Embothrium coccineum (Proteaceae) is an emblematic tree of the South American temperate forest (around 20°S of latitude) that has strongly been affected by topographic and climatic events. Previous studies have shown a marked genetic structure in this species, and distinct ecotypes have been described. Yet, little is known about their adaptive genetic responses. The main goal of this study was to investigate the effects of historical and contemporary landscape features affecting the genetic diversity and connectivity of E. coccineum throughout its current natural distribution. Using over 2000 single nucleotide polymorphisms (SNPs), we identified two genetic groups  that diverged around 2.8 million years ago. The level of genetic structure was higher among populations within the Northern genetic group than within the Central‐Southern group. We propose that these differences in genetic structure may be due to differences in the assemblages of pollinators and in the evolutionary histories of the two genetic groups. Moreover, the data displayed a strong pattern of isolation by the environment in E. coccineum, suggesting that selection could have led to adaptive divergence among localities. We propose that in the Chilean temperate forest, the patterns of genetic variation in E. coccineum reflect both a Quaternary phylogenetic imprint and signatures of selection as a consequence of a strong environmental gradient.The South American temperate forests were subjected to drastic topographic and climatic changes during the Pliocene‐Pleistocene as a consequence of the Andean orogeny and glacial cycles. Such changes are common drivers of genetic structure and adaptation.  Embothrium coccineum. Both glacial cycles and selection to the strong environmental gradients have largely shaped the genetic diversity of this native Chilean tree. Using 2155 SNPs we detected two main genetic groups, which have diverged some 2.5 million years ago, in  Forts. & G. Forst  obtained by genotyping‐by‐sequencing (GBS). Patterns of genetic structure were evaluated independently using all SNP loci  and those only found in genomic regions that may have been subjected to selection . We hypothesized that patterns of genetic variation in E. coccineum will reflect the impact of historical processes  and the impact of contemporary landscape features, including the selective effects of environmental heterogeneity.The main goal of this study was to evaluate the effects of historical and contemporary landscape features affecting the genetic diversity and connectivity of 22.1E. coccineum's , were used to characterize/define spatial environmental variation. We tested for covariation among environmental variables using the vifcor function from the “usdm” R package  of 19 environmental variables  and the elevation of each location  and the raw GBS dataset can be found in the SRA repository (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA783610).Library preparation and high‐throughput sequencing took place at the University of Wisconsin Biotechnology Center (DNA Sequencing Facility). The preparation of the GBS genomic library followed the protocol in Elshire et al.  using th2.4populations pipeline of Stacks v.2.2 , only one randomly selected SNP per locus was kept to minimize the probability of coanalyzing linked markers.2.5IS) of each location. The percentage of polymorphic loci and private alleles were estimated using the R package “hierfstat v. 0.04‐22”  and expected (He) levels of heterozygosity and the inbreeding coefficient  was used to determine the optimum K, based on L(K) and ΔK  test statistic using a False Discovery Rate (FDR) of 5%. Second, we implemented a Bayesian test to detect outlier loci using BAYESCAN 2.1 software  of all environmental variables allowing for the identification of those that best explain the observed genetic variation. The allele frequencies of candidate adaptive loci  were used as the response variable. The GF was fitted using 2000 regression trees per SNP and a variable correlation of 0.5  and a partial db‐RDA at the individual level; first, on the complete PG_dataset and second, only on loci potentially influenced by selection. The RDA is a multiple linear regression method performed between a matrix of dependent variables and a set of matrices for independent variables and has been demonstrated as the more appropriate method (as opposed to for example a Mantel test) when multiple variables are analyzed to identify drivers of population genetic structure , ecological gradients (IBE), co‐ancestry (IBA), or their combination , using the vartpart function in the R package vegan v.2.5.7 . We used a lognormal distribution for the crown age of the Embothrieae tribe, which was estimated at 66 million years  using a phylogeny of Proteaceae that included all the genera currently recognized in the family . Alpha and beta parameters were left with a uniform prior of an arbitrary value of 1.0  were checked for mixing all parameters with Tracer v1.7 . A total of 116,606,122 reads were generated and de novo assembled to 1,303,750 loci, from which 473,461 SNPs were called. The application of filtering criteria resulted in a final PG_dataset of 2155 SNPs. For the DT_dataset, comprising 12 individuals of E. coccineum and two individuals of L. hirsuta, a total of 320 SNPs were called.In order to evaluate the population structure, the divergence time among genetic groups, and the local adaptation in 3.1IS), the percentages of polymorphic loci (PPL), and the number of private alleles (PA) are given in Table IS value; and of those with positive FIS values, four locations  had FIS superior to 0.1.The expected and observed heterozygosity , the inbreeding coefficient (F3.2p < .05).The highest pairwise FST values were observed between locations from the northern and southern regions (0.319 < FST < 0.424), followed by those calculated between locations from southern and central regions (0.222 < FST < 0.429), and then by the ones calculated between locations from Central and Southern regions (0.070 < FST < 0.193). The levels of divergence between locations within the same region were generally lower  and K = 4 as a possible secondary level of substructure Figure a,b. The The dendrogram reconstructed with Nei's distance tree using the PG_dataset allowed to separate the Northern and Central‐Southern samples Figure , which w3.3E. coccineum.Within the 2155 loci included in the PG_dataset, PCAdapt detected 132 outlier loci and BAYESCAN detected 89 outlier loci. The two methods concurred in the detection of 59 loci (2.73%), which were kept and designated as potentially adaptive loci in R2) on E. coccineum populations, and comprised precipitation of the driest month (bio 14), mean temperature of the wettest quarter (bio 8), elevation (Elv), and mean temperature of the driest quarter  was at least two times higher than those among the other three variables of the highest impact.The GF analysis revealed four environmental factors to have the highest impact  strongly correlated with the factor precipitation of the driest month  database using BLASTn, but no significant alignments were found (data not shown).Furthermore, the allelic frequencies of four of the five aforementioned outliers  gradually changed with latitude Figure ; while t3.4R2 = .420, p < .001; Geo: adjusted R2 = .226, p < .001; Anc: adjusted R2 = .105, p < .001; Table R2 = .288; Geo | Env + Anc: adjusted R2 = .118; Anc | Env + Geo: adjusted R2 = .011; Table E. coccineum , geographic (Geo), and co‐ancestry (Anc) influence on the allelic variation , near the end of the Pliocene and at the beginning of the Pleistocene. The locations within the Northern genetic group (Nah and Cu) or within the Central‐Southern genetic group  seem to have diverged much more recently, toward the end of the Pleistocene  on its genetic diversity.In the present study, samples of 4.1E. coccineum, one in the Northern and one in the Central‐Southern part of the species distribution. These results support previous studies, yet at different levels of spatial resolution . Still, it has been shown that consistent FST values can be estimated between populations constituted by only two individuals when a high number of SNP markers (>1500 loci) are used  than the divergence between the two main lineages. The divergence between Northern and Central‐Southern lineages was estimated at 2.8 Myr ago, which corresponds to the Late Pliocene and Early Pleistocene. Herewith, our study provides a refined picture of the possible divergence time between the main genetic clusters of E. coccineum. In line with our conclusions, the impact of glacial/interglacial contractions and expansions has already been reported on the genetic makeup of South American terrestrial temperate species of herbaceous plants and animals  and then rapidly increasing after the LGM . The existence of IBD indicates that gene flow is limited as geographic distance increases  in populations located at high latitudes and altitudes compared with the rest of the distribution  and those of neutral processes, such as isolation by distance (IBD) or co‐ancestry  on the genetic differentiation in E. coccineum. The level of precipitation of the driest month discriminated the Mediterranean biome to the north and the Patagonian steppe to the east (both with very low summer precipitation) from the temperate biome inhabited by E. coccineum  to the environment  are more resistant to wilting ; data curation ; formal analysis (lead); investigation (lead); methodology (lead); validation (lead); visualization ; writing – original draft ; writing – review and editing . Ariana Bertin‐Benavides: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); investigation (lead); methodology ; project administration (lead); resources (lead); supervision ; validation ; writing – original draft ; writing – review and editing . Rodrigo Hasbún: Formal analysis (supporting); investigation (supporting); methodology ; writing – original draft ; writing – review and editing . Óscar Toro‐Núñez: Conceptualization ; formal analysis ; investigation ; methodology ; validation ; writing – review and editing . Antonio Varas‐Myrik: Data curation ; formal analysis ; investigation ; methodology ; validation ; writing – review and editing . Diego Alarcón: Formal analysis ; investigation ; methodology ; visualization ; writing – review and editing . Marie Guillemin: Data curation ; formal analysis (lead); investigation (lead); methodology ; supervision (lead); validation ; writing – original draft (lead).On behalf of all authors, the corresponding author states that there is no conflict of interest.https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA783610.This article has earned Open Data, Open Materials and Preregistered Research Design badges. Data, materials and the preregistered design and analysis plan are available at Appendix S1.Click here for additional data file.


ST, f3-statistics, f4-statistics, qpAdm/qpWave, qpGraph, ALDER, and TreeMix]. We found that Fuxin Mongolians had a close genetic relationship with Han people, northern Mongolians, other Mongolic speakers, and Tungusic speakers in East Asia. Also, we found that Neolithic millet farmers in the Yellow River Basin and West Liao River Basin and Neolithic hunter–gatherers in the Mongolian Plateau and Amur River Basin were the dominant ancestral sources, and there were additional gene flows related to Eurasian Steppe pastoralists and Neolithic Iranian farmers in the gene pool of Fuxin Mongolians. These results shed light on dynamic demographic history, complex population admixture, and multiple sources of genetic diversity in Fuxin Mongolians.The Mongolian population exceeds six million and is the largest population among the Mongolic speakers in China. However, the genetic structure and admixture history of the Mongolians are still unclear due to the limited number of samples and lower coverage of single-nucleotide polymorphism (SNP). In this study, we genotyped genome-wide data of over 700,000 SNPs in 38 Mongolian individuals from Fuxin in Liaoning Province to explore the genetic structure and population history based on typical and advanced population genetic analysis methods [principal component analysis (PCA), admixture, F Northeast Asia is a vast geographical region encompassing the Mongolian Plateau (MP), Yellow River Basin (YRB), West Liao River Basin (WLRB), Amur River Basin (ARB), Russian Far East, Korean Peninsula, and Japanese Islands. Recent studies indicated that frequent and complex population migration, exchange, and admixture events had happened in this region. The West Liao River Basin is considered the cradle of the Transeurasian language family. The “Transeurasian hypothesis” is supported by evidence from linguistics, archaeology, and genetics. It indicates that Japonic, Koreanic, Tungusic, Mongolic, and Turkic languages are split from a proto-Transeurasian language, and the diffusion of Transeurasian language is related to the expansion of early millet farmers in the West Liao River Basin , but WanMongolians play an indispensable role in the formation of culture and genetic structure in Eurasia, the Mongol Empire controlled vast territories and trade routes stretching from East Asia to Europe in the 13th century, and there are genetic imprints of Mongolians which can be found in modern Eurasian populations . The MonIn this study, we collected peripheral blood samples from 38 Mongolian individuals  in Fuxin, Liaoning Province, China. The detailed sample information is listed in ST, admixture, TreeMix, and ALDER. Also, we merged newly genotyped data with the 1240K dataset from the Reich Lab  to obtain a dataset with a larger number of SNPs  for performing f3-statistics, f4-statistics, qpWave/qpAdm, and qpGraph.In this study, we merged our newly genotyped 36 individuals’ data with the Affymetrix Human Origins (HO) array dataset  and receIn this study, PCA and admixture were used to explore the population structure in Eurasia. First, we performed the PCA based on the merged-HO dataset containing 52,403 SNPs from 1,488 individuals in 151 populations using the smartpca program in EIGENSOFT software  with theST genetic distances between Fuxin Mongolian and other modern reference populations. The heatmap and phylogenetic tree were used to shed light on the relationship between Fuxin Mongolians and other populations. The heatmap was plotted using the R package pheatmap, the neighbor-joining (N-J) tree was constructed using MEGA X (https://itol.embl.de/).We used the smartpca program in EIGENSOFT software  with parg MEGA X , and thef-statistics were carried out using ADMIXTOOLS (f3-statistics). We calculated outgroup-f3  to measure shared genetic drifts between Fuxin Mongolians and reference populations (Y) and computed admixture-f3  to explore the potential admixture signals. Also, we then performed the four-population test (f4-statistics) using the qpDstat program in ADMIXTOOLS  and mtDNA PhyloTree17 (http://www.phylotree.org/). The haplogroup information of mtDNA and Y chromosome is listed in We used an in-house script to assign the Y-chromosomal and mitochondrial haplogroups following the recommendations of the International Society of Genetic Genealogy , Baotou Mongolian (0.002), Xibo (0.002), Tu (0.002), and Korean (0.002)  . In the e lowest . The rese lowest . The yelt    , we founn_Fuxin)  to modelf4-statistics to explore the asymmetric genetic relationship and gene flow direction between Fuxin Mongolians and other modern/ancient populations in the forms of f4  and f4 . In the form of f4   , we found Fuxin Mongolians shared more alleles with ancient YRB, WLRB, ARB, and MP populations  (We then performed  Yoruba) , we obse Yoruba) , and theulations , and the Yoruba)  further qpWave/qpAdm methods to explore the possible ancestral contributors and estimate their admixture proportions in Fuxin Mongolians. We used millet farmers related to Neolithic Yangshao, Longshan, and Lower Xiajiadian cultures, Neolithic hunter–gatherers in the Amur River Basin and Mongolian Plateau, Iranian Neolithic farmers, Bronze-Age Yamnaya pastoralists, and Bronze-Age Sake peoples in Central Asia as the possible ancestral sources  which was associated with the prosperity of the Silk Road and the westward expansion of the Mongol Empire . The timIn this study, we successfully identified paternal and maternal lineages in Fuxin Mongolians; 13 paternal haplogroups and 36 maternal haplogroups are listed in the f3 statistics further supported the aforementioned conclusions. We found that Fuxin Mongolians shared more genetic drifts with the Han people than other East Asians, and f4-statistics results further indicated the genetic similarity between Fuxin Mongolian and Han people, with insignificant Z-scores in the form of f4 . Also, Fuxin Mongolians also shared alleles with Tungusic and Mongolic speakers which are supported by the results of Outgroup-f3  and f4 . The results of admixture-f3  which have a significant Z-score further indicated that Han people and Altaic speakers played an indispensable role in the formation of genetic makeup in Fuxin Mongolians. In addition, we also found the genetic contribution of the Western Eurasian population in Fuxin Mongolians. The admixture-f3 results showed that Fuxin Mongolians could be modeled as the mixture between Han people and Western Eurasian populations, and the ALDER-based admixture time revealed that the eastern–western Eurasian population admixture occurred during ∼600–∼1,300 years ago which is the period from the Tang dynasty to the Ming dynasty. The Silk Road and Maritime Silk Route were further developed during the Tang Dynasty; the Mongol Empire rose and expanded westward in the 13th century, and ancient Chinese navigators explored the ocean during the Ming dynasty. These events promoted prosperous economic, political, and cultural interactions and genetic exchanges between western and eastern Eurasia  indicated that the genetic structure was consistent with the language category and geographical distribution, and populations from the same language group or adjoining geographic position had a close genetic affinity with East Asia. Fuxin Mongolians showed genetic similarity with the Mongolian population in Inner Mongolia (Hulunbuir and Baotou) and other Mongolic speakers such as Daur, Tu, and Bonan. Also, there were close genetic relationships between Fuxin Mongolians and Han people, Tungusic speakers (Hezhen and Xibo), and other geographically adjoining populations. Outgroup- Eurasia . Also, d Eurasia , and Wes Eurasia . Also, e Eurasia , and the Eurasia .f3  and f4  indicated that Fuxin Mongolians shared more genetic drifts and alleles with ancient YRB, WLRB, ARB, and MP populations. There were insignificant Z-scores in the form of f4 . The genetic contribution of ancient Mongolians also existed in the Fuxin Mongolian gene pool, Mongolia_8_EIA, Ulaanzukh_2_LBA, SlabGrave_1_EIA, Xianbei_IA, Xiongnu_7_EIA, CenterWest_4_LBA, Ulgii_1_EBA, and Khovsgol_6_LBA shared alleles with Fuxin Mongolians, and the results of f4-statistics in the form of f4  revealed that Fuxin Mongolians received more genetic influences of Mongolia_8_EIA and Ulaanzukh_2_LBA. According to historical records, modern Mongolians in Liaoning are mainly descended from two ancient Mongolian tribes, the “Mongolia Zhen” tribe, and the “Harqin” tribe. Fuxin Mongolians belong to the “Mongolia Zhen” tribe which has a history of more than 1,200 years, it originated in the Orkhon River Basin and Selenga River Basin, and this tribe expanded westward into the regions of Central Asia and Xinjiang with the development of strength. From the late 15th century to the early 16th century, the “Mongolia Zhen” tribe migrated to Hetao Plain in the Yellow River Basin and became an alliance with the “Tumet” tribe. In the 16th century, the “Tumet-Mongolia Zhen” tribe migrated eastward, and they finally settled in the western regions of Liaoning Province. The complex migration and admixture history shaped genetic differences between Fuxin Mongolians and other Mongolians. The admixture-f3  results exhibited that Fuxin Mongolians could be modeled as the mixture between Neolithic and Iron–Age YRB and WLRB populations and Eurasian Steppe pastoralists, ancient Mongolians, and ancient Iranian. The models of the population mixture based on the qpAdm method showed YRB and WLRB millet farmers and ARB and MP hunter–gatherers were the dominant ancestral contributors, and there were additional gene flows related to Eurasian Steppe pastoralists and Iranian farmers. The qpGraph-based phylogenetic framework further supported the aforementioned results. The millet farmer-related ancestry was 53%–57%, and the MP and ARB hunter–gatherer-related ancestry and Western Eurasian-related ancestry were 43%–47%. We also found the Western Eurasian-related ancestry possessed 9% in the gene pool of Fuxin Mongolians. Also, the qpAdm results also indicated genetic differences between Fuxin Mongolians and other Altaic language speakers, and we could find there were more YRB-, WLRB-, and ARB-related ancestries in Fuxin Mongolians, which suggested that Fuxin Mongolians were more influenced by the expansion of millet farmers and retained more genetic contribution of ARB hunter–gatherers. In general, we found there were dynamic demographic history, complex population admixture, and multiple sources of genetic diversity in Fuxin Mongolians.Paleogenomic studies demonstrated that ancient Mongolians at different times and geographic regions exhibited disparate genetic profiles. There was a dynamic demographic history in the Mongolian Plateau, and multiple populations contributed to genetic ancestries to shape the genetic differences of ancient Mongolians: the main ancient Northeast Asian ancestry, the ephemeral ancient North Eurasian ancestry, the limited genetic contributions of Western Eurasian Steppe pastoralists and Iranian farmers, and recent genetic influence of the Han people . Our resf3-statistics, f4-statistics, qpAdm/qpWave, qpGraph, ALDER, and TreeMix] to explore genetic structure and admixture history of Fuxin Mongolians. We found that Fuxin Mongolians had a close genetic relationship with the Han people, northern Mongolians, other Mongolic speakers, and Tungusic speakers in East Asia. Also, we found that Neolithic millet farmers in the Yellow River Basin and West Liao River Basin and Neolithic hunter–gatherers in the Mongolian Plateau and Amur River Basin were the dominant ancestral sources, and there were additional gene flows related to Eurasian Steppe pastoralists and Neolithic Iranian farmers in the gene pool of Fuxin Mongolians. These results shed light on dynamic demographic history, complex population admixture, and multiple sources of genetic diversity in Fuxin Mongolians.In this study, we reported genome-wide SNP data of Fuxin Mongolians in Liaoning Province. We applied typical and advanced population genetic analysis methods [principal component analysis (PCA), Admixture, FST,


Phaseolus vulgaris L.) local varieties and 5 commercial cultivars collected from 8 different locations in Erzurum-Ispir by using inter-primary binding site (iPBS) retrotransposon markers. For molecular characterization, the 26 most polymorphic iPBS primers were used; 52 bands per primer and 1350 bands in total were recorded. The mean polymorphism information content was 0.331. Various diversity indices, such as the mean effective allele number (0.706), mean Shannon’s information index (0.546), and gene diversity (0.361) revealed the presence of sufficient genetic diversity in the germplasm examined. Molecular analysis of variance (AMOVA) revealed that 67% of variation in bean germplasm was due to differences within populations. In addition, population structure analysis exposed all local and commercial bean varieties from five sub-populations. Expected heterozygosity values ranged between 0.1567 (the fourth sub-population) and 0.3210 (first sub-population), with an average value of 0.2103. In contrary, population differentiation measurement (Fst) was identified as 0.0062 for the first sub-population, 0.6372 for the fourth subpopulations. This is the first study to investigate the genetic diversity and population structure of bean germplasm in Erzurum-Ispir region using the iPBS-retrotransposon marker system. Overall, the current results showed that iPBS markers could be used consistently to elucidate the genetic diversity of local and commercial bean varieties and potentially be included in future studies examining diversity in a larger collection of local and commercial bean varieties from different regions.Beans are legumes that play extremely important roles in human nutrition, serving as good sources of protein, vitamins, minerals, and antioxidants. In this study, we tried to elucidate the genetic diversity and population structure of 40 Turkish bean ( It has been reported that the rate of disappearance of plant species has increased in recent years and it is thought that the rate of genetic erosion of plant species will increase in the coming years . To miniPhaseolus vulgaris L.) is one of the most valuable herbal products in the world due to its nutritional properties, benefits to human health and economic importance [Bean  and non-LTR retrotransposons. LTR-retrotransposons are more common in plants than the other group . Due to Phaseolus vulgaris L.) local varieties were used as plant material. The names and gathering places of the regional varieties are presented in In this study, 45 Turkish bean (P. vulgaris L.) approximately 15-day-old plants were ground in liquid nitrogen at the molecular biology and genetics laboratory of Ataturk University. The collective DNA of 45 individuals per participation was then prepared, using the DNA extraction method of Zeinalzadehtabrizi et al. [Young leaves of beans . In the present study, 26 iPBS primers developed by Kalendar et al.  were useThe PCR was performed in three replicates for each primer to verify the band pattern consistency. The DNA bands were scored, using TotalLab TL120 software . For the iPBS amplification products, a band is scored “1” or absent “0” for each locus. Only clear, strong bands were scored, while faint, weak bands were ignored. The Numerical Taxonomy and Multivariate Analysis System for personal computer (NTSYSpc) V.2.0 programs based on the Dice similarity matrix  were useSufficiently clear and scoreable bands were obtained from all primers included in the study. With these 26 primers, 1350 visible and scoreable bands were generated. The number of alleles in the primers varied between 23 (iPBS 2077 and 2383) and 80 (iPBS 2274) (Mean 37.14). When the analysis was performed with the iPBS markers, the PIC varied between 0.151 (iPBS 2298) and 0.495 (iPBS 2383) (Mean 0.331). Major allele frequency ranged from 0.528 (iPBS-2383) to 0.888 (iPBS-2298). The mean major allele frequency was 0.706 .The number of effective alleles (ne), genetic diversity of Nei (h) and Shannon’s information index (I) value of the bean varieties is presented in The summary statistics for nine populations  dendrogram placed the 45-bean variety into three clusters. There were only 18 (40%), 14 (31.11%) and 13 (28.88%) varieties in the first to three clusteres, respectively . ClusterST) value was determined as 0.0002, 0.4371, 0.4061, 0.6372, and 0.5440 in the first to fifth subpopulations, respectively. Likewise, the expected heterozygosity values (He) were determined as 0.3210, 0.1858, 0.1947, 0.1567, and 0.1907 in the first to fifth subpopulations, respectively  (membership probability < 0.8) . At K = ectively .P. vulgaris L.) species at the molecular level. This provided important information about the genetic associations between these varieties. The information produced by the iPBS marker system suggests that it can be used effectively for diversity studies and genetic analysis in bean varieties. Using this marker system, other researchers have successfully examined similar bean species in genetic diversity studies [Determining the genetic diversity levels of the germplasm of a plant species is essential for the designing and structuring of plant breeding programs . Molecul studies ,8,24. Th studies . This re studies . In our  studies . The resThe mean number of effective alleles (ne), genetic diversity of Nei (h) and Shannon’s information index (I) value of the bean varieties were calculated as 1.566, 0.361, and 0.546, respectively. , in theiThe population structure identified in this study was consistent with distance-based clustering from principal coordinate analysis (PCoA). In our study, we showed that intraspecific crosses, especially those between the Ic/Kv (0.232), Yy/Ky (0.229), and Kt/Ic (0.222) populations, may produce stronger hybrids, due to their greater genetic distance. We also performed PCoA analysis to examine the genetic associations between bean varieties. In the first three axes, PCoA analysis explained 43.92% of the total variation. In PCoA analysis, cluster analysis data obtained from this matrix are generally considered reliable when the axes explain 25% or more of the total variation . PCoA isThe findings showed that the bean varieties were divided into five groups according to their genetic structures. Varieties accumulate several living mutations throughout the evolutionary process, which form the basis of genetic diversity. Moreover, recombination, random drift, natural selection, such forces shape the genetic makeup of populations. In the recent past, understanding population structure has become a feature of great interest, as it can be helpful in selecting various parents and mapping sign-trait relationships. As a tool, analysis of population structure can predict similarity levels between individuals, subpopulations, and contributions. When samples are plotted with different geographic origins, analysis of population structure shows the pattern of geographic distribution among populations . In a siP. vulgaris L.) genetic re-sources, conservation, and selection of suitable parents to provide greater genetic diversity for use in breeding programs. We showed here that the iPBS marker system is a powerful and easy method for detecting variation among bean varieties. The current findings reveal the diversity in local bean varieties collected from Erzurum-Ispir and will provide a basis for subsequent bean breeding programs, as well as integrity in bean identification studies. According to the information obtained in the study, it was determined that the genetically most distant cultivars were the G1 and G36 local varieties. With future studies, it is thought that these varieties can be used in breeding and hybridization studies, taking into account their agro-morphological characteristics, their resistance to biotic and abiotic conditions. There are many tools for determining and revealing genetic diversity in plant breeding. However, in plant breeding programs, it is extremely important to know the genetic distance of the varieties that are not clearly defined are unknown in the germplasm. Although classical breeding studies have reached the desired rate in many plant species and varieties, molecular markers provide very important information in breeding programs in genotypes development studies. In addition, the determination of distance and proximity conditions between varieties by performing genetic analyses contributes to the creation of new populations and to obtaining high-yielding varieties with heterosis. Therefore, evaluation of the genetic diversity of local bean varieties is needed for the conservation and breeding of this genetic material. Molecular markers and genetic diversity studies provide the useful information that is so critically needed about population structure. More informative molecular markers, such as iPBS, are being increasingly used in the study of bean genetic diversity, and their power cannot be underestimated. In conclusion, we used the iPBS retrotransposon marker system to generate pre-breeding data that could potentially be applied to the identification of common bean (


Knowledge of genetic interrelationships and grouping among pigeonpea germplasm collections is fundamental to selecting breeding parents with unique genetic constitutions. The objectives of this study were to assess the genetic diversity and genetic grouping present among 81 pigeonpea genotypes collected from Malawi, Tanzania and Kenya using 4122 single nucleotide polymorphism (SNP) markers and complementary morphological traits. The SNP markers and phenotypic traits revealed significant genetic variation among the assessed genotypes. The test genotypes were resolved into three distinct clusters based on both marker systems. The mean gene diversity and the polymorphic information content (PIC) were 0.14 and 0.11, suggesting moderate genetic differentiation among the genotypes. The analysis of molecular variance revealed that differences among populations accounted for only 2.7% of the variation, while within the population  accounted for 97.3% of the variation. The results based on the DArT SNP genotyping complemented the phenotypic data and led to the selection of unique pigeonpea genotypes for effective breeding programs in Malawi and related agroecologies. This suggested that unique breeding populations could be created by identifying and selecting divergent individuals as parental lines. There is a need to create a new genetic variation or introgress genes from genetically unrelated parents to increase the genetic base of the current pigeonpea breeding populations. Pigeonpea is also a major income source for many small-scale farmers in Africa and Asia . Pigeonpogen (N)  with a n factors , 5. Furt factors . DespiteConsequently, the production and productivity of pigeonpea are still low to attract interest from commercial and large-scale farming enterprises. The neglect of orphan crops such as pigeonpea by crop improvement research programs compared to other commodity crops such as maize, wheat, and rice has contributed to a lack of improved and high-yielding cultivars in sub-Saharan Africa (SSA). To date, very few commercially grown pigeonpea varieties are available, meeting farmer and market preferences in SSA. This includes hybrids such as ICPH 2671, ICPH 2740, and ICPH 3762 developed by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- India) using the cytoplasmic nuclear male sterility (CMS) system [® platform called CcSNPnks’ has been developed [et al. [Cajanus species. The authors reported a narrow range of genetic diversity among the tested genotypes. More than 15000 SNPs were discovered recently across the pigeonpea genome [The development of new cultivars will require understanding the existing diversity to inform breeding programs and germplasm management strategies. Knowledge of the genetic basis of yield, quality and stress tolerance is important for the genetic improvement of pigeonpea. Varshney et al.  reportedeveloped , which p [et al.  developea genome .The recently compiled diversity arrays technology (DArT) library on pigeonpea genome provides opportunities for gene discovery and developing strategies for marker-assisted selection to accelerate breeding progress in pigeonpea. Pigeonpea breeding in Malawi is lagging and is mainly focused on conventional breeding methods. Conventional breeding should be complemented with genomic-assisted selection for precision and accelerated breeding and variety release. Yohane et al.  reportedA population of 81 pigeonpea genotypes were used for this study. Test genotypes were collected from the Department of Agricultural Research Services, Lilongwe, Malawi  and the National Plant Genetic Resource Centre, Lilongwe, Malawi , the International Crops Research Institute for the Semi-Arid Tropics, Nairobi, Kenya 1°14’10”S 36°49’07”E 1697 masl), and Tanzania Agricultural Research Institute, Ilonga, Tanzania (6°42”S 37° 2”E 506 masl). The germplasm included landraces, breeding lines, and released cultivars obtained from different sources, as presented in https://www.diversityarrays.com/files/DArT). Fifty milligrams of total genomic DNA were extracted from the well-developed trifoliated leaves using the NucleoSpin Plant II kit  with the Lysis Buffer I (based on the cetyl trimethylammonium bromide (CTAB) method). The DNA quality and quantity of each sample were determined on 2% agarose gel followed by quantification using a Nanodrop 2000 Spectrophotometer . The DNA samples were sent to the Biosciences eastern and central Africa International Livestock Research Institution (BecA-ILRI-hub in Kenya (https://hub.africabiosciences.org/) for genotyping.Ten seeds of each pigeonpea genotype were planted in plastic pots and allowed to grow for three weeks before DNA was extracted. Fresh leaf samples of 10 individual plants per genotype were pooled so that each genotype was well represented. Similarly, 15 pigeonpea plants per genotype were sampled and bulked DNA used for genetic analysis . This waThe phenotypic evaluation of the accessions was conducted in the 2017/18 and 2018/19 growing seasons at Bvumbwe, Chitedze, and Makoka. The Bvumbwe site (15°55′ S 35°04′ E) receives an average of 1208.6 mm of rainfall per year, and the average temperature ranges between 16.2 and 24.9 °C. The Chitedze site (13°59′ S 33°38′ E) receives 811.6 mm of rainfall annually with an average temperature between 18.5 and 29.4 °C. At Makoka (15°32′ S 35°11′ E), the average rainfall received per year is 875.7 mm while the average temperature ranges between 15.6 and 28.2 °C. The full description of the sites’ environmental and edaphic conditions is presented in Yohane et al. [2. Seeds were planted 0.75 m apart within a row. Three seeds were planted per planting station and thinned to one plant two weeks after emergence. The phenotypic data collected included qualitative and quantitative attributes  of phenotypic data was presented in Yohane et al. . Summaryet al. . Phenotyet al. .DArTseq SNP delivered markers were filtered for quality control to remove bad SNPs and genotypes using the “impute” package in R software version 1.42.0 . A totalThe gene diversity, minor allele frequency (MAF), polymorphic information content (PIC), and heterozygosity (Ho) were calculated using the “diveRsity” procedure in R software . The anaThe genetic groups of the 81 genotypes was determined using the admixture model-based clustering method in STRUCTURE Harvester . The burGenetic groups were defined using a combination of the phenotypic and genotypic dissimilarity matrices. The joint matrix was generated by the summation of the genotypic and phenotypic dissimilarity matrices. The phenotypic dissimilarity matrix was generated using Gower’s distance matrix, while the genotypic dissimilarity matrix was based on Jaccard’s coefficients. The groups generated from the phenotypic and genotypic sets were compared using the “viridis” procedure in R version 5.0 , and theThe SNP call rate and average reproducibility ranged from 0.34 to 0.98, and 0.9 to 0.99, respectively. Heterozygosity values varied from 0.21 to 0.23, with a mean of 0.22 . Gene diThe SNPs resoved, reolved three distinct sub-populations among the 81 accessions  based onThe genetic differentiation among the populations ranged from -0.011 to 0.002 . The higThe results obtained from genetic structure analysis based on the phylogenetic tree resolved the 81 genotypes into three groups . Group I-1, ranging between 0.11 t ha-1 and 3.67 t ha-1.Genotypic variation was significant for most quantitative traits . The earUsing morphological attributes, the phenotypic diversity assessment  grouped The phylogenetic tree generated from the phenotypic data was compared to the genotype grouping based on the SNP data . The resPreliminary evaluation of the pigeonpea germplasm revealed significant genetic variation based on phenotypic traits . Such vaDetermination of genetic diversity among genotypes, populations, and gene pools is essential to identify unique individuals as sources of genes for improving quantitative or qualitative traits. Several studies have been conducted that assessed the genetic diversity in pigeonpea using morphological descriptors , 39, 40,Similarly, relatively low PIC values were reported in common bean and cowpea germplasms, respectively in Malawi and Zambia , 45 due The negative inbreeding coefficient values indicate the presence of considerable heterozygosity in the test populations owing to the inherent outbreeding of pigeonpea. The excess heterozygosity observed in this study may have resulted from the markers deviating from the expected Mendelian ratios, referred to as “segregation disorders”. Although the actual cause of this disorder is unknown, sampling error favouring heterozygotes is among the suspected causes of excessive heterozygosity , 46. HowThe presence of low levels of heterozygosity among individuals and the low frequency of rare variants in the population could present bottlenecks for breeding. Adequate genetic diversity facilitates the adaptation of populations to changes in environmental conditions . High heThe highest delta value occurred at K = 3 in the population structure analysis , showingBrassica cultivars based on PCR-based DNA polymorphism. The tested cultivars were clustered into two major groups and four sub-groups. However, there was a narrow genetic base among the Pakistan oilseeds Brassica cultivars.The grouping of genotypes into three clusters Figs  and 3 usA joint analysis of phenotypic and genotypic data was conducted to capture the genetic variability and grouping of the test population. The comparison between the phenotypic and genotypic information showed that 45.7%  of the aThe present study assessed the genetic diversity and grouping among the 81-pigeonpea accessions sourced from Malawi, Tanzania, and ICRISAT/Kenya. The genetic diversity and grouping of the test populations were confirmed using morphological traits, SNPs data, and joint analysis. The test genotypes were grouped into three genetic clusters, enabling the selection of divergent parents for hybridization and the development of new pigeonpea breeding populations in Malawi. There is a need to create a new genetic variation or introgress genes from genetically contrasting parents to harness the genetic variation in the presently assessed pigeonpea population.S1 Fig(DOCX)Click here for additional data file.S2 FigSee (DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(TXT)Click here for additional data file.S4 File(CSV)Click here for additional data file.S5 File(CSV)Click here for additional data file.S6 File(CSV)Click here for additional data file.


As a typical microscopic imaging technology, the emergence of the microscope has accelerated the pace of human exploration of the micro world. With the development of science and technology, microscopes have developed from the optical microscopes at the time of their invention to electron microscopes and even atomic force microscopes. The resolution has steadily improved, allowing humans to expand the field of research from the initial animal and plant tissues to microorganisms such as bacteria, and even down to the nanolevel. The microscope is now widely used in life science, material science, geological research, and other fields. It can be said that the development of microscopes also promotes the development of micro- and nanotechnology. It is foreseeable that microscopes will play a significant part in the exploration of the microworld for a long time to come. The development of microscope technology is the focus of this study, which summarized the properties of numerous microscopes and discussed their applications in micro and nanotechnology. At the same time, the application of microscopic imaging technology in micro- and nanofields was investigated based on the properties of various microscopes. A microscope is an instrument that magnifies the image of an observed object. It has a history of 400 years since its invention at the end of the 16th century. Because of its ability to magnify images, the microscope has played a vital part in the investigation of the microscopic world. Microscopic imaging technology evolves in tandem with human awareness of the microcosmic world. For the time being, the microscope includes the following three types according to the principles: optical microscope, electron microscope, and scanning probe microscope. The different principles make the use and contribution significantly different. In this study, we will discuss the evolution of the microscope and its applications in micro- and nanotechnology.The optical microscope is the first human-made microscope. It has played an extremely important role in the initial stage of human understanding of the micro world. For the past 400 years, the basic construction of the optical microscope remained stable without major changes, including objective lens, eyepiece, light source, condenser, and the mechanical structure. The resolution of traditional optical microscopes meets the Abbe formula: δ is the optical microscope resolution, λ is the wavelength of light, n is the refractive index, and α is the aperture angle. The resolution limit of an optical microscope calculated is about 0.2 microns, so an optical microscope is widely used at a micron level, especially for the study of cells in biology and medicine. However, further observations of smaller objects are limited.where λ or aperture With the development of science and technology, near-field optical microscopy (NSOM) has been developed in recent years. Compared to the traditional optical microscope, its resolution has substantially enhanced. The structure includes the local light source, laser, fiber probe sample station, and optical amplification system. The characteristic of the near-field microscope is that at least one illumination and imaging must work in the near field, while both traditional optical microscopes work in the far field. The resolution of traditional far-field optical microscopes has been limited to wavelength Increasing the information dimension is one of the important directions to improving the traditional optical microscope. Polarization is an inherent property of light, which is more sensitive to microstructure. Through polarization measurement, we can obtain more sample information including light intensity . TherefoAt the same time, it should be noted that a fluorescence microscope is another traditional optical principle-based microscope. Many materials fluoresce when exposed to a specifically engineered excitation light source, which is often ultraviolet, blue violet, or green light. Other non-fluorescent materials can be stained, and associated materials can then be monitored using UV light. Weixing Li et al. (2015) are cited in this study to demonstrate the structural properties of a low-temperature fluorescence microscope as shown in It is worth noting that in recent years, with the advent of super-resolution fluorescence microscopy, fluorescence microscopy can get rid of the resolution of traditional optical microscopy. Photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and stimulated emission depletion (STED) are currently used to improve microscopic imaging in the direction perpendicular to light propagation, while STED-4 3D imaging and 3D SSIM imaging saturated structure illumination (SSIM) are used to improve microscopic imaging in the direction of light propagation. The resolution is at the nanoscale. It is widely used in the field of life science to observe the expression and embodiment of biological macromolecules and cellular functions in living cells at the molecular level.It must be mentioned that laser scanning confocal microscopy has become one of the important microscopy tools nowadays. A laser scanning confocal microscope is a set of observation, analysis, and output systems. The key technology is conjugate focusing based on a traditional optical microscope. It consists primarily of a laser light source, an automatic microscope, a scanning module , a digital signal processor, a computer, and also an image output device, among others. In The imaging process in optical microscopy is nondestructive to the material. It is a reasonably simple observation technique, which can realize real-time imaging. The contemporary conventional optical microscope, however, has fewer applications at the nanoscale scale than other microscopes because its resolution is limited by light diffraction, and its applications are mostly centered on the micron size. Traditional optical microscopy is still one of the most important detection tools in the field of life sciences and clinical medicine because of the advantages of non-destructive observation of samples, real-time imaging, and a relatively simple observation process. In a study published in 2015, Fu Rong et al. used urine routine combined with optical microscopy for morphological examination of urine erythrocytes and thus for early diagnosis of Alport syndrome . R HauseThe NSOM successfully breaks the resolution limit of traditional optical microscope by detecting evanescent waves containing object details. Near-field scanning optical microscope has made significant advances in theory and practice, and is now being used in micron and nanotechnology. In a study published in 2002, RS Decca, Lee, and others proposed a system for tracking single molecules using a near-field scanning optical microscope . MeanwhiAt present, polarized light microscope is playing a unique role in life science and material science. The polarization microscope is useful in biological studies because different biological tissues and even distinct proteins have variable polarization characteristics . In a 20The fluorescent microscope is now employed in a variety of fields. Fluorescence microscopes are employed in biological molecular identification and genetic engineering in life science. Takao et al. examined the interior structure of a single DNA molecule employing optics under a fluorescent microscope in 2016. This research will be used to inform future DNA microstructure design . Fluoresin situ immunolocalization of factors associated with joint pathology in intact cartilage  used SEM to examine a soil sample. The findings demonstrate that observing crust morphology and development mechanisms using SEM on soil is beneficial. It has a significant impact on geology . JL PiloCrewe et al. (1968) pioneered field emission scanning transmission electron microscopy research, combining the advantages of the scanning electron microscope and transmission electron microscope, and using a field emission electron cannon as an electron source, considerably improving microscopic performance. In materials science and life science, scanning transmission electron microscopy has become a useful instrument.in situ liquid scanning transmission electron microscopy to study the growth process of platinum nanocrystals, which is important for optimizing nanocrystal preparation conditions and nanostructure synthesis  is currently widely employed in material research. At the same time, because the resolution of scanning tunneling microscopy reaches a single atom, it is employed as a detection method in nanotechnology to determine the particle size of nanoparticles. SNV A used a scanning tunneling microscope to observe gold nanoparticles made by the sputtering approach in 2020 and went on to conduct nanotechnology research . STM canWith the invention of the scanning tunneling microscope, the probe microscope has become a new type of high-precision microscope. In 1986, G. Binning invented AFM using the STM probe measurement method. AFM currently has a bright future . The basBecause the atomic force between atoms is a function of distance, measuring the atomic force of individual atoms can be used to determine the surface topography of samples. The atomic force microscope measures the atomic force on the sample surface through the probe. When the atomic force changes, the probe produces a small deformation relative to the initial state, which is amplified and transmitted to the position sensitive detector (PSD) through the optical lever, so as to convert the surface morphology signal of the sample into an electrical signal. Through continuous measurement, the morphology, particle size, chemical composition, and other characteristics of the sample to be tested are finally obtained.As one of the few tools that directly utilize atomic force, AFM has a resolution up to the atomic level, while the scanning tunneling microscope can arrange and process atoms at low temperatures. The atomic force microscope is now widely used in life science, material science, and other disciplines. The atomic force microscope (AFM) is an important tool in nanotechnology for detecting the particle size and chemical composition of nanoparticles as well as processing them. M Kim used an atomic force microscope to manipulate and assemble super-spherical AuNPs in 2015, resulting in supramolecules with uniform structure. Hyperspherical AuNPs’ dependable and deterministic operation will increase the accessibility of supramolecular bases and improve spatial positioning precision . H Jin eFrom the perspective of development, this study systematically summarizes the development of microscopic imaging technology, and the principles, characteristics, and applications of various technologies in the field of micro-nanoscience as shown in the following table. The development of micro-imaging technology has greatly promoted the development of micro-nanoscience, and the human exploration demand in the field of micro-nanoscience also drives the development of micro-imaging technology. At present, the lower resolution limit of the traditional optical microscope is not enough to support the needs of micro-nanotechnology. The polarization microscope, fluorescence imaging microscope, and super-resolution microscope developed on the basis of the traditional optical microscope have brought new possibilities for further tapping the application potential of the optical microscope in micro-nanoscience. Although the resolution of an electron microscope is higher than that of a traditional optical microscope, its application environment must be vacuum. The resolution of the probe microscope reaches the atomic level, but the imaging speed is relatively slow. With the deepening of human understanding of the micro field, the requirements for micro-imaging technology will be higher and higher. Higher resolution, fewer environmental restrictions, and faster imaging speed will be the development direction of the new generation of the micro-imaging technology, which will be the breakthrough for the further development of micro-nanotechnology, and worth more energy from scientists,


Larimichthys polyactis, is a commercial fish of the order Perciformes that mainly inhabit estuaries and coastal waters.In recent years, the resources and catch of L. polyactis have undergone huge fluctuations. To detect genetic variations caused by the fluctuation of resources, genetic diversity of L. polyactis in the coastal waters of China were analyzed in this study using microsatellite DNA marker. The results revealed high genetic diversity of this species. The STRUCTURE, DAPC and FST results all indicated that there was no genetic structure consistent with the distribution pattern. Overall, our main findings are in agreement with previous studies, indicating that L. polyactis showed high genetic diversity and low genetic differentiation. Our results for high genetic connectivity among L. polyactis localities provide insights into the development of management strategies, that is, to manage this species as a single management unit.The small yellow croaker,  Marine fish have complex genetic structures because of their special geographical environment . RelativLarimichthys polyactis is a famous seafood product in China, and its population structure is the cause of major concern   , Fig. 1. grounds . Adult s grounds . ExperimL. polyactis developed by L. polyactis . The PCR was performed in an A300 Fast Thermal Cycler . The reaction system and amplification conditions were carried out according to the methods of A total of 12 microsatellite loci for olyactis . ForwardLD) and the Hardy-Weinberg equilibrium (HWE) test were performed.The data was initially processed as follows. Genemarker v.1.91 was used to score the microsatellite alleles . The genA), polymorphic information content (PIC), expected heterozygosity (HE), observed heterozygosity (HO) and unbiased expected heterozygosity (u HE) . The inbreeding coefficient (FIS) between L. polyactis localities was also estimated by Fstat v.2.9  , were cay (u HE)  and POPGy (u HE) . This soat v.2.9 .FST and RST was obtained using FSTAT v.2.9, and the significance was evaluated using Bonferroni correction tests ), (http://earth.google.com/) was used to measure the Euclidean distance between localities. Three-dimensional factorial correspondence analyses (3D FCA) and Discriminant Analysis of Principal Components (DAPC) were analyzed by the software of Genetix v.4.5.0. The “ggplot2 package” and “adegene package” in R 3.2.2 software was used to examine the genetic relationships among L. polyactis localities , estimated with the admixture model, ranged from 1–7 . To verify the results, each K values were run independently. To confirm the consistency of analysis, we carried out ten independent runs for each specific K-value. The most appropriate number of K value was estimated according to the change rate of the data log probability between the successive K values based on the ad hoc estimated likelihood of K ),  by Mante- FST)), . Google calities . STRUCTUcalities . The parood of K .L. polyactis was estimated using the Wilcoxon’s test in Bottleneck v.1.2.2 with three different mutation models: two-phased model of mutation (TPM), stepwise-mutation model (SMM) and infinite allele model (IAM)  , where 9LD test for localities and loci after Bonferroni correction. This locus was not kept in the further analyses. There were only four loci  that deviated from the HWE after Bonferroni correction in this study.A total of four null alleles were found in this study. The value of the null allele frequencies ranged from 0.001  to 0.038 (ZS in locus lop116), which were all low . There wA) per loci ranged from nine to 20, with a total of 186 alleles (A) detected in seven localities. The average allele richness (AR) ranged from 11.8 (WZ) to 14.0 (LYG). The average observed heterozygosity (HO) of all localities ranged from 0.970 (WZ) to 0.981 (LYG) and the average expected heterozygosity (HE) ranged from 0.916 (RS) to 0.924 (ZS). The range of uHE was 0.910 to 0.925. The average polymorphic information content (PIC), which ranged from 0.889 (YT and RS) to 0.894 (QD and YC), revealed high genetic diversity in all localities (PIC>0.5). The range of FIS was 0.033−0.086, which these values were not significant.Summary statistics of the genetic diversity parameters are shown in L. polyactis localities was estimated based on pairwise F-statistics (FST). The FST among localities ranged from −0.0073 (LYG vs ZS) to 0.0153 (QD vs WZ) (RST was −0.0083 (RS vs WZ) to 0.0145 (QD vs ZS)  and geographic distance . The result of 3D-FCA showed that the contribution rates of three principal components were 46.37%, 21.79% and 17.21%, respectively, and no significant population structure existed throughout the examined range of L. polyactis (The genetic structure of D vs WZ) . BesidesD vs ZS) . Signifiolyactis . This reolyactis .K value indicated that the model where K = 3 was the most appropriate . Also, the L-shaped distribution of allele frequency in the mode shift test indicated that L. polyactis were at mutation-drift equilibrium (Under the assumption of the two-phased model of mutation (TPM), the stepwise-mutation model (SMM) and infinite allele model (IAM), seven localities did not show a recent genetic bottleneck   . Lin et  species . Also, t species . Previouasciatus , Sebastirmoratus . TherefoFST was detected in most localities, revealing high flow among L. polyactis. This pattern was common for other marine organisms across this area (Engraulis japonicus in the northwestern Pacific based on restriction-site associated DNA (RAD) sequencing, suggesting high gene flow between the Bohai sea and the Yellow sea E. japonicus populations and no sign of local adaptation was found. High gene flow may be attributed to the spawning migrations and strong dispersal ability of L. polyactis (There are two traditional patterns of population division in the evaluation and management of fishery resources, including geographical boundaries and administrative areas . Howeverhis area . Zhang eolyactis .L. polyactis in the coastal waters of China should be managed as a single management unit. There may be a single genetic stock of L. polyactis in different sampling sites due to long-distance migration and larval dispersal (L. polyactis, therefore, we suggest that the marine national nature reserve should be established in Zhoushan Archipelago sea area. Overall, effective management for important economic species should not only consider administrative division and geographical boundaries but also the biological characteristics of the species such as migration and genetic structure.Many studies have confirmed that it is important for the fishery management to consider the spatial structure of marine organisms . Speciesispersal . Moreoveispersal . Previouispersal . To protL. polyactis in offshore areas of China had high genetic diversity, indicating that they had large resources and population size. No genetic structure consistent with the distribution pattern was detected. However, a possible genetic differentiation existed in Zhoushan as detected in this study, which should arouse the attention of researchers. This study can provide the foundation for the division of management units and the formation of laws and regulations on fishery protection.In summary, 10.7717/peerj.13789/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13789/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13789/supp-3Supplemental Information 3Click here for additional data file.


Higher concentration of elements such as C, N, O, Na, Mg, Al, Si, S, Fe, Cl, Ca, Ti, Cr, Pb, Fe, K, Cu, Cl, P, and F were observed during the smog along with dominant organic functional groups , ether, etc.), which supported potential contribution from transboundary biomass-burning activities along with local pollution sources and favorable meteorological conditions. The morphology of individual particles were found mostly as non-spherical, including carbon fractals, aggregates, sharp-edged, rod-shaped, and flaky structures. A multiple path particle dosimetry (MPPD) model showed significant deposition potential of PM2.5 in terms of deposition fraction, mass rate, and mass flux during smog conditions in all age groups. The highest PM2.5 deposition fraction and mass rate were found for the head region followed by the alveolar region of the human respiratory tract. The highest mass flux was reported for 21-month-old (4.7 × 102 µg/min/m2), followed by 3-month-old (49.2 µg/min/m2) children, whereas it was lowest for 21-year-old adults (6.8 µg/min/m2), indicating babies and children were more vulnerable to PM2.5 pollution than adults during smog. Deposition doses of toxic elements such as Cr, Fe, Zn, Pb, Cu, Mn, and Ni were also found to be higher (up to 1 × 10−7 µg/kg/day) for children than adults.The present work studies a severe smog event that occurred in Delhi (India) in 2017, targeting the characterization of PM For chi × 10−8) . These h × 10−8) . ADD for × 10−8) . In addin adults . Accordin adults .2.5, elemental composition, and organic functional groups were observed during pre-smog conditions which drastically increased during Smog Event-2017 in Delhi. Pre-smog PM2.5 concentration was reported as 124.0 µg/m3 which drastically increased up to 717.2 µg/m3 (~6 times higher) during the studied smog episode. The meteorological conditions, such as lower temperature, low wind speed, and higher RH played a crucial role in PM2.5 accumulation, thereby, reducing the air visibility to a greater extent in Delhi during the smog. During smog conditions, significantly higher concentration of elements such as N, S, Cl, K, Cr, Fe, Zn, Pb, Cu, and Br and the presence of dominant organic functional groups in PM2.5 confirmed biomass-burning signatures along with local emission sources in Delhi. Non-spherical-shaped individual particles dominated, followed by some spherical/nearly spherical-shaped particles with round, flaky structures, solid and irregular-shaped particles, carbon fractals, sticky aggregates, sharp-edged particles, and rod-shaped and rectangular particles during Smog Event-2017.In the present study, lower concentrations of PM2.5 deposition potential (mass rate and mass flux) were reported than that of pre- or post-smog days. The highest deposition fraction and mass rate (µg/min) were reported for the alveolar region of the lungs, followed by the head region in some age groups of babies and children, whereas, for the head region, followed by alveolar region in the remainder of the age groups, including adults. This shows that mostly the baby and child groups were affected by particle deposition in the alveolar region, causing adverse health effects, whereas in adults and other age groups, most of the particle deposition took place in the head region, affecting upper respiratory tract more than the alveolar region. Mass flux (µg/min/m2) in 3-month-old babies was found to be highest, followed by 21-month-old children, which shows babies and children were the most vulnerable to PM2.5 mass flux during the smog period due to lower lung surface area. In addition, ADD (µg/kg/h) for PM2.5-associated elements were found higher for children than adults. ADD for elements with potential toxicity, such as Cr, Fe, Zn, Pb, Cu, Mn, and Ni, were also found higher for children during smog conditions followed by the post-smog period, showing potential adverse effects of these toxic elements to children, even after the smog event was over. On the basis of this study, it can be suggested that there is an urgent need for atmospheric scientists, meteorologists, and policy makers to contemplate more stringent mitigation policies to combat pollution episodes such as smog to minimize the adverse health effects to the sensitive population, such as babies and children, in Delhi.The MPPD model outputs suggested that during Smog Event-2017 much higher rate of PM


Imidacloprid is one of the most commonly used insecticides for managing pests, thus, improving the quality and yield of vegetables. The abuse/misuse of imidacloprid contaminates the environment and threatens human health. To reduce the risk, a colorimetric enzyme-linked immunoassay assay (Co-ELISA) and chemiluminescence enzyme-linked immunoassay assay (Cl-ELISA) were established to detect imidacloprid residues in vegetables. The linear range of Co-ELISA ranged between 1.56 μg/L and 200 μg/L with a limit of detection (LOD) of 1.56 μg/L. The values for Cl-ELISA were 0.19 μg/L to 25 μg/L with an LOD of 0.19 μg/L, which are lower than those of Co-ELISA. Fortifying Chinese cabbage, cucumber, and zucchini with imidacloprid at 10, 50, and 100 μg/L yielded recoveries between 81.7 and 117.6% for Co-ELISA and at 5, 10, and 20 µg/L yielded recoveries range from 69.7 to 120.6% for Cl-ELISA. These results indicate that Cl-ELISA has a high sensitivity and a rapid detection time, saving cost (antigen and antibody concentrations) and serving as a more efficient model for the rapid detection of imidacloprid residue. Imidacloprid (IMI), a neonicotinoid insecticide, is widely used for pest control in agriculture, ensuring the yield and quality of vegetables . AlthougMany instrumental methods, such as liquid chromatography (LC) , liquid To improve the LOD of IC-ELISA, this study reports colorimetric and chemiluminescent IC-ELISAs for detecting IMI . CompariImidacloprid monoclonal antibody and ovalbumin-coated hapten (OVA-Hapten) were provided by Zhejiang University . Peroxidase-conjugated AffiniPure goat antimouse IgG was purchased from Jackson ImmunoResearch Laboratories Inc. . Imidacloprid standard (100 mg/kg) was procured from Beijing Manhage Bio-Technology Co., Ltd. . Acetonitrile (chromatographic grade) was procured from Merck AG . TMB  single-component substrate solution, phosphate buffer saline tablets, and tris (hydroxymethyl) aminomethane were secured from Beijing Solarbio Science & Technology Co., Ltd. . HRP-chemiluminescent reagents A and B were supplied by Beijing Keyuebio Technology Co., Ltd. . Albumin (bovine serum) was obtained from Shanghai Yuanye Biotechnology Co., Ltd. . Tween 20 was acquired from Shanghai Macklin Biochemical Co., Ltd. . A 96-well opaque assay plate was obtained from Shanghai Jing An Biological Technology Co., Ltd. . An immuno clear flat-bottom 96-well plate (item #439454#) was supplied by Thermo Fisher Scientific .max/IC50 was greater than 5 × 105, the minimum amount of antigen–antibody was desirable. The Cl-ELISA detected IMI under various concentrations of BSA , implementing methanol to the final volume  in the IMI dilution, and chemiluminescence reaction times (from 0 to 20 min). The negative well RLU value, positive well RLU value, and N/P value  were used as evaluation criteria in the optimization scheme to select the best physicochemical parameters. For Co-ELISA, the optimal parameters of the Co-ELISA protocol were screened according to the chessboard titrations, including OD = 1 and cost savings [The experimental parameters  were evaluated to improve the sensitivities of the Cl-ELISA. Seven different combinations of antigen–antibody concentrations were screened , and the savings . The optg (4 °C) for 15 min. Afterwards, 3 mL of supernatant was transferred to a 10 mL plastic centrifuge tube. Finally, 100 µL of supernatant was transferred to a 2 mL plastic tube, and 900 µL of PBS buffer solution  was added to ensure a 10% ratio of methanol.Method 1: Chinese cabbage, cucumber, and zucchini  were selected as spiked samples. IMI standard concentrations  were used as fortification levels in homogenized samples (5 g) in 10 mL centrifuge tubes. The mixtures were stirred with 5 mL of methanol with manual shaking for 10 s and then left to extract for 30 min, followed by centrifugation at 8824 × g (4 °C) for 15 min. The entire supernatant was transferred to a 15 mL centrifuge tube, and water was added to adjust the volume to 10 mL. Finally, 200 µL of the supernatant was transferred to a 2 mL plastic tube, and 800 µL of PBS buffer solution  was added to ensure a 10% ratio of methanol.Method 2: Chinese cabbage, cucumber, and zucchini squash  were selected as spiked samples. IMI standard concentrations  were used as spiking levels for homogenized samples (5 g) in 10 mL centrifuge tubes. The mixtures were stirred with 5 mL of methanol with manual shaking for 10 s and then left to extract for 30 min, followed by centrifugation at 8824 × 650nm) was read with a multifunctional microplate reader . All incubations were carried out on a thermomixer  at 37 °C and 400 rpm, followed by washing three times with PBST  using a DEM-3 washing machine .A 100 μL/well OVA-Hapten (10 mg/L) in PBS  was coated on an immuno clear flat-bottom 96-well plate for 2 h. The plates were then washed three times using PBST, followed by adding a BSA (300 μL/well) blocker for 1 h. After washing three times with PBST, IMI (50 μL/well) dissolved in 10% MeOH−PBS  and antibody  diluted with tris  were added and incubated for 2 h on a blocked plate. After washing, 100 μL/well of diluted (1/1000) goat antimouse IgG-HRP was added. The mixtures were incubated for 1 h, followed by the addition of TMB (100 μL/well). After incubation for 10 min, the absorbance (AA 100 μL/well OVA-Hapten (5 mg/L) in PBS  was coated on 96-well opaque assay plates for 2 h. The plates were then washed, followed by the addition and incubation of BSA (300 μL/well) blocker for 1 h. After washing, analyte (50 μL/well) dissolved in 10% MeOH−PBS  and antibody  diluted with tris  were added and incubated for 2 h on a blocked plate. After washing 3 times, 100 μL/well of diluted (1/1000) goat antimouse IgG-HRP was added. The mixtures were incubated for 1 h, followed by the addition of HRP-chemiluminescent reagents A and B (50 μL/well). After incubation for 3.6 min, the luminescence  was read with a multifunctional microplate reader . All incubations were carried out on a thermomixer  at 37 °C and 400 rpm, followed by washing three times with PBST  using a DEM-3 washing machine .The calibration curve for Cl-ELISA was plotted by considering IMI concentrations on the x-axis against percent inhibition on the y-axis. The percent inhibition was calculated using the following Equation (1) ,26. Simimax and RLUx are the response without IMI and the response value when the concentration of the standard solution is x, respectively. RLUmin is the response value of the blank control.In Equation (1), RLUmax and ODx are the response without IMI and the response value when the concentration of the standard solution is x, respectively. ODmin is the response value of the blank control.In Equation (2), OD5, high RLUmax/IC50, significant pesticide inhibition effects, and cost savings [max/IC50 means a larger detection range and the highest sensitivity for chemiluminescence [2 = 0.996, RLUmax 6.24 × 106 was greater than 5.0 × 105, and cost savings. As shown in The parameters of the Cl-ELISA protocol were optimized according to the chessboard assay, including signal values greater than 5.0 × 10 savings ,26. For nescence . As showFor Co-ELISA, we used a chessboard assay to screen for a combination of antigen–antibody concentrations. OD = 1 is used as a target because it can save the amount of antigen and antibody used and meet the method’s sensitivity ,30. A coAs a critical step, protein antibodies or antigens are immobilized on plastic surfaces through nonspecific binding in ELISA. Binding nonspecific proteins may lead to a decline in sensitivity and specificity and can also produce false negative results . To avoiIMI solubility in water is less than that in organic solvents , and add7, cucumber 2.0 × 107, and zucchini 1.7 × 107.The substrate action time is an important factor affecting the sensitivity of Cl-ELISA. A reaction time that is too short or too long might result in a false negative. The chemiluminescence dynamic curves of the 0-well plates were analyzed under three different matrices to control the reaction time. The results a showed Under optimal conditions, Co-ELISA was set at 37 °C with substrate action times of 0, 5, 10, 15, and 20 min b. With imax/IC50 ratio; 10% methanol, 2% BSA, and 3 min substrate action time were used for Cl-ELISA. Under optimal conditions, 50) of Cl-ELISA were 0.19 μg/L and 2.66 μg/L, respectively. Similarly, the optimal conditions for Co-ELISA were as follows: coated antigen (10 mg/L) and antibody (2 mg/L) produced the OD = 1; 10% methanol, 2% BSA, and 10 min substrate action time were used for Co-ELISA. Under optimal conditions, 50) of Cl-ELISA were 1.56 μg/L and 8.15 μg/L, respectively.All these factors were accounted for, and the optimal conditions for Cl-ELISA were as follows: coated antigen (5 mg/L) and antibody (0.5 mg/L) produced the highest RLU2 = 0.9893. In comparison, the Cl-ELISA linearity range (0.19–25 μg/L) with R2 = 0.9940 may be caused by the concentration of antigen and antibody of Co-ELISA being greater than that of Cl-ELISA. The sensitivity of Cl-ELISA (IC50 = 1.56 μg/L) was greater than that of Co-ELISA (IC50 = 8.15 μg/L), which may be due to the sensitivity of the chemiluminescence assay compared to that of the colorimetric assay (50 (1.56 μg/L) of Cl-ELISA was more sensitive than the IC50 (8.15 μg/L) of Co-ELISA, which was caused by the chemiluminescence assay being more sensitive than the colorimetric assay. In addition, chemiluminescent assays were time-saving and efficient compared to colorimetric assays.ic assay . From TaMethod 1 was the sample preparation for Cl-ELISA and Co-ELISA. Co-ELISA showed good recovery, while Cl-ELISA had poor recovery (26.8–237.5%) and RSD (1.4–52.7%) . The rec2 = 0.9746), 1.56 μg/L–200 μg/L in cucumber (R2 = 0.9743), and 1.56 μg/L–200 μg/L in zucchini (R2 = 0.9756). The linearity of Cl-ELISA was observed in the range of 0.39 μg/L–25 μg/L in Chinese cabbage with a good regression coefficient (R2) of 0.9656, 1.56 μg/L–50 μg/L in cucumber (R2 = 0.9577), and 0.39 μg/L–50 μg/L in zucchini (R2 = 0.9873). Chinese cabbage, cucumber, and zucchini were spiked at concentrations of 10, 50, and 100 μg/L for Co-ELISA and concentrations of 5, 10, and 20 μg/L for Cl-ELISA. The recovery was performed under optimized conditions and compared with Cl-ELISA for its performance. According to The inhibition curves for IMI in Chinese cabbage, cucumber, and zucchini matrices using Co-ELISA and Cl-ELSIA are shown in 50 = 2.66 μg/L), benefiting the trace detection level, which was one order of magnitude higher than that of Co-ELISA. Cl-ELISA is a cost-saving alternative to Co-ELSIA in terms of the amount of antigen and antibody used and detection time. This study builds colorimetric and chemiluminescent assays based on ELISA detection of IMI and provides the theoretical basis for selecting an optical detection assay.Herein, Co-ELISA and Cl-ELISA were developed for detecting IMI in Chinese cabbage, cucumber, and zucchini. The recoveries ranged from 81.7 to 117.6% for Co-ELISA and 69.7 to 120.6% for Cl-ELISA. The principle of detection used for both assays can also be applied to detect IMI at a sensitive level in other vegetable samples. Co-ELISA exhibited a wide linear range (1.56–200 μg/L) suitable for IMI testing at the microgram level. Compared with Co-ELISA, Cl-ELISA showed higher sensitivity (IC


Setaria italica (L.) P. Beauv.) is highly valued for nutritional traits, stress tolerance and sustainability in resource-poor dryland agriculture. However, the low productivity of this crop in semi-arid regions of Southern India, is further threatened by climate stress. Landraces are valuable genetic resources, regionally adapted in form of novel alleles that are responsible for cope up the adverse conditions used by local farmers. In recent years, there is an erosion of genetic diversity. We have hypothesized that plant genetic resources collected from the semi-arid climatic zone would serve as a source of novel alleles for the development of climate resilience foxtail millet lines with enhanced yield. Keeping in view, there is an urgent need for conservation of genetic resources. To explore the genetic diversity, to identify superior genotypes and novel alleles, we collected a heterogeneous mixture of foxtail millet landraces from farmer fields. In an extensive multi-year study, we developed twenty genetically fixed foxtail millet landraces by single seed descent method. These landraces characterized along with four released cultivars with agro-morphological, physiological, yield and yield-related traits assessed genetic diversity and population structure. The landraces showed significant diversity in all the studied traits. We identified landraces S3G5, Red, Black and S1C1 that showed outstanding grain yield with earlier flowering, and maturity as compared to released cultivars. Diversity analysis using 67 simple sequence repeat microsatellite and other markers detected 127 alleles including 11 rare alleles, averaging 1.89 alleles per locus, expected heterozygosity of 0.26 and an average polymorphism information content of 0.23, collectively indicating a moderate genetic diversity in the landrace populations. Euclidean Ward’s clustering, based on the molecular markers, principal coordinate analysis and structure analysis concordantly distinguished the genotypes into two to three sub-populations. A significant phenotypic and genotypic diversity observed in the landraces indicates a diverse gene pool that can be utilized for sustainable foxtail millet crop improvement.Foxtail millet ( Millets are among the oldest cultivated cereals that carry great importance in agriculture. Among the minor millets, foxtail millet is an ancient diploid, C4 cereal crop from Eurasia . It is aLandraces are locally adapted domesticated species often used by local farmers, serving as valuable genetic resources for sustainable agriculture. Landraces are generally known to yield less compared to released cultivars but may carry superior stress tolerance . The lowEven though 26,670 accessions of foxtail millet from 26 countries are stored at the Chinese National Gene Bank (CNGB), they are not easily accessible outside of China  and 1,47S. viridis) and foxtail millet , yet this crop is extensively grown in semiarid states of Andhra Pradesh, Karnataka, Tamil Nadu, Uttar Pradesh, Uttarakhand, Maharashtra, Rajasthan, Gujarat and North Eastern States . Therefol millet . Estimatl millet , but stul millet . Simple l millet . Comparel millet . Previoul millet . Functiol millet .In recent years, there is a scientific and social concern on the erosion of genetic diversity in biological systems, in specific, the crop diversity of genes, species and their ecosystems. Agro-biodiversity plays a major role to deal with this challenge. Keeping in view, there is an urgent need for conservation of genetic resources. To our knowledge, less efforts have been made for the systematic collection, agro-morphological, physiological, yield and yield-related traits and molecular markers characterization of foxtail millet landraces from local farmer fields who are not yet adapted the modern released/improved cultivars. With this background we hypothesized the necessity of the systematic characterization of the land races from a semi-arid tropic region would provide a novel genetic source for improvement of foxtail millet. These landraces will have novel allelic variants to cope up with extreme environmental conditions with enhanced yield. The objectives of the study is to develop genetically fixed foxtail millet landraces from a heterogeneous mixture and assess the genetic diversity based on agro-morphological, physiological, yield and yield-related traits and molecular markers. Hence, the present study has been carried to systematically characterize gathered heterogeneous mixture of foxtail millet landraces from local farmer fields in semi-arid region of Rayalaseema in South India. The collections were separated using the Single Seed Descent (SSD) method for eight generations, after which the lines were characterized based on morpho-physiological, yield and yield related traits and molecular markers along with four released cultivars. In the process, we identified superior high yielding landraces and genetic diversity that could be tapped for future breeding and research.https://milletadvisor.com/foxtail-millet-farming/). In each genotype, 10 individuals with uniform traits were tagged and harvested separately and evaluated for the morpho-physiological, yield and yield related traits. For simplicity, the four major locations-Ipperu, Korrapadu, Chittoor and Madikera are designated as location 1 (S1), location 2 (S2), location 3 (S3) and location 4 (S4), respectively. The G in the genotype designations represents Green and the C indicates Color; the latter represents plants that display coloration in at least one of the following regions: culm, stem base, leaf sheath, panicle or bristles. Based on seed coat color, the genotype from Basavanapalli is designated as Black and the one from Punganur is designated as Red . Each genotype was grown in three rows with two replications with intra and inter-row spacing of 5 and 22.5 cm, respectively, and a depth of 2–3 cm , leaf size (LS), panicle lobbing (PB), inflorescence compactness (IC), bristles (BSL), type of bristles (TB), color of bristles (CB), apex sterility (APX), seed color (SC), root angle @1 , root angle @2 , root angle @3 , root angle @4 , plant height , plant dry weight , days to 50% flowering (D50%F), days to maturity (DM), panicle exertion , panicle length , panicle weight , total seed weight , 1000 seed weight , chlorophyll content (SPAD cc), number of stomata (NS), number of epidermal cells (NE), stomatal index (SI), relative water content (RWC), ion leakage (ILN) were used in this study and descriptor traits of foxtail millet for characterization given in DNA was isolated from leaves using the CTAB method described by All the statistical analysis among and between the variants was determined by Analysis of Variance (ANOVA) by using Indostat 9.1 software. The same software was used for broad-sense heritability (H2), genetic advance and Path coefficient analysis. Correlation analysis was performed using R  and PCA In order to develop genetically fixed landraces, heterogeneous mixture of foxtail millet landraces were collected from farmer fields and subjected to SSD method for eight generations to develop pure lines with stabilized traits. Four commercial varieties/released cultivars  were used for trait comparison. A detailed scheme of the landrace development and characterization with released cultivars presented in p < 0.05 and p < 0.01 , as shown in p < 0.05 to 0.001 with traits like RA1, RA2, RA3, but not RA4, PHT, PDW, D50%F, DM and panicle characteristics like PE, PL and PWT. TSDW also showed significant negative associations with the almost all physiological traits i.e., SPADcc, NS, NEC SI and RWC (except ILG)  were calculated for the 19 quantitative traits and were found to be statistically significant (ept ILG) . Since cept ILG) . Since bept ILG) . Based oPCA analysis was performed to identify the sources of variation among the different landraces. PCA reveals the similarities and differences among the genotypes based on contribution of each qualitative and quantitative trait to the total variance. PCA can also aid in the identification of traits that contribute significantly in partitioning the 24 foxtail millet genotypes based on the 28 studied traits. A total of nine qualitative traits were subjected to PCA with four principal components  accounting for 84.93% of the variation . The firLikewise, 19 quantitative traits were also used to perform PCA; PC 1 was contributed by PHT, PDW, D50%F, DM, PE, PL, PWT, TSDW, SPADcc and ILG  which acTwo way cluster analysis is a re-validation of PCA and the twenty-four foxtail millet genotypes were grouped into 2 major clusters I and II based on the Euclidean Ward approach . ClusterBased on morpho physiological, yield and yield-related traits were grouped twenty-four genotypes into two major clusters I and II . ClusterA total of 34 SSR, 21 EST-SSR, and 9 TE based markers dispersed across the nine chromosomes were used to assess the genetic diversity in 24 foxtail millet genotypes except 3 rice SSR markers  and a toA dendrogram was constructed by cluster analysis (Euclidean Ward’s method) to reveal the genetic relationships between landraces and cultivars, based on the 127 genotyping amplicons generated in this study. These 24 foxtail millet genotypes were separable into two major clusters, cluster I (6 genotypes) and cluster II (18 genotypes), with the latter including two prominent subclusters, a II (8 genotypes) and b II (10 genotypes) . The denPCoA was also performed to find genetic relationships among landraces and commercial varieties. The location of individuals was defined by PC 1 and PC 2, which combined to explain less than 10% of the variation; PC 1 and PC 2 explained 5.3% and 4.5% variation, respectively . This imSTRUCTURE 2.3.4 employing Bayesian clustering was used to decipher the population structure genetic relationships among foxtail millet genotypes . PopulatMillets like foxtail millet are minor cereals widely farmed by resource-poor farmers in semi-arid regions of Asia and Africa. Even though foxtail millet is more nutritious, climate-resilient, more compatible with infertile soil than wheat and rice. Its production and research have been neglected, perhaps partly due to its poor yield . TherefoAn understanding of morphogenetic variation in agronomic traits of a crop can assist in the identification of superior lines with desirable traits of high yield, abiotic stress tolerance and biotic resistance . LandracFor qualitative traits, first two components of PCA, accounted for 61.86% of the variation which suggests as a good correlation between these characters . RemarkaNatural genetic diversity provides the raw material for plant breeding and understanding the diversity in available germplasm is a prerequisite to utilizing them for breeding and crop improvement . Higher In this study, 67 SSR, EST-SSR and TE-based markers was employed for assessing the genetic relatedness, diversity and population structure of the landraces. SSR markers, were preferred as they are known to be more informative than other markers . A totalMolecular markers can also be employed to assess the phylogenetic relationship between genotypes. Ward’s clustering analysis, principal coordinate analysis and population structure analyses based on the molecular markers partitioned the landraces into two to three subpopulations or genetic groups, partly according to the genotypes and partly corresponding with their geographic location . In addiThe present work highlights 20 foxtail millet landraces development and evaluated along with four released cultivars. Of the 9 qualitative and 19 quantitative traits, days to flowering, days to maturity and panicle characteristics contribute significantly towards the genetic divergence and these traits can be utilized in breeding programs for crop improvement. Polymorphic marker, identified in the study is useful in marker assisted selection and rare alleles can be used as a marker trait association and diagnostic marker for specific varietal identification. A moderately high phenotypic and genotypic diversity was observed in landraces, which is an indication of a diverse gene pool. Among the 20 developed landraces short-duration high yielding superior lines namely S3G5, Red, Black and S1C1 were identified. These superior lines have been selected for multi-location trials for its high yield and high stability performance. These stable landraces may be recommended for varietal release. This study also highlights the value of landraces and demonstrates that a wider geographical coverage and a more diverse panel of markers could unearth a goldmine of genetic resources for millet crop improvement for low-input sustainable agriculture.