition to initial sampling, we recommend maintaining detailed metadata  and tissue samples for all pedigreed individuals in subsequent generations. Tissue banks for all pedigreed individuals can help jumpstart genomic research, coupled with collected metadata embedded in the pedigree. Tissue samples can also be used by conservation genetic researchers to periodically validate the pedigree for accuracy and assist with any uncertain parentage assignments. Finally, we advocate for clear communication between researchers, practitioners, and IPLC to ensure pedigrees continue to be built and used as a tool for management for generations to come.The conception of this piece was led by Stephanie J. Galla and Tammy E. Steeves, with support from all authors. Writing and illustrations were led by Stephanie J. Galla with contributions from all authors. Box


Parents pass on both their genes and environment to offspring, prompting debate about the relative importance of nature versus nurture in the inheritance of complex traits. Advances in molecular genetics now make it possible to quantify an individual’s genetic predisposition to a trait via his or her ‘polygenic score’. However, part of the risk captured by an individual’s polygenic score may actually be attributed to the genotype of their parents. In the most well-studied example of this indirect ‘genetic nurture’ effect, about half the genetic contribution to educational attainment was found to be attributed to parental alleles, even if those alleles were not inherited by the child. Refractive errors, such as myopia, are a common cause of visual impairment and pose high economic and quality-of-life costs. Despite strong evidence that refractive errors are highly heritable, the extent to which genetic risk is conferred directly via transmitted risk alleles or indirectly via the environment that parents create for their children is entirely unknown. Here, an instrumental variable analysis in 1944 pairs of adult siblings from the United Kingdom was used to quantify the proportion of the genetic risk (‘single nucleotide polymorphism (SNP) heritability’) of refractive error contributed by genetic nurture. We found no evidence of a contribution from genetic nurture: non-within-family SNP-heritability estimate = 0.213  and within-family SNP-heritability estimate = 0.250 (0.152–0.372). Our findings imply the genetic contribution to refractive error is principally an intrinsic effect from alleles transmitted from parents to offspring. A mismatch between the focal and axial lengths of the eye causes blurry vision, known as myopia or hyperopia Fig. . ApproxiRefractive error is a highly heritable trait –7, with The genetic risk transmitted from parents to offspring generally occurs via the transmission of parental alleles to children. However, alleles present in the parents can potentially influence the phenotype of a child through the child’s environment, independently of their transmission to the child (‘genetic nurture’) . AccordiA genetic variant displaying a significant genotype-phenotype association provides evidence of a causal path affecting the phenotype . HoweverTo date, the role of genetic nurture has only been examined for a few traits. Most studies have focused on either educational attainment or birth weight , 19–28. A polygenic score (PGS) quantifies a person’s genetic predisposition for a specific trait  and can family-based GWAS will only capture the genetic effects of transmitted alleles. Here, we carried out analyses to assess the evidence for genetic nurture by comparing SNP-heritability estimates obtained in within-family vs. non-within-family analyses [When a GWAS is carried out in a sample of unrelated individuals, the SNP beta coefficients will not only capture effects of transmitted alleles, but also any effects due to genetic nurture, population stratification and assortative mating. By contrast, an appropriately designed analyses . Given tanalyses , 12, 34,EduYears) was defined as age participants reported completing their full-time education , except that those who reported leaving school before age 15 or after age 21 years-old were assigned a school leaving age of 15 or 21 , respectively [EduYears of 16 years). An ophthalmic assessment was introduced late in the UK Biobank recruitment period, with only about a quarter of participants undergoing this assessment. The mean spherical equivalent (sphere plus 0.5 × cylinder) for each eye was calculated from the autorefraction readings. The average mean spherical equivalent (avMSE) of the two eyes was taken as the participant’s refractive error phenotype value [UK Biobank is a prospective study of health and well-being of adults living in the United Kingdom. Ethical approval for the study was obtained from the NHS Research Ethics Committee . The pohttps://alkesgroup.broadinstitute.org/LDSCORE/w_hm3.snplist.bz2). Using PLINK v2.00a2LM [n = 43,168 for avMSE and n = 147,813 for EduYears) using the --predBetasFile option in BOLT v2.3.5 [avMSE and two polygenic scores for EduYears. The incremental R2 was defined as the variance in the trait-of-interest explained by the polygenic score in a linear regression model, over and above the variance explained by a baseline linear regression model that included the predictors age, age-squared, sex, genotyping array and the first 10 genetic PCs.A list of autosomal genetic variants in the HapMap3 reference panel was downloaded from the LDSC website  . The aim of the current study was to examine the extent to which the genetic risk captured by a polygenic score for refractive error is mediated by genetic nurture effects. In this context, use of an imprecise polygenic score will tend to underestimate the contribution of the mediating pathway [Each participant’s genetic propensity for the trait of interest was quantified using a polygenic score (PGS), using Eq. .1\docume pathway . One metple GWAS , 33. Thihe other , 33.α0 is the mean phenotype level in the sample, yfi is the phenotype of sibling i = A or B from family f who has an instrumented polygenic score of Wf is the mean level of the phenotype in family f. Terms m=2)fi derived by Bycroft et al. [α1 in Eq. (plm and ivmodel (code in Supplementary Methods).We fitted Eq. , followit et al.  were incTable EduYears were carried out in two non-overlapping samples of UK Biobank participants  from the genetic nurture analysis sample for EduYears, which comprised of 18,146 sibling pairs who were unrelated to the participants in the GWAS samples. Each polygenic score explained approximately 8% of the variance in EduYears. Specifically, the incremental R2 for the first polygenic score PGSm=1)( was R2 = 0.077 and for the second polygenic score PGSm=2)( it was R2 = 0.080. The correlation of the two polygenic scores (PGSm=1)( vs. PGSm=2)( in sibling A) in this sample was 0.509 (95% CI 0.498–0.519).Split-sample GWAS analyses for nts Fig. . From thm=1). For PGSm=2)( the corresponding within-sibling-pair correlation was 0.553 (95% CI 0.542–0.563). These within-sibling-pair correlations were above the level of 0.5 expected under random mating, suggesting the presence of assortative mating, as reported previously for the trait EduYears [For the first polygenic score PGSEduYears .EduYears are shown in Table EduYears from genetic nurture and assortative mating combined was larger than the contribution from transmitted alleles. This is consistent with previous work, which has suggested that as much as half of the variance in educational attainment captured by a polygenic score acts via alleles present in the parents irrespective of transmission to the proband or reflects assortative mating.The results of the genetic nurture analysis for EduYears, split-sample GWAS analyses for avMSE were carried out in two further non-overlapping samples of UK Biobank participants ( and R2 = 0.059 for PGSm=2)( (n = 1944). The correlation of the two polygenic scores for avMSE (PGSm=1)( vs. PGSm=2)( in sibling A) in this sample of unrelated individuals was 0.316 (95% CI 0.275–0.355).Following the same methodology described above for studying the contribution of genetic nurture to nts Fig. . From thn = 1944 sibling pairs with information on avMSE, the within-sibling-pair correlation of PGSm=1)( was 0.477 (95% CI 0.442–0.511) and the within-sibling-pair correlation for PGSm=2)( was 0.499 (95% CI 0.465–0.532). These correlations were close to the value of 0.5 expected under random mating, although the confidence intervals were wide. To provide more precise estimates, the within-sibling-pair analyses were repeated in the larger sample of siblings  used for the genetic nurture analysis of EduYears. . Calculated for this sample of 18,146 sibling pairs, the within-sibling-pair correlation in polygenic scores for avMSE was 0.526 (95% CI 0.516–0.537) for PGSm=1)( and 0.525 (95% CI 0.514 –0.535) for PGSm=2), leading to wide confidence intervals for the key parameters of Strengths of the current work were the highly standardized method for measuring refractive error, use of powerful PGS constructs implemented in an instrumental variable framework , 33. Theh weight . Although weight , differeWhen considering these new results, it is important to bear in mind that our analyses were conducted in a group of participants who grew up in the United Kingdom during the 1940s to 1980s. As such, our findings do not rule out a greater role for genetic nurture in more recent birth cohorts, or in communities with differing lifestyle-related risk factors, such as urban regions in East and South-East Asia where the prevalence of myopia is much higher than the United Kingdom , 7. SecoIn summary, in this study, we obtained minimally-biased estimates of the genetic contribution to refractive error and educational attainment; specifically, free from bias due to genetic nurture and assortative mating. Building on prior work, we estimated that ~25% of the SNP-heritability of educational attainment was attributed to alleles transmitted from parents to children and that the remaining ~75% occurred as a result of genetic nurture and assortative mating. In stark contrast, our results suggested little contribution from genetic nurture to the SNP-heritability of refractive error. While we did find evidence of assortative mating for refractive error, there was no evidence that this appreciably inflated its SNP-heritability estimate. We conclude that, whereas the genetic contribution to educational attainment is in large part inherited through the environment, the genetic contribution to refractive error occurs mainly through direct parent-to-child transmission of alleles. Our findings validate the assumption that genetic variants associated with refractive error highlight potential therapeutic targets.Supplementary Material


Colletotrichum graminicola isolates which infect maize by sequencing the genomes of 108 isolates collected from 14 countries using restriction site-associated DNA sequencing (RAD-seq) and whole-genome sequencing (WGS). Clustering analyses based on single-nucleotide polymorphisms revealed three genetic groups delimited by continental origin, compatible with short-dispersal of the pathogen and geographic subdivision. Intra- and intercontinental migration was observed between Europe and South America, likely associated with the movement of contaminated germplasm. Low clonality, evidence of genetic recombination, and high phenotypic diversity were detected. We show evidence that, although it is rare  C. graminicola can undergo sexual recombination. Our results support the hypotheses that intra- and intercontinental pathogen migration and genetic recombination have great impacts on the C. graminicola population structure.Understanding the genetic diversity and mechanisms underlying genetic variation in pathogen populations is crucial to the development of effective control strategies. We investigated the genetic diversity and reproductive biology of  Crop diseases are one of the biggest concerns in global agriculture, responsible for major losses in food production , 2. Fung5–Zea mays) is the second most extensively cultivated cereal crop globally, with a harvested area of almost 200 million hectares   , which wnditions , 75, thenditions , i.e., pnditions . Howevernditions , which mColletotrichum species  revealed population subdivision into three genetic clades with isolates grouped according to their country of origin, with distinct levels of genetic diversity between them, suggesting different evolutionary histories. We found that intra- and intercontinental migration, mainly between Europe and South America, is likely associated with the movement of contaminated seeds. We discussed the possible evolutionary origins of these genetic groups, the impact of recombination on the genetic structure, and the potential for maize anthracnose to become a much more significant disease in the future.Genetic recombination is reported to be an important factor in the ecology and evolution of many  species –83. Howe cereale –85, and isolates . TherefoC. graminicola isolates collected from 14 countries, and these were used for population genetic analyses  and discriminate analysis of principal components (DAPC) grouped individuals according to their geographic origin , Fig. S110.1128/mbio.02878-22.1FIG S1Colletotrichum graminicola isolates assigned with a priori geographical information (by continent). Download FIG S1, PDF file, 0.1 MB.(A) Bayesian information criteria (BIC) indicating the most likely number of genetic groups by discriminant analysis of principal component analysis (DAPC). (B) Scatterplot from DAPC of Copyright © 2022 Rogério et al.2022Rogério et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the K = 3 clusters  criterion, supported the clustering pattern, showing an optimum value of clusters . A neighclusters . Interes to 0.42 .10.1128/mbio.02878-22.2FIG S2FIG S2, PDF file, 0.1 MB.Bayesian information criteria (BIC) indicating the most probable number of genetic groups using the snapclust function. Download Copyright © 2022 Rogério et al.2022Rogério et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mbio.02878-22.7TABLE S3Colletotrichum graminicola. Download Table S3, XLSX file, 0.1 MB.Pairwise genetic differentiation between genetic clusters of Copyright © 2022 Rogério et al.2022Rogério et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Among the 108 isolates, we detected 76 unique multilocus genotypes (MLGs) and 15 MLGs shared between at least 2 isolates. Most MLGs included isolates from the European clade  and were shared between isolates from the same clade and country . However10.1128/mbio.02878-22.8TABLE S4Colletotrichum graminicola isolates. Download Table S4, XLSX file, 0.01 MB.Multilocus genotypes (MLGs) among Copyright © 2022 Rogério et al.2022Rogério et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the To estimate genetic diversity, we computed the number of segregating sites within genetic groups. Genetic diversity was twice as high in the North American clade compared to the Brazilian and European clades . Based oP < 0.001).We assessed the genomic impact of recombination by analyzing patterns of linkage disequilibrium (LD). For this, we computed LD on the genetic clusters separately to avoid combining evolutionarily distinct populations, which can lead to overestimation of the linkage equilibrium . For allC. graminicola sexual reproduction- and meiosis-associated genes were maintained, we explored the presence of 96 genes previously identified as having roles in sexual reproduction in Ascomycetes. We observed the presence of 91 out of 96 sex-related genes in three C. graminicola genomes  and a post hoc Tukey’s test revealed seven isolates which were significantly more virulent and two which were significantly less virulent than M1.001  and whole-genome sequencing (WGS), we investigated the distribution of genetic variation considering our samples as a global population. We found populational differentiation on a global scale, with three genetic groups delimited by continental origin, consistent with short dispersal of the pathogen and geographic subdivision. However, we observed intra- and intercontinental migration, possibly associated with the movement of contaminated seeds. The genetic clusters, corresponding to the Brazilian, European, and North American clades, showed distinct levels of genetic diversity which could reflect different evolutionary histories. Analysis of linkage disequilibrium and the pairwise homoplasy index (PHI) test for clonality provided evidence of genetic recombination. Investigation of sex-related genes revealed gene losses in most isolates. We have shown evidence that, although it is rare , C. graminicola can undergo sexual recombination based on lab assays and genomic analyses. We present the possible evolutionary origins of these genetic groups, the impact of recombination on genetic structure, and the potential for maize anthracnose to become a much more significant disease in the future.We used a population genomics approach to determine the genetic diversity of Clustering analyses based on single-nucleotide polymorphisms showed strong population differentiation, with the isolates grouped according to their country of origin. The European clade showed less genetic variation, a higher number of repeated MLGs, and lower pairwise differentiation among clusters, suggesting a larger clonal component compared to the other clades. Interestingly, all the shared genotypes were almost genetically identical, suggesting that gene flow is dependent on the clonal isolates generated by asexual reproduction. Most MLGs were shared between isolates from the same cluster and country; however, one MLG encompassed isolates from France and Switzerland  and another included isolates from Argentina and Croatia. In our cluster analyses, the European clade encompassed all isolates from Argentina. Together, these results indicate intra- and intercontinental migration of this pathogen. We argue a likely European origin of the isolates from Argentina due to the movement of maize germplasm into South America. Maize breeders in North America and Europe commonly use fields in South American (including Argentina) to advance breeding programs during the Northern Hemisphere winter. Because Argentina is a maize exporter, breeding programs represent a possible source of pathogen importation and consequently a source of pathogen introduction into new areas, for instance, in Europe or elsewhere. This result is particularly important from an epidemiological point of view, with direct implications for disease management because migration can cause the movement of more virulent and/or fungicide resistant genotypes .C. graminicola , may have overestimated the genetic diversity of this group. A more contemporary sampling could reveal the current genetic variation of North American populations.The North American clade is more genetically diverse and seems to have a separate evolutionary history. This clade encompassed isolates from the United States and Canada and one strain from Nigeria obtained from a culture collection. Although the first report in the literature of a  in 1852 , we argu in 1852 . Conside in 1852 , 93 and  in 1852  this lin in 1852 . C. gramIn South America, isolates from Argentina and Brazil were grouped into separate clades, suggesting independent introductions. In Brazil, the first disease report was in 1965 in the state of São Paulo (Campinas)  reportedC. graminicola populations detected in this study. The rapid decay of linkage disequilibrium and extensive reticulations in the neighbor network is consistent with a possible history of recombination. Furthermore, the PHI test for clonality detected recombination within each population. These results corroborate a previous study that found evidence of recombination using the PHI test at the chromosome level  maintained all the genes required to undergo sexual reproduction. This finding also suggests that some lineages have lost the ability for sexual reproduction or that not all sexual reproduction-related genes reported in other species are required in Colletotrichum spp.  and M5.001 (CBS130839). The cross-inoculation experiment was performed under laboratory conditions as described by Vaillancourt and Hanau 1991  AgaCopyright © 2022 Rogério et al.2022Rogério et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. graminicola populations may have been affected by genetic recombination, but also that, even if it is rare , C. graminicola can undergo sexual recombination. Fine-scale investigations at the genome level can be used to determine the real impact of genetic recombination on genetic diversity and adaptation; for instance, whether genetic exchange enhances virulence factors  in these populations, as has been suggested for other fungal species  have different levels of genetic diversity, suggesting that each has a unique evolutionary history. We also found evidence of recent migration of isolates between Europe and Argentina, possibly due to the importation of infected plant material. Although the sexual state of C. graminicola has only been reported under lab conditions, we show strong evidence that genetic recombination, whether by sexual reproduction or alternative mechanisms , plays an important role in C. graminicola population structure. In contrast to the traditional view of C. graminicola being mainly asexual, we conclude that genetic recombination is frequent. Fine-scale investigations at the genome level can be employed to understand the real impact of recombination and gene flow on the genetic diversity and adaptation of this species, with a direct effect on control measures and breeding programs aimed at resistance. Finally, we warn that maize anthracnose has the potential to become much more significant in the future, mainly due to isolates which are highly aggressive against the cultivar evaluated, the geographic range expansion of the pathogen, and the increased susceptibility of agroecosystems accentuated by ongoing climate changes.Our study represents the first investigation of the population structure of C. graminicola samples were obtained from symptomatic maize stems provided by an international network of maize researchers and producers, allowing sampling from nine different countries where anthracnose disease is prevalent   region of rDNA using the primers ITS4 and ITS5  , 127 werimbabwe)  and inclimbabwe) , 54. Funimbabwe) . Isolateand ITS5 , followiand ITS5 . The ampC. graminicola isolates used for whole-genome sequencing was extracted using the cetyltrimethylammonium-bromide (CTAB) protocol adapted from Baek and Kernerley   for 3 days at 25°C and 150 rpm under continuous light . Genomicernerley .C. graminicola isolates using either RAD-seq or WGS; sequences for an additional 11 isolates were also included in the analyses  from libraries prepared using a Nextera XT DNA kit  and sequenced on an Illumina HiSeq 4000 (2 × 150 bp). WGS data were obtained from six C. graminicola isolates  from paired-end libraries prepared using an Illumina TruSeq DNA Nano kit and sequenced on an Illumina MiSeq (2 × 300 bp). Sequence quality was checked on FastQC v0.11.7  and low-quality reads and adaptors were trimmed with Trimmomatic v0.3.8  as the query sequence. To identify the rRNA gene clusters, we used the following query sequences: XR_001139483 ; XR_001139484.1 (18S); and DQ003110.1 . Matching contigs were also removed. The assembly completeness was assessed using Busco v.3  using BWA-MEM v0.7.8  to confirm quality and were demultiplexed and quality-filtered with the process_radtags program in Stacks v.2.10 . The demM v0.7.8  with the  was calculated with the R package hierfstat v0.5-11 using the WC84 method . This analysis was conducted in R with the package adegenet v2.0.1 (K) based on the BIC. Additionally, we implemented a neighbor network using the software SplitsTree v4  was calculated using the R package Poppr v2.8.6 . We usedGeneious .r2) (The coefficient of linkage disequilibrium (r2)  was calcr2) . The ratr2)  to test C. graminicola genomes, we used a set of 96 genes with known roles in sexual reproduction in fungal model systems , C. fru000000.2 ), C. glo000000.1 ), C. hig000000.1 ), C. nym000000.1 ), C. sal000000.1 ), C. sim000000.1 ), C. sub000000.1 ), C. orb000000.2 ), and Ve000000.1 ). For alin vivo leaf blight assays using a derivative of the susceptible maize inbred line Mo940 (C. graminicola spore suspensions (3 ×105 spores ml−1) according to the methods of Vargas et al. (P < 0.0001), indicating a clear batch effect. Therefore, to eliminate the systematic differences among batches, we standardized each strain using the mean and standard deviation of the control (strain M1.001) for its batch. An ANOVA of the corrected data was used to determine the presence of significant differences among isolates. The second test was to determine whether there were differences among isolates, after correcting the lesion sizes to account for batch differences. The predictor variable was isolate, the response variable was the corrected lesion size, and the null hypothesis was no difference in lesion size among the isolates. The ANOVA test was significant (P < 0.0001), so we performed a Tukey’s comparison-of-means test.Pathogenicity assays were performed by ne Mo940 . Plants s et al. . Virulens et al. , with thThe genome assemblies and raw Illumina RAD-seq reads were submitted to GenBank and their accession numbers are listed in


Oryza rufipogon Griff. is a valuable germplasm resource for rice genetic improvement. However, natural habitat loss has led to the erosion of the genetic diversity of wild rice populations. Genetic diversity analysis of O. rufipogon accessions and development of the core collection are crucial for conserving natural genetic diversity and providing novel traits for rice breeding. In the present study, we developed 1,592 SNPs by multiplex PCR and next-generation sequencing (NGS) technology and used them to genotype 998 O. rufipogon accessions from 14 agroclimatic zones in Guangdong and Hainan Provinces, China. These SNPs were mapped onto 12 chromosomes, and the average MAF value was 0.128 with a minimum of 0.01 and a maximum of 0.499. The O. rufipogon accessions were classified into ten groups. The mean Nei’s diversity index and Shannon–Wiener index (I) were 0.187 and 0.308, respectively, in all populations, indicating that O. rufipogon accessions had rich genetic diversity. There were also differences in the genetic diversity of O. rufipogon resources in the 14 regions. Hainan populations possessed higher levels of genetic diversity, whereas the Guangzhou population had lower levels of genetic diversity than did the other populations. Phylogenetic analysis revealed that the genetic relationship among the distribution sites of O. rufipogon was closely related to geographical location. Based on genetic distance, a core collection of 299 accessions captured more than 99% of the genetic variation in the germplasm. This study provides insights into O. rufipogon conservation, and the constructed core collection provides valuable resources for future research and genomics-assisted breeding of rice. In recent times, with the popularization of single varieties, pressure from environmental selection and the aggravation of pest and disease issues, the genetic basis of cultivated rice has inevitably narrowed . It is uO. rufipogon Griff. , an important CWR of cultivated rice, has accumulated many important agronomic characteristics over its long evolution, such as wide adaptability, resistance to insects, diseases and abiotic stresses, cytoplasmic male sterility, and better quality, which are desirable for the improvement of rice crops  construction is a favoured approach to the efficient exploration and conservation of novel variation in genetic resources . A core aracters . Pan et O. rufipogon resources in Guangdong and Hainan Provinces, China. However, previous research was limited to individual populations from a few ecological regions. The genetic diversity of O. rufipogon in Gaozhou, Guangdong, China, was studied  in Guangdong Province was studied for preserving the genetic diversity  following the manufacturer’s recommendations, and index codes were added to each sample.Genomic DNA was isolated with the cetyltrimethylammonium bromide (CTAB) method from young leaves of each ccession . A total® 3.0 was used to determine the concentration of the library. An Agilent 2100 Bioanalyzer system was used to determine the length of the library fragments. Qualified libraries were sequenced on an Illumina platform.A total of 456 pairs of sample-specific multiplex PCR primers were designed and PCR amplification was performed at iGeneTech Bioscience Co., Ltd. The first round of multiplex PCR was performed as follows: 95̊ initial denaturation for 3 min 30 s, then 98̊ denaturation for 20 s, 60̊ primer annealing and extension for 2 min for 20 cycles, and finally extension for 5 min at 72̊. Products were purified using AMPure XP beads. The corresponding adapter oligonucleotides were joined by the second round of PCR, with the following procedure: 95°C initial denaturation for 3 min 30 s, 98°C denaturation for 20 s, 58°C primer annealing for 1 min, and 72°C extension for 30 s, with 10 cycles and finally extension for 5 min at 72°C. Products were purified using AMPure XP beads. QubitOryza sativa MSU V7.0  was set from 1 to 10  ≥ 1% and integrity ≥ 50% were used for phylogenetic and population structure analyses. The genetic diversity parameters of er V3.25 . Principer V3.25 . The ADM 1 to 10 .O. rufipogon accessions were selected based on the strategy of population priority and gradual clustering. First, according to the geographical distribution of O. rufipogon, the accessions from the same population were divided into a group, and only one O. rufipogon accession in the group was directly selected into the core germplasm. Then, the genetic diversity and cluster analysis of O. rufipogon germplasms in each group were carried out with the SNP data, and the core germplasm was screened according to different sampling ratios to capture most of the allelic diversity in the germplasm using Core Hunter II software  were examined under the models with K = 1-10 using admixture software. As suggested, a good K value will exhibit the lowest CV compared to other K values , representing the genetic diversity of populations, had a value of 0.5022 in the Hainan subgroup, while the I in the other subgroups was lower than 0.5. The range of the PIC was from 0.248 to 0.2674, with an average of 0.2576. Nei’s diversity index ranged from 0.3047 to 0.3347, with an average of 0.32.O. rufipogon accessions of 14 regions were grouped into six main clusters when the tree scale was 0.005  were restricted to the major cluster 6. When the tree scale was 0.01, all O. rufipogon accessions were clustered into 11 groups. The accessions from Dongguan and Huizhou were grouped together, and the accessions from Qingyuan was grouped separately. Moreover, another cluster containing accessions from Jieyang and Shanwei were characterized.With phylogenetic analysis, a neighbour-joining tree based on Nei’s genetic distance was also clearly constructed. The 998 as 0.005 Figure 4O. rufipogon accessions, the core germplasm was developed using Core Hunter II software with a modified Rogers distance of 0.7 and Shannon diversity index of 0.3. Our analysis indicated that the 499 top-ranked accessions could capture 100% of the allelic diversity of the entire O. rufipogon resource collection  and Shanwei , while the proportion of core germplasm from Shenzhen and Yangjiang was relatively low, accounting for only 0.67% and 2.34% of the total core germplasm, respectively. The detailed information is listed in Construction of a core collection provides a subset of representative accessions that captures most of the allelic diversity in the entire collection and can be used for genome-wide association studies (GWAS), gene cloning and marker development. According to the geographical distribution and genotype information of 998 O. rufipogon resources was determined  was from 0.056 to 0.693, with an average of 0.308. Nei’s diversity index ranged from 0.02 to 0.5, with an average of 0.187. The results revealed that O. rufipogon populations of Guangdong and Hainan Provinces had rich genetic diversity and that variations were widely distributed in the populations. However, if the number of SNPs would be further increased, the more perfect genetic relationships would be obtained. There were also differences in the genetic diversity of O. rufipogon resources in 14 regions, which suggested that the geographical environment might be responsible for the genetic background differences in O. rufipogon in different regions. It was consistent with previous research result , the Special Fund for Science and Technology Innovation Strategy  (201926), the Subproject of National Key Research and Development Program (2021YFD1200101-05) and Guangdong Key Laboratory of New Technology in Rice Breeding (2020B1212060047).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Species' ecological traits influence their spatial genetic patterns. Bedrock preference strongly shapes the phylogeography of alpine plants, but its interactions with other ecological traits have rarely been disentangled. Here, we explore whether dispersal ability and degree of habitat specialization account for divergent postglacial expansion patterns of high‐elevation plants in spite of similar bedrock preference.The Pyrenees, southwestern Europe.Cirsium glabrum (Asteraceae), Salix pyrenaica  and Silene borderei (Caryophyllaceae).Phylogenetic, genetic structure and demographic modelling analyses based on restriction‐site‐associated DNA sequencing (RADseq) data from a range‐wide populational sampling were conducted. Occurrence data and environmental variables were used to construct species distribution models, which were projected under current and Last Glacial Maximum conditions, and were combined with RADseq data to reconstruct the postglacial history of the study species. The degree of habitat specialization of each species was estimated based on the plant communities within which they occur, and their climatic niche breadth.Salix pyrenaica, which occupies a broad range of habitats, shows a high level of range filling, a blurred genetic structure and an admixture cline between the two main genetic groups, congruent with rapid postglacial expansion. The microsite specialists C. glabrum and S. borderei exhibit a strong genetic structure and low levels of range filling, indicative of slow postglacial expansion. The good disperser C. glabrum shows higher levels of admixture between genetic groups and weaker population differentiation than the poor disperser S. borderei.Factors other than bedrock preference have a strong impact on the postglacial range dynamics of high‐elevation species. Habitat specialization plays an important role, allowing species occupying a broad range of habitats to more rapidly expand their ranges after environmental change. The effect of dispersal ability is lower than expected for the study species. BecauseA further decisive factor shaping the distributions of species and their spatial genetic patterns is climate , while the main uplift occurred during the Alpine orogeny (37–24 Ma). The main chain is mostly built of granite and metamorphic rocks of siliceous nature, flanked by carbonate‐rich sedimentary rocks. However, this large‐scale pattern is interrupted by several islands of carbonate‐rich substrates along the main chain, as well as silicate‐rich sediments in the Pre‐Pyrenees, resulting in a geologically heterogeneous landscape. The typical postglacial landscape, with U‐shaped valleys, moraines and glacial cirques, evidences the extensive glaciation during the cold stages of the Pleistocene Figure . HoweverSalix pyrenaica, a species occupying a broad range of habitats with good dispersal ability, Cirsium glabrum, a microsite specialist with good dispersal ability, and Silene borderei, a microsite specialist with low dispersal ability. All of them are Pyrenean endemics, which allows ruling out recent immigration or gene flow from neighbouring mountain ranges.Here, we used phylogeographical analyses based on genomic data and species distribution modelling to study the genetic structure and diversity as well as the environmental niche of three calcicolous species with different dispersal abilities and degree of habitat specialization within the diversity of calcareous habitats. In particular, we explored the relative impact of three factors, that is bedrock preference, habitat specialization and dispersal ability, on species' current spatial genetic patterns. Specifically, if bedrock alone is a good predictor of spatial genetic patterns, we expect to find congruent phylogeographical patterns across the three species. However, if dispersal ability and degree of habitat specialization had a strong impact on the range contraction to glacial refugia and posterior expansion strategies, we expect species‐specific spatial genetic patterns and differential degrees of range filling. Specifically, we expect to find a blurred genetic structure and a high degree of range filling for good dispersers and species occupying a broad range of habitats, and a strong genetic structure and incomplete range filling for poor dispersers and microsite specialists. Furthermore, we aimed at disentangling the contribution of habitat specialization and dispersal ability to the species' postglacial range expansion. The three studied species are 22.1Cirsium glabrum is a herbaceous geophyte with a subterranean rhizome  1500–2800 m.  Blanco, . It occu2.2C. glabrum and S. pyrenaica, sampling was conducted on individuals spaced at least 5 m to avoid sampling clones. From each population, a herbarium voucher was made and deposited in the herbarium of the University of Barcelona (acronym BCN). Sampling was carried out in accordance with the Nagoya Protocol, and the EU regulation 511/2014, that is, prior informed consent was requested from the competent national authorities of Nagoya protocol members (https://www.cbd.int/abs/about) and a collection permission was obtained if required. Cirsium erisithales Scop., C. spinosissimum Scop., Salix hastata L., S. lanata L., Silene ciliata Pourr., S. multicaulis Durand. and S. saxifraga L. were sampled as outgroups.Samples of the study species were collected from 52 sites in 2018 covering the entire distribution range of each species Figure . Sites  measurements were conducted with leaf material from all collected populations, aiming at checking for uniform ploidy level across populations for each species Table . Three i2.4Salix, twice for Silene and three times for Cirsium samples with the wash buffer containing sorbitol. The quality of the extracts was examined photometrically. Extractions were then purified with the Nucleospin gDNA clean‐up kit. The DNA concentration was estimated using a Qubit 4 fluorometer (ThermoFisher Scientific).Total genomic DNA was extracted from ca. 10 to 20 mg dried leaf material following the CTAB protocol  and a protocol adapted from Paun et al. (m P1 adapters to the restricted samples. Shearing by sonication was performed with a M220 Focused‐ultrasonicator (Covaris) with settings targeting a size range of 200–800 bp and a mode at 400 bp . Libraries were sequenced on Illumina HiSeq at VBCF NGS Unit (http://www.vbcf.ac.at/ngs/) as 100 bp single‐end reads.Single‐digest restriction‐site‐associated DNA sequencing (RADseq) libraries were prepared from five individuals per each es Table  using thn et al. . Brieflyhttps://github.com/wtsinpg/illumina2bam) and the program process_radtags.pl implemented in Stacks 2.3  from 0 to 8 and a percentage of individuals that must possess a particular locus for it to be included in the calculation of population‐level statistics (r) of 80%. For every value of M, the minimum number of raw reads required to form a stack (m) and the maximum number of differences among loci to be considered as orthologous across multiple samples (n) were given values equal to M. After the optimization, M = 5 was selected as the value optimizing the number of de novo assembled loci for the three species. The values of m and n were as well set to 5.The raw reads were quality filtered and demultiplexed based on individual‐specific barcodes using Picard BamIndexDecoder included in the Picard Illumina2bam package (available from https://CRAN.R‐project.org/package=adegenet) on files generated under different filtering parameters allowed selecting a filtering scheme with a good balance of missing data and amount of informative characters. Samples with high levels of missing data were excluded  and Felsenstein's ascertainment bias correction was further used to account for missing invariant sites as recommended by Leaché et al. .Adegenet was used to calculate Nei's distance matrices Nei, , which wé et al. . Tree seé et al. . The besé et al. . The resK (number of groups) ranging from 1 to 10 were carried out using a burn‐in of 50,000 iterations followed by 500,000 additional MCMC iterations. CLUMPAK  per population and genetic group and FST between genetic groups. For the estimation of population‐level summary statistics, geographically close populations of C. glabrum and S. borderei were defined as single populations  using the software Stairway plot  by downprojecting the datasets to a minimum sample size, which maximizes the number of SNPs kept  Heynh.  to calculate slope inclination. These variables were checked to exclude high correlations (Pearson's r > |0.7|).To account for the rough terrain of mountain regions, the available historic climate dataset, accessed via PaleoView . We classified the geological entities into four categories reflecting the amount of carbonate, from pure silicate to pure carbonates  and Silene (http://flore.silene.eu), resulting in 38 data points for C. glabrum, 27 for S. borderei and 137 for S. pyrenaica. Absences were generated from a dataset comprising 2229 non‐forested vegetation plots above 1000 m, georeferenced at a precision of 1 × 1 km and containing at least information on elevation from the European vegetation archive  to project the environmental suitability of the study area for the three studied species at the LGM and under current conditions. Species occurrence data were obtained from our sampling sites and GPS data obtained during field work, and a selection of precise localities from the databases of the Atlas of the Flora of the Pyrenees  using the R‐package ‘vegan’  million. The resulting number of SNPs per species and filtering scheme is presented in Table C. glabrum showed two clear groups, the eastern group comprising five populations and the western group 11 populations . The same pattern was observed in fineRADstructure results, with additional traces of admixture between populations adjacent to the gap between the western and eastern groups  and S. borderei (ΔAIC = 86.8) involved a founder event plus asymmetric migration, in both cases higher from the source population to the founded population than vice versa , with 10‐fold higher migration from the eastern to the western population than vice versa  than that of C. glabrum (0.12) and S. borderei (0.14)  Figure . The resa Figure .4S. pyrenaica, and, on the other hand, postglacial migration lag in C. glabrum and S. borderei. A strong source of evidence comes from the species' level of range filling (Dullinger et al., S. pyrenaica indicates successful postglacial colonization of areas, which were glaciated or non‐suitable at the LGM, from peripheral refugia (Figure C. glabrum and S. borderei cover only a small fraction of their potential range (Figure Our results demonstrate two main responses of high‐elevation plant species of the Pyrenees to the temperature increase at the end of the last glacial period, that is on the one hand a rapid range expansion towards distribution equilibrium in a Figure . Nunataka Figure . In conte Figure , most ofe Figure , wherease Figure .C. glabrum (Figure S. borderei can be explained by a strong postglacial migration lag, since most populations occur in previously non‐glaciated areas. In contrast, in S. pyrenaica major glacial refugia outside the ice sheet need to be invoked (Figure The increase in effective population size is synchronous with the end of the glacial period observed in m Figure  and can m Figure  in the vm Figure . The lacd Figure . Howeverd Figure .S. borderei (Figures C. glabrum showed a strong structure within the western Pyrenees (Figures C. glabrum, and in the eastern Pyrenees for S. borderei, as previously evidenced for several European mountain ranges including the Pyrenees (Bidegaray‐Batista et al., S. pyrenaica, where two groups were only observed in the STRUCTURE results (Figure C. glabrum and S. borderei (Table S. pyrenaica could be the result of isolation by distance (Wright, Even if compromised with uncertainties regarding the exact timing, the different pace of postglacial colonization and extent of postglacial range filling shaped the spatial genetic architecture of the studied species. The strong structure observed in  Figures  and 3 an Figures  reveal l Figures . Similar Figures  and 3. Ts Figure , but nots Figure . This paei Table . A likelei Table  and evidei Table . Secondaei Table . Alterna Wright, , which w Wright, .C. glabrum and S. borderei, as compared to S. pyrenaica, a species occupying a broader range of habitats. These results add to the suggestion of Dullinger et al. (C. glabrum grows exclusively in stable scree slopes with sparse vegetation cover and deep, nutrient‐rich soils, and S. borderei is found in crevices of vertical to overhanging cliffs (Talavera, S. pyrenaica can be found in a much wider range of habitats (Figure C. glabrum is obviously not sufficient to enable rapid colonization of its potential range, rendering this species a similarly poor colonizer as S. borderei, which is lacking apparent adaptations for long‐distance dispersal.Habitat specialization strongly shapes postglacial colonization in the study species, partly overruling their dispersal abilities. This is reflected in the lesser extent of range filling and stronger genetic structure of the microsite specialists r et al.  that othr et al. , which cs Figure . Thus, wOur results, together with those of Clark et al. , may expC. glabrum and S. borderei. Specifically, the poor disperser S. borderei shows a higher level of local spatial genetic structure, a higher number of private alleles and a lower degree of admixture between groups as compared to C. glabrum (Figures Whereas we did not detect strong effects of dispersal ability on postglacial expansion on the range‐wide scale, it clearly drives small‐scale differences in spatial genetic patterns of  Figures  and S4.  Figures . Among tNone declared.Pau Carnicero is an Assistant Professor at the Department of Botany, University of Innsbruck. He is particularly interested in the interaction between microevolution and environmental changes acting on mountain plants.Author contributions: Pau Carnicero, Peter Schönswetter and Stefan Dullinger designed the study. Pau Carnicero conducted the genomic analyses. Dietmar Moser, Johannes Wessely and Stefan Dullinger performed the SDMs. Xavier Font prepared the vegetation database. Pau Carnicero and Peter Schönswetter wrote the first version of the manuscript. All authors commented on the manuscript.Figure S1Figure S2Figure S3Figure S4Figure S5Figure S6Figure S6Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Appendix S5Click here for additional data file.


Amur leopard genetic diversity was found low  with no visible population substructure and recent bottleneck signature. Although a historical bottleneck footprint was observed. Mitochondrial DNA diversity was also found low with two haplotypes differing by a point mutation reported in 1,769 bp of investigated sequence covering parts of cytochrome b gene (846 bp), NADH-5 gene (611 bp) and control region (312 bp). We recommend periodic genetic monitoring of wild Amur leopards following the proposed methodology to achieve cost effectiveness and efficiency.Small populations of the endangered species are more vulnerable to extinction and hence require periodic genetic monitoring to establish and revisit the conservation strategies. The Amur leopard is critically endangered with about 100 individuals in the wild. In this study, we developed a simple and cost-effective noninvasive genetic monitoring protocol for Amur leopards. Also, we investigated the impact of fecal sample’s age, storage, and collection season on microsatellite genotyping success and data quality. We identified 89 leopard scats out of the 342 fecal samples collected from Land of the Leopard between 2014–2019. Microsatellite genotyping using 12 markers optimized in 3 multiplex PCR reactions reveals presence of at least 24 leopard individuals . There was a significant difference in the success rate of genotyping depending on the time from feces deposition to collection ( Increased anthropogenic disturbances, such as land development, over exploitation, pollution, introduction of exotic species, are threatening many wildlife around the world . In the Panthera pardus orientalis Schlegel, 1857), the northernmost leopard subspecies, is critically endangered [th century mainly due to excessive hunting, declining of prey species and marginalization of habitat resources, and only a small population currently exists in the mountain-forest region near the borders of Russia, China, and North Korea [The Amur leopard .In this study, we developed a simple and cost-effective microsatellite based DNA fingerprinting protocol to identify leopard individuals using the DNA extracted from feces. Further, the genetic diversity of critically endangered Amur leopards was analyzed noninvasively  by monitoring microsatellite markers and mitochondrial DNA (mtDNA). Additionally, factors  influencing the performance of noninvasive genetic studies were assessed. The study is part of an ongoing Korea–Russia collaborative wildlife research project to develop long–term population management plan for critically endangered Amur leopard and Amur tiger (2). Precautions were enacted during sample collection to avoid sample contamination and the collection date, Global Positioning System (GPS) coordinates, approximate age of the sample, type of collection site, and presumed species were recorded. Scat sample age at the time of collection was determined by experienced field biologists based on scat morphology (intact or disintegrated), appearance (colour) and moisture content (moist or dry). Also, nearest surrounding area for traces and scrapes were investigated to determine the sample age. Each sample was assigned a unique identification number, and stored in plastic bags at −20°C in a freezer prior to genomic DNA (gDNA) extraction, which was conducted using the QIAamp Fast DNA Stool Mini Kit  in accordance with the manufacturer’s protocol. Negative controls were included during DNA extraction to monitor contamination and success. DNA extraction was conducted in Russia and the DNA extracts were exported to South Korea for genetic analysis (CITES permit no. ES2019-03989). All samples were legally and ethically collected following noninvasive sampling approaches. The study involved no capture or handling of any live animal. The study protocol was conducted in accordance with the ethical guidelines of the Institutional Animal Care and Use Committee of Seoul National University .Fecal samples (n = 342) were collected by park rangers and researchers opportunistically from the Land of the Leopard  during sb region using the universal primers L14841 and H15149 [https://www.ncbi.nlm.nih.gov/) using the Basic Local Alignment Search Tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi).Land of the Leopard hosts both tigers and leopards. During sample collection, the collected samples were assigned to a specific species by field biologists and re-verified by multiplex polymerase chain reaction (PCR) and agarose gel electrophoresis as described by Sugimoto et al. . Multipld H15149 , followeThe gender to fecal sample was determined based on amplification of genes located on X chromosome (ZFX) and Y chromosome (DBY-7) . The fraID) and lower genotyping errors  were selected in order to use the minimum number of markers with sufficient discrimination power. The use of fewer markers can lower the probability of errors and increase efficiency. The remaining leopard samples were amplified by the selected marker and individual identification for the entire leopard samples proceeded.DNA fingerprinting based on microsatellite genotyping of the fecal samples was used to identify unique individuals and subsequently estimate the genetic diversity of the Amur leopard population. The whole process included following three steps: (i) microsatellite prescreening , (ii) identification of unique individuals by genotyping of fecal samples with select markers, and (iii) estimation of the genetic diversity of the Amur leopard population. Microsatellite markers developed for big cat species were used to identify individuals and assess genetic diversity . In totaIndividual identification was performed using samples with more than 70% of the loci successfully genotyped. Genotyping errors inevitably occur with the use of fecal DNA extracts because degraded DNA remains in small amounts with inhibitors , 26. Thuhttps://www.geneious.com).The microsatellite loci were amplified by PCR in 10 μL reaction volumes consisting of 5 μL of Qiagen multiplex kit master mix, 0.5 μL of Q-Solution, 0.25 μL of each primer (10 pmol), and 2.0 μL of DNA extract. The PCR conditions for amplification of microsatellites included an initial denaturation step at 94°C for 15min followed by 45 cycles of denaturation at 94°C for 40 s, annealing at 60°C for MPP1–7 or 55°C for MPP8 for 1 min, and extension at 72°C for 1min, and a final extension step at 72°C for 30 min. Each round of PCR included a positive and negative control to validate the PCR procedure and detect contamination. The PCR products were analyzed using ABI 3730xl genetic analyzer  at NICEM Inc. , and the genotyping data were collected using Geneious Prime 2020.1.2 (E) and gene diversity , and marker’s polymorphic information content (PIC) were estimated using GenAlEx 6.5 [ID and probability of identity among siblings (PIDsib) were calculated using GIMLET [Consensus genotypes of samples were determined by threshold method (consensus threshold = 3) of GIMLET, which was also used to calculate the allelic dropout and false allele rates . The preAlEx 6.5  and CERVAlEx 6.5 . The PIDg GIMLET .Since the Amur leopard population has experienced a significant decline in the past, the presence of a contemporary and historical bottleneck signature was investigated. BOTTLENECK 1.2.02 was used to investigate the presence of a contemporary bottleneck with 1000 iterations at two-phase mutational model  . The Wilb gene (846 bp), NADH-5 gene (611 bp) and control region (312 bp). The cytochrome b gene of the Amur leopard was amplified with the primer sets PSF1/PSR1 and PSF2/PH1 as described by Sugimoto et al. [NADH-5 gene was amplified with the primer sets F/RL2 and FL2/RL4 as described by Uphyrkina et al. [https://www.geneious.com)  on an ABI 3730xl genetic analyzer . The sequences were analyzed using Geneious Prime 2020.1.2 (https://www.geneious.com) and the peak quality of each sequence was checked manually. Segments were aligned to the published whole mitochondrion sequence of P. p. orientalis (KX655614) and to the comparable partial sequences of P. p. orientalis . The emergence and loss of unique genetic variants were also investigated.Three segments of mtDNA  were amplified, which included parts of the cytochrome o et al. , and NADa et al. . To ampla et al.  embeddedous.com) . PC2 wasRangers recorded the sample collection date and estimated the age of fecal sample based on the scat morphology, appearance and moisture content. During analysis, the time from deposition to collection was categorized as <2, 2–4, or >4 weeks, while the time from collection to DNA extraction was categorized as <2 years, 2–4 years, and >4 years, and collection month were categorized as November–December, January, February, and March–April. Samples without information were not categorized. To investigate whether the age of sample, storage duration, or the month of collection were correlated with the success rate of genotyping, the Fisher’s exact test was conducted for three separate cases between sample quality and (1) time from deposition to collection, (2) time from collection to DNA extraction and (3) month of collection. The samples were classified as good, medium, or poor quality based on the amplification success (consensus genotype) of the 12 microsatellite loci selected for individual identification  assigned to the same species. We further compared field biologist observations about species of origin of each of the collected samples and results of genetic experiment. Of the 342 scats, field biologists assigned species of origin to 309 scats. Genetic species identification results were found similar to field observation for 284 samples (92%). The remaining samples include 2 Tiger  and 14 Leopard . Nine samples were not amplified or genetically identified as different species although field observation was thought to be tiger or leopard.Of the 342 fecal samples, 89  were from leopards . Of the −5 and 7.16 × 10−3, respectively, which were deemed sufficient to distinguish among the estimated 100 individuals inhabiting the Land of the Leopard [Of the 32 screened microsatellite markers, 17 were monomorphic and 15 were polymorphic . Of the  Leopard , 42  and no significant difference was found depending on the time from collection to DNA extraction  and month of collection . Since long term storage of the samples in a freezer was not problematic and there was no difference in the month of collection, obtaining fresh feces should be considered first for efficient genetic monitoring. The proportion of samples categorized as good quality were 73% in <2 weeks old samples, 56% in 2–4 weeks old samples, and 31% in >4 weeks old samples, indicating a notable decrease in quality with prolonged exposure to the environment  of Amur leopards of Russia Far East based on 25 microsatellites and 7 individuals captured between 1993–96. Sugimoto et al. [Comparisons of genetic diversity are ideally conducted with the use of the same set of molecular markers. Estimation of genetic diversity with the use of microsatellites involves assessment of allelic diversity  and percentage of heterozygosity within the genome. Allelic diversity is marker dependent whereas heterozygosity represents the state of a population rather than the markers used. Uphyrkina et al.  first reo et al.  studied o et al.  also stuNADH-5 and control region. Sugimoto et al. [b and the ratio of Type T was about three-fold greater than that of Type A in the Amur leopard population of the Land of the Leopard. We amplified partial fragments of the cytochrome b, NADH-5 and control region and reported two haplotypes that differ by a point mutation in cytochrome b. The observed point mutation in the cytochrome b represent the types A and T reported earlier by Sugimoto et al. [The haplotype diversity of the Amur leopard population is relatively small as compared to other leopard subspecies. Uphyrkina et al.  did leopo et al.  reportedo et al. . HoweverE and HEQ (heterozygosity expected at mutation-drift equilibrium) values to find excessive HE, and find the evidence of a relatively recent bottleneck occurred between generations 0.2–4Ne (bottleneck effective size) [The Amur leopard population was reduced to about 20 individuals in the late 1900s due to rampant hunting and habitat destruction . Thus, ave size) , 50. A rve size) . Samplinve size) , 54. On ve size) . This suMonitoring individuals with genetic tagging has many advantages. It is possible to identify demographic structures and to keep up with population trends, such as abundance and birth rates and death rates, using noninvasive samples . Also, tAn estimation of the population was not conducted because the samples were obtained opportunistically, but a systematic design of fecal sample collection would even allow reliable population estimation using the capture recapture method . In partS1 Fig(A) Log posterior probability of 14 microsatellites data for K number of clusters visualized by STRUCTURE HARVESTER web 0.6.94 . (B) Bar(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.