STAT3 and to provide causative genetic variants associated with piglet diarrhea. We firstly confirmed that STAT3 expressed higher in the small intestine than in the spleen, stomach and large intestine of SPF piglets, respectively (P < 0.05). Then, 10 genetic variations in the porcine STAT3 promoter region was identified by direct sequencing. Among them, three mutations SNP1: g.−870 G>A, SNP2: g.−584 A>C and a 6-bp Indel in the promoter region that displayed significant differential transcriptional activities were identified. Association analyses showed that SNP1: g.−870 G>A was significantly associated with piglet diarrhea (P < 0.05) and the GG animals had lower diarrhea score than AA piglets (P < 0.01) in both Min and Landrace population. Further functional analysis revealed that E2F6 repressed the transcriptional efficiency of STAT3 in vitro, by binding the G allele of SNP1. The present study suggested that SNP1: g.−870 G>A was a piglet diarrhea-associated variant that directly affected binding with E2F6, leading to changes in STAT3 transcription which might partially contribute to piglet diarrhea susceptibility or resistance.The present study aimed to search for functional mutations within the promoter of porcine  Pork produced by the modern pig industry accounts for more than one third of global meat consumption . Piglet SLC11A1 gene associated with diverse expression of NRAMP1 in bovine macrophages. It is now widely accepted that the promoter region is critical for identifying variants associated with complex traits . Liu et al. (MRC1 transcription after porcine circovirus type 2 (PCV2) infection. But literature on the identification of causative SNPs associated with piglet diarrhea is limited.Genetic-based selective breeding firstly involves identifying causative genetic variants associated with animal health. Traditional studies focused on the protein-coding regions to identify trait-associated variants which altered the amino acid sequences and eventual protein functions. For example, Holder et al.  reportedx traits , 5. Recex traits  showed tu et al.  showed tSTAT3 mutations cause autosomal dominant hyper-IgE syndrome, a rare disorder of immunity characterized by elevated IgE levels and recurrent bacterial infections in skin and lung  are a family of ubiquitous transcription factors located at the junction of several cytokine-signaling pathways which exercises multiple cellular functions. Like other STAT family members, STAT3 is known to exert widespread effects in immunity and disease through mediating a variety of specific gene transcription responses to cytokines –10. Humaand lung –15. Gene disease , 17, a cSTAT1, STAT2, STAT3, STAT4, and STAT5A, were significantly induced by PEDV infection in IPEC-J2. Subsequently, the bioinformatics analysis revealed that 59.02% of the differentially expressed genes after PEDV infection possess binding sites for STATs in the putative promoter regions. Piglet weaning stress is another common factor for piglet diarrhea. Yi et al. (STAT3 could be regarded as a candidate gene for piglet diarrhea.Porcine epidemic diarrhea virus (PEDV) is closely linked to piglet viral diarrhea , 19. Yani et al.  found wehttp://afs.okstate.edu/breeds/swine/minzhu/index.html/). Michaels et al.  were observed to obtain the diarrhea score. From birth to weaning at day 35, all the piglets' feces were observed at 6:00 am and 2:00 pm every day, then the daily diarrhea score was assigned to every piglets according to the following standard: normal solid feces 0, slight diarrhea with soft and loose feces 1, moderate diarrhea with semi-liquid feces 2 and severe diarrhea with liquid and unformed feces 3 . Piglet For mRNA expression profile analysis, four 28-day SPF piglets provided by Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS)  were slaughtered and the tissues including spleen, stomach, duodenum, jejunum, ileum and cecum were collected.STAT3 (NM_001044580.1), E2F4 (XM_003126933.6), E2F6 (XM_005655271) or genomic sequences retrieved from Ensembl at http://www.ensembl.org/ in Primer pairs used in this study were designed with Primer 5 software using the porcine GAPDH) was used as reference gene and the relative mRNA expression of a specific gene in different tissues or cells was calculated using the 2−ΔΔCT method  in line with the manufacturer's protocol and reverse transcribed into cDNA using PrimerScript RT Master Mix . According to the protocol of SYBR Premix Ex Taq , the qPCR reaction contained 100 ng cDNA, 0.2 μM of each primer as in T method .STAT3 promoter region were amplified using the primer pair STAT3-P in http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3) and JASPAR (http://jaspar.genereg.net/) with a threshold score of 90. The CpG islands were predicted by MethPrimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) with an expected ratio exceeding 0.6 and G+C exceeding 50%.Porcine STAT3 promoter region was amplified using the primer pair pGL3-P in Kpn I and Mlu I restriction sites of the pGL3-Basic luciferase reporter vector , which was named pGL3-STAT3-P. With pGL3-STAT3-P plasmid as the template, three mutants termed SNP1 or g.−870 G>A, SNP2 or g.−584 A>C and SNP3 or g.34 G>C, were generated with mutagenic primers in Kpn I and Mlu I sites of the pGL3-Basic vector and named pGL3-STAT3-SNP1, pGL3-STAT3-SNP2, and pGL3-STAT3-SNP3, respectively. In addition, fragments containing 6-bp inserts or deletions (Indel) were amplified with the primer pair listed in Kpn I and Mlu I restriction sites to construct pGL3-STAT3-In and pGL3-STAT3-Del, respectively. All the positive plasmids were confirmed by DNA sequencing.To verify functional genetic variations predicted by JASPAR and PROMO, the Dual luciferase reporter assays were conducted in IPEC-J2 cells with the plasmids constructed above, the pGL3-Basic and pRL-TK . In brief, IPEC-J2 cells were cultured in DMEM supplemented with 10% FBS . After 18 to 24 h, 0.5 μg of reconstructed luciferase reporter plasmids or pGL3-basic as a negative control and 0.005 μg of internal control (pRL-TK) were transiently transfected into cells using 1.5 μl of X-treme GENE HP DNA Transfection Reagent . After 24 to 48 h, all the cells were lysed and the enzymatic activity of firefly and Renilla were examined using the Dual-Luciferase Reporter Assay System (Promega). Relative luciferase activity was calculated as the ratio of firefly to Renilla. Three replicas were used in cell transfection and the measurement of luciferase.Msp I PCR-fragment length polymorphism (PCR-RFLP) method was used to genotype SNP1 of porcine STAT3. Briefly, a mismatched primer as a G to C mutation at the second base of the 3′ of the forward primer was designed to introduce a recognition site of the restriction enzyme Msp I for genotyping SNP1 as in Msp I  at 37°C for 5 h, separated on an 8% polyacrylamide gel electrophoresis (PAGE).An enforced STAT3 was genotyped by PCR-based single strand conformation polymorphism (PCR-SSCP). In brief, fragments containing SNP2 were amplified by PCR using primer pair in STAT3, an end-point PCR was used. The fragments containing this 6-bp Indel were amplified with the primer in The SNP2 of the porcine E2F4 and E2F6 were determined in IPEC-J2 cells. The complete CDS of porcine E2F4 and E2F6 was first amplified with the primer in EcoR I and Xho I or Kpn I sites of the pCMV-HA vector . The positive plasmids were confirmed by DNA sequencing and named pCMV-HA-E2F4 and pCMV-HA-E2F6, respectively. The expression of E2F4 and E2F6 were validated by Western blot analysis. In brief, IPEC-J2 cells transfected with E2F4 or E2F6 were harvested after lysing with RIPA buffer , boiled with 5 × denaturing loading buffer and resolved in 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), then transferred to 0.22 μm of Immuno-Blot polyvinylidene fluoride (PVDF) membrane . Subsequently, the membrane was blocked with 5% BSA, washed with TBST and incubated in 1:1,000 dilution with a primary antibody for HA-tag  for 12 h at 4°C, and a secondary antibody for 2 h at room temperature. The membrane was washed with TBST and the bands were detected using the Super ECL kit .Overexpression of To validate the role of E2F4 and E2F6 on transcriptional activity of reporter gene carrying SNP1, IPEC-J2 cells were transfected with 0.25 μg of luciferase reporter vector pGL3-STAT3-P or pGL3-STAT3-SNP1, 0.25 μg of corresponding expression plasmid pCMV-HA-E2F4, pCMV-HA-E2F6 or pCMV-HA, and 0.005 μg of pRL-TK. Relative luciferase activity was calculated as described above.2O) was incubated on normal atmospheric temperature for 20 min. For the competition assay, a 50-fold, 100-fold and 200-fold excess of unlabeled probes was added to the reaction mixture described above. Additionally, 1 μg of antibody for HA-tag  was added to the reaction mixture described above and incubated for 30 min at 4°C prior to incubation with the biotin-labeled probes. The DNA-protein complexes were then separated by electrophoresis on a 6.5% PAGE, transferred onto nylon membranes and cross-linked for 1 min with a UV cross-linker and the signal was detected in line with the manufacturer's instructions.The nuclear extracts of the IPEC-J2 cell transfected with pCMV-HA-E2F4 or pCMV-HA-E2F6 were prepared using the nuclear and cytoplasmic protein extraction kit  and quantified by the BCA method . Specific DNA probes for SNP1 A or G alleles were synthesized with or without a 5′-end biotin label  in T-test in GraphPad Prism 5 software . The observed heterozygosity (Ho), expected heterozygosity (He), effective allele numbers (Ne) and chi-square test for Hardy-Weinberg equilibrium of the genetic variation polymorphisms were calculated using Popgene software (version 1.32). The linkage disequilibrium (LD) of three functional loci were calculated by the online SHEsis software . The haplotypes were build and the corresponding frequency were calculated using SAS version 8.0.The STAT3 mRNA expression level among the tissues or cells was analyzed using one-way ANOVA. The luciferase reporter activity between different reconstructions were evaluated using the The effects of genotype or haplotype, breed and the sow on piglet diarrhea trait were analyzed using a general linear model (GLM) procedure of SAS version 8.0 according to the following statistical model:Yijk is the observed traits, μ is the population mean, Gi is the fixed effect of genotype or haplotype, Bj is the effect of breed, Sk is the maternal effect and eijk is the random residual.Where The association analysis between genetic variation polymorphisms (or haplotype) and the piglets' phenotype traits in Min or Landrace population were assessed using a GLM procedure of SAS version 8.0 according to the following statistical model:Yij is the observed traits, μ is the population mean, Gi is the fixed effect of genotype or haplotype, Sjis the maternal effect and eij is the random residual.Where STAT3 in the gastrointestinal tract of SPF piglets. As show in STAT3 was expressed in all the examined tissues including spleen, stomach, duodenum, jejunum, ileum and cecum. Higher mRNA expression of STAT3 existed in the small intestine including the duodenum, jejunum and ileum than in the tissues of the spleen, stomach and large intestine  of Min and Landrace population was 1.97 and 1.77, respectively. The polymorphism information content (PIC) values were 0.37 and 0.34 in Min and Landrace pigs, respectively. The SNP2 was genotyped using the PCR-SSCP in Ho and He of SNP2 were 0.47 and 0.50, respectively, the Ne was 2.00 and the PIC was 0.37. For the 6-bp Indel polymorphism, PCR products were observed as II with 124-bp, ID with 124-bp and 118-bp and DD with 118-bp after the PAGE in Ho and He of this 6-bp Indel loci were 0.34 and 0.36, respectively. And the Ne and PIC values were 1.57 and 0.30, respectively. The chi-square test indicated that all these loci was in Hardy-Weinberg equilibrium.To explore the association between predicted functional variations and piglet phenotype, SNP1, SNP2 and the 6-bp Indel of porcine r2). In Min population, the D' value between SNP1 and SNP2 was 0.83; the r2 was 0.50  (P < 0.01) as shown in P < 0.05) or Hap3:AAD (P < 0.01) animals. Hap2:GAI might be resistant to Landrace piglet diarrhea, whereas Hap1:AAI and/or Hap3:AAD may be the susceptible haplotype. Collectively, the consistent statistic results in Min and Landrace breeds indicated piglets with SNP1-GG genotype were healthier than SNP1-AA carriers.Statistical analysis showed that effects of SNP1 genotype, breed, and the maternity on phenotype trait were all significant ( < 0.05) . However < 0.05) . The folSTAT3 transcriptional regulation, six luciferase reporter vectors including one control named pGL3-STAT3-P which containing SNP1 (−870A), SNP2 (−584A), SNP3 (+34G) and Indel (6-bp deletion), three single SNP reporter vectors including SNP1 (−870G), SNP2 (−584C) and SNP3 (+34C) and two 6-bp indel reporter vectors were constructed, as shown in To verify the effects of predicted functional genetic variations on To further define the role of transcriptional factors on the transcriptional activity of the reporter gene carrying SNP1, the E2F4 or E2F6 expression vector were cotransfected into IPEC-J2 cells with the luciferase reporter construct pGL3-STAT3-P containing SNP1-A or pGL3-STAT3-SNP1 containing SNP1-G. Statistical results showed the overexpression of E2F4 significantly increased the luciferase activity of the SNP1-G by 34%, but not the A allele as in in vitro EMSA was performed to verify the effect of SNP1 on the binding of E2F4 or E2F6 to the cis-acting element of porcine STAT3. The western blot results showed that E2F4-HA or E2F6-HA existed at elevated levels in nuclear extracts derived from IPEC-J2 cells transfected with the respective expression vector in Integrating the above association analysis and the bioinformatics predictions, an STAT3 was altered significantly in IPEC-J2 after infection by PEDV or in the jejunum of piglets during weaning , transmissible gastroenteritis virus (TGV), porcine delta coronavirus (PoDV) and swine acute diarrhea syndrome coronavirus (SADS-CoV), was limited to intestinal villus epithelial cells , 30. Ear weaning , 22, butSTAT3 promoter region were first screened by in silico tools and the subsequent luciferase reporter assay verified that SNP1 and SNP2, which were predicted to alter transcriptional factor binding sites by both JASPAR and PROMO tools showed diverse transcriptional activity. Considering the key role of CEBPB in the immunity  were in non-coding sequences and that several variants were involved in the transcriptional regulatory of a candidate gene –35. Althimmunity , 37 and sistance , SNP3 waAssociation analysis suggested that SNP1 rather than SNP2 or the 6-bp Indel was associated with piglet diarrhea and the frequency of the resistant allele SNP1-G present in the Min pig was higher than in the Landrace. It has been observed that Min and Landrace breeds vary in their capacity to resist diarrhea , so the According to JASPAR, E2F4 and E2F6 can bind the SNP1-G allele but not the A allele and the EMSA results verified this prediction. The overexpression of E2F6 suppressed the luciferase activity of SNP1 G alleles by 68%, while E2F4 increased G alleles by 34%. Both E2F4 and E2F6 belong to the ubiquitous E2F family of transcription factors which control cell proliferation and terminal fate –40. E2F4STAT3 was consistently found in this study. Considering the positive role of STAT3 on PEDV replication (STAT3 and then decreases the replication of PEDV. Human papillomavirus type 16 E7 (HPV E7) and adenoviral E1A protein can interact with E2F6 to inactivate the transcriptional repression activity of E2F6 and subvert the critical cellular defense  and the Natural Science Foundation of Heilongjiang Province (LH2020C015).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.


Market competition is considered to have a significant impact on product diversification, but related issues are rarely discussed on a city scale. To analyze the diversification of export commodities and export market of 270 Chinese cities, this study uses data from 2000 to 2017 based on the commodity concentration index, market concentration index, export similarity index, spatial stratified heterogeneity, and the Almon lag model. The study’s findings are: (1) The diversification of exports in most Chinese cities increased, which was higher in more developed cities in the southeast than in less developed ones in the northwest. With time, the degree of commodity and market diversification in some developed southeast Chinese cities  declined. This indicates the difference in and complexity of the evolution of export development in hundreds of Chinese cities between 2000 and 2017. (2) The export competition between cities became increasingly fierce, which effectively urged most of them to improve their export diversification levels. Facing increasing export competition pressure, 81.11% of the total number of cities will enhance the diversification of export commodities to cope with challenges posed by other cities. But only 56.67% will further expand the export market when the pressure of export competition increases. The biggest contribution of this study is the finding that for most Chinese cities, when export competition from other domestic cities increases, increasing diversification of products becomes a wise choice. However, the influence of competition on the diversification degree of the city’s export market is relatively weak. This study provides not only a new perspective for existing research on urban export trade, but also valuable information for cities to form a more profitable and robust trading system. In this study, we use this method to analyze the extent to which the independent variable X (AESI) explains the spatial differentiation of the dependent variable Y (CCI and MCI). The calculation formula is as follows :q=1−∑hg et al. ). Nh andThe Almon lag model (ALM)Yt is the variable to be studied ; a is the free term; bv is the sum of the values of the pulse response function; X is the explanatory factor ; μt is white noise; t is time; v is the lag period; and s is the number of lag periods. This model describes how the lagged effects of changes in X are distributed over time [There is a lag in the influence of competitive pressure (AESI) on city export diversification (CCI and MCI). This is because the export enterprises in a city experience a change in competitive pressure first and then adjust the structure of export products. This process requires time. In this study, the distributed lag model is used to analyze the influence of AESI on CCI and MCI in the current and lag periods. The finite distributed lag model can be presented as follows :Yt=a+∑ver time .φ is low order polynomial; and m represents lag weights.To reduce the impact of multicollinearity, the Almon lag model (ALM) is used in this research , 61. Thehttps://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp), this study uniformly adjusts the eight-digit commodity codes of every year of the customs data to the HS1996 version with six-digit codes, so that the CCI, MCI, and AESI between the different years can be measured comparatively. Based on data availability, 270 cities in mainland China are selected.The export data for each city are obtained from the customs database of China which is a database that many economists use –65 , Chengdu City , and Shanghai City , increased by more than 100% between 2000 and 2017.As can be seen in Unlike the CCI, the number of cities with MCI below 20 was relatively small. For example, in 2017, only nine cities, including Jinan City  and Jinhua (in Zhejiang Province), had an MCI less than 20. In 2010, although the MCI of many cities was below 20, the distribution was dispersed, and there was no spatial feature of clustering distribution like the CCI.Most cities’ export markets became more diversified, but some did not. Of the 270 cities, 51, including Xi’an City  and Wuhan City , showed a trend of growth in MCI from 2000 to 2017. In other words, their export markets were less diverse. For example, 14.49% and 8.64% of Xi’an City’s total export value in 2000 were exported to the United States and Hong Kong, respectively. In 2017, these figures increased to 17.35% and 26.99%, respectively.As can be seen, the spatial difference of MCI is smaller than that of CCI. This is because a city can choose from thousands of commodities to export (the mix of exports varies greatly from city to city) but will have only a hundred or so markets to choose from. Taking 2017 as an example, 45.56% of cities chose the United States as their largest export market, while 14.81% and 8.52% chose Hong Kong and South Korea, respectively, as their largest export destinations  can be significantly rejected  . Using Stata/MP 16.0, we found that AESI and CCI passed the panel unit root test (PURT)  and paneow 0.01) . Similarrational economic man to some extent. Increasing commodity diversification can prevent trade risks caused by commodity concentration and builds a healthier and stronger export system. China’s export trade has developed rapidly in the more than ten years since China’s accession to the WTO. In the face of the vast world market, although Chinese cities are competitive with each other, the huge scale of the destination market still makes it one of the important development paths for foreign trade enterprises in most cities to increase product types that will help obtain profits and cope with competition [As shown in petition .However, 18.89% of the cities registered a positive effect of AESI on CCI Click here for additional data file.S1 Data(RAR)Click here for additional data file.


Geochelone elegans is poorly understood throughout its range in South Asian countries. The mitochondrial gene sequence and distribution modeling analyses demonstrated lower intraspecific genetic diversity and more highly fragmented habitats in India than in Sri Lanka. The present study recommends the intensive genetic screening of wild and trade/captive individuals before translocation, effective enforcement to prohibit wildlife trafficking, and habitat restoration urgently demanded to conserve this highly-threatened species in the wild.Genetic diversity and habitat suitability of the star tortoise, Geochelone elegans) is a massively traded animal in South Asia. To mitigate this risk, the conservation agencies recommended guidelines to safeguard this charismatic species in nature. We adopted mitochondrial DNA-based investigation and performed species distribution modeling of G. elegans throughout its distribution range in the Indian subcontinent. The genetic analyses revealed weak genetic landscape shape interpolations, low intraspecific distances (0% to 1.5%) with mixed haplotype diversity, and a single molecular operational taxonomic unit (MOTU) in the cytochrome b gene dataset. The star tortoise, G. elegans, and its sister species Geochelone platynota showed a monophyletic clustering in the Bayesian (BA) phylogeny. We also attempt to understand the habitat suitability and quality of G. elegans in its distribution range. Our results suggest that, out of the extant area, only 56,495 km2 (9.90%) is suitable for this species, with regions of highest suitability in Sri Lanka. Comparative habitat quality estimation suggests the patch shape complexity and habitat fragmentation are greater in the western and southern ranges of India, which have been greatly influenced by an increased level of urbanization and agriculture practices. We have also provided a retrospect on the potential threat to G. elegans related to the wildlife trade on the regional and international spectrum. Our results detected multiple trading hubs and junctions overlying within the suitable ranges which need special attention in the vicinity. The present study calls for a proper conservation strategy to combat the fragmented distribution and explicitly recommends intensive genetic screening of founder individuals or isolated adult colonies, implementing scientific breeding, and subsequent wild release to restore the lost genetic diversity of star tortoises.The Indian star tortoise ( Geochelone elegans is a medium-sized reptile species classified under the family Testudinidae (order Testudines). The species was originally described under the genus Testudo and later on transferred to Geochelone with its closest relative, the Burmese star tortoise (Geochelone platynota) [G. elegans confronts habitat loss, anthropogenic threats, and severe menaces due to illegal hunting, with approximately one hundred thousand individuals traded every year [G. elegans under the ‘vulnerable’ category in the IUCN Red List of Threatened Species [The Indian star tortoise, atynota) . This laatynota) . In the ery year ,4. Hence Species .G. elegans from different regions [G. elegans in a restricted manner. The first approach aimed to elucidate the phylogenetic assessment of G. elegans  through nuclear and mitochondrial DNA analyses [G. elegans from Pakistan, India, and Sri Lanka, and a comprehensive systematic screening was recommended across the entire distribution range [Several studies have aimed to comprehend the morphology, ecology, captive care, and breeding of  regions ,7. More analyses . Consecuon range .G. elegans is endemic to the Indian subcontinent and demarcated its distribution range in three broadly isolated geographic areas viz., north-western India and adjacent south-eastern Pakistan, southern and south-eastern India, and Sri Lanka [G. elegans into the wild for sustainable conservation is still poorly known.In response to illegal wildlife trafficking, successful enforcement often involves immediate confiscation of animals and their holistic management to make them born free . To avoiri Lanka . HoweverG. elegans throughout its distribution range. In addition, species distribution modeling is often able to develop substantial knowledge of the habitat and biogeography of many chelonian species under past, current, and future climate conditions [G. elegans through distribution modeling (SDM) to forecast a piece of vital information regarding the prioritized area for developing conservation and management in the Indian subcontinent.The genetic data has been evidenced as a successful tool in conservation genetics and facilitating the rapid return of trafficked turtles back to the wild . In thisnditions ,17,18. Tnditions ,20. OwinG. elegans based on its unique geometric pattern of carapace with light radiating lines on a dark background and plastron with dark radiating lines on a light background [https://arriveguidelines.org) rules [The biological samples were collected from 13 star tortoises, kept locally as pets, from seven different localities. After communicating with the pet keepers, we came to understand that seven individuals were collected from the inside of the known distribution range ; however, the remaining were collected outside of this range . The species was identified as g) rules . No ethi2, 10 pmol of each primer, 0.25 mM of dNTPs, 0.25 U of high-fidelity polymerase, and 20 ng of template DNA in a Veriti Thermal Cycler  with specific thermal profiles. The amplified DNA was purified using a QIAquick Gel Extraction Kit (QIAGEN) following the standard protocol. The cycle sequencing was performed using a BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) with 3.2 pmol of each primer. Subsequently, the PCR products were purified using a BigDye X-terminator kit  and sequenced bi-directionally by a 3730 Genetic Analyzer .The genomic DNA was extracted using a Qiagen DNeasy Blood & Tissue Kit . To compare the genetic diversity from earlier and present distant localities, a partial fragment of the cytochrome b (Cytb) gene was amplified using a published primer pair (mcb398/mcb869) . The PCRhttps://blast.ncbi.nlm.nih.gov, accessed on 25 November 2022) and ORF finder  search tools to confirm the appropriate amino acid array of the vertebrate mitochondrial genome. The spatial patterns of genetic divergences were investigated through a genetic landscape shape interpolation analysis using the Alleles in Space v1.0 program [https://posit.co/). To estimate the molecular operational taxonomic units (MOTUs), the Automatic Barcode Gap Discovery (ABGD) and Poisson Tree Processes (PTP) were applied [www.abi.snv.jussieu.fr/public/abgd/, accessed on 25 November 2022) using the Jukes-Cantor (JC69) model. The maximum-likelihood tree was constructed in RAxML v2.0.1 to perform the PTP analysis  [The bi-directional chromatograms were screened using a SeqScanner version 1.0 , and the noisy parts were trimmed from both ends to avoid the nuclear mitochondrial DNA segments (NUMTs). The consensus sequences were reviewed through the nucleotide BLAST ( program . The Kim program . The rep applied . The ABGer 2022) . The numer 2022) . The TCSer 2022) ,30.G. elegans and 7 sequences of G. platynota were acquired from GenBank and aligned by ClustalX to build a final dataset (458 bp) [Manouria emys (DQ080040) was also acquired from GenBank and used as an outgroup in the phylogenetic inference. The suitable model was confirmed using Mr. MODELTEST v2 with the lowest BIC (Bayesian information criterion) score [https://itol.embl.de/) [A total of 46 mtCytb sequences of (458 bp) . The seqn) score . The Bayn) score . The genmbl.de/) .G. elegans were collected from the TFTSG assessment and associated literature, as well as from the GBIF online data repository  [n = 205) spatially independent occurrence points for G. elegans  [https://lcviewer.vito.be/download, accessed on 25 November 2022). Further, we used the Human Influence Index (HII) to understand human influences on the target species [http://srtm.csi.cgiar.org/srtmdata/, accessed on 25 November 2022). For the final model run, predictors were resampled at 1 km spatial resolution using spatial analysis within ArcGIS 10.6. We used SDMtoolbox v2.4 to check the spatial multicollinearity among the predictors, and the variables with r > 0.8 Pearson’s correlation were dropped from the final modeling environment  . The arier 2022) . The LUL species . The topironment .G. elegans. The variable influence was estimated using the Jackknife test of developed regularized training gain [G. elegans to evaluate the zonal statistics and area calculations.We implemented maximum entropy modeling (MaxEnt) v3.4.4 for the present study, as it is a widely used predictive modeling tool that is known to perform well even if the number of covariates exceeds the number of occurrences for a predictive model ,41. We uing gain . For moding gain . The AUCing gain ,47. We pG. elegans. The study used FRAGSTATS v4.2.1 to calculate the class-level landscape metrics using the PLAND (percentage of landscape), the number of patches (NP), patch density (PD), aggregation (AI), largest patch (LPI), total edge (TE), interspersion and juxtaposition (IJI), edge density (ED), and landscape shape (LSI) as the indices of the level of habitat quality and level of fragmentation indicators in the present area [For the habitat quality assessment, we compared the suitable areas of distinct suitable ranges of ent area ,49,50.G. elegans were contributed to the global GenBank database under the accession numbers (OP684115–OP684127). The generated sequences showed 99–100% similarity with the database sequences of the same species. The overall mean K2P genetic distance was 0.6% in the G. elegans dataset. The genetic landscape analysis also revealed small zones of low genetic differentiation across the distribution range of G. elegans (G. elegans in India and Sri Lanka. The intra-species genetic distance ranged from 0% to 1.5% in the present dataset (n = 17) were observed compared with the database sequences (DQ497299) of G. elegans vouchered in the Ambrose Monell Cryo Collection, American Museum of Natural History. Hence, this database sequence was not incorporated into multiple species delimitation and phylogenetic analyses. The ABGD and PTP analyses revealed a single MOTU of G. elegans in the initial partitioning and maximum-likelihood-supported solutions, respectively  = 0.7195. The TCS network depicted a mixed haplotypic distribution of G. elegans in terms of their collection sites , about 56,495 km2 (9.90%) is suitable for G. elegans . Further, in India, the most suitable and continuous habitat patches were distributed in the far southern portion of Andhra Pradesh and Tamil Nadu, with a total area of 15,699 km2  and high values of LPI (72.67), suggesting habitat continuity and less habitat fragmentation ; zone 2 ; zone-3 ; and zone 4 (comprising the north-western and south-eastern portions of Sri Lanka) E. Our reentation E. Moreoventation E.The Convention on Biological Diversity (CBD) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) made us aware of the significant reduction in the rate of biodiversity loss and extinction risks to earth life, driven by anthropogenic activity, invasive alien species, overexploitation, climate change impacts, and ecological collapse ,52. To rG. elegans and G. platynota were irrespectively generated to examine the evolutionary relationships of Testudines [G. elegans and exhibited the loss of genetic diversity among the different populations due to their enormous trade volume [G. elegans in near future as well as utilized in wildlife forensics. Nevertheless, the present genetic analysis reveals lost genetic diversity across the distribution range of G. elegans with weak intraspecific differences, which is congruent with previous studies. The partial mitochondrial cytochrome b gene sequences were inadequate to distinguish the different populations of G. elegans from distant localities. Due to the unscientific release of confiscated animals in the wild, and the subsequent hybridization between different populations over the years, star tortoises have lost genetic diversity and have experienced increases in the vulnerability of wild populations. Beyond the legal restrictions of global biodiversity research, we suggest that the genetic screening of G. elegans by other genes be required for Pakistan, India, and Sri Lanka, which would provide a better genetic explanation for their phylogeographic footmark across their distribution range [G. elegans.The molecular data have proven to be a successful tool for illuminating various biological questions of Testudines around the globe. The genetic architecture of land tortoises is often used to identify new species , detect studines ,76,77,78e volume ,79,80. Ton range ,82. CurrG. elegans), has reached an alarming level [G. elegans are under significant human pressure, which is an issue that requires special attention for their conservation [G. elegans, it is also important to have species-specific knowledge about their habits and habitats as well as their trade routes. Previous studies have suggested that areas near Ahmedabad, Bengaluru, and Chennai are illegal trading hubs for Indian star tortoises [The massive unlawful trade of reptiles, including the Indian star tortoise (ng level ,84. To png level . Our reservation . Furtherortoises . While iDue to habitat fragmentation, anthropogenic threats, and illegal hunting, star tortoise populations have declined significantly in South Asian countries. Unscientific translocations have led to genetic admixture between different populations and wiped out their phylogeographic differentiation throughout the range. Both mitochondrial Cytb genetic data and MaxEnt species distribution modeling corroborated these facts. We recommend that a comprehensive genetic survey be required to search the isolated wild colonies and subsequent scientific breeding to retrieve their lost genetic diversity. More effective conservation action plans by the Turtle Survival Alliance and other organizations are needed to reduce habitat destruction and precisely identify suitable protected areas for the conservation of this species.


The importance of the coat color in horses has been present since their domestication. The interactive effect of coat color-determinant genes on other traits, such as behavior, makes it a factor of importance in horse selection, as it determines the way the horse perceives and reacts to its environment. Eye temperature at rest (ETR) assessed with infrared thermography was used as a suitable non-invasive tool to assess basal reactivity. The aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography, determine their relationship with the results obtained in show jumping competitions, and to estimate the genetic parameters for ETR to test its suitability for genetic selection. Our results indicated differences in ETR due to six different factors. We also discovered a positive correlation with ranking in show jumping, indicating that less reactive horses were more likely to achieve better rankings. The genetic results indicated that part of the variability present in the basal reactivity due to coat color has an environmental origin. The heritability values indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.p < 0.05) was found with ranking, indicating that less reactive horses were more likely to achieve better rankings. Heritability values ranged from 0.17 to 0.22 and were computed with a model with genetic groups and a model with residual variance heterogeneity. Breeding values were higher with the last genetic model, thus demonstrating the pleiotropic effect of coat color. These results indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.Horses have been valued for their diversity of coat color since prehistoric times. In particular, the pleiotropic effect that coat color genes have on behavior determines the way the horse perceives and reacts to its environment. The primary aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography as eye temperature at rest (ETR), determine their relation with the results obtained by these horses in Show Jumping competitions and to estimate the genetic parameters for this variable to test its suitability for genetic selection. A General Linear Model (GLM) and Duncan post-hoc analysis indicated differences in ETR due to coat color, sex, age, location, and breed-group factors. A Spearman’s rank correlation of 0.11 ( The importance of coat color diversity in horses dates to prehistoric times . The relReactivity and stress induce behavioral and physiological changes on the animal. Behavioral changes will vary on intensity and nature due to the stressor considered ,22. WherRecently, a non-invasive tool to measure physiological changes, eye temperature assessed with infrared thermography (IRT) technology, has demonstrated great potential for assessing physiological stress in horses and also correlates with traditional, invasive physiological measures such as lactate concentration in blood ,33,34. TAs occurs with heart rate or lactate levels, eye temperature assessed with IRT is not only dependent on the aerobic capacity of the horse ,34, but The aims of this study were, first, to test the influence of coat color and different environmental effects  on basal reactivity in CDE before a performance test, using IRT; second, to assess the relation between this measurement and the horses’ results in show jumping competitions; and third, to estimate the genetic parameters and breeding values for this variable, to test its suitability for genetic selection.Measurements were taken from 471 animals  of the Caballo de Deporte Español (CDE) breed, aged from 4 to 18 years old. From this group of animals, a pedigree matrix of 8245 animals was built , eastern Spain  and southern Spain . All equestrian events were held during the same season  in two consecutive years, 2021 and 2022, with temperatures ranging from 10 °C to 28 °C and humidity ranging from 35% to 60%. Events programming was designed to avoid overlapping between competitions. Furthermore, the ranking of these horses in the show jumping competitions held the day after the study was collected and recorded as values ranging from 1 (first and best ranking position) to 72 (last and worst ranking position).The data for this study was obtained from the animals at rest in the boxes provided for them at the equestrian centers of the show jumping competition they were participating in. The measurements were taken one day before the official competition was held. The temperature in the boxes ranged from 14 °C to 24 °C and the relative humidity was between 40% and 50%. However, the owners were asked to arrive at the equestrian center between 12 and 24 h before measurement time, so that the horses could adapt to the new environmental conditions at the center before the measurements were taken. Thus, the horses were at rest at least 36 h before the official show jumping competitions. During their stay, the animals were housed in 3 × 3 mThe physiological changes undergone by the animals at rest in the stall boxes were assessed with eye temperature (ET) measurements. To take the infrared photographs, all the horses were handled by their owners or their regular horse keeper inside their stall box. The camera operator stood 0.5–1 m away from the horse, perpendicular to its left eye, where the images were taken without touching the horse. To get the horses used to the operators and to the camera itself, a short period of habituation was performed 12 h before the test day with all the horses. During this habituation period, the horse could freely sniff the camera and the camera operator. It must be mention that most horses were already familiar with the camera operator from previous and regular sport competitions, as the researches from the study were also the responsibility of the management of the CDE’s Breeding Program. Thus, no long habituation periods were required.Eye temperature images were always taken by the same person, with a portable infrared thermography camera . The emissivity was set to 0.98. In order to calibrate the camera results, environmental temperature and relative humidity were recorded with a digital thermo-hygrometer  every time an eye temperature sample was taken. To determine eye temperature, FLIR Tools 6.0.17046.1002  image analysis software was used, measuring the temperature (°C) within an oval area traced around the caruncle of the eye, including approximately 1 cm around it. The program provided the maximum, minimum, and mean temperature of this oval area, but for the study purposes, only the maximum temperature was used . Three tA previous Shapiro–Wilk test (results not shown) presented a normal distribution of the ETR variable. Hence, parametric statistical analyses were used.To test the influence of horse characteristics on the physiological reactivity of the horses studied, a univariate one-way general linear model (GLM) analysis was performed for the ETR variable measured, considering the following 4 effects: age, with 3 levels: ≤4 years old (10.6%), 5 to 10 years old (13.2%), and ≥11 years old (76.2%); sex, with 2 categories: male (66.0%) and female (34.0%); location, with three categories: central Spain (27.2%), eastern Spain (46.9%), and southern Spain (25.9%); and breed group , the ETR assessed in all animals and the coat color, a Spearman rank analysis was performed.For the statistical analyses, the ‘Statistica for Windows’ software v. 12.  was used.The genetic parameters for the ETR variable were estimated. To do this, pedigree information was gathered from the studbook provided by the National Association of Spanish Sport Horse Breeders (ANCADES). The pedigree was traced back to 4 generations, with a total of 8245 animals .A BLUP genetic evaluation was computed based on a univariate animal model, with ETR as the variable. For the analyses, two genetic models were applied and compared: one model with genetic groups (MGG) and one with heterogeneous residual variance (MHRV). Both were performed using a Bayesian approach via Gibbs sampling using the GIBBSF90+ module of the BLUPF90 software . The GibA the numerator relationship matrix.The equation in matrix notation to solve the mixed model was:More specifically, for the genetic groups model, b included age , sex within coat color (8 levels), location , breed group  for both genetic models and standardized them for an interval of 80–120, with a population average of 100.No specific ethical approval was required for this study, since the infrared thermography measurements were obtained at rest. As regards the temperature samples obtained from the animals, since the eye temperature assessed with infrared thermography was a non-invasive physiological measure collected from a minimum distance of 0.5–1 m away from the animal, it was not necessary to obtain a specific permit for animal experimentation since the animals were not subjected to any pain or physical stress. Likewise, all the owners of the animals had been previously informed of the entire procedure and the type of samples to be taken, and the approval of all the owners of the animals participating in the study had been obtained.In order to study the influence of environmental effects on the basal reactivity of an animal, a univariate one-way GLM variance analysis was performed including age, coat color, sex, location, breed group, sex within coat color, age within coat color, breed group within coat color, and location within coat color as fixed effects, and ETR as the variable analyzed .To study the differences identified by the GLM analysis, a least square means analysis with a Duncan post-hoc test was computed for the statistically significant effects found in p < 0.05) in the GLM analysis. Of these, gray horses had the lowest ETR values, whereas the youngest horses (≤4 years old) had the highest. Conversely, female horses generally demonstrated higher ETR values. Additionally, when the interaction with coat color was analyzed, female chestnut, black, and bay horses and male black horses portrayed higher ETR than male chestnut, bay, and gray horses. With respect to the location where data was collected, horses had the highest ETR values in competitions from central and eastern Spain. Finally, horses from BG4 had the lowest ETR results, with no differences between the rest of the breed groups.The results indicated that first, only six effects from the nine analyzed were statistically significant (p < 0.05) positive correlation of 0.11. When the correlation was assessed according to the horse’s coat color, only statistically significant (p < 0.05) correlations were discovered for black horses, with a value of 0.23. Our results indicated a positive relation with ETR and the ranking position achieved in the sport competition for all the animals in general, although this was more evident in black horses. Thus, it followed that the higher the ETR, the worse the final classification of the animal in the show jumping competitions.Spearman rank correlations were computed between ETR values and rank level for the animals analyzed, obtaining a statistically significant  and the black coat showing the lowest (0.17). Conversely, the model with genetic groups demonstrated an intermediate heritability value of 0.20.Finally, in order to assess the differences between the estimations of the two genetic model, The results indicated that the highest EBVs were achieved for chestnut-coated horses estimated with the MGG model (111.9), followed by black-coated horses estimated with the MHRV model (111.8). In contrast, the lowest EBV estimation was obtained for gray-coated horses with the MGG model. With respect to the number of coincident animals between the genetic models, the black- and chestnut-coated horses indicated the highest percentage of coincidence, with 98.2% and 98.1%, respectively, whereas the gray- and bay-coated horses had the lowest (76.9% and 75.2%), respectively. Generally, EBVs were higher using the MHRV model than the MGG, thus indicating a higher environmental variance of the ETR due to coat color.Behavior, reactivity, and temperament are valuable quantitative traits in all domesticated livestock species. No production system can operate effectively when excessive fear, aggression, or abnormal behavior jeopardizes the safety and well-being of the animals and their human caretakers . This isThe pleiotropic effects of coat color associated with mutations in the same genes have been widely reported in horses in relation to different pathologies, like osteochondrosis  or melanFirst, with respect to GLM and the post-hoc results, Gray-coated horses portrayed the lowest ETR values, compared with chestnut-, bay- or black-coated horses. Bartolomé et al. , found dResults discovered for sex and age were in accordance with previous studies on stress in horses assessed with infrared thermography ,36,38, cAs regards sex–coat color interaction, chestnut and bay female horses showed the highest ETR differences, with male horses having the same coat colors. These results were in line with previous studies which linked bay coat colors with more nervous characters, not only in horses , but alsResults demonstrating that competitions held in southern Spain were linked to horses with the lowest ETR values could be because CDE horses competing in this area tend to be calmer and less reactive to their environment. These results could also bear a relationship to the breed group results, as the PRE breed group also demonstrated the lowest ETR values and, since this breed is mainly bred in southern Spain , most ofCorrelations discovered between ETR and RANK results in the show jumping competition held after the sample collection corroborated previous studies in horses indicating that basal reactivity and tendency to get easily stressed by novel stimuli affects the horse’s sport performance, giving them a lower chance of reaching the top positions in the classification ,30,33,36With respect to the genetic models used for the genetic evaluation, with the MGG, a homogeneous residual variance for any given genotype is assumed ,64 and tEstimates of h2 for residual variance (0.17–0.22) were similar to those reported by Domínguez-Viveros  in behavFurthermore, when EBVs were computed with both genetic models, the generally higher EBVs computed with the MHRV indicated that part of the variability present in the basal reactivity because coat color has an environmental origin. Thus, when the genetic models used for the genetic parameters take that factor into consideration (using MHRVs instead of MGGs), the data fit better and the EBVs are higher. These results are aligned with previous studies comparing different genetic models for animal production ,71,72. OThese coat color differences are of crucial concern in selection plans for a composite breed without morphological/phaneroptic standards like the CDE, since they could be used to offer different breeding recommendations to CDE breeders according to the horse’s coat color. Thus, in further studies, it would be interesting to assess basal reactivity in horses before a sport competition together with other physiological and genomic tools, in order to corroborate the IRT results obtained in this study.Basal reactivity, assessed in CDE sport horses before a show jumping competition using infrared thermography, is influenced by several environmental effects, such as coat color, sex, age, location of the competition and CDE breed group. A correlation with the ranking position in show jumping was also found, with less reactive horses being more likely to achieve better rankings. The genetic parameters estimated for the ETR variable were similar for both genetic models, with higher heritability values obtained with the MHRV. However, the genetic evaluation resulted in higher EBVs for the ETR when coat color was due to heterogeneous residual variance, thus indicating the pleiotropic effect of coat color, both with genetic and environmental effects. Black coat color was the only one demonstrating higher EBVs for the genetic groups model, probably due to a stronger genetic effect behind the IRT measured in this coat color than for the others. These results indicate that this parameter has a suitable genetic basis to be used in the CDE Breeding Program to select for basal reactivity due to coat color.


Arachis hypogaea L.) is an important oil crop but has a narrow genetic diversity. Molecular markers can be used to probe the genetic diversity of various germplasm. In this study, the restriction site associated DNA (RAD) approach was utilized to sequence 31 accessions of Taiwanese peanut germplasm, leading to the identification of a total of 17,610 single nucleotide polymorphisms (SNPs). When we grouped these 31 accessions into two subsets according to origin, we found that the “global” subset (n = 17) was more genetically diverse than the “local” subset (n = 14). Concerning botanical varieties, the var. fastigiata subset had greater genetic diversity than the other two subsets of var. vulgaris and var. hypogaea, suggesting that novel genetic resources should be introduced into breeding programs to enhance genetic diversity. Principal component analysis (PCA) of genotyping data separated the 31 accessions into three clusters largely according to the botanical varieties, consistent with the PCA result for 282 accessions genotyped by 14 kompetitive allele-specific PCR (KASP) markers developed in this study. The SNP markers identified in this work not only revealed the genetic relationship and population structure of current germplasm in Taiwan, but also offer an efficient tool for breeding and further genetic applications.The cultivated peanut ( Arachis hypogaea L.) is an allotetraploid  and an important legume crop worldwide. Humans benefit from peanut seeds as food and source of oil due to their high percentage of proteins and fatty acids1. The annual production of peanuts has increased in the past 20 years to reach 53 million tons in 2020 according to FAOSTAT (http://www.fao.org/faostat). To fulfill the increasing peanut demand under the threat of climate change, breeding new varieties is an effective strategy to improve peanut qualitative and quantitative traits.Originated from South America, the cultivated peanut , United States Department of Agriculture (USDA), and the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (OCRI-CAAS). More than 15,000, 9,000 and 8,000 accessions were collected in ICRISAT, USDA, and OCRI-CAAS2, respectively. On the other hand, understanding the genetic diversity of in-hand germplasm is the prerequisite before launching breeding programs, and the utilization of molecular markers is the predominant strategy to evaluate the genetic diversity of germplasm at present3. Cultivated peanut has its low genetic diversity due to the recent hybridization of its two ancestors and selection in breeding programs7. Even though the narrow genetic diversity of cultivated peanuts has hindered the development of molecular markers, it has been possible to develop and utilize simple sequence repeat (SSR) markers to assess the genetic diversity in cultivated peanut11. In particular, the population structures of 92 accessions in the US Peanut Mini Core Collection and 196 major peanut cultivars in China were revealed by SSR markers13. Although SSR markers were widely used for identifying genetic diversity of peanut populations, these studies had limited population size due to the challenging genotyping process.The conservation of Arachis hypogaea L. and its two diploid ancestors, A. duranensis (AA) and A. ipaensis (BB), have been sequenced14. These high quality genome sequences have paved the way for developing high-throughput single nucleotide polymorphism (SNP) markers e.g. via genotyping-by-sequencing (GBS) that can then facilitate peanut molecular breeding. The 58 K SNP array ‘Axiom_Arachis’, developed by resequencing 41 peanut accessions, was used to identify genetic diversity across 384 Arachis genotypes including USDA Mini Core Collection and wild species16, while 787 accessions from the U.S. Peanut core collection were genotyped by the 14 K ‘Arachis_Axiom2’ SNP array to reveal their genetic diversity17. Compared to SNP arrays, GBS is a more cost-effective technique based on sequencing of the reduced genome associated with restriction sites using NGS19. In peanut research, this technique was applied in SNP development, enabling the construction of genetic maps for quantitative trait locus (QTL) mapping and the analysis of population structure22.Recently, the peanut genome projects made possible by next generation sequencing (NGS) have revolutionized genetic research in cultivated peanuts. So far, the genomes of In Taiwan, peanut breeding programs can be traced back to the late 1950s. To date, most varieties developed locally have been obtained by conventional breeding based on evaluating morphological traits. In such breeding programs, the parental selection mainly relied on the pedigree information or the collection source to infer genetic relationships. Thus, exploiting available molecular tools to characterize the present peanut varieties in Taiwan should allow improved breeding programs in the future. In this study, we performed the restriction site-associated DNA (RAD) approach to sequence 31 genotypes—including current elite varieties developed in Taiwan and important accessions introduced from abroad—to reveal the underlying genetic diversity. Furthermore, 14 kompetitive allele-specific PCR (KASP) markers were designed and then used to genotype 282 other accessions. Overall, this work reveals the genetic structure of peanut germplasm in Taiwan through SNP markers identified by RAD-seq and these markers can be used for a number of applications such as variety identification and breeding programs.31 peanut accessions, maintained by Taiwan Agricultural Research Institute (TARI) and Tainan District Agricultural Research and Extension Station (Tainan DARES), were chosen for RAD-seq construction. These accessions consist of elite cultivars, advanced breeding lines, and “introduced” old accessions acquired in South American countries close to the geographic origin of peanut  to purify these DNA samples which were then quantified and qualified by NanoDrop 2000 spectrophotometer . The purified DNA samples with (1) the 260/280 ratio from 1.8 to 2.0, (2) the 260/230 ratio from 2.0 to 2.4, and (3) the concentration ≥ 25 ng/µl were further checked for the DNA integrity by agarose gel (1.0%) electrophoresis.The DNA extraction of all accessions was based on young leaves collected from seedlings within two weeks using the modified CTAB method which replaces phenol and chloroform with potassium acetate to remove protein and polysaccharides2), 100-pod weight, 100-seed weight, rust resistance and leaf spot resistance. The susceptibility to these two peanut diseases was quantified under natural conditions in the field since these diseases develop spontaneously during the fall, and the disease symptoms were scored from 1 (having no symptoms) to 9 (highly susceptible)24. Depending on the degree of inclination from verticality, plant architecture was scored from 0 (the most upright) to 9 (the most prostrate).24 out of the 31 peanut accessions used in RAD-seq and 282 additional peanut accessions of the germplasm were phenotyped in the fall of 2016 in TARI (coordinates 24° 01′ 47.5″ N 120° 41′ 47.4″ E), and 20 plants of each accession were evaluated for 8 quantitative traits, including days to flowering (between the sowing and flowering date), plant architecture, number of pods, yield  with 100 bp single-end reads in two lanes. The sequencing data have been deposited at National Center for Biotechnology Information (NCBI) under BioProject PRJNA811600. For SNP calling, single-end reads were first debarcoded by Stacks using the program “process_radtags”26. Then, we used Burrows-Wheeler Alignment (BWA) v0.7.17-r1188 “aln” to align the reads of each accession onto the reference genome of cultivated peanut and its diploid ancestors for identifying SNPs used in the genetic diversity analysis and the development of KASP markers, respectively27. When the genome of cultivated peanut was published14, all of the KASP markers used in this study had already been designed using the merged genomes of two diploid ancestors of cultivated peanut6. Thus, identified SNPs based on the genome of cultivated peanut were only used for the in-silico analyses that investigated the genetic diversity of the 31 accessions, but in all cases the SNP calling pipeline was the same. After the alignment was finished, Samtools and BCFtools were utilized for SNP calling and filtering, SNPs were kept with (1) base quality ≥ 20, (2) mapping quality score ≥ 20, and (3) depth ≥ 3. Then, a customized R script was used to create Variant Call Format (VCF) files encompassing qualified SNPs that discriminated the 31 accessions28.After finalizing DNA extraction, purification and quality control of 31 peanut accessions, we used 1.1 µg of each high-quality DNA sample to make two RAD-seq libraries from 16 and 15 accessions, respectively, following the published protocol, and 6, we extracted 1,230 homozygous SNPs with informative alleles in all 31 accessions, and then discarded 783 SNPs having Polymorphic Information Content (PIC) values lower than the average over all SNPs. Finally, 29 out of 477 SNPs with an average PIC value of 0.28 were selected for developing KASP markers. These 29 putative SNPs with 100 bp flanking sequences on both sides were used for designing KASP primers that were then synthesized by LGC genomics . The validation of KASP markers was performed on the 96-well StepOnePlus™ Real-Time PCR System , and each 10-μL reaction consisted of 12.5 ng of DNA, 0.14 μL of KASP assay mix and 5 μL of KASP Master Mix (2X). The PCR protocol was carried out as follows: (1) pre-read stage at 30 °C for 1 min, (2) hold stage at 94 °C for 15 min, (3) PCR stage 1 of 10 touchdown cycles using 94 °C for 20 s and 61 °C (decreasing 0.6 °C per cycle) for 1 min, (4) PCR stage 2 with 26 amplification cycles at 94 °C for 20 s and 55 °C for 1 min, and (5) post-read stage at 30 °C for 1 min. When the PCRs were completed, the fluorescent signals of samples were analyzed by the StepOne™ software for determining genotypes.Among SNPs available for distinguishing 31 peanut accessions based on the genome of the two diploid ancestors30. Principal component analysis (PCA) using phenotypic data was performed by the “PCA” function in the “FactoMineR” package31. In the “poppr” package, the “bitwise.dist” function was used to calculate the genetic distances between the 31 accessions, and these distances were calculated depending on the fraction of loci which differ between germplasm33. The “aboot” function was utilized to construct the dendrograms based on the unweighted pair group method with arithmetic mean (UPGMA) with 1000 bootstraps. In the “adegenet” package, PCA for SNP data from 31 accessions was carried out by the “glPCA” function, and population structure of 282 accessions was addressed by successive K-mean clustering and discriminant analysis of principal components (DAPC) using the “find.clusters” and “dapc” function, respectively35. In addition to the dendrogram plot, created by the “plot.phylo" function in the “ape” package36, all visualization was performed using the “ggplot” function in the “tidyverse” package37.All statistical analyses were carried out in R 3.63. The PIC value and the expected heterozygosity (He) were determined for each SNP markerIn this study, 31 peanut accessions were chosen to conduct RAD-seq, of which 17 accessions were introduced from abroad and 14 accessions developed or collected in Taiwan. This collection has important agronomic traits including yield-related traits, resistances to biotic and abiotic stresses, and valuable characteristics at the genetic diversity level  of the total cultivated peanut genome (2.7 Gb). The estimated sequencing depth in the 31 accessions ranged from 4.26 (HL2) to 15.01 (Red), and the average depth was 9.47. In addition, more than 99.0% of sequenced reads from all samples were properly aligned to the reference genome. Compared to the reference genome, A. hypogaea cv. Tifrunner, there were 1475 to 14,471 SNPs identified from these 31 accessions with an average of 5249 SNPs, and more than 3 quarters of these polymorphisms were homozygous. In addition, the transition/transversion (Ts/Tv) ratio ranged from 0.48 to 1.19 (Table fastigiata var. vulgaris (Spanish type), subsp. fastigiata var. fastigiata  and subsp. hypogaea var. hypogaea (Virginia/Runner types) had a total of 5006, 5119 and 5905 SNPs, i.e., the differences across botanical varieties were very small. Interestingly, the 31 accessions separated well according to the global and local collection, corresponding to 17 genotypes introduced from other countries and 14 genotypes from Taiwan, respectively. The global collection had an average of 6071 SNPs which was higher than the average of 4526 SNPs for the local collection. Moreover, 8 introduced accessions, collected in South America close to the center of origin of cultivated peanut, led to an average of 7139 SNPs, even higher than that of the global collection. This result suggested that the global collection germplasm from various countries had more polymorphisms than the local one containing mainly Taiwanese cultivars.In the RAD-seq approach, the six-cutter enzyme, Then, the next stage of filtration was performed to keep only SNPs differentiating these 31 accessions. As a result, 3474 out of 17,610 SNPs were finally kept for the genetic diversity analysis using a tolerance of 6 missing values (20%) at most for each polymorphism.32. The pairwise comparison of the genetic distance between accessions is listed in Supplementary Table fastigiata var. fastigiata had the largest average He, PIC and genetic distance  and smallest MAF (0.87), to be compared to that of the germplasm from subsp. fastigiata var. vulgaris  or subsp. hypogaea var. hypogaea   was clustered into group I with mainly Valencia type germplasm, while two Valencia type accessions, Red and HL2, were clustered into group II with mostly Spanish type germplasm. Interestingly, TN16 and TN17, two Valencia type cultivars, were clustered into group III, but they were separated from accessions of subsp. hypogaea var. hypogaea with a distance of 0.18.The genetic diversity of the 31 peanut accessions was quantified by a number of measures, including the expected heterozygosity (He), the major allele frequency (MAF), polymorphic information content (PIC), and genetic distance. The genetic distance was based on the bitwise distance, identical to Provesti's distance, growing with the fraction of genetically different loci between 31 accessionsfastigiata var. vulgaris (Spanish type), subsp. fastigiata var. fastigiata  and subsp. hypogaea var. hypogaea (Virginia/Runner types), while the second and third pair were capable of separating TN16 and TN17 from three clusters assigned by the first pair  explained 24.2%, 20.8% and 8.2% of the variance, respectively, totaling 53.2% of the overall genetic distance variance . For PCA based on 29 SNPs, 31 accessions were grouped into three clusters according to their botanical varieties Fig. . The topfastigiata var. vulgaris, subsp. fastigiata var. fastigiata and subsp. hypogaea var. hypogaea). In addition, the KASP markers mostly contributing to the variance of PC1 and PC2 were B04_84804214, B09_6670331, A01_9265671, A02_65802170 and A05_80673567, A01_90916564 (Supplementary Table fastigiata var. vulgaris/var. fastigiata and subsp. hypogaea var. hypogaea). Most Spanish and Valencia type accessions were difficult to distinguish using phenotypic data, and the grouping between Spanish/Valencia and Virginia/Runner accessions was less clear than in the PCA result based on genotyping data  was performed based on increasing number of clusters (K) assigned by successive K-means. In such an approach, the Bayesian information criterion (BIC) was used to assess the model relevance, and the result showed that it was best to go to values of K of at least 3 for clustering the 282 accessions , reasonably informative (0.25–0.5) and only slightly informative  according to their PIC values29. The average PIC of 0.16 from these 31 accessions fell in this last class, while 32% of the identified SNPs corresponded to the reasonably informative class. In studies of three other germplasm collections genotyped by 48 K and 58 K SNP arrays, two collections comprising accessions of three botanical varieties like this study had a mean PIC value of 0.1944, and the third germplasm collection, having only accessions from two botanical varieties, had the mean PIC value of 0.0843, implying that the germplasm panel of 31 accessions chosen in this study preserves a high proportion of overall genetic diversity in spite of a smaller sample size compared to the ones in these three studies.The genetic diversity of these 31 accessions was then investigated using several approaches based on 3474 SNPs. As a whole, this panel had an average PIC, expected He, MAF and genetic distance of 0.16, 0.19, 0.87, 0.17 Table , respectfastigiata var. fastigiata  had larger He (0.18), PIC (0.15) and genetic distance (0.15) on average than subsp. fastigiata var. vulgaris  and subsp. hypogaea var. hypogaea . In the 31 accessions, 11 genotypes were from subsp. fastigiata var. fastigiata including 7 introduced accessions, 3 cultivars and 1 landrace; in particular, 5 of 7 introduced accessions were from countries in South America including Brazil, Uruguay, Peru and Venezuela. The cultivated peanut originated from South America45, it is thus expected that genotypes of subsp. fastigiata var. fastigiata have larger genetic diversity than genotypes of subsp. fastigiata var. vulgaris and subsp. hypogaea var. hypogaea composing most cultivars in Taiwan. They also are expected to have higher diversity than this study’s introduced accessions coming from regions away from the center of origin of cultivated peanut.Focusing on subsets associated with the origin of our germplasm, Table fastigiata var. fastigiata and subsp. fastigiata var. vulgaris, respectively, were separated by a genetic distance of 0.19, and Group III was separated from Group I/II by a genetic distance of 0.21 (Fig. 22.Genetic relationships among the 31 accessions were investigated using the pairwise genetic distance for the construction of the dendrogram and PCA (Supplementary Table This dendrogram result also suggested that the local cultivars in Taiwan might be suffering from low genetic diversity due to the excessive exploitation of narrow genetic resources as breeding material, notably genotypes of Spanish type germplasm Fig. . In the To understand the genetic diversity beyond the 31 accessions, 14 KASP markers developed by RAD-seq data of 31 accessions were utilized to assess the population structure of 282 peanut accessions from the germplasm conservation center in TARI. On the other hand, we also considered phenotypic data as an alternative tool for the assessment of population structure; specifically, 8 agronomic quantitative traits were evaluated in the field trial in the fall of 2016 using 306 accessions including 282 peanut accessions for the KASP marker validation and 24 out of 31 accessions used in RAD-seq.fastigiata and subsp. hypogaea into two groups47. Similar to the results of two previous studies, we found that the subsp. fastigiata accessions in Taiwan had early maturity characteristics. However, our work showed that the subsp. fastigiata accessions have more pods than previously reported, their yield-related characteristics indicated the subsp. fastigiata accessions produce higher yields than subsp. hypogaea accessions in Taiwan (Supplementary Table fastigiata and subsp. hypogaea (Fig. The phenotyping results were consistent with similar field trials conducted in India and Turkey, which separated the subsp. aea Fig. . In the aea Fig. , S10.fastigiata and subsp. hypogaea accessions at K = 2, and then separated var. fastigiata and var. vulgaris from the same subspecies fastigiata at K = 3, which was compatible with previous work22. However, when setting K = 4, the additional group had a mixture of four market types of germplasm belonging to all three botanical varieties, suggesting that exchanges of genetic background among these accessions may have occurred. This result of a fourth cluster not corresponding to subspecies or market types was also reported in other works42, and it may result from phenotyping difficulties49. In both sets, containing respectively 31 and 282 accessions, PCA was used to compare the effectiveness of molecular markers and phenotypic data to cluster samples, and it was demonstrated that PCA based on molecular markers provides more reproducible and satisfactory results than PCA based on phenotypic data (Figs. While we validated these KASP markers using 282 genotypes, the PCA results showed that these genotypes were distinctly separated into 3 groups according to three botanical varieties Fig. . This cota Figs. .Overall, the genetic diversity and relationship among peanut germplasm in Taiwan was revealed by SNPs identified through the RAD-approach. Our analyses suggest that one should broaden genetic diversity by introducing novel germplasm to prevent genetic vulnerability. In addition, the KASP markers successfully developed here could be useful tools for identifying the population structure of other peanut germplasm collections or for conducting further genetic studies related to breeding.Supplementary Figures.Supplementary Tables.


Phrynosoma blainvillii), in the urbanized landscape of the Greater Los Angeles Area in Southern California, USA. Using single nucleotide polymorphism data, we assessed genetic structure among populations occurring at the interface of two abutting evolutionary lineages, and at a fine scale among habitat fragments within the heavily urbanized area. Based on the ecology of P. blainvillii, we predicted which environmental variables influence population structure and gene flow and used gravity models to distinguish among hypotheses to best explain population connectivity. Our results show evidence of admixture between two evolutionary lineages and strong population genetic structure across small habitat fragments. We also show that topography, microclimate, and soil and vegetation types are important predictors of functional connectivity, and that anthropogenic disturbance, including recent fire history and urban development, are key factors impacting contemporary population dynamics. Examining how natural and anthropogenic sources of landscape variation affect contemporary population genetics is critical to understanding how to best manage sensitive species in a rapidly changing landscape.Identifying how natural  and anthropogenic landscape variables influence contemporary functional connectivity in terrestrial organisms can elucidate the genetic consequences of environmental change. We examine population genetic structure and functional connectivity among populations of a declining species, the Blainville's horned lizard ( Urban development and land use change can act as a barrier or a conduit to gene flow, depending on the species and the composition of the urban landscape , a Priority II Species of Special Concern in California due to range wide decline  , which play an important role in seed dispersal and redistribution of soil and nutrients , which displaces native ant species, alters faunal and floral community structure, and reduces habitat quality , and high levels of genetic divergence among isolated habitat fragments due to contemporary barriers to connectivity. However, we expected to find instances of shared ancestral polymorphism despite lack of current connectivity due to the recency of these barriers. Second, we generated hypotheses for the ecological factors that limit or promote functional connectivity among populations of P. blainvillii . We used RAD‐sequencing to develop a SNP data set following the BestRAD library preparation protocol , maximum three mismatches per locus within an individual (M parameter), and three mismatches between loci across different individuals in the global data set (n parameter). Finally, we mapped scaffolds for all individuals against the catalogue using sstacks , Simi Hills (SIM), Franklin Canyon (FRA), Griffith Park (GRI), Santa Susana Mountains (SSM), Piru Creek (PIR), San Gabriel Mountains (SGM), Verdugo Mountains (VER) and El Segundo Dunes (ESD). Topanga Canyon (TOP) was combined with the SMM samples due to proximity. Additionally, we combined the Verdugo Mountains and San Rafael Hills samples due to their close geographic proximity and the small sample size from the San Rafael Hills Figure .plink version 1.90. Principal components were color‐coded by geographic area and visualized with 95% confidence intervals in ggplot2 in R version 3.6.2 , less complex broad‐scale topography, and minimal urban development. The same variables used to represent native chaparral vegetation and heat for thermoregulation for the at‐site conditions were calculated for the corresponding between‐site conditions. We estimated the time since the last fire by subtracting the year of the most recent burn from the current year. We used surface relief ratio , so native resolution (30‐m) values for both at‐ and between‐site variables were used in the models  were retained. The lanes had average retained reads of 88.1% and 88.7%, indicating good alignment. The average final sequencing coverage  was 15x. Before applying allele filtering, we removed 10 individuals with high missing data (>95%). The final filtered population genetic data set contained 9448 SNPs for 134 individuals Table .3.2PC1 explained 34.9% in our PCA, with samples from El Segundo Dunes clustering apart from the remaining individuals Figure A. FranklFST) ranged from 0.015 to 0.433 (average = 0.183) with all being significantly greater than zero  support two and seven clusters, respectively, but we detected informative patterns at K = 2–7 , we found population structuring at multiple scales. 7 Figure . We repo3.3Both the 50‐ and 10‐km pruned networks achieved the same results, but the 10‐km pruned network improved model fit without changing model rankings . Prior to the 1900s, sand dune habitat was abundant along much of the Los Angeles coastline from the Palos Verdes Peninsula in the south to Ballona Creek, which is immediately north of the ESD. Up until the 1860s, Ballona Creek was frequently the outflow for the Los Angeles River watershed . There are no historical records upriver from these localities, suggesting a distributional gap and lack of recent gene flow, but increased connectivity prior to urbanization may have facilitated limited gene flow between these coastal populations and those along the upper margins of the Los Angeles Basin at Griffith Park, the Verdugo Mountains, and the San Rafael Hills. Further north, the contact zone between the Northern and Southern California lineages through the San Gabriel Mountains remains unclear. Increased sampling in the eastern and central San Gabriel Mountains is needed to understand contact zone dynamics in this area  were significant at‐site variables in the final gravity model, which represent conditions that facilitate P. blainvillii reproductive productivity  facilitate movement among sites .Despite the naturally patchy distribution of y Figure . This paContrary to the weak genetic structure in the western portion of the Santa Monica Mountains, a stronger signal of genetic structure among Topanga Canyon and surrounding localities to the east and west may signify the early stages of genetic divergence due to barriers on the landscape Figure . Althoug4.3P. blainvillii. Strong population structure among urban‐isolated habitat fragments and reduced connectivity across impervious surfaces suggests that P. blainvillii is susceptible to urban fragmentation effects  while ameliorating loss of genetic diversity due to drift , such as gravity models, may be particularly important for estimating functional connectivity for species with ecological specialization and/or patchy distributions, where the production of migrants is a critical component of gene flow (e.g., Murphy, Dezzani, et al., This research applies landscape genetics to enhance our understanding of the effects of environmental factors on contemporary functional connectivity of Sarah M. Wenner, Kathleen S. Delaney, Gregory B. Pauly, Jonathan Q. Richmond, Robert N. Fisher, and Jeanne M. Robertson conceived and designed the study. The National Park Service and the U.S. Geological Survey provided samples, and Sarah M. Wenner and Gregory B. Pauly conducted additional field sampling in 2018 and 2019. Sarah M. Wenner performed the molecular laboratory work. Jeanne M. Robertson provided laboratory and computational resources for generating genetic data. Sarah M. Wenner and Melanie A. Murphy conducted data analysis. Sarah M. Wenner drafted the manuscript and all authors critically revised and approved the final version.The authors have no conflicts of interest to declare.Table S1Figure S1Figure S2Figure S3Click here for additional data file.


Vicia faba L. (faba bean) is one of the most promising pulse crops due to its nutritional value and high nitrogen fixation capacity. The aim of the present study was to compare the genetic diversity and the seed metabolite profiles of five genetic materials of faba bean. Specifically, three newly developed advanced lines  and two commercial cultivars (POLIKARPI and TANAGRA), were evaluated for this purpose. Genetic diversity among populations was assessed by SCoT molecular markers. Through UPGMA dendrogram, genetic distances between populations were estimated. Untargeted metabolomics analysis of the seeds was performed employing GC/EI/MS. The cultivar POLYKARPI exhibited the highest polymorphism. All varieties showed a higher within-cultivars and advanced lines variability than between. POLYKARPI and KK14 had the lowest genetic distances, while KK18 and TANAGRA presented the highest ones. The advanced line KK18 displayed the best nutritional profile, the highest concentration of desirable metabolites , the lowest concentration of anti-nutritional factors  and the lowest concentration of saturated fatty acids . According to the results of the present study, KK18 line is a very promising material for further exploration and utilization in breeding programs. Genetic diversity among species members is a crucial pre-requisite for successful plant breeding. Phenotypic traits and DNA markers, which represent various aspects of genome polymorphism and are occasionally used independently in parental selection, are the two main data sources. However, integration of phenotypic data with genotypic information is highly advised when neutral DNA markers are used for this purpose ,2. This V. faba), also referred to as broad bean, horse bean and field bean, is one of the oldest cultivated plants whose cultivation dates back to 3000 B.C. in Ancient Egypt   and N-Trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The samples were transferred to glass microinserters  into glass autosampler vials (2 mL) for analyses. Additionally, selected analytical standards of primary plant metabolites were used for the absolute annotation of metabolite features.rotocols ,104. Bri process ,104 usin−1 rate, stable for 5 min. Full scan mass spectra were acquired over the range 50–800 Da (4 scans s−1).Metabolomics analysis was performed using an Agilent 6890 analyzer  applying previously described settings). Samples (1 μL) were injected on column at a 5:1 split ratio. The temperature of the oven was 70 °C, stable for 5 min, increased to 295 °C at a 5 °C minThe obtained GC/EI/MS total ion chromatograms (TIC) were deconvoluted using the software AMDIS v.2.66 and the metabolite libraries of the National Institute of Standards and Technology library, NIST ‘08 . Their processing for metabolomics analysis was performed using the MS DIAL v.4.80 software . The detThe quality traits data were checked for regularity with a Shapiro–Wilk test, homogeneity with Levene and Barlett tests and were found to meet both criteria, as well as for their independence which is ensured through the Random Complete Group design that we implemented in the experiment. This was followed by ANOVA test and comparison of the mean values of the characteristics of the varieties with the method of Minimum Significant Difference (LSD). The statistical processing of the data was performed through the JMP program.Two commercial cultivars and three advanced breeding lines were evaluated, comparing their genetic diversity and their main metabolites.SCoT markers are useful tools for the detection of genetic diversity among faba bean germplasm and help in studying genetic relationships. Genetic diversity is essential for the creation of new varieties with interesting characteristics. The commercial cultivars POLIKARPI and TANAGRA exhibited higher genetic diversity compared to advanced lines. Genetic and metabolomic analyses give similar results regarding the relationships between the studied genetic material. TANAGRA and KK10 are the genetic material with high similarities both at genetic and metabolic levels. Moreover, POLIKARPI and KK18 are genetically and metabolically the most distinct from the others.On the other hand, line KK18 showed the highest accumulation of desirable nutritional compounds such as lactic-acid and trehalose and the lowest for anti-nutritional factors . Additionally, KK18 is characterized by high seed yield under low rainfall environments.This study showed that KK18 is the most promising line for further exploitation due to the production of highly nutritional quality seeds and high seed yield under low rainfall environments . On the


The data are available in the YHRD under accession numbers YA006010 and YA006009. Genetic analysis (AMOVA and MDS) did not show significant differences between the two groups  in terms of Y-chromosome diversity. Both groups are characterized by haplogroup C2a1a2 as a founder effect, which dominated two of the three tribes: Alimuly (67%), Baiuly (74.6%), and Zhetiru (25.8%). At the same time, the phylogenetic network for each tribe found its own clusters within C2a1a2. Western Kazakhs and Karakalpakstan Kazakhs present high values of unique haplotypes (84.44% and 96.61%), discrimination capacity (90.37% and 98.30%), and haplotype diversity (0.9991 and 0.9994). A set of 27 Y-STR loci distinguishes closely related individuals within the Western Kazakh tribes quite well. It is suitable for forensic application, and is also optimal for population genetics studies.Data on the genetic polymorphism of 27 Y-STR in Kazakhs of the Junior Zhuz has been presented and analyzed in relation to forensic features. A total of 464 representatives of the Western Kazakh tribes of Kazakhstan (Western Kazakhs,  There are no data for Kyrgyzstan, Tajikistan, or Turkmenistan. Uzbekistan has data (n = 176) for a minimal set of 12 Y-STR loci  , in which there are only two haplotypes from Western Kazakhstan. At the same time, North Kazakhstan, Kazakhstan [Kazakh] are genetically located farther  than Afghanistan [Hazara] , Xinjiang, China [Mongolian] , or Urumqi, China [Uighur] . Such results require a more detailed study of intrapopulation differences among Kazakhs.The pairwise genetic distance (n = 464) but between the three Kazakh tribes: Alimuly (n = 103), Baiuly (n = 272), and Zhetiru (n = 89). Results for 27 Y-STR haplotype distributions are presented in Genetic polymorphism of 27 Y-STR haplotypes were also reanalyzed within the same sample , while copy number variations in the DYS19 locus are more typical for Baiuly (80%). The phylogenetic network covers individuals from the Western Kazakhs group and the Karakalpakstan Kazakhs group. Geographical differentiation in Kazakhstan and Karakalpakstan (Uzbekistan) is not expressed A. Three In conclusion, this study presents data on the genetic polymorphism of the Y-chromosome of the western Kazakh tribes of Kazakhstan and Karakalpakstan, for 27 STR loci. The results indicate a common genetic origin of Western Kazakhs and Karakalpakstan Kazakhs, as well as their genetic distinction from Northern Kazakhs. The genetic links between the Alimuly, Baiuly, and Zhetiru tribes are expressed in their high differentiation along the paternal line. This indicates the relevance of research on the genetic polymorphism of the Y-chromosome in connection with the tribal structure of the Kazakh population. A wide set of 27 Y-STR loci distinguishes closely related male haplotypes quite well. New data added to YHRD (YA006010 and YA006009) will add diversity to the Eurasian-Altaic metapopulation. The new data will definitely help with forensic DNA applications and population genetic studies.