Nymphaea, commonly known as water lily, is the largest and most widely distributed genus in the order Nymphaeales. The importance of Nymphaea in wetland ecosystems and their increased vulnerability make them a great choice for conservation and management. In this work, we studied genetic diversity in a collection of 90 N. micrantha and 92 N. nouchali individuals from six different states of India, i.e., Assam, Manipur, Meghalaya, Maharashtra, Goa, and Kerala, using simple sequence repeat (SSR) markers developed by low throughput Illumina sequencing (10X coverage of genome) of N. micrantha. Nymphaea nouchali is native to India, whereas N. micrantha is suggested to be introduced to the country for its aesthetic and cultural values. The study revealed extensive polymorphism in N. nouchali, while in N. micrantha, no apparent genetic divergence was detected prompting us to investigate the reason(s) by studying the reproductive biology of the two species. The study revealed that N. micrantha predominantly reproduces asexually which has impacted the genetic diversity of the species to a great extent. This observation is of immense importance for a successful re-establishment of Nymphaea species during restoration programs of wetlands. The information generated on reproductive behaviors and their association with genotypic richness can help in strategizing genetic resource conservation, especially for species with limited distribution. The study has also generated 22,268 non-redundant microsatellite loci, out of which, 143 microsatellites were tested for polymorphism and polymorphic markers were tested for transferability in five other Nymphaea species, providing genomic resources for further studies on this important genus. Nymphaea, an aquatic perennial herb commonly known as water lily, is the largest and most widely distributed genus in the order Nymphaeales. The genus is represented by about 45–50 species with a worldwide distribution in the temperate and tropical regions , act to reduce water turbidity and provide in-stream sediment stabilization  are wild and four  are cultivated , amplified fragment length polymorphism (AFLP), and random amplified polymorphic DNA (RAPD) for assessing the genetic diversity and resolving relationships among Nymphaea species  to develop and characterize SSR markers for Nymphaea in terms of frequency, genomic distribution, information content, and transferability to related species, and (2) to assess genetic diversity and population structure of the two aforementioned Nymphaea species. We discuss the importance of genetic diversity as a resource used to ensure the species success in varied environments. Emphasis is placed on the significance of genetic diversity in the conservation of biodiversity. This is the first report on the genetic diversity and population structure of Nymphaea species using genomic SSR markers. Therefore, the genetic information obtained in this study will provide useful resources for various genetic studies in Nymphaea and guide strategies to conserve wild species.In this work, we collected 90 Nymphaea. Sixteen populations of N. micrantha and 27 populations of N. nouchali consisting of 90 and 92 individuals, respectively, were collected from different locations in Assam, Meghalaya, Manipur, Goa, Maharashtra, and Kerala. The species were identified by Prof. Santhosh Nampy and Dr. A. K. Pradeep, University of Calicut, Kerala, and the voucher specimens were deposited at the herbarium of Department of Botany, University of Delhi (DUH14710-DUH14732) and Botanical Survey of India, Eastern Regional Centre, Meghalaya (ASSAM96585-ASSAM96610). The plant material of 3–5 individuals per population was collected, dried in silica, and brought to the laboratory for DNA extraction. To avoid sampling from the same plant and capture more diversity, the samples were collected from the plants growing at a greater distance than 5 m. Individuals collected from a single wetland were assigned to one population. All the accessions studied in the present study are tabulated in Wetland areas of different states of North-East India , Western India , and Southern India  were explored during the years 2017–2020 to survey and collect biological samples of the genus Nymphaea spp. were also collected  and N. caerulea were collected from North-eastern Hill University (NEHU), Shillong, and Yamuna Biodiversity Park, Delhi, respectively.To assess the cross-species transferability of the SSR markers, a few other ollected . Three sGenomic DNA was extracted from the silica dried leaf tissues brought from the field using CTAB (hexadecyltrimethylammonium bromide) method described by N. micrantha was estimated by flow cytometry. About 1 cm2 of the young fresh leaf was used for sample preparation using the CyStain® PI Absolute P kit  following manufacturer instructions. Solanum lycopersicum L. “Stupicke’ polnı’ rane”’ 1C = 0.98 pg  for each sample. For both, the standard reference and N. micrantha samples were prepared in replicates, and each was run three times with a minimum of 2,500 nuclei count per sample. The fluorescence values were converted to the DNA content using the following formula:The genome size of  0.98 pg  was usedN. micrantha was used for genome sequencing. The sequencing library was prepared using the NEBNext Ultra DNA library prep kit for Illumina  following the manufacturer’s instructions. In brief, the genomic DNA was fragmented by sonication, followed by adaptor ligation . The adaptor-ligated fragments were then PCR amplified using NEBnext universal  and Index3 tagged NEBnext indexed primer  and gel purified. The concentration of the prepared library was quantified by Qubit 4.0 Fluorometer  and sequenced on Illumina HiSeq 2000 platform in paired-end sequencing mode  by Macrogen Inc., (South Korea). The FastQ files containing the raw reads were submitted to the sequence read archive (SRA) at National Centre for Biotechnology Information (NCBI) under the accession number PRJNA750447. The raw data was filtered for high-quality reads [Reads with ≥ 70% HQ bases (Q ≥ 20)] using the NGS QC toolkit (de novo assembly using Velvet (v1.2.10), with the k-mer length of 55. The assembled sequences were filtered to all contigs greater than or equal to 1,000 bp to remove fragmentation of the assembly.The high-quality genomic DNA of  toolkit  at defauN. micrantha genome were mined for perfect microsatellites using MISA  terms were assigned by adopting the terms that had been assigned to the top hit sequences of the database and mapping them to the higher-level categories using the Functional Annotation Tool of DAVID Bioinformatics Resources 6.8  were used to design primers. The web-based program, Primer3Plus., India . Additio., India .N. micrantha DNA samples. The PCR reaction was conducted in a total reaction volume of 15 μl containing 1x PCR buffer, 2 mM of MgCl2, 0.2 mM of each dNTP, 0.4 mM each of forward and reverse primers, 50 ng of template genomic DNA, and 1.25 U of Taq DNA polymerase . The DNA amplification was performed in a SimpliAmp™ thermal cycler  with the following cycling parameters: initial denaturation at 94°C for 3 min followed by 30 cycles at 94°C for 20 s, optimized primer annealing temperature (between 5 and 60°C) for 20 s, DNA extension at 72°C for 30 s, and a final extension at 72°C for 7 min. The amplified products were initially resolved on 2% agarose and visualized under UV-light after ethidium bromide staining.The 43 genic-SSR, designated as NymTr_1 to NymT_43 and a subset of 100 non-genic SSR markers, designated as Nym_NGS1 to Nym_NGS100 were chosen for experimental validation. These markers were tested for amplification using two N. micrantha and N. nouchali. Amplicon size was determined by comparing it with a 100 bp DNA ladder . Unambiguously amplified alleles for each SSR locus were scored individually and coded as for their integer size in base pairs (bp). To avoid genotyping errors due to PCR-based amplification failures, the software Micro-Checker 2.2.3 , overall, an effective number of alleles (Ne), observed (Ho) and expected (He) heterozygosity, Fst = Nei’s differentiation index (Fst), and Gene flow (Nm) were calculated for each locus using GenAlEx 6.5 , the mean number of effective alleles (Ne), percentage of polymorphic loci (PPL), number of private alleles (Ap), and mean observed heterozygosity (Ho) were calculated using Genalex and the Nei’s unbiased gene diversity/expected heterozygosity  were calculated using poppr R package. Private alleles were defined as those discovered only in the population considered, discarding those that were found only once as they could reflect genotyping errors.To estimate within population diversity, the genetic diversity parameters were calculated using GenAlEx 6.5 and poppr R package. Observed mean number of alleles  approach to estimate every individual’s admixture proportions for a predefined K value . We ran The DAPC combines the advantages of principal component analysis (PCA) and discriminant analysis (DA) . It firsSNAPCLUST allies the advantages of both model-based approaches as used in STRUCTURE and geometric approaches like those used by DAPC and provides a fast maximum-likelihood solution to the specific genetic clustering problem . We usedTo further assess the population genetic structure and evaluate the genetic interrelationships among the individuals collected from different locations, unrooted neighbor joining (NJ), and principal coordinate analysis (PCoA) were performed based on pairwise Nei’s genetic distance matrix. The PCoA was generated using dudi. pco function implemented in ade4 R-package , and theThe analysis of molecular variance (AMOVA) analysis was carried out to test the structure of genetic variation within and among genetic clusters inferred based on population structure analysis.ST as the distance measure. Within individual variance was calculated to quantify variation due to heterozygosity within genotype. The F-statistics was calculated to summarize the degree of differentiation at each hierarchical level. The significance of variance components and F-statistics was tested using 9,999 random permutations of data matrices.We performed hierarchical AMOVA analysis using the poppr R package  to quanto), the mean number of effective alleles (Ne), percentage of polymorphic loci (PPL), number of private alleles (Ap), and mean observed heterozygosity (Ho) were estimated using GenAlEx 6.5 ]} and natural log-transformed geographic distance [ln (1 + geographic distance)] matrix as outlined by To determine if the genetic variability is structured in geographic space, we performed an isolation by distance (IBD) test  between via the method of r = 0). A bootstrap of 10,000 trials was used to estimate a 95% confidence interval around the observed r values for each class.The spatial autocorrelation analysis was computed using the multi-locus genetic distance between individuals calculated n = 20) of both species were collected from the natural populations and dissected to observe the seed set.Besides making a record of pheno-events covering onset/duration of flowering, fruiting, and production of vegetative propagules through regular field visits (2017–2020), the plantlets/propagules were grown in experimental ponds at the Department of Botany, the University of Delhi to monitor the events. The pollinated flowers/fruits  was determined using 1% acetocarmine , while pn = 15) or (ii) cross-pollinated (n = 15), with fresh pollen on floral stages harboring the most receptive stigma and bagged with a muslin cloth. The pollinated pistils were collected 6 h after pollination (HAP) and fixed in acetic: alcohol , cleared with NaOH (1N), and stained with decolorized aniline blue, and visualized under the epifluorescence microscope following n = 20) were left untreated (open-pollination) to determine the fruit-set in the plants under cultivation. The pollinated pistils were collected 15 days after pollination (DAP) and dissected to observe a seed set.Flowers were either (i) selfed  protein database. A total of 3,809 (30.3%) sequences were found to have similarities to the sequences in the NCBI nr protein database, and 1,623 (12.9%) were found to have at least one functional annotation. The remaining 8,747 (69.67%) did not show significant similarity to the known sequences and were not annotated.Gene Ontology (GO) enrichment analysis, using DAVID Bioinformatics Resources 6.8 , classifN. micrantha individuals. Surprisingly, all the markers tested in N. micrantha produced the same DNA profile and were monomorphic.The 143 SSR primer pairs were analyzed for polymorphism among 90 N. nouchali, of 143 SSR markers tested, 50 could not produce clear and scorable bands on PAGE, and 36 were monomorphic and, thus, were excluded from further analysis. The analysis using Micro-Checker software revealed no evidence for stutter error or large allele dropout at any locus for all the populations. The probable presence of null alleles was detected for locus Nym_NGS82 (K1 population) and locus NymTr_41 (K3 population). Accordingly, the adjusted frequencies of amplified alleles for these populations were used for further analysis. Descriptive characterization and genetic diversity analysis among 92 N. nouchali individuals, for each of the remaining 57 polymorphic SSR loci, is given in e) ranged between 1.056 (NymTr_13) and 2.339 (NymTr_24), averaging 1.821 alleles per locus. The observed (Ho) and expected (He) heterozygosity ranged from 0.04 (NymTr_13) to 0.975 (NymTr_21), and 0.03 (NymTr_13) to 0.559 (NymTr_24), respectively. The average observed heterozygosity (Ho = 0.578) was higher than the average expected heterozygosity (He = 0.386). Polymorphic information content (PIC) values varied from 0.042 (NymTr_13) to 0.826 (NymTr_5), with a mean PIC of 0.466. Twenty-six SSR markers were considered as highly informative (PIC > 0.5), twenty-five were moderately informative (0.5 > PIC > 0.3) and six markers  were considered of little polymorphism information having PIC of < 0.3 values (t) was highest for the marker NymTr_5 (Ht = 0.86), followed by NymTr_31 (Ht = 0.751) and Nym_NGS45 (Ht = 0.737), and lowest for the marker NymTr_13 (Ht = 0.04). Null allele frequencies obtained for all loci were close to zero. No significant linkage disequilibrium was found between the locus pairs based on log-likelihood ratio statistics (G-test). Forty-two out of 57 loci significantly deviated from HWE (p < 0.05), of which 35 deviated due to heterozygote excess and 7 loci deviated due to heterozygote deficiency (is = −0.456) and deviations from HWE indicate an excess of heterozygosity among N. nouchali populations.In 3 values . Total gficiency . NegativN. nouchali are tabulated in o) was higher than the expected heterozygosity (He) for each population except A6. Ten out of 27 populations showed a significant deviation (p < 0.05) from HWE . The genotype abundance/evenness (E5) for all the populations except one (K2) indicated that the multi-locus genotypes (MLG) in the populations are equally abundant. More than 50% of the studied loci were polymorphic in all the populations with the highest percentage of polymorphic loci in 6S, K3, and A7 (PPL = 87.72%), and lowest in A2 (PPL = 59.65%).Genetic diversity indices obtained for each population of from HWE . Nei’s uST values ranged between 0.00 (between A4 and A5 populations) and 0.390 (between K2 and A2 populations), showing no genetic differentiation to fairly high genetic differentiation among populations . Significant genetic differentiation (p < 0.001) was detected among genetic clusters for both K = 3 and K = 4 genetic clusters  and Nei’s unbiased gene diversity (He) were highest in cluster II  and lowest in cluster IV . Simpson’s index (H) and Shannon–Wiener index were highest in cluster I  and lowest in cluster III . The genotype abundance/evenness (E5) in each cluster was either 1 or close to 1 which indicates the equal abundance of all multi-locus genotypes (MLGs) in these genetic clusters. More than 90% of the loci studied were polymorphic in all four clusters with the highest percentage of polymorphic loci in cluster II (98.25%). All the accession groups amplified private alleles (Ap) varying between 6 and 30 with the highest number of Ap detected in cluster II.Genetic diversity statistics obtained for each genetic cluster are tabulated in st values between clusters ranged from 0.053 to 0.136, and all the cluster pairs were significantly differentiated p-value < 0.01 for each pair (ST = 0.053), followed by accessions from cluster I and cluster II (FST = 0.068). Cluster I and II accessions were more distinct from cluster III and IV accessions than they were from each other, and vice-versa.The pairwise Fach pair  from eacST and geographical distance based on Mantel permutation test . The multivariate spatial autocorrelation analysis displayed positive and significant r values up to 12 km  model, significant positive correlation was observed between pairwise Fto 12 km  illustrar2 = 0.187, p < 0.001) and cluster II , which mostly included the localities from Goa-Maharashtra and Kerala respectively. The multivariate spatial autocorrelation analysis within the genetic groups displayed significant r values for cluster I and suggested a non-random genetic similarity between individuals who are less than 32 km apart. For clusters II, III, and IV, the r values fell within the confidence interval of the null hypothesis . Controlled pollinations (both selfed and outcrossed) resulted in the initiation of fruits formation but failed to mature into fruits. The pollinated flowers turned brown and started decomposing after 12–15 days of the anthesis phase . However, only 4.33 ± 1.91% of the ovules showed receptivity. The fertility of pollen grains was significantly greater (87.89 ± 5.18%) than the viability  were greater than an effective number of alleles (Ae). The negative inbreeding coefficient (mean Fis = −0.456) values indicate an excess of heterozygosity in the studied populations of N. nouchali. The average total gene diversity (Ht = 0.531) among all polymorphic SSR markers was higher than polymorphic information content (PIC = 0.466) suggesting that it is less likely that the N. nouchali individuals have identical heterozygote genotypes  within populations ranged between 0.292 and 0.535 , which might also explain the observed high within-population gene diversity in the species. Several studies on other aquatic plants, however, have reported low genetic variation  the low effective size of populations studied, (2) the outcrossing breeding system, and (3) selection favoring heterozygotes.The average gene diversity . These approaches recovered three or four different clusters with a slightly different partitioning of genotypes into genetic clusters. All the three approaches recovered a high degree of correspondence between genetic clusters and geographical origin, thus, indicating that the individuals with less geographical distances were more genetically similar than those from a greater geographical distance.To estimate genetic populations within a group of ST between each population pair revealed that populations collected from Assam were more differentiated from the populations collected from Goa, Maharashtra, and Kerala  pattern also suggested restricted dispersal and regional equilibrium between gene flow and genetic drift in controlling the distribution of genetic variation . The detK = 4 and K = 3. The highest variance was present within geographical among individuals, followed by among genetic clusters and zero variance was detected among individuals within the genetic cluster  the four clusters. The pairwise FST values between the clusters showed greater genetic differentiation with increased geographic distance between the two clusters. Clusters I and II individuals were more genetically differentiated from clusters III and IV individuals than from each other. The cluster III and cluster IV individuals, which includes individuals from Assam, were least differentiated (FST = 0.053), followed by cluster I and cluster II (FST = 0.068), which included individuals from Goa and Maharashtra. The regional population genetic structure and weak or lack of IBD within clusters  indicated that the populations collected from southern Indian states, Goa, Maharashtra, and Kerala  was detected in the samples studied.Sparganium emersum. These studies suggest that the plants reproducing asexually can preserve well-adapted genomes in stable environments, which permits the fixation of well-adapted phenotypes over generations , and poor ovule receptivity . These results were comparable to the flowers that were marked for natural fruit-set (open-pollination). As seen in the present work as well, the species is known to propagate predominantly through leaf propagules  predominant vegetative propagation, broad tolerance range, and plasticity of genotypes; (2) introduction of the same genotype once or multiple times in the North-eastern parts of India; (3) fixation of well-adapted genomes over generations.Various species of  bulbils . The preopagules . The extto India , althougto India . The sucN. micrantha to conquer water bodies in the North-East regions of India as a silent invader could also be attributed to human interventions. The ornamental and religious values of the species aided by human involvement might have played a role in its spread. Several previous studies have also demonstrated the presence of low genetic diversity in invasive and clonally reproducing plant species supporting our results. Our results suggest that a single genotype was introduced to India and expanded its distribution range through massive vegetative reproduction. The ability of Nymphaea in wetland ecosystems and their increased vulnerability make them a great choice for conservation and management. This will also be useful for the sustainable use of Nymphaea species. The existing re-establishment practices, which include restoration of degraded habitats and introduction of individuals into locations, may help recover the declining populations of Nymphaea  can be found in the article/SG conceived and designed the experiments, analyzed and interpreted the results, and authored and reviewed drafts of the manuscript. SP collected the plant material, designed and performed lab experiments, performed the statistical analysis, analyzed and interpreted the results, and authored the manuscript. NS collected the plant material and performed lab experiments. AA performed pollen biology and ovule receptivity tests and wrote the manuscript. SK designed the experiments and organized the collection of plant material. RT designed the experiments, analyzed and interpreted the results, and wrote the manuscript. MA and AJ participated in designing lab experiments and reviewed the drafts of the manuscript. All authors have read and approved the manuscript.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.


Fragaria nilgerrensis is adapted to a variety of diverse habitats across its native range in China. Thus, discoveries made in this species could serve as a useful guide in the development of new superior strawberry cultivars that are resilient to new or variable environments. However, the genetic diversity and genetic architecture of traits in this species underlying important adaptive traits remain poorly understood. Here, we used whole-genome resequencing data from 193 F. nilgerrensis individuals spanning the distribution range in China to investigate the genetic diversity, population structure and genomic basis of local adaptation. We identified four genetic groups, with the western group located in Hengduan Mountains exhibiting the highest genetic diversity. Redundancy analysis suggested that both environment and geographic variables shaped a significant proportion of the genomic variation. Our analyses revealed that the environmental difference explains more of the observed genetic variation than geographic distance. This suggests that adaptation to distinct habitats, which present a unique combination of abiotic factors, likely drove genetic differentiation. Lastly, by implementing selective sweep scans and genome–environment association analysis throughout the genome, we identified the genetic variation associated with local adaptation and investigated the functions of putative candidate genes in F. nilgerrensis.The crop wild relative  Crop wild relatives (CWRs), commonly defined as the progenitors and other close relatives of agricultural and horticultural crops, contain a reservoir of beneficial traits for crop improvement and food security –3. The mFragaria × ananassa) is one of the most economically and commercially important fruits throughout the world. The Fruits are rich in a variety of nutritive compounds, including vitamin C, folate, minerals, and dietary fibers, and are a valuable source of phenolic compounds, which are known to have antioxidant and anti-inflammatory properties . Both our PCA and phylogenetic analysis indicated fine-scale structure within F. nilgerrensis. Thus we considered four groups according to their genetic composition and geographic distribution. We found that the western group located in the Hengduan Mountains exhibits the highest level of genetic diversity (Table 1), followed by the central group, which is located to the east of the Hengduan Mountains. This has been observed previously in other plant species, such as Quercus aquifolioides [Taxus wallichiana [Circaeaster agrestis [D value, indicating that intermediate-frequency alleles appeared more frequently than other groups [F. nilgerrensis. This result supports the hypothesis that the Qinghai-Tibet Plateau and the adjacent area were the glacial refuges of Fragaria, which could explain why southwest China was the center of species diversity for this genus [The population genomic approach provides a novel perspective for deciphering patterns of genetic variation and structure, and demographic history. Furthermore, recent progress in genomic tools enables the identification of the adaptive genomic footprints shaped by heterogeneous environments, contributing to understanding how climate has shaped and will continue to shape the genome of this species. In this study, we sequenced 193 individuals from 28 geographical populations to obtain genome-wide SNPs of olioides , Taxus wlichiana , and Ciragrestis . The wesr groups . Our resis genus .F. nilgerrensis groups. The analyses of IBD and IBE suggest that both geographic and environmental factors have contributed to the genetic differentiation observed within this species. The geographic distance explained 11.58% of the observed variance, and a strong pattern of isolation by distance (0.5039) was observed between populations, suggesting the considerable contribution of geographic isolation to genetic variation. The complex topography and natural barriers such as the north–south mountain series of the Hengduan Mountains might limit the dispersal between the three genetic groups in the southwest to a certain extent. However, the substantial collinearity observed between geographic and environmental distances made it difficult to disentangle the relative contributions of geographic and environmental factors in shaping the genomic variation. Thus, we further performed RDA to quantify the relationship of genomic variation with climate and geography. RDA analysis identified that both climatic factors and geographic distance shaped a significant proportion of genomic variation: 12.25 and 11.58% respectively. Despite the large proportion of collinearity (28.29%) between environment and space, our analysis identified that specific environmental factors such as temperature seasonality and precipitation seasonality explained a substantial portion of SNP variation among populations when the effects of spatial structure were also considered. Our study supplements and reinforces some previous findings [Fragaria species. Johnson et al. [Fragaria were the coefficient of variation of precipitation seasonality, annual mean temperature, temperature seasonality, and mean altitude. Similarly, Yang et al. [F. nilgerrensis.Environmental factors have been widely reported to drive differential selective pressures leading to genetic divergence during adaptation to heterogeneous environments , 58. We findings , 60 in sn et al.  found thF. nilgerrensis, like most plant species, is sensitive to temperature changes. Our findings revealed that temperature seasonality was the strongest climate predictor of the degree of genetic differentiation among the four groups. Interestingly, the southern group located in Yunnan slightly expanded during the LGM. This scenario had been previously reported for several other organisms, such as Primula obconica [Microvelia douglasi [We investigated the long-term changes in effective population size of the four different groups and uncovered evidence for changes in effective population sizes post-Pleistocene. The geological epoch following the most recent glaciation event (LGM) is associated with dynamic shifts in climates worldwide. During and/or following the LGM, four groups experienced population declines followed by subsequent population expansions. These patterns highlight that obconica  and Micrdouglasi .F. nilgerrensis were, in general, significantly diverged from each other based on their native environments  light receptor previously shown to be involved in UV-B sensing and tolerance in other species [HAC1 detected in the central group played an important role in vegetative and reproductive development; a previous study suggested that it is essential for regulating flowering time, and lesions in HAC1 can cause a late-flowering phenotype in Arabidopsis [At5g45160 gene found in the southern group and the CGR2, ERG28, and TOR genes found in the eastern group. Selection on genes involved in vegetative growth has previously been reported in populations from relatively low altitudes in Arabidopsis lyrata [Arabidopsis thaliana [F. nilgerrensis provide new information about the loci related to the adaptive responses to diverse abiotic stresses, and provide prime candidates for future functional research and potential molecular markers to guide breeding efforts in strawberry.Identifying the genomic regions that evolved in response to various abiotic factors in strawberry species could contribute to furthering our understanding of the ability of populations to sustain or respond to rapid changes in the environment , 63, 64.ronments . Despite species . Plants  species . The genbidopsis . The cens lyrata . Previouthaliana  also shoF. nilgerrensis and provide novel insights into the population structure and demographic history of this CWR. We explicitly measured the relative impact of geographic and environmental variables on population divergence to dissect these two features in shaping patterns of observed genomic variation. Our analysis identified that climatic variables explained more genomic variation than geographic distance, with temperature seasonality explaining the most SNP variation when conditioned on spatial structure, which suggests that local adaptation greatly promotes population genetic differentiation. By combining selective sweep analysis and GEA, we identified several candidate genes possibly related to adaptation to heterogeneous climate environments. Our results provide many avenues for conservation and utilization of F. nilgerrensis germplasm and the breeding of cultivated strawberries that can grow in environments affected by climate change.In summary, we report new genomic resources for F. nilgerrensis in China (We collected 193 samples from 28 populations (3–10 specimens per population) across the distribution range of in China . We extrF. nilgerrensis [http://broadinstitute.github.io/picard/). After that, SNP calling in each individual was performed using HaplotypeCaller of GATK v4.1.4.0 [For raw sequencing reads, we (i) removed reads with >10 nucleotides aligned to the adapter, allowing ≤10% mismatches, and (ii) filtered out low-quality read pairs including reads with >10% unidentified nucleotides (N) and with >50% of base quality ≤5 in either of the paired reads. All clean reads were then mapped to the reference genome of errensis  (272 Mb)errensis . The reserrensis , and dupv4.1.4.0  to generK value from 2 to 10, where the length of the burn-in period and number of MCMC replications after burn-in were set to 50 000 and 100 000, respectively. We used STRUCTURE HARVESTER [K groups through the Evanno method [p-distance model using MEGA X [r2) between each pair of SNPs within a sliding window of 50 SNPs with a step of 5 SNPs and removes one of a pair of SNPs if r2 > .2.We used a Bayesian clustering approach implemented in the software STRUCTURE  to delinARVESTER  to deteco method . The cluo method  and the o method . We alsoo method  to assesg MEGA X  with 100F. nilgerrensis based on genetic clustering and phylogenetic analysis, we used VCFtools [π), Tajima’s D for each group, and population- and group-level pairwise FST. Specifically, we computed π per site using the parameter -site—pi on all SNPs of individuals from each group. The total nucleotide diversity for each group was computed by summing the π values of all SNPs and dividing by the total number of callable sites. Tajima’s D and pairwise FST were calculated in a non-overlapping 20-kb sliding window.After clarifying the population structure of VCFtools  to calcuNe through time. Prior to performing the analysis, all segregating sites within each group were phased and imputed using Beagle v4.0 [Ne of the four genetic groups based on sets of two individuals (four haplotypes), respectively. For each group, 50 rounds of random samplings were run to determine the mean and standard deviation of Ne changes. The input files for MSMC analysis were generated according to MSMC Tools (https://github.com/stschiff/msmc-tools). One-year generation times and a mutation rate of 7 × 10−9 substitutions per site per year were used to estimate times and population sizes.We used MSMC v2  to recongle v4.0 . We thenNe. SMC++ takes advantage of both information contained in the site frequency spectrum and LD to make demographic inferences. In addition, SMC++ is phase-insensitive, limiting switch errors in phasing that can bias Ne estimates for recent times. A polarization error of 0.5 was used since the identity of the ancestral allele could not be determined for many loci. The years for a generation and the mutation rate were set as MSMC.We also used the sequential Markovian approach implemented in SMC++  to inferFST and geographic distance and environmental distance by the Mantel test with 10 000 permutations implemented in the R package vegan v2.5.4 [To illustrate the effects of geographic and environmental variables on shaping genetic structure, we conducted IBD and IBE analyses to assess associations between pairwise n v2.5.4 . We calcn v2.5.4 . The 19 n v2.5.4  were useR2 of the RDA model including all initial variables. The RDA analysis was performed using the rda function in vegan. The overall significance and the significance of each variable were assessed using the anova.cca function in vegan with 999 permutations.We further performed RDA to estimate the degree to which genome-wide SNP variation among populations is explained by geographic or environmental variables. To avoid the influence of multicollinearity, we eliminated one of the variables in each pair with a correlation value >0.9 through Pearson correlation analysis and retained the remaining nine variables. These nine climate variables were further tested using the forward.sel function in the R package adespatial  to identμ statistic across the genome from SNP-driven, overlapping windows. We calculated μ using default settings in four groups separately. Finally, the μ statistics were averaged across non-overlapping 20-kb windows on each chromosome. Windows with <10% of covered sites left from previous quality-filtering steps were excluded. Only the top 10% of windows for each group were retained for downstream analysis.We performed scans on each group separately by using two approaches: (i) a method relying on multiple signatures of a selective sweep via the enumeration of SNP vectors   and (ii)https://www.github.com/szpiech/selscan). Using either of the other groups as the reference, for each group, windows' with the highest fraction of extreme scores higher than the 99th percentile of its distribution were identified as candidate regions.XP-nSL summarizes haplotype diversity by calculating the average number of variant sites in a genomic region that are identical across all haplotypes, and then compares haplotype pools between two different populations, which makes it possible to detect differential local adaptation . XP-nSL The overlaps of the results from the two methods were identified and regarded as the high-confidence selective sweep regions. We then identified genes that localized within or were closer than 5000 bp to the selective sweep regions to exclude the border effect .P-value (eBPmc) >3 were considered significantly associated.To detect genome-wide signatures of local adaptation, we applied two GEA tests that can infer SNPs that have significant associations with specific environmental factors. First, we tested for the correlation of environmental covariates with SNPs using the standard covariate model in BayPass . For a cP-value <.05 were considered significant and were used to evaluate candidate SNPs. Candidate SNP for the first four RDA axes were identified as being +/− three standard deviations from the average RDA loading, creating a cutoff of two-tailed P-value of .0027  and the National Natural Science Foundation of China (31501799) to M.K., and the National Science Foundation (2029959) and United States Department of Agriculture (2020-67013-30870) to P.E.M.K. conceived and designed the project. Y.H., C.F., and L.Y. performed the sampling and experiments, and the data analysis. Y.H. and M.K. wrote the manuscript. All authors read and approved the final manuscript.The whole-genome sequencing (WGS) raw reads have been deposited in the National Center for Biotechnology Information Sequence Read Archive (SRA) database under BioProject accession number PRJNA748880.The authors declare no competing interests.Horticulture Research online.Web_Material_uhab059Click here for additional data file.


The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.Even though statistical tests have been utilized to conduct case–control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data  and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann–Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann–Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.The online version contains supplementary material available at 10.1186/s12936-022-04104-x. Anopheles mosquito bite. However, as the parasites involved are highly adaptable to nature, it is tremendously challenging to control the outbreak of this disease [Malaria is a life-threatening disease caused by a parasite transmitted to humans by an infected female  disease . MoreoveGWAS is a popular approach that investigates associations between genetic information, in particular, specific Single Nucleotide Polymorphisms (SNPs), and disease. An SNP is the most common type of genetic variation of a disease and is henceforth a resistance or susceptibility marker. For example, a resistance marker can prevent the risk of developing the disease as well as reducing the severity of the symptoms. In contrast, a susceptibility marker increases the risk of developing the disease instead. Thus, SNPs can be used as genetic markers to represent disease-associated risk factors.In malaria research, GWAS has successfully been applied in multiple malaria-endemic areas –11, wherA statistical test is a powerful tool widely used throughout the scientific research process to conclude from mass data, where it can be applied to study the relationship between risk factors and diseases . For exaHowever, several challenges were faced when applying statistical tests to the work. The first major challenge is that researchers to date have studied the statistical significance of disease risk factors by applying the tests on ordinal clinical data, i.e., continuous variables and not genetic markers. This data includes demographic characteristics, lifestyle habits, physical measurements, medical records, family history of the related disease, and disease-related knowledge data. To overcome this challenge of non-ordinal data, the genetic risk scores were calculated from the genetic markers, i.e., SNPs. As these scores indicate the impact of genetic variations in populations, the genetic markers that contribute to malaria resistance or susceptibility between populations can be explored by adopting a statistical test. However, malaria is a complex disease involving various genetic markers from many different genes, which leads to the genetic basis of malaria resistance or susceptibility being complicated at multiple levels . TherefoChoosing the correct statistical test is another challenge. There are two types of statistical tests: parametric test and non-parametric test. There has always been a dispute over the preferred test in medical research –22. The Introduces how a statistical test can potentially be adopted to analyse genetic risk scores obtained from large-scale genetic variation data , i.e., SNPs genetic marker;Analyses statistical significance of malaria genetic markers between populations, and;Identifies highly differentiated genetic markers among populations.In malaria research, statistical tests have mainly been utilized to capture an individual's genes characteristics towards malaria resistance or susceptibility , 29; andhttp://www.malariagen.net/.The human GWAS data utilized in this study was generated from the MalariaGEN Consortial Project 1, entitled: “Genome-wide study of resistance to severe malaria in eleven populations”. The study comprised genotype data of 20,854 individuals from 11 worldwide populations: 10,791 severe malaria-affected individuals and 10,063 control subjects. Table Through the review and analysis of 31 academic articles related to malaria research , 32–52, Thus, 104 SNPs were extracted from the study subjects to analyse their genetic markers . The wGRS is calculated by multiplying the number of risk alleles  by the estimated effect size reported for each variant . The logSome observations in the literature , 58 indiAn individual tends to be resistant to the development of malaria if the two alleles are not identical (heterozygous). Conversely, an individual tends to be susceptible to the development of malaria if the two alleles are identical (homozygous). Therefore, the inclusion of genotype patterns is essential for differentiating genetic markers. Inspired by its importance, this study will include genotype frequency with wGRS to formulate more comprehensive genetic risk scores, namely wGRS + GF.Genotype frequency indicates the relative frequency of a particular genotype in a population. The genotype frequency of each population is calculated from the genotype data by using the Hardy–Weinberg equation, as this equation calculates an individual’s genetic variation at equilibrium. The wGRS + GF is calculated by multiplying the genotype frequency by the wGRS mentioned above.Descriptive statistics was performed to understand the characteristics of the data. The summary of mean, median, minimum, maximum, and standard deviation values of genetic risk scores based on continents, populations, and case/control is presented in Figs. Based on the results in Fig. Mann–Whitney U test is based on the median, whereas Welch’s t-test is based on the mean. However, median is the preferred measurement when data is measured on an ordinal scale, which is most suitable for real-world data . NormallThe Mann–Whitney U test was implemented in Python using the pingouin.mwu function  to test The statistical analysis for the work here comprises two parts. Part 1 involves general analysis to evaluate the association between the population and the cumulative effects of SNPs to study the statistical significance of multilocus genetic markers among populations. The cumulative effect is calculated by summing the genetic risk scores of all the 104 SNPs. On the other hand, Part 2 involves a detailed analysis to evaluate the association between the population and the genetic risk score of each SNP (single locus) to identify the highly differentiated genetic markers between populations. In other words, Part 2 analyses the effect of each SNP instead of the combined effect of all the 104 SNPs. Both parts are analysed based on two groups: case and control, and are performed based on wGRS + GF as the genetic risk score described in the previous section.Figure This section describes the experimental results based on Part 1 and Part 2.The first part is to study the statistical significance of multilocus genetic markers among populations by evaluating the association between the population and the cumulative effects of 104 SNPs.Table On the other hand, Table The second part is to identify the highly differentiated genetic markers between populations by evaluating the association between the population and the genetic risk score of each SNP (single locus).Additional file Following this, Additional file Up-to-date, not many statistical analysis studies have been carried out to study the relationship between malaria risk and populations. However, these studies utilized environmental data such as low altitude, high temperature, and humidity with malaria incidences . For exaGenetic markers are essential in providing a basis for understanding genetic differences between populations and malaria risk. These markers have been utilized to characterize the genetic composition and complexity of the disease. However, no study has analysed the significant differences of the genetic markers. The initial exploration results based on descriptive statistics indicated case/control distribution data to be symmetric. However, the Mann–Whitney U test was chosen over Welch’s t-test, as there are no significant differences in the p-values obtained from both the tests via one-way ANOVA test. Moreover, prior studies recommended the use of the median for real-world data.The Mann–Whitney U test was performed to study the statistical significance of genetic risk scores by population groups. This study introduces a statistical test to analyse large-scale genetic variation data of case and control groups to study the statistical significance of genetic markers. In particular, the human GWAS datasets obtained from MalariaGEN were analysed, which contains 11 worldwide populations.To formulate a more comprehensive genetic risk score, genotype frequency was combined with wGRS. This score represents the impact of genetic variation for each individual, which further contributes to the population genetic risk score. Inclusion of genotype frequency is essential because studies have shown that genotype patterns play a crucial role in malaria resistance or susceptibility. The performed statistical tests were based on the case and control groups with a significance level of p < 0.01.The association between population and cumulative effects of all the 104 SNPs was evaluated to study the statistical significance of multilocus malaria genetic markers between populations. The test results revealed a significant difference (p < 0.01) for all populations in the case group. Likewise, in the control group, a significant difference (p < 0.01) was present for all populations, except between Cameroon and Papua New Guinea, Ghana and Papua New Guinea populations. These results further confirm that genetic markers vary between populations.The significant differences in genetic variation used as markers to distinguish populations have not yet been discovered. Therefore, the association between the population and the genetic risk score of each SNP (single locus) was evaluated to identify the highly differentiated genetic markers. The test results showed a significant difference (p < 0.01) for most genetic markers between the case and control groups. Moreover, the results show that the p-value of the genetic markers vary between populations. More highly differentiated genetic markers with very significant differences (p < 0.001) were observed in the Gambia, Kenya, and Malawi populations. In addition, several genetic markers have very significant differences (p < 0.001) across all populations, while others were only observed between certain specific populations. Also, several genetic markers have no significant differences between populations. The findings indicate that the highly differentiated genetic markers that contribute to malaria risk differ between populations due to genetic differences.This study has presented a method to analyse large-scale genetic variation data through the Mann–Whitney U test to explore genetic markers of malaria. Many previous studies have analysed malaria genetic markers, focusing on either resistance , 37, 59 Besides that, previous studies have used statistical tests to explore malaria risk depending on the purpose of analysis. For example, the Chi-square test was used to estimate the prevalence of specific genes in malaria-endemic populations , 73; StuOn the other hand, this study support prior research that indicates the Mann–Whitney U test is the preferred test for analysing real-world medical data , especiaThis study conducted a malaria risk analysis based on the MalariaGEN human GWAS datasets that contain 11 populations. More precisely, a statistical test was adopted to explore genetic risk scores obtained from SNPs genetic markers. The analysis of the association between population and the cumulative effects of SNPs was carried out to study the statistical significance of multilocus malaria genetic markers. Then, the association between the population and the genetic risk score of each SNP (single locus) was further explored to identify the highly differentiated genetic markers. The findings indicate that populations have different genetic markers affecting malaria resistance or susceptibility levels. Therefore, there are significant differences in the likelihood of malaria infection among populations. It is believed that the findings of this study can help further characterize the complexity of the disease and provide additional knowledge regarding the association of malaria risk among populations. To a larger extent, the study has shown a promising method that demonstrates how statistical tests can be adopted to analyse large-scale genetic variation data to explore genetic markers associated with complex diseases.Additional file 1. General information on the 104 SNPs used in this study.Additional file 2. Highly differentiated genetic markers with very significant differences (p < 0.001) analysed on population case groups.Additional file 3. Population case groups test results of each SNP with their corresponding p-value.Additional file 4. Highly differentiated genetic markers with very significant differences (p < 0.001) analysed on population control groups.Additional file 5. Population control groups test results of each SNP with their corresponding p-value.


Galeolaria caespitosa, to a factorial manipulation of current and projected temperatures at fertilization and development. We find that adaptive potential in early life is substantially reduced, to the point of being no longer detectable, by genotype‐specific carryover effects of fertilization under projected warming. We interpret these results in light of our fitness landscape model, and argue that the thermal environment at fertilization deserves more attention than it currently receives when forecasting the adaptive potential of populations confronting climate change.Additive genetic variation for fitness at vulnerable life stages governs the adaptive potential of populations facing stressful conditions under climate change, and can depend on current conditions as well as those experienced by past stages or generations. For sexual populations, fertilization is the key stage that links one generation to the next, yet the effects of fertilization environment on the adaptive potential at the vulnerable stages that then unfold during development are rarely considered, despite climatic stress posing risks for gamete function and fertility in many taxa and external fertilizers especially. Here, we develop a simple fitness landscape model exploring the effects of environmental stress at fertilization and development on the adaptive potential in early life. We then test our model with a quantitative genetic breeding design exposing family groups of a marine external fertilizer, the tubeworm  Galeolaria caespitosa. Our findings suggest that harmful carryover effects of fertilization under projected warming lower adaptive potential in early life, and failing to account for them may risk overestimating the adaptive potential of many species with similar biology. Our work has new implications not only for external fertilizers, but also for internally fertilizing ectotherms where fertilization is still vulnerable to ambient conditions, and argues that the thermal environment at fertilization deserves more attention than it currently receives when forecasting the adaptive potential of populations in a rapidly warming world.Natural populations need additive genetic variation for fitness at vulnerable life stages to adapt to climate change, and adaptive potential can depend on current environmental conditions as well as ones experienced by past stages or generations. Fertilization is the key life stage that links generations in most species, yet the impacts of fertilization environment on the adaptive potential at the vulnerable stages that then unfold during development are unknown and rarely considered. This knowledge gap is particularly concerning for the many aquatic species  that undergo fertilization and development in the external environment, directly exposed to rising water temperatures. We combine classic evolutionary theory with a novel breeding design to explore how external fertilization and development under projected ocean warming shape adaptive potential in early life for an intertidal ecosystem engineer, the marine tubeworm Climate change is exposing populations to heightened stress and extinction risk  whose vulnerability to stress makes them weak links in the life cycle . We then use our model to interpret the sensitivity of genetic variation for offspring survival to external fertilization and development under projected ocean warming  in the marine tubeworm, i and whose parents spawned in environment j, trait expression (ijz) is:x is the environment‐independent genetic effect on the trait, iy is the genetic effect on the trait in the ith developmental environment, jgb is the genetic effect on the trait in the jth environment of parents’ gametes (g is a random variable and jb is a constant describing the magnitude and direction of the effect of the jth fertilization environment), and εi is residual variation in environment i. We assume that x,yi,g,andεi are independent, normally distributed random variables with means of x¯,y¯i,g¯,andε¯i, and variances of Vx,Vyi,Vg,andVεi, respectively. Assuming that, ε¯=0 the trait mean and genetic variance (respectively), are z¯ij=x¯+y¯i+bjg¯andVij=Vx+Vyi+bj2Vg.We assume that genetic variation for offspring fitness  is affected by a single major trait whose expression depends on offspring genotype, developmental environment, and the environment of parents’ gametes during fertilization . For offspring that develop in the environment i follows a Gaussian function with trait optimum θi, and width ωi  of eq. ((θi), which increases genetic variation for fitness if the optimum shifts away from the mean , which decreases genetic variation for fitness if the mean shifts toward the optimum , which decreases genetic variation for fitness if the width increases , in which genetic variation for fitness increases with the trait's genetic variance  of eq.  and 2)   is the trait's genetic covariance across environments, and dij=θi−z¯ijanddkl=θk−z¯kl are the displacements of trait means from their optima. The cross‐environment genetic correlation for fitness is then:Extending the model to predict genetic correlations for fitness across fertilization or developmental environments, the genetic covariance for fitness across environments is:dijanddkl have the same sign, so that dijdkl>0). Changes in the trait's genetic basis ) or direction of selection (so that dijdkl<0) between environments lead to weakly positive or negative genetic correlations for fitness, implying genetic trade‐offs for fitness across environments.Eqs.  imply thGaleolaria caespitosa  is a calcareous tubeworm native to rocky shores of southeastern Australia, where it is an ecosystem engineer whose dense colonies of adult tubes provide habitat for endemic communities  from April to June 2018, transferring individuals to Monash University in insulated aquaria. To reduce environmental differences among adults sampled at different times, we acclimatized adults at ∼16.5°C for 14–17 days  and females (dams) in a cross‐classified North Carolina II (NCII) breeding design Fig.  to estimPederson . In eachPederson . Each siPederson . Our exp5 of a sire's sperm in 1 ml of filtered seawater. For Galeolaria, these conditions maximize fertilization success while minimizing lethal polyspermy  to minimize aging . Each fertilization trial ran for 30 minutes, maximizing fertilization success in pilot work. Each trial was agitated every 10 minutes to reduce oxygen depletion, then ended by thoroughly rinsing embryos through 25 μm Nitex mesh with filtered seawater.Fertilization was initiated by adding ∼900 of a dam's eggs in 0.1 ml of filtered seawater to a vial holding ∼5 × 10Next, embryos were transferred from fertilization trials to developmental temperatures Fig. . BecauseGaleolaria suggests that 48 hours is the best time for reliably assessing survival to independence . We used this period because previous work on y is survival, X is the design matrix for the fixed effects of fertilization and developmental environments (β), while Z1, Z2, Z3, and Z4 are design matrices for the random effects s, d, sd, and b, estimating sire variance (σ2S), dam variance (σ2D), sire × dam variance (σ2SD), and block variance (σ2B), respectively. The latter was modeled as a single variance, and each of the others as a block‐diagonal matrix with a separate submatrix per fertilization environment. Each submatrix modeled the variances for, and covariance between, survival in each developmental environment. Residual variance was modeled separately for each combination of fertilization and developmental environments. Residual diagnostics indicated no distributional problems, which was not the case for the equivalent animal model . We multiplied sire and sire × dam variances by four to calculate additive genetic and nonadditive genetic variances, respectively, and subtracted dam variances from sire ones to calculate maternal environmental variances  in ASReml‐R 3.0 . This model estimated the same effects as before, but with a structure that allowed us to test if pooling each effect across developmental environments resulted in worse fit than the original model.2 = 0.99, d.f. = 1, P = 0.32). Fertilization at warmer temperature reduced mean survival by ∼7% , irrespective of developmental temperature, and development at warmer temperature reduced survival by ∼6% , irrespective of fertilization temperature.Warmer fertilization and developmental temperatures significantly reduced mean offspring survival, without interacting in their effects  will tend to reduce genetic variance for fitness, it will also increase mean survival, which is opposite to what was observed. The likely reason, in light of our model, is that genotype‐environment interactions carrying over from fertilization at warmer temperature decrease additive genetic variation (and covariation) for survival in warmer conditions .Data and R‐script have been deposited in Dryad  and projected (24°C) fertilisation and developmental temperatures.Figure S1. No impact of sperm ramping period on mean fertilisation success (±S.E.).Click here for additional data file.


More than 41 percent of amphibians evaluated by International Union for Conservation of Nature are threatened. It is vitally important to establish scientific and effective protection strategies for these organisms. Leishan Spiny Toad is endemic to China and it has a narrow distribution area. Long-term intentional human use and habitat destruction has caused the species to suffer. Here, we developed newly reliable and efficient molecular markers based on its genome to assess its genetic diversity and population history and provided support for conservation of this toad. Our results show that this toad still possesses high genetic diversity, but population decline may increase the possibility of inbreeding, which could work against persisting survival. Recovering the toad’s habitat and strengthening the publicity and education of wildlife protection can be helpful to its sustainability.Leptobrachium leishanense, an endangered toad and receiving secondary protection on the list of state-protected wildlife in China. However, current molecular markers with low reliability and efficiency hinder studies. Here, we sampled 120 adult toes from the population in the Leishan Mountain, 23 of which were used to develop tetranucleotide microsatellite markers based on one reference L. leishanense genome. After primer optimization, stability detection, and polymorphism detection, we obtained 12 satisfactory microsatellite loci. Then, we used these loci to evaluate the genetic diversity and population dynamics of the 120 individuals. Our results show that there is a low degree of inbreeding in the population, and it has a high genetic diversity. Recently, the population has not experienced population bottlenecks, and the estimated effective population size was 424.3. Accordingly, stabilizing genetic diversity will be key to population sustainability. Recovering its habitat and avoiding intentional human use will be useful for conservation of this species.Persisting declination of amphibians around the world has resulted in the public attaching importance to the conservation of their biodiversity. Genetic data can be greatly helpful in conservation planning and management, especially in species that are small in size and hard to observe. It is essential to perform genetic assessments for the conservation of  Amphibians have long been declining on a global scale, and this trend will continue . FurtherLeptobrachium leishanense) is an endemic amphibian to China and is mainly restricted in Leishan county of Guizhou Province. This species inhabits broadleaf forests at elevations ranging from 1100–1800 m and breeds in slow-flowing streams via larval development [The Leishan Spiny Toad  develop microsatellite loci with high reliability and efficiency, (2) evaluate the genetic diversity of the L. leishanense population, (3) detect if the population is experiencing a population bottleneck, (4) estimate the effective population size, and (5) provide molecular support for L. leishanense conservation planning.Here, we totally sampled 120 adult toes of L. leishanense adults per year in Maoping village of Leishan County, Guizhou, China  procedure was conducted in a 10 μL volume, in which the premix was 5 μL, each primer was 0.3 μL, template DNA was 0.6 μL, and ddH2O was 3.8 μL. The procedure was performed with initial denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at temperature Ta for 30 s, extension at 72 °C for 45 s, and extension at 72 °C for 5 min. PCR products were detected by 1% agarose gel electrophoresis. By adjusting the Ta temperature, a product with a clear band was obtained. The Ta temperature corresponding to the product was used as the optimum temperature for PCR amplification. Under the optimal amplification conditions, we used the DNA of three different individuals to detect the stability of primers in different individuals and screened the primers that could be amplified stably.DNA samples were extracted using the TIANamp DNA kit  and stored at −20 °C. MicroSatellite identification tool   was usedPIC value higher than 0.5 [PIC), expected heterozygosity (He), and observed heterozygosity (Ho). Micro-Checker 2.2.3 [p < 0.05.The screened primers were used to synthesize 5′ upstream fluorescent primers . DNA amplification was performed on 23 individuals collected in 2012 and 2013 by PCR with fluorescent primers, and the amplified fluorescence PCR products were sent to Tsingke Biological Company, Beijing, China for SSR scanning and sequenced by an ABI 3730xl analyzer. Then, the products were genotyped and calculated, and the evaluation criterion of the polymorphisms was a than 0.5  to read than 0.5 . Cervus than 0.5  was useder 2.2.3  was useder 2.2.3  was usedFst), and Cervus 3.0 software was used again to calculate the values described above. Excel and Microsatellite Toolkit v3.1.1 software were used for preliminary genetic data statistics and data format conversion. FSTAT 2.9.3.2 software [Fis).A total of 120 DNA samples were amplified by PCR with the screening fluorescent primers described above. The PCR products were sent to Qingke Biological Company for SSR scanning. An ABI 3730xl analyzer was used for sequencing. Data were analyzed using GenAlEx 6.502  to calcuBottleneck 1.2.02 software  was usedNeEstimator 2.1  was usedL. leishanense. Monobase repeat microsatellite markers and dibase repeat microsatellite markers were the most common among all microsatellite markers. There were 874,773 monobase repeat microsatellite markers and 263,927 dibase repeat microsatellite markers, accounting for 60.16% and 18.15% of the total number of microsatellite markers, respectively, followed by 71,167 tribase repeat microsatellite markers and 23,332 tetrabase repeat microsatellite markers, accounting for 4.89% and 1.60% of the total number of microsatellite markers, respectively. The number of pentabase repeat microsatellite markers and hexabase repeat microsatellite markers was the lowest, with 909 pentabase repeat microsatellite markers and 844 hexabase repeat microsatellite markers, accounting for only 0.12% of the total microsatellite markers , and there were no loci deviated from HWE (p > 0.05). According to Micro-Checker 2.2.3, there was no large allele dropout of these microsatellite markers and no scoring error caused by the shadow peak.Eighty-seven pairs of primers randomly chosen from 23,332 tetrabase repeat microsatellite marker. After primer optimization, 64 pairs of primers were successfully amplified. Then stability detection was used, and 46 pairs of primers were obtained. Employing polymorphism detection, we obtained 12 satisfactory microsatellite loci. The Na of these loci ranged from 6–16. ectively . All 12 L. leishanense population was performed. After Bonferroni correction, the significance level was p < 0.0042. The results are shown in LEA23, LEA7, LEA47, LEA2, and LEA53 deviated from HWE significantly, with Fis values as 0.179, 0.112, 0.119, 0.230, and 0.134, respectively  of the population in the IAM, SMM, and TPM models was calculated (p < 0.05), among which LEA22, LEA25, LEA20, LEA35, LEA14, LEA23, and LEA24 were extremely significantly higher than Heq (p < 0.01). In the TPM and SMM models, only He at LEA5 was significantly higher than Heq, showing heterozygote surplus. The sign test and Wilcoxon test were used to detect the heterozygosity surplus of the population under the three models of IAM, TPM, and SMM  from NeEstimator 2.1.3.1.L. leishanense. Then, we used these loci to study the genetic diversity and population dynamics of this species. We found that the genetic diversity of the population was high and that there was a low degree of inbreeding in the population. Moreover, the population has not recently experienced bottleneck effects, and the estimated effective population size is 424.3.We isolated and characterized 12 tetrabase repeat microsatellite markers with polymorphisms from one reference genome of PIC values of all 12 loci were higher than 0.5, suggesting that the loci we developed had higher polymorphism. Stable and reliable microsatellite markers are a necessary prerequisite for population estimation in the wild [Although the results above are similar to those of Zhang’s research , which uthe wild . Thus, aL. leishanense does not have significant genetic differentiation among these five years (PIC, Ho, He, and Hs. According to our results, the toad still has high genetic diversity. Threatened species usually have small or declining populations and are prone to loss of genetic diversity due to inbreeding or genetic drift [We speculated that several loci deviated from HWE  mainly cve years ; accordiic drift . As an eic drift ,47,48,49ic drift . The Leiic drift . The toaic drift , suggestFis value of the population is on the low degree, this does not mean that there is no inbreeding between the individuals in the population. According to our year-by-year field work, its population size is declining. This undoubtedly increases the possibility of its inbreeding. Inbreeding has a negative effect on the fitness of the population, including fertility and viability [Although the iability , which iL. leishanense, indicating that the population has not recently experienced a bottleneck.Combining the results of model simulation with allele frequency distribution, we find that the population has not recently experienced a bottleneck effect. We tested three models, and IAM was significant both in the sign test and the Wilcoxon test. Both SMM and TPM were not significant in the sign test and Wilcoxon test . IAM assHowever, the ability to detect the population bottleneck based on heterozygosity is limited, and the number of alleles is more sensitive to population fluctuation, so it is more reliable to analyze the distribution of allele rates in the case of heterozygous residues to determine whether the population has experienced the bottleneck effect . To enhaL. leishanense is 424.3. Nei et al. [L. leishanense.The LD distance between microsatellite markers can be used to estimate effective population size, and this method has been applied to mammals, fish, amphibians, and other animals . Effectii et al.  deemed ti et al. . More saL. leishanense. Although the genetic diversity is still high based on our results, a low degree of inbreeding indicates that the population is declining. Avoiding habitat fragmentation and intentional human use will be key to the conservation of this species. Furthermore, recovering the streams and woodlands where the species once existed abundantly will also help to stabilize its genetic diversity.Our study has provided 12 reliable tetranucleotide microsatellite loci with polymorphisms, enriching the information regarding the genetic diversity and population dynamics of