Stat3 specifically deleted from the osteoblasts showed both sexes exhibited decreased bone mineral density (BMD) and strength. Using the Cre-Lox system with Cathepsin K promotor driving Cre to target the deletion of the Stat3 gene in mature osteoclasts (STAT3-cKO mice), we observed that 8-week old STAT3-cKO female femurs exhibited significantly lower BMD and bone mineral content (BMC) compared to littermate control (CN) females. There were no differences in BMD and BMC observed between male knock-out and male CN femurs. However, micro-computed tomography (μCT) analysis showed that both male and female STAT3-cKO mice had significant decreases in bone volume/tissue volume (BV/TV). Bone histomorphometry analysis of the distal femur, further revealed a decrease in bone formation rate and mineralizing surface/bone surface (MS/BS) with a significant decrease in osteoclast surface in female, but not male, STAT3-cKO mice. Profiling gene expression in an osteoclastic cell line with a knockdown of STAT3 showed an upregulation of a number of genes that are directly regulated by estrogen receptors. These data collectively suggest that regulation of STAT3 differs in male and female osteoclasts and that inactivation of STAT3 in osteoclasts affects bone turnover more in females than males, demonstrating the complicated nature of STAT3 signaling pathways in osteoclastogenesis. Drugs targeting the STAT3 pathway may be used for treatment of diseases such as Job’s Syndrome and osteoporosis.Signal Transducer and Activator of Transcription 3 (STAT3) has recently been shown to be involved in bone development and has been implicated in bone diseases, such as Job’s Syndrome. Bone growth and changes have been known for many years to differ between sexes with male bones tending to have higher bone mass than female bones and older females tending to lose bone mass at faster rates than older males. Previous studies using conditional knock mice with  Previous studies have shown that bones develop differently between the sexes. From birth to death, bone size and mass are observed to be greater in males than in females , 2. StudOsteoblast regulation of osteoclast function may be further modified by members of the IL-6 family . The IL-We and others , 21 haveWhile the functions of STAT3 in osteoblasts and osteocytes are emerging, the roles of STAT3 in osteoclasts have not been clearly defined. A recent study in which STAT3 in osteoclasts was deleted by use of the Cathepsin K promoter driving Cre recombinase demonstrated that mice exhibited an increase in BMD with a decrease in bone resorption . HoweverClinically, STAT3 has been implicated in the autosomal-dominant form of Hyperimmunoglobulin E Syndrome (HIES), also known as Job’s syndrome , 26. HIEin vitro. We determined the function of STAT3 in osteoclasts by using the Cre-lox system with the Capthepsin K promoter driving Cre recombinase as a regulator of osteoclast function in bone development. Interestingly, STAT3 regulation of bone resorption appears to be differently regulated between sexes. Furthermore, microarray analysis of RAW264.7 cells with STAT3 miRNA has identified a number of differentially expressed genes that are potential targets of STAT3 regulation, including some known to be regulated by female hormones.The purpose of this study was to examine the role of STAT3 in mature osteoclasts during skeletal development and to further examine the regulation of STAT3 in osteoclast differentiation Stat3 gene . Conditional knock-out (STAT3-cKO) mice on the C57BL/6 background carried the Cre recombinase transgene and floxed stat3 region, while the littermate control (CN) C57BL/6 mice carried only the Cre recombinase transgene. Calcein (30mg/kg) and alizarin (50mg/kg) were injected intraperitoneally 7 days and 2 days before euthanasia, respectively. The right femurs from the experimental mice were fixed in 10% formalin overnight and then were transferred into 70% ethanol for bone histomorphometry. The left femurs from the experimental mice were frozen in saline-soaked gauze at -20°C for bone mineral measurement, micro-CT scanning, and biomechanical testing.Indiana University Purdue University Indianapolis School of Science IACUC committee has approved the study protocol. Mice were anesthetized or euthanized with inhalation of isoflurane or carbon dioxide. Eight weeks old C57BL/6 female and male osteoclast-specific STAT3 knock-out mice were created using the Cre-loxP system. Mice expressing Cre recombinase driven by a Cathepsin K (CTSK) promoter  were bre2) and BMC (g).Left femurs from 8 week old STAT3-cKO and CN C57BL/6 mice underwent ex vivo dual-energy X-ray absorptiometry (DEXA) scanning  to calculate BMD . Scans were performed using a 0.7-degree angle increment, two frames averaged, through a 0.5 mmAL filter (V = 50 kV). The raw images were reconstructed using NRecon software. All data were analyzed using the CTan program and 3D models were constructed using the CTvol program. All volumes of interest were analyzed at a section of the proximal femur beginning 0.5 mm proximally to the growth plate and continuing 1.0 mm proximally.max and Imin).For the femoral cortical bone analysis, a 1-mm region of interest was selected at the midshaft of the bone. The cortical shaft was analyzed  to determine the total cross-sectional area (Tt.Ar), cortical area (Ct.Ar), and marrow area (Ma.Ar), cortical thickness (Ct.Th), cortical area fraction (Ct.Ar/Tt.Ar) and principal moments of inertia (I3/μm2 per year) (bone surface referent), percentage of osteoclast surface  and osteoclast number per tissue area .Right femurs from 8 week old STAT3-cKO and CN C57BL/6 mice were dehydrated in graded alcohol and methyl methacrylate, then embedded in methyl methacrylate. From the distal femurs, 5-μm-thick sections were cut using a microtome . For each set of bone sections from the femurs, two unstained sections were mounted on microscope slides, while other sections were stained with tartrate-resistant acid phosphatase (TRAP) to identify active osteoclasts. Plastic embedded sections of the right femur were analyzed with an Olympus BX53 light/fluorescent microscope and Olympus DP72 camera interfaced with Osteomeasure™ software version 1.01 . Images were analyzed at 200x magnification. Parameters measured were as described by Parfitt et al. . Mice laThe bone sections from 8 week old STAT3-cKO and CN C57BL/6 mice were deplastified in acetone and rehydrated using ethanols. Slides containing the sections were then incubated in a sodium acetate buffer pH 5.0 containing 0.2 M sodium acetate and 50mM sodium tartrate dibasic dihydrate for 20 minutes. Slides were then transferred to a pH 5.0 sodium acetate buffer containing 0.2 M sodium acetate, 50mM sodium tartrate dibasic dihydrate, 0.5mg/mL napthol AS-MX phosphate, and 1.1 mg/mL fast red TR salt for 1 hour at 37°C. Sections were then counterstained with hematoxylin .Bone mechanical integrity was calculated by 3-point bending using femurs from 8 week old STAT3-cKO and CN C57BL/6 mice . Previou6 cells/well. Cells were incubated in alpha-MEM containing 10% FBS and 20 ng/ml M-CSF for 2 days. The media was removed and replaced with fresh media containing 20 ng/ml M-CSF and 80 ng/ml RANKL until mature osteoclasts were observed. Mature osteoclasts were detached by trypsinization, washed once, re-plated onto Corning Osteo Assay Surface plates and cultured for an additional 48 hours in media containing 20 ng/ml M-CSF and 80 ng/ml RANKL. The wells were washed, incubated in 6% NaCl for 5 min, and sonicated for 20 s to remove cells. Plates were stained with a solution containing 1% toluidine blue and 1% sodium borate for one minute, washed with water and air-dried. Resorbed surface area (resorption area) was quantified using the ImagePro7.0 on a Leica DMI4000 with a 10X objective and normalized by the total measurement area. The percentage of resorption area was further normalized by TRAP+ osteoclast number.Osteoclast-like cells were generated from murine bone marrow monocytes (BMMs) from two groups of 6 week old, male and female, STAT3-cKO and CN C57BL/6 mice. Experiments using BMMs from each group of mice were completed in triplicate. The resorption activity of cultured osteoclasts was evaluated using a standard resorption assay. Murine BMMs were plated into 6-well culture plates at 2x105) were seeded in six-well plate in 5 mL of antibiotic-free 10% FCS MEM-alpha medium. The plates were incubated 24 hours before the addition of siRNA Stat3 Silencer (ThermoFisher Scientific). Twenty-five nM of Select siRNA was used to knock down Stat3 gene expression and New NC2 Custom Select siRNA was used as a negative control. Lipofectamine RNAiMax was used to transfect the cells per the manufacturer’s protocol. After transfection, the plates were incubated for 6 or 24 hours and the medium was changed to 5 mL of MEM-alpha media with 10% FBS and 1% penicillin and streptomycin antibiotic. After the incubation period, the cells were stimulated with 50 ng/ml of RANKL (R & D Systems) or PBS control for 24 hours prior to harvesting the cells and were processed for either Western blot or microarray analysis.RAW 264.7 cells (1 x 10o freezer. Antibodies used for this study included1o Ab specific to p-STAT3 (Ser727), NFATc1, c-Fos, and Cathepsin K; all were purchased from Santa Cruz Biotechnology ; 1o Ab specific to ß-actin Ab was obtained from Sigma-Aldrich, .Radioimmunoprecipitation assay (RIPA) lysis buffer  was prepared by mixing RIPA with protease inhibitor and phosphatase inhibitor (1:100). The medium was aspirated from the wells and the cells were washed 3x with 5 mL PBS. A volume of 300 μl RIPA buffer was added to each well. The cells were scraped off the wells and transferred to 1.5 mL tubes incubated on ice. The cells were sonicated on ice and centrifuged at 4°C for 10 minutes at 13,200 rpm. The supernatant was transferred to separate tubes and placed on ice for PAGE or stored in the -80TM Mouse 2.0ST arrays (ThermoFisher Scientific). Both RANKL groups were compared and genes with significant changes (p < 0.05) and with a ≥ 1.2 fold change were selected for further analysis using real-time PCR (qPCR).RAW264.7 cells were cultured as described above. The cells were divided into 4 groups: 1) RAW264.7 cells with non-specific siRNA and not stimulated with RANKL, 2) RAW264.7 cells with Stat3 siRNA and not stimulated with RANKL 3) RAW264.7 cells with non-specific siRNA stimulated with 50 ng/ml of RANKL, and 4) RAW264.7 cells with Stat3 siRNA stimulated with 50 ng/ml of RANKL. RAW264.7 cells with and without siRNA STAT3 were either untreated or treated with RANKL for 24 hours in triplicate samples before RNA extraction, membrane-hybridization, and microarray were performed. Triplicate RNA samples were extracted for 12 GeneChipTotal RNA was prepared using RLT Plus buffer  plus 2-mercaptoethanol per the manufacturer’s protocol. Briefly, cells were scraped thoroughly and transferred to a Qiashredder tube and centrifuged to remove supernatant. RNeasy spin columns were used to purify total RNA . The cDNA was prepared using 1000 ng of each RNA sample, random hexamers and MultiScribe Reverse Transcriptase (ThermoFisher Scientific). Real-time PCR was performed in a 20 μl reaction using cDNA’s, 2x Master Mix  in a PCR7500 Fast system (ThermoFisher Scientific) and custom-designed primers . Data for animal experiments were tested using two-way ANOVA with STAT3 cKO and sex as main effects followed by a Student’s t-test post hoc test for comparing the littermate control and STAT3-cKO parameters as two groups for each sex. Microarray and RT-qPCR data were analyzed between the groups with and without Stat3 siRNA under either vehicle or RANKL treatment by a Student’s t-test. Statistical significance was assumed for p < 0.05.To examine the function of the STAT3 specifically in mature osteoclasts, we analyzed femurs isolated from 8 week old STAT3 -cKO female and male mice and their littermate controls by measuring BMD, BMC and bone geometry . No signFemurs from 8 week old male and female STAT3-cKO C57BL/6 mice and their littermate CN were analyzed by μCT. Histochemical staining for the presence of TRAP+ cells (osteoclasts) at the distal femurs showed that N.Oc/B.Pm (osteoclast number/bone perimeter) was not significantly different among 8 week old STAT3-cKO female and male mice and littermate controls . But, osTo determine whether the STAT deficiency in osteoclasts has any effects on bone formation, we used dynamic histomorphometry . There wTo test whether the changes in bone parameters analyzed could affect bone strength, left femurs from 8 week old mice were tested to failure by 3 point bending. The data of the structural mechanical properties and femoral midshaft geometry are shown in in vitro. To accomplish this, the whole bone marrow was isolated, and enriched for bone marrow monocytes (BMMs). BMMs from six week old STAT3-cKO male and female mice and littermate controls were then stimulated with M-CSF and RANKL . Of the 296 genes whose fold change is greater than 1.2 in the group with STAT3 siRNA and stimulated by RANKL versus the group with non-specific siRNA and not stimulated by RANKL, 192 genes were significantly upregulated (red) and 104 were significantly downregulated (blue) (p < 0.05). In groups that were stimulated with RANKL with and without siRNA STAT3, 41 differentially expressed genes were identified with a p < 0.05 that had at least a 1.2 fold change  mice. This observation is opposite to our current finding. Of note, the Yang group also report a decrease in osteoclast number in bones with the loss of STAT3 in osteoclasts. In our study, osteoclast number was not different between STAT3-cKO and control mice, but the resorption surface was greater in female STAT3-cKO mice than female control mice. This might be due to the fact that Ctsk-Cre mice used in the Yang et al study were generated by knocking in the Cre recombinase into the Ctsk gene locus [In a recent study, also with conditional knock-out of STAT3 in osteoclasts using a cathepsin promoter driving Cre recombinase, Yang et al  showed tne locus , whereasne locus . The YanIn the Yang et al study , the dowATP6v0d2 gene which encodes the ATPase H+ transporting V0 subunit d 2 (ATP6v0d2). ATP6v0d2 function includes acidification of intracellular organelles [hyal1 gene which encodes for lysosomal hyaluronidase [arly estrogen-induced gene 1 (ee1g1), which encodes a novel RANK signaling component important for osteoclastogenesis. The EE1G1 protein was shown to be recruited to the RANK receptor upon it stimulation, where it facilitates PLCγ2 phosphorylation of NFATc1 induction [Our microarray analysis also showed that many genes are regulated by STAT3 in osteoclasts. Indeed, STAT3 may regulate osteoclast resorption function through the lysosome pathway identified by the expression of the gene coding for cathepsin K, Serpinb6b (inhibitor of cathepsin G ) and theganelles  and miceganelles . Additioronidase  was alsoronidase . We alsonduction . CollectAnother intriguing pathway that STAT3 may regulate in osteoclasts is the modulation of the oxidative stress within cells. In this context, we observed an upregulation of the gene encoding for the osteoclast-associated receptor (OSCAR) when STAT3 was knocked down. OSCAR is required for osteoclast differentiation but it is involved NFATc1 regulation of oxidative stress-mediated atherogenesis . Likewislfi202 gene has been shown to be stimulated by IL6 and is directly regulated by STAT3 [ifn202 gene is differentially regulated by sex hormones with female hormone 17beta-estradiol significantly increasing its expression while the male hormone dihydrotestosterone decreased expression [c-src gene deletion have lost their bone resorption activity, suggesting that SRC activity may regulate osteoclast function [More importantly, many of the genes that were identified as possible targets of STAT3 regulation are directly regulated by estrogen. The expression of by STAT3 . The exppression . SRC exppression . Osteoclfunction . Furtherfunction . The serfunction , 52. Serfunction . How theOur microarray analysis has also identified five genes that appear to be directly regulated by STAT3: CD97, C/EBPa, Gadd45g, HOX-a1, and Sla. Two of particular interest are CD95 and C/EBPa. CD95 is an adhesion G protein-coupled receptor which upon binding to its ligand CD55 activates the Jak2/STAT3 pathway . RANKL wOne limitation of this study is that we failed to find the difference in bone formation and resorption between male STAT3 cKO mice and their sex-matchced controls. Because the significant difference in BV/TV between male STAT3 cKO mice and the controls exists at age of 8 weeks, there is a possibility that we could detect a difference in bone formation and/or resorption parameters at a younger age. On the other hand, our data imply a potential interaction between STAT3 and sex hromones because bone formation and resorption differ between male and female STAT3 cKO mice with an increase in age. Another limitation is that RAW264.7 preosteoclast cell line used in these studies was derived from a male mouse which had been transformed by the Abelson murine leukemia virus. In order to control for the variability of working with primary cultures as well as to compare our data with data from other laboratories, we selected to work with this well-known pre-osteoclastic cell line, RAW264.7 cells. However, the male osteoclast cell line could complicate the interpretation and conclusion made especially in regard to its relevance to the role of STAT3 in female osteoclasts.Collectively, our data suggest that STAT3 functions in regulating trabecular bone development. Our findings also suggest that STAT3 may be linked to estrogen and this may explain the more severe phenotype of a STAT3 knockdown in female bones compared with male bones. STAT3 regulates osteoclast differentiation and activity via cathepsin K. STAT3 signaling may interact with estrogen signaling pathways. Further, our data reported here show the complicated nature of STAT3 signaling pathways in osteoclasts. Finaly, modulation of STAT3 signaling may be targeted to treat bone metabolic diseases, such as Job’s Syndrome and menopause-associated osteoporosis.S1 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Archaeologically defined Upper Palaeolithic  “cultures” are often used as proxies to designate fossil populations. While recent genomic studies have partly clarified the complex relationship between European UP “cultures” and past population dynamics, they leave open numerous questions regarding the biological characterization of these human groups, especially regarding the Mid-UP period , which encompasses a pan-European cultural mosaic (Gravettian) with several regional facies. Here, we analyse a large database of well-dated and well-preserved UP crania, including MUP specimens from South-West France (SWF) and Moravia, using 3D geometric morphometrics to test for human group affinities. Our results show that the Gravettian makers from these two regions form a remarkably phenetically homogeneous sample which is different from, and more homogeneous than, the Late UP sample. Those results are congruent with genomic studies indicating a genetic continuity within the Gravettian manufacturers and a discontinuity marked by the Last Glacial Maximum (LGM). Moreover, our study expands the geographical range of the MUP phenetic continuity to SWF, for which aDNA data are scarce, and clarifies the post-LGM European population structure in SWF, with a possible dual ancestry stemming from different LGM refugia. In the last decade, genomic studies on ancient human DNA (i.e. aDNA) have increased drastically our understanding of the complex relationships between UP techno-complexes and European past population dynamics (e.g.8). This is particularly true for the Gravettian techno-complex, during the Mid-UP period . The large distribution of the Gravettian techno-complex across the continent has led to the concept of a pan European cultural mosaic, with several identified facies evolving regionally13. These smaller identified entities often present similarities, but only at certain times and without forming a homogeneous unit on a continental scale for 9,000 years15. Thus, the Gravettian, from a theoretical point of view, can be described as a “vast meta-culture composed by a patchwork of several distinct typo-technological traditions spread over the European continent”15.Archaeologically defined Upper Palaeolithic  cultural taxonomic units  are often used as proxies to designate fossil populations. During the last 100 years, changes in the UP cultural archaeological record have often been scrutinized to debate about population extinctions, migrations, replacements, etc. (for recent controversies and discussions about past models see17), and a relative proximity between these individuals and some earlier specimens attributed to the Early UP . These results have led to the hypothesis of an East European founder population ~ 36,000 years ago from which Gravettian-related groups and industries would have dispersed16. The Věstonice ancestry is not seen any more after the Last Glacial Maximum , in the makers of Late UP  industries22 such as Magdalenian and Epigravettian.Ancient DNA studies indicate underlying genetic continuity between manufacturers of MUP industries from Czech Republic, Ukraine, Belgium, and possibly Southern Italy , an area that yielded one of the richest corpus of MUP human fossils chronologically distributed through the whole period26. In this region, Gravettian is characterized by a succession of phases  showing a diversity of lithic projectile points and implements and a no less remarkable diversity of chaines opératoires of blades and bladelets production28. In SWF, Gravettian art is also prominent, with several ornamented caves with stylized animal figures carved or painted and negative hands29. Several sites in SWF also delivered female figures carved on cave-wall (Cussac) or fallen rocks (Laussel) or made of ivory  or soft stones , that are often stylistically compared to the ones known in the rest of Europe (see for instance31). Gravettian in SWF is also characterized by complex funerary behaviours implying deliberate commingling of the remains of several individuals, unique in the MUP mortuary landscape34. To date, no nuclear DNA has been extracted from SWF MUP human remains, but a mitochondrial DNA (i.e. mtDNA) sequence belonging to the M haplogroup was published for the La Rochette individual dated at ~ 27,500 years ago. This haplogroup has also been identified in four EUP specimens from Goyet (Belgium) and Bacho Kiro (Bulgaria) and in one Italian MUP fossil from Ostuni 36, but has not been found in EUP or MUP individuals from Central Europe. In this context, the genetic history of the MUP groups remains unclear, and the biological affinities that existed between the SWF MUP local population and other MUP groups are not properly understood.If the hypothesis of a spread of the Gravettian techno-complex related to migrations of people with their equipment and traditions may stand for some areas in Central or Eastern Europe, more complex evolutionary mechanisms have likely played a role in the genetic structuration of the MUP local populations in Western part of the continent. On the one hand, in several Western European areas , typotechonological data do not support the notion of the Gravettian being an “intrusive culture” that would have simply replaced EUP ones37). It has been shown that patterns of cranial morphology largely reflect genetic distances among human populations and that this morphology is evolving largely neutrally41. Geometric morphometrics have been used extensively in palaeoanthropological studies in the past two decades, more specifically to address early periods of human evolution at a time when different human species coexisted (see for instance45), but to our knowledge, they have never been used to discuss European UP human group affinities. Here, we apply geometric morphometrics using cranial 3D landmarks and semi-landmarks  and to their geographical location and chrono-cultural attribution for the LUP (i.e. LUPswf and LUPita). The results displayed on Fig. The Mahalanobis, Procrustes and Euclidean distances Tables  and 5 co48  but showed more variation than the ethnically defined samples (i.e. Inuit and OcPap). The MUP sample also seemed to be more homogeneous than the LUP sample which was formed by two different techno-complexes: Epigravettian and Magdalenian.19 and cranial osteometric49 studies. The close morphological affinity between the two samples may have been partly influenced by the very limited number of available EUP specimens, but it could also reflect true human group affinities. For instance, the phenetic similarities between the EUP specimens Kostënki 14 (likely related to the Early Aurignacian techno-complex) and Sungir’ 1 , and the MUP morphotype in our analyses, concur with recent genomic results showing genetic proximity between these fossils and those from the Věstonice cluster18.The present study is the first geometric morphometric analysis focusing on cranial morphological affinities between European UP local populations. No significant differences in shape between EUP and MUP individuals were found confirming previous genomic19, and previous anthropological studies on cranial and infracranial morphology have already highlighted the striking morphological differences between these two groups54. Our results are mostly congruent with previous cranial osteometric analyses which identified most of the differences on the face50: LUP specimens display on average a less projecting face, shorter minimal frontal breadth and nasal aperture dimensions, and slightly larger orbital breadth .Our results highlight cranial morphological differences between MUP and LUP fossils from the same region (SWF), though they are less marked than in other MUP and LUP comparisons . These differences may be related to the suggested population bottleneck during the LGM22 identified two main ancestries during the LUP. The Goyet Q-2 ancestry is predominant in 19,000–15,000-year-old individuals associated with the Magdalenian culture. This genetic ancestry was largely replaced by the ‘Villabruna ancestry’ ~ 14,000 years ago, suggesting migrations or population shifts at that time. None of the LUPswf specimens has been genetically characterized so far, but considering that they are dated to ~ 19, 000 years ago while the LUPita sample dates to the second part of the LUP , significant morphological differences between these two samples would be expected. However, it has been shown recently that Iberian hunter-gatherers, including the El Mirón individual dated to ~ 19,000 years ago, carried dual ancestry from both Villabruna and Goyet Q-2 clusters22. Given that South-Western Europe (i.e. the Iberian Peninsula and SWF) was likely a refugium for human groups during the LGM, it seems possible that our LUPswf sample also carries a dual genetic signature similar to that of the El Mirón cluster. The presence of Villabruna-like ancestry in this genetic signature could explain the morphological similarities observed between the LUPswf and the latter LUPita sample. Alternatively, the LUP from SWF may carry a full Villabruna ancestry. This hypothesis implies that both LUPswf and LUPita would have originated from a unique founding event which would have most likely taken place in the Italian peninsula at least ~ 20,000 years ago. Given the high degree of within-sample variation in the LUP sample , it is also possible that people carrying the Věstonice ancestry and coming from the East have mixed with local populations in SWF France. In any case, the remarkable morphological homogeneity seen for the MUP sample, despite the distance between SWF and Moravia (~ 1200 km) and the time span (over 6000 years), suggests that the two areas did not stay isolated throughout the MUP. Indeed, the gene flow rate appears to have been high enough to prevent local populations from diverging through genetic drift, and both groups may have belonged, at least for some times, to the same large mating networks. These complex processes of population migrations and interactions and the spread of genes (and likely of knowledge and ideas as well) through mating networks reinforce the idea of the Gravettian as a vast meta-culture composed by a patchwork of several regional distinct typo-technological traditions.The two geographical MUP samples could not be statistically distinguished as they occupied the same morphospace. This may indicate that the MUPswf sample is associated to the Věstonice cluster, and hence, both human groups may have originated from a single founding event. Alternatively, as EUP fossils could not be easily distinguished from MUP ones based on their morphology  and Moravia (Pavlovian), are phenetically homogeneous and could be considered as a widespread unique paleodeme (sensu37). They share affinities with EUP fossils, but are different from post-LGM LUP specimens, which within group diversity is much more important than what can be observed within the MUP sample. After the LGM, and as suggested by genomic results22 for the Iberian Peninsula, the human groups in SWF were likely partly replaced and/or admixed with groups coming from a refugium in Southern Italy, comforting the idea that LUP human groups were likely structured around at least two genetic ancestries stemming from different LGM refugia.The present study used 3D geometric morphometrics to analyse for the first time the largest well-dated and well-preserved European UP crania sample. This approach offers a unique means to decipher the complex phenetic signals within the European UP fossil sample. The affinity of the different regional UP human groups considered to be the makers of the various meta-cultures composed by a patchwork of several distinct typo-technological traditions have recently been the focus of numerous genomic studies which have paved the way to a better understanding of the complex population structures within European UP human groupsThe UP sample was composed of 26 well-preserved crania . The sex ratio  for each sample is indicated in Supplementary Table The sample is estimated to be composed of 38.6% female individuals , they nevertheless still correlate with population history60. All semi-landmarks are technically curve semi-landmarks (two degrees of freedom) and not surface semilandmarks (three degrees of freedom). Missing landmarks were estimated by mirroring and, where mirroring was impossible, by a thin-plate-spline (i.e. TPS) interpolation using the available landmarks61. The TPS estimation was based on a global average from the entire sample. This approach was applied to 18 specimens  to estimate less than 20% of the landmarks  only. To correct for bilateral asymmetry, we used symmetrized landmark configuration of each specimen for subsequent analyses62. Outliers from the original dataset were checked for both sets of analyses  using 57 landmarks and 182 semi-landmarks .The first set of analysis focused on morphological variations of the MUP fossils compared to the fossil samples of EUP and LUP specimens. After alignment of the landmark dataset (i.e. GPA), the Procrustes residuals were used to compute Principal Component Analyses (i.e. PCA) and the first 15 PCs .The second set of analyses concentrates on the within-group variation of the MUP and LUP specimens. It includes the four extant modern human groups  as a comparative sample for the expected variation within modern human groups. The North American and Southern European samples represents human groups, define by their approximate geographical origin, while the Inuit and the Papuan samples represent ethnically defined groups. The EUP sample was excluded from these analyses due to the low number of available specimens (i.e. n = 4). After the Procrustes superimposition of both datasets, we used the Procrustes residuals to compute a PCA (Supplementary Table 65) and Geomorph (v 2.1.266) packages for the 3D geometric morphometric analyses, the Ade4 (v 1.7-467) package for the bgPCA, vcvComp package (v 1.0.164) for the study of the variation within and between groups and ggplot2 (v 3.3.068) to perform graphical representations. Mahalanobis distances were calculated using the MorphoJ software (v 2.069) and surface deviation analysis was performed with Geomagic Studio v.2013.0.1.Analyses were performed on the R platform using the Morpho (v 2.1Supplementary Information 1.Supplementary Table S2.


Acacia salicina and Acacia stenophylla) in the Murray–Darling Basin (MDB), a large agriculturally utilized region in Australia, and assessed the impact of landscape structure on genetic differentiation. We used AFLP genetic data and sampled a total of 28 A. salicina and 30 A. stenophylla sampling locations across southeastern Australia. We specifically evaluated the importance of four landscape features: forest cover, land cover, water stream cover, and elevation. We found that both species had high genetic diversity  and differentiation among local sampling locations . Population structure analysis showed that both species had high levels of structure (6 clusters each) and admixture in some sampling locations, particularly A. stenophylla. Although both species have a similar geographic range, the drivers of genetic connectivity for each species were very different. Genetic variation in A. salicina seems to be mainly driven by geographic distance, while for A. stenophylla, land cover appears to be the most important factor. This suggests that for the latter species, gene flow among populations is affected by habitat fragmentation. We conclude that these largely co‐occurring species require different management actions to maintain population connectivity. We recommend active management of A. stenophylla in the MDB to improve gene flow in the adversity of increasing disturbances  driven by climate change and anthropogenic factors.Restoring degraded landscapes has primarily focused on re‐establishing native plant communities. However, little is known with respect to the diversity and distribution of most key revegetation species or the environmental and anthropogenic factors that may affect their demography and genetic structure. In this study, we investigated the genetic structure of two widespread Australian legume species , are poorly understood. Here, we studied the effects of habitat fragmentation and geographical barriers on the gene flow of two broadly distributed Acacia species in the MDB. We found that despite being ecologically similar, each Acacia species had different factors affecting their genetic connectivity in the MDB, with important effects of habitat fragmentation in only one of the species. Acacias form major components of many ecosystems across the continent including many arid ecosystems with poor soils  in shrubs and trees was estimated to be twice that observed in global studies. While observations such as these are useful for high‐level comparisons, understanding the major drivers of among‐species variation in genetic diversity and structure may be more important for guiding conservation decisions.Restoration using Acacia salicina and Acacia stenophylla) to improve our understanding of the main environmental factors shaping genetic connectivity in these two species. Acacia salicina and Acacia stenophylla are both broadly distributed across the Murray Darling Basin (MDB)  Figure . ImportaA. stenophylla to be more sensitive to habitat fragmentation, historical changes in water fluctuations of the MDB colonize degraded areas , and stored for DNA analysis.Location data from herbarium specimen records of the Australian Virtual Herbarium were obtained to guide the selection of sites. A survey was then conducted to multiple agricultural areas of the MDB . We selected sites where the number of mature individuals of a Figure . 25 and 2.2EcoRI‐MseI, the EcoI‐A‐MseI‐C preamplification reaction was diluted 1:30 prior to selective amplification and selective amplification primers were fluorescently labeled. Initial screening of 12 primer combinations identified six with polymorphic and repeatable banding patterns (Eco‐AGC/Mse‐CTC (FAM), Eco‐AGT/Mse‐CTC (PET), Eco‐ACC/Mse‐CTC (VIC), Eco‐AGC/Mse‐CTG (FAM), Eco‐ACC/Mse‐CTG (VIC), Eco‐AGA/Mse‐CTG (NED)) (dye used shown in brackets). Amplicons were visualized on an ABI 3130XL sequencer using a LIZ 500‐bp internal standard (Applied Biosystems) and scored using GeneMapper Version 4.0 software (Applied Biosystems). A binary matrix of present (1) and absent (0) bands was constructed for each species. Ninety‐five samples from 3 to 4 populations from across the geographic range of both A. salicina and A. stenophylla were run twice for each of the proposed primer pairs to test for reproducibility across a range of 120 to 450 bp. Markers with an error rate of >5% were discarded with the error rate for markers selected for A. salicina ranging from 0–3.4 and for A. stenophylla being 1.5–4.3. A negative control was run with every set of samples in a 96‐well block.DNA was extracted from ~10 mg of dried tissue ground to a fine powder using 3‐mm tungsten carbide beads in a Retsch MM300 mixer mill using the Qiagen 96‐well DNEasy Extraction Kit  following the manufacturer's protocol. AFLP amplification largely followed that of  for each sampling location using GenAlEx version 6.41 ; except for sampling location 48 (N = 7) of A. stenophylla, which showed significant differences of genetic diversity (He)  between all sample sizes. GenAlEx was also used for an analysis of molecular variance  based on 999 permutations. An exploratory analysis of population genetic structure was undertaken using principal coordinate analyses (PCoA) based on between‐plant pairwise genetic distances (ΦPT) using the GenAlEx covariance standardized method with the first two dimensions then plotted. These analyses identified divergent sampling locations in each species, which were removed and the analyses rerun. STRUCTURE version 2.3.2  and expected heterozygosity  where every pixel contained a value of forest cover , (b) “land cover” based on a categorical Global Land Cover Map for 2009 (GlobCover: http://due.esrin.esa.int/globcover) where every pixel contained a land cover class code, (c) “water stream cover” based on categorical “Surface Hydrology Lines” map of Australia (http://pid.geoscience.gov.au/dataset/ga/83130), and (d) elevation based on a GEODATA 9 Second digital elevation model (DEM) version 3 2008 for the main study area (https://data.gov.au/data/dataset/0fc3357c‐5852‐4e6b‐992c‐c78bd10e9234) where every pixel contained an elevation value expressed in meters. To minimize border effects, we cropped all rasters to the extent of the study region for both Acacia species and masked them using a shape file of Australia. We also standardized by reprojecting (EPSG:4326) and aggregating all rasters to similar size grid cells. Spatial data were prepared using the R packages raster  “forest cover” based on a continuous forest cover map for 2000 (University of Maryland: Acacia gene flow and (b) that water streams might be positively affecting gene flow between Acacia populations (particularly A. stenophylla). To do this, we created resistance surfaces with pixels at higher elevations having higher resistance values using the raw elevations from the DEMs as resistance values for each pixel  . MantelLegendre . The firLegendre  method a33.1A. salicina and A. stenophylla. The percentage of polymorphic loci ranged from 36%–76.4% in A. salicina and 46.2%–83.9% in A. stenophylla while expected heterozygosity for A. salicina was 0.117–0.216 and 0.151–0.280 in A. stenophylla  and mean expected heterozygosity , slight differences in the primer pair combinations used to generate the datasets suggest caution when comparing these results. The AMOVAs indicated substantial and significant differences in genetic variation among sampling locations of both species . Significant isolation by distance was also detected in both species (p < .001).Genetic diversity measures varied among sampling locations within species as well as between A. salicina and 49.3% of the variation for A. stenophylla  for both species  might also be present  showed evidence of admixture or potentially immigration from sampling locations belonging to other groups. The A. salicina clusters were somewhat geographically partitioned across the study region  for A. stenophylla and A. salicina, except for forest cover effects on genetic clusters of A. salicina . More specifically, the admixed cluster (white) consisting of two sampling locations was mostly related to areas of high forest cover resistance , while for A. stenophylla, only land cover resistance appeared to be important  and a vital economic resource for the agricultural industry  from six genetic clusters detected by STRUCTURE. This suggests that gene flow of A. stenophylla is relatively high across many northern and southern sampling locations within its geographical range. However, few sampling locations were composed of distinct genetic clusters [e.g., cluster 3 (green) only present in the south and cluster 4 (black) in the far southwest . Although we did not find an effect of hydrological connectivity, this partially confirms results from a previous study of population genetics of A. stenophylla, which shows high levels of population connectivity across the northwest region of the MDB mainly driven by hydrochory  and geographical distance, seem to shape the observed pattern of genetic variation. However, model fitting (R2 = 0.03) was poor, suggesting that other environmental factors, not considered here, might have an effect on the genetic connectivity of A. salicina. For example, extensive droughts in the Murray Darling Basin area are known to have an impact on Acacia demography  did have a significant effect on genetic structure. Thus, genetic connectivity among A. stenophylla sampling locations does not seem to be mainly driven by geographic distance  and MRM showed that elevation might also act as a potential barrier for gene flow between sampling locations. Interestingly, given the high levels of admixture in many sampling locations of A. stenophylla, the effects of land cover and elevation are likely to explain the presence of distinct genetic clusters in southern sampling locations  in A. stenophylla, but our results do not support that for A. salicina. Management actions to improve connectivity of these species (including through water management) need to be tailored accordingly based on our findings. For example, southern sampling locations of A. stenophylla who continue to suffer severe water fluctuations that will decrease seed dispersal between populations might be benefited by translocation of individuals where we can target populations with relatively large effective sizes and that are relatively well connected by gene flow to other large populations.Several studies have pointed out the negative impacts of habitat fragmentation on plant population viability and genetic diversity  habitat fragmentation and elevation do not equally affect the genetic connectivity of these two woody legumes supporting our hypothesis. While it seems that A. salicina genetic differentiation and connectivity are mainly driven by geographic distance, anthropogenic disturbances in the MDB do have an important impact on gene flow in A. stenophylla and it is likely that it affects other less resilient plant species in the region  both species had relatively high levels of genetic diversity and differentiation; (b) both species also had high levels of genetic structure across the MDB, although None declared.Francisco Encinas‐Viso: Conceptualization (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Project administration ; Software (lead); Supervision (lead); Validation ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Christiana McDonald‐Spicer: Data curation ; Formal analysis ; Investigation ; Methodology ; Visualization ; Writing‐review & editing . Nunzio Knerr: Data curation ; Formal analysis ; Investigation ; Software ; Visualization (lead); Writing‐review & editing . Peter Thrall: Conceptualization ; Data curation ; Funding acquisition (lead); Investigation ; Methodology ; Project administration ; Resources (lead); Supervision ; Validation ; Writing‐review & editing . Linda Broadhurst: Conceptualization (lead); Data curation ; Formal analysis (supporting); Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Resources (lead); Software (supporting); Supervision ; Validation ; Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing .Supplementary MaterialClick here for additional data file.


Prunus spp. accessions distributed in five ecological groups, including 74 accessions of cultivated Prunus armeniaca L. and 94 accessions of wild apricots (P. armeniaca L. and Prunus sibirica L.), which generated 417,961 high-quality SNPs. We used cluster, genetic structure, and principal component analyses to examine the genetic diversities and genetic relationships of the 168 accessions. The Dzhungar-Ili ecological group accessions showed the highest genetic diversity in terms of private allele number, observed heterozygosity, and nucleotide diversity. We speculate that the Central Asian ecological group accessions were domesticated from the Dzhungar-Ili ecological group accessions. The population structure and gene flow of the North China and European ecological group accessions suggested a genetic background of P. sibirica. We argue that the two groups should be considered hybrid swarms connected to P. sibirica by continuous and extensive gene flow. P. armeniaca originated in Northwest China , subsequently spread throughout Central Asia, and eventually spread to Europe. In addition, selective sweep signatures in P. armeniaca during domestication from wild to cultivated apricots, combined with differentially expressed genes, underlie distinct fruit traits, including sugars, aromas, organic acids, and carotenoids. This study provides substantive and valuable genomic resources that will significantly advance apricot improvement and effective utilization.Single-nucleotide polymorphisms (SNPs) are the most abundant form of genomic polymorphisms and are widely used in population genetics research. Here, high-throughput sequencing was used to examine the genome-level diversity, population structure, and relationships of apricot, which are important for germplasm conservation and molecular breeding. Restriction site-associated DNA sequencing (RAD-seq) was adopted to sequence 168  Apricot belongs to section Armeniaca (Lam.) Koch, subgenus Prunophora Focke, and genus Prunus (Rosaceae)2. According to different taxonomic systems, apricots are divided into three to twelve species4. However, six species are recognized by most scholars6: Prunus armeniaca L., Prunus sibirica L., Prunus mandshurica (Maxim.) Skv., Prunus holosericea  Kost., Prunus mume Sieb. et Zucc., and Prunus brigantina Vill. Almost all cultivated apricots originated from P. armeniaca7.Apricot is a fruit of temperate and subtropical regions and is distributed worldwide. China, Iran, Pakistan, Uzbekistan, Morocco, Algeria, Ukraine, and the USA are the main producers of apricot8 divided apricot into six ecological groups based on geographical distribution. The Central Asian ecological group (CAG) accessions are the most abundant, with vigorous tree growth, many slender twigs, and small leaves. These accessions flower late, are self-fruiting, and produce soft, small fruits with a sweet kernel8. The European ecological group (EG) accessions are medium-sized trees with thick branches, weak growth, and a short dormancy period, and mostly produce firm fruits through self-fertilization. The flowers, leaves, and fruits of EG accessions are usually large8. Accessions in the Dzhungar-Ili ecological group (DZG) are small trees that produce small fruits with a pubescent peel and exhibit strong cold, drought, and waterlogging resistance8. The North China ecological group (NCG) accessions are tall trees with vigorous growth. These accessions have intermediate cold and drought resistance and produce fruits via self-fertilization that are large and juicy8. The Northeast Asian ecological group (NAG) accessions are small shrubs that have strong cold resistance. These accessions can tolerate temperatures as low as −50 °C in winter, exhibit early leaf fall, and produce small and dry fruits with bitter flesh that are not suitable for consumption8. The East China ecological group (ECG) accessions include shrubs or medium-sized trees and tall trees and have strong disease resistance. The seed-setting rate of self-pollinated flowers is very low, the colors of the flowers vary, and the fruits are mostly green8. Therefore, the groups exhibit large morphological and physiological differences and wide adaptability to ecological conditions9.Chinese scholars10, apricots have three origin centers: China, Central Asia, and the Near East. Most scholars12 support the history of ancient apricots in Central Asia and China, regarding these regions as independent centers of domestication. However, the early history of apricot is not completely clear, and the main question of whether the cultivation history in Central Asia is longer or shorter than that in China remains unanswered13. De Candolle et al.14 argued that apricot cultivation in China dates back to the end of 3000 BC. However, there is no archeological evidence for the beginning of apricot domestication and cultivation. In Central Asia, apricot cultivation was recently introduced in ~1000 to 2000 BC15. Many scholars have shown that the genetic diversity of apricots in China is the richest worldwide18.According to Vavilov18. It is an important part of the deciduous broad-leaved forest under the mountain coniferous forest and above-mountain grassland in Xinjiang8. There is only one mountain between the southern Xinjiang and Ili areas in the northern Tianshan Mountains, and there are several corridors between the northern and southern Tianshan Mountains. Geographically, the apricots cultivated in Xinjiang, southern Tianshan Mountains (CAG), most likely evolved from the spread of wild apricots in the Ili Valley (DZG)8.The Kashgar, Hotan, and Kuqa oasis areas around the Tarim Basin in the southern part of the Xinjiang Uygur Autonomous Region of China are the main apricot-producing areas and contain the greatest abundance of apricot cultivars. The wild apricot forest in Ili, Xinjiang, China, is a relic of a broad-leaved forest from the late Tertiary community that played a decisive role in the domestication and cultivation of apricot worldwide19. Identifying genetic variations in wild and cultivated populations and within populations can provide insights into the general mechanisms of plant domestication and diversification and guide crop genetic improvement in future breeding programs20. There is evidence that domestication can lead to a decrease in the genetic diversity of cultivated crops21. The genetic information present before domestication and artificial selection may be retained in wild populations, which are special resources for studying the effects of variation in the apricot genome. However, the natural apricot population in China is strongly influenced by human activities, such as logging and deforestation. Consequently, protective measures must be taken to prevent further declines in wild apricot resources. Information on genetic diversity and population structure is essential for the development of management and conservation approaches. We studied the genetic diversity and population structure of apricot and identified the representative core germplasm. We can preserve the genetic diversity of the whole population to the greatest extent by using the smallest amount of genetic resources, and further improve the protection efficiency of apricot germplasm resources22. Developing genomic resources, increasing the knowledge of the apricot gene pool, and obtaining information about genetic diversity and population structure can accelerate biological research and genetic improvement.Crop domestication not only changes economic and agronomic traits but also retains the genetic characteristics that affect the genetic diversity and population structure of domesticated plants25. Previous studies used nuclear DNA, mitochondrial DNA, and chloroplast DNA28 sequence fragments to study the genetic diversity of apricots and the relationships between apricot and other related species29. Compared with genome-level sequencing, plastid and nuclear gene molecular markers have a lower accuracy and resolution. The limited number of polymorphic loci produced by conventional molecular markers hinders genetic research on apricot.The rapid development of high-throughput sequencing technology has provided whole-genome information for nonreference crops. The use of this technology has produced large data sets of short sequences within millions of genomes with deep coverage. These methods, including restriction site-associated DNA sequencing (RAD-seq) and genotyping-by-sequencing (GBS), have been successfully applied in population genomics studiesThe main purposes of this study were to evaluate the genetic diversity, population structure, and relationships of apricots in different ecological groups using RAD-seq of the whole genome, clarify the phylogenetic relationships between five ecological groups and provide a theoretical basis for further improving and effectively utilizing apricot germplasm resources.Prunus spp. accessions were successfully sequenced using the HiSeq 2000 system  producing a total of 192 Gb of raw reads, with an average of 1.14 Gb for each accession. After filtering low-quality reads, a total of 180 Gb of clean data were retained, with an average of 1.07 Gb per accession and an average success rate of 98.44%. The quality of the sequencing data was high , the GC content was stable at 37.33–41.25%, and all samples were free from contamination  had base transitions, and the transition vs. transversion (ts/tv) ratios for the populations ranged from 1.787 to 1.799. The ts/tv ratios of SNPs in the two wild populations (NAG and DZG) were larger than those in the cultivated populations  the DZG (group I), with four subgroups based on the strong geographical distribution pattern among the branches (Yining County (group I-1), Huocheng County (group I-2), Xinyuan County (group I-3), and Gongliu County (group I-4)) in the Ili region, Xinjiang; (2) the CAG (group II), with apricot cultivars mainly from the Kashgar, Hotan, and Kuqa areas in Xinjiang that could not be clearly distinguished based on their geographical distributions, which may be due to the high genetic diversity and frequent gene exchange generated by selection or introduction, reflecting the complex domestication history of cultivated apricots18 , and the optimal value obtained in this study was K = 5  to better understand the relationships between ecological groups Fig. , and them = 1)   Fig. , we obse 2) Fig. , the EG, 2) Fig. .Fig. 5DeP. armeniaca populations ranged from 0.378 to 0.407; the observed heterozygosity (Ho) of the P. armeniaca populations ranged from 0.218 to 0.260; Wright’s F-statistic (FIS) of the P. armeniaca populations ranged from 0.030 to 0.078; and the number of private alleles (AP) in the populations of P. armeniaca ranged from 11–11,069. The average nucleotide diversity (π) and polymorphism  estimates of the P. armeniaca population mutation parameters were 0.0027 and 0.0016 at the genome level, respectively 31 and cassava (π = 0.0026)32, but lower than that of date palm (π = 0.0092)33.The expected heterozygosity (He) of the FIS) ranged from 0.015 to 0.055, and the private allele number (AP) ranged from 2026 to 8847. The Ho and AP in Xinyuan County were higher than those in Gongliu, Yining, and Huocheng Counties.Further analysis of the DZG accessions Table  showed tP. armeniaca was larger than that of P. sibirica.Notably, the Ho and π  of the DZG accessions were higher than those of the CAG accessions , NCG accessions  and EG accessions   between populations ranged from 0.1080 to 0.1946  revealed that 12.02% of the genetic variation occurred between populations and 87.98% occurred within populations Table . AlthougSeparate selective sweeps driven by artificial selection were detected in the CAG, EG, and NCG accessions. For CAG accessions, selective sweep signatures were detected for 0.27 Mb of the genome sequence, containing 53 putative genes Fig. .Fig. 7DiOne carotenoid biosynthesis gene, one cellulose biosynthesis gene, two serine and threonine metabolism genes, and two GTPase regulatory genes were identified in the selective sweep regions of the CAG accessions  and random amplified polymorphic DNA (RAPD) markers.36, maize37, Amorphophallus paeoniifolius38, and Arabidopsis39. Due to the biased mutation process in the plant genome, the predicted number of transitions is much larger than the predicted number of transversions38. Consistent with these predictions, we found that the base transition values were higher than the base transversion values and that the ts/tv ratios of the five geographical groups ranged from 1.787 to 1.799  (Table P was most significant in the wild population (DZG), measuring 11,069, indicating a large pool of genetic variation, which could guide the genetic improvement of crops in future breeding programs. In contrast, AP values of only 11–3784 (Table P. armeniaca). These differences may be due to inbreeding or continuous directional selection during the domestication process, which narrows the genetic basis of germplasm, reduces genetic diversity, and increases the possibility of genetic drift during the domestication process. Similar results were obtained by Li et al.26.The genetic diversity index of the cultivated population was much lower than that of the wild population (a) Table , Fig. 6,10, China and Central Asia were the two main centers of apricot domestication. Most cultivated apricots belong to P. armeniaca. Among the four main ecological groups of P. armeniaca species, Kostina40 argued that DZG accessions are the most primitive and that CAG accessions have the longest cultivation history. The cultivation of EG accessions has a relatively recent history, being traced back to ~2000 years ago16. In Central Asia, local cultivars are most likely to have originated from wild apricots, moving southward from the Kazakhstan–China border (Dzhungar-Ili) to Kashmir and westward into the mountains of Afghanistan16. In our study, 168 accessions were divided into five ecological groups based on population structure, PCA and ML tree analysis. The CAG and DZG accessions were tightly clustered, indicating that the DZG (wild apricot) may be the ancestor of the CAG. Zhebentyayeva et al.18 and Li et al.26 reached similar conclusions. The genetic background of the CAG was most similar to that of the DZG  of the DZG accessions were higher than those of accessions in the other ecological groups , subsequently dispersed throughout Central Asia, and eventually spread to Europe. These findings are reasonable from a historical perspective, as there was extensive cultural contact along the Silk Road from 207 BCE to 220 CE41. Therefore, historical and commercial influences may have contributed to the development of this unique species of cultivated apricot.According to Vavilov42 also found that wild apricots in the Yili Valley maintained a high level of diversity and that genetic variation mainly occurred within populations.Further analysis of the DZG Fig.  clustereP. sibirica. We argue that the two groups should be considered hybrid swarms connected to P. sibirica by continuous and extensive gene flow.The population structure and gene flow analyses of the NCG and EG accessions suggested a genetic background of 43. The different genes identified in selective sweeps of CAG, EG, and NCG apricots were found to be enriched in biological processes, including the metabolism of sugars, aromas, organic acids, and carotenoids. These genes may have been involved in different domestication pathways, leading to differences in traits among apricot ecological groups. Some sugar-related genes have been found in selective sweeps, suggesting a preference for sweet fruit during domestication. For the NCG accessions , NCG (13 cultivars), EG (8 cultivars), DZG (90 cultivars), and NAG (4 wild accessions), were considered for RAD-seq.A diverse collection of 168 P. armeniaca) were collected from the natural distribution areas of wild fruit forests in Xinyuan County, Gongliu County, Yining County and Huocheng County in Ili, Xinjiang. Four wild apricots (P. sibirica) were provided by the Xiongyue National Germplasm Resources Garden of the Liaoning Institute of Pomology . The DNA concentration was accurately quantified with a QubitTM 2.0 fluorometer , after which the DNA was stored at −20 °C34. RAD library preparation and sequencing were conducted by Novogene Bioinformatics Technology Co. Ltd., Beijing. The DNA concentration used for library preparation was 75 ng/μl, and the qualified DNA was digested by the EcoRI (GAATTC) restriction enzyme. Each sample was physically randomly fragmented by the Covaris S220 system , and then subjected to barcode ligation, gel fragment selection, adapter connection and PCR fragment amplification, and 300–500 bp sequences were recovered38. Paired-end sequencing was performed on the Illumina HiSeq 2000 platform , and each sample generated ~2.32 Gb of raw data. The clean sequencing data have been submitted to the NCBI Short Read Achieve with accession number PRJNA592274.DNA was extracted from young leaves using a Plant Genomic DNA Kit P. armeniaca cv. “Cuijianali”; NCBI accession number PRJNA592274) by Burrows-Wheeler Aligner (version 0.7.8-r455)45. We used SAMtools software30 to detect SNPs in the population. The final product was converted to variant call format (VCF).Qualified sequencing data were aligned to the CAG 09 reference genome 30.To obtain high-quality data, the raw data were sequenced, compared, assessed for mutations, screened, and analyzed. After the reads were successfully aligned to the reference genome through quality control, SAMtoolsAfter filtering 1,475,632 SNPs  < 0.05), 417,961 SNPs were obtained for PCA, population structure analysis and phylogenetic tree construction.M. sieversii46 (NCBI accession number SRX2934932) as an outgroup. TreeBeST (version 1.9.2)47 was used to calculate the distance matrix, RAxML (version 8.2.12)48 was used to construct the ML phylogenetic tree, and 1000 bootstrap replicates were used.To clarify the genetic relationships among the five ecological groups, phylogenetic trees were constructed. We used the published RAD-seq data of 49 by setting the number of genetic groups from 2 to 8 and using 1000 bootstrap replicates. To determine the optimal group structure classification, the optimal group number was identified as that with the minimum CV50. Group structure was analyzed by PCA using GCTA51 software, and the significance level of feature vectors was determined by using a Tracy–Widom test. Genome-wide complex trait analysis (GCTA) software integrates the layered correction method of Eigenstart53, which can clearly distinguish cases and controls.Population structure was analyzed by the frappe programBased on the SNPs, we used TreeMix (version 1.13) to evaluate the gene flow between the five ecological groups with the “-se-bootstrap-k 1000 -m” setting, where the number (-m) varied from 1 to 3.P, Ho, He, π43, θw54, FIS, and Tajima’s D43. Sliding windows with a 5 kb step size and 10 kb size were used to calculate Ho, He, π, θw, and Tajima’s D. For each window, these values were run using the Bio::PopGen package with a custom Perl script55. Paired Fst values56 were calculated in the same window to measure the difference between populations. FIS29 was calculated using the POPULATION program in the STACKS pipeline. The statistical values were all calculated using VCFtools (version 0.1.14)57.The genetic diversity indexes included A58, different genetic structures were defined and statistically tested by classifying and dividing the populations at different levels.Using AMOVA in Arlequin (version 3.5.1)P. mume L59. reference genome to identify selective sweeps in the 168 Prunus spp. accessions distributed in five ecological groups. The results were plotted using the ggplot2 package in R software.Qualified sequencing data were aligned to the Table S1,Table S2,Table S3,Table S4,Table S5,Table S6,Table S8,Fig. 1,Fig. 2


Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population  and the progeny  derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad-sense heritability (H2 = 0.95) and narrow-sense heritability (h2 = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H2 = 0.06 and h2 = 0.00). We observed the highest genetic correlation (rg= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (rg= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (rg= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C0 and C1 was not well defined, implying that the 100 selected progenitors of C1 captured the diversity in the C0. Overall, genetic gain for most traits were observed from C0 to C1.Cassava ( Manihot esculenta Crantz) is a crop that provides staple food for >700 million people worldwide , rice (Oryza sativa L.), and wheat (Triticum aestivum L.)  involves the prediction of breeding values and selection of parents based on marker-estimated effects, enabling more cycles of selection and recombination per unit time than phenotypic recurrent selection  of Uganda is one of the first cassava breeding programs in Africa to implement GS. Genomic prediction accuracies from the initial training population (C0) at NaCRRI have been estimated to be reasonably accurate for highly heritable traits, such as dry matter content (DMC) and CMD, but less for low heritability traits, such as fresh root yield  is high. At the International Center for Tropical Agriculture (CIAT), the 8-yr cassava breeding cycle was reportedly shortened to only 3 yr, when parental selections for subsequent crossing were made at the seedling stage for total carotenoid content  between DMC and total carotenoid content has been observed in African cassava breeding population  of GS, including seedling and clonal phenotypic evaluations of a large portion of the C1 population. This presents the opportunity to address a number of unanswered questions and assess the progress made so far relative to GS in this population, using both the available C0 and C1 datasets. Thus, our overall objective was to estimate genetic parameters to guide routine implementation of GS in an East African cassava breeding population. Our specific objectives were (i) to assess trait variability, genetic diversity, and inbreeding level in C0 and C1 genotypes that constitute the GS training population at NaCRRI, Uganda; and (ii) to examine the phenotypic and genetic correlations for selected agronomic and virus resistance traits evaluated at the seedling and clonal stages.Furthermore, cassava is known to suffer from inbreeding depression  for GS. A subset of 100 C0 clones was selected for hybridization to produce the C1 population.In response to the CBSD outbreak in Uganda . Our selection index included four traits, which collectively represent the major breeding objectives of our program: CBSD root severity (CBSDRs), DMC, harvest index (HI), and root weight per plot (RTWT). As indicated above, our breeding program is implementing GS. We derive genomic estimated breeding values (GEBVs) for each of the traits mentioned using mixed-model methods described in detail below. Since our selection criteria are already estimates of breeding value, it was not necessary to further account for the difference between phenotypic and genetic variance-covariance  was planted at three locations: Namulonge in central Uganda , Ngetta in northern Uganda , and Kasese in southwestern Uganda  to assess their agronomic performance and reaction to CMD and CBSD. Importantly, Namulonge is known to be a hotspot for CMD and CBSD , all plants were assessed for CMD and CBSD shoot symptoms, whereas CBSD root necrosis severity was scored at harvest (12 MAP). Shoot severity for CBSD was assessed on a scale of 1 to 5 , plants were harvested and CBSD root necrotic symptoms were assessed, as described above. We did not have budget to genotype all C1 seedlings. For this reason, we decided to cull plants with CMD severity score of 3 or greater, as well as those with insufficient stem biomass to generate at least 10 cuttings for subsequent clonal evaluations. We made this decision because CMD is a high-heritability trait and is easily scored on seedlings. Because we did not cull on the basis of any other trait, the only bias in selection response should be for CMD. Prior to harvesting, leaf samples were collected from 2113 selected C1 seedlings for DNA extraction, as described above. Of the 2113 seedlings, 1420 were cloned and evaluated at Namulonge. A subset (1088) of the clones established at Namulonge was evaluated at Serere in eastern Uganda  to capture further variability that might be associated with environmental differences. The clonal trials were established in August 2016, using an augmented design comprising 30 to 34 plots per block. Each clone was represented by a row of 10 plants and each block contained four checks. Assessment for CMD, CBSD, DMC, and HI was done, as described for C0 evaluations. Because of missing plots, a total of 1056 C1 clones remained for downstream data analyses, of which 432 and 624 were full-sib and half-sib progenies, respectively. Similarly, the top 110 clones were selected from C1 population as parents to generate C2 using the selection index described above, and the parents were clustered using SNP markers, as described previously.The C0 and C1 clonal evaluations, phenotypic data from the two sets of experiments were fitted to a linear mixed model using the lme4 package for the R statistical computing software Z is the corresponding incidence matrix; and e is the residual, such that H2) estimates as follows:where 1 trial, including a fixed effect of location and grand mean, a random effect for clones, blocks nested in location, and the random residual term. Accordingly, variance components were extracted to compute broad-sense heritability estimates for C1 clones.where 0 and C1 populations separately, and later we combined the two populations for joint analysis. From separate analyses, we estimated SNP-based heritability (narrow-sense heritability) for all traits, using the formula as described below:In addition, we fitted a single-step genomic best linear unbiased predication (G-BLUP) model, first for Ch2 is the narrow-sense heritability, and where 0 and C1 data, we fitted a mixed model where the trial location and grand mean were treated as fixed effects, and clone effects were considered random, with realized genomic relationship matrix K constructed using the A.mat function in the rrBLUP package for the markers . Furthermore, we performed a t test to compare mean differences in the GEBVs between C0 and C1 populations. Boxplots were generated from the GEBVs, using the ggplot function built in ggplot2 in R to visualize variability for each trait between the two populations.For the combined C1 traits evaluated at the seedling stage, (ii) between C1 traits measured at the seedling and at clonal stages, and (iii) combined data for C0 and C1 traits evaluated at the clonal stage. To estimate the phenotypic correlations, we used the raw data without accounting for the field trial designs. For estimation of genetic correlations, we first fitted multi-locational models described above for C0 and C1 datasets separately. From linear mixed models, best linear unbiased predictors (BLUPs) were extracted for both C0 and C1 clones and deregressed using the formula described by Garrick et al. (Both phenotypic and genetic correlations were estimated for three scenarios: (i) among Ck et al. .emmremlMultivariate function in the EMMREML package . However, to estimate genetic correlations for Scenarios (ii) and (iii), we used a two-step procedure. The BLUPs obtained from the first-step analyses described above were deregressed. The deregressed BLUPs for C0 and C1 were combined into a single dataset and used as response variables in the second step for fitting the multivariate genomic mixed models described for seedling data. This approach estimates a genetic variance-covariance matrix for the traits. The trait variance-covariance matrices were converted to genetic correlation matrices with cov2cor function in R.The genomic breeding values were estimated using multivariate G-BLUP with the 0 and C1, we used 46,760 SNP markers in both C0 and C1 populations. The markers were filtered to have a minor allele frequency ≥0.01 and formatted as a dosage matrix, with SNP genotypes coded as −1, 0, or +1. The realized genomic relationship matrix (K) was constructed with this dosage matrix as input using the A.mat function in the rrBLUP package  were used to visualize population structure. The mean of the diagonals of the matrix K is known to be proportional to the inbreeding coefficient  than for the C0 (0.38 for CBSDRs and 0.37 for CBSDRi) base population. On the other hand, estimates of broad-sense heritability for foliar CBSD ranged from 0.26 for CBSD3s to 0.49 for CBSD3i among selected parents out of C0, whereas the broad-sense heritability ranged from 0.52 for CBSD6i to 0.69 for CBSD3i among the selected parents out of C1. Overall, the broad-sense heritability estimates of CBSD root necrosis were higher for C1 (0.70 for CBSDRs and 0.63 for CBSDRi) selected as parents than for C0 (0.29 for CBSDRs and 0.39 for CBSDRi) clones selected as progenitors to generate the C1 population.Estimates of broad-sense heritability for foliar CBSD scored at 3 and 6 MAP  ranged from 0.28 for CBSD3s to 0.47 for CBSD3i in the Cpulation . In gene0 base population, whereas the broad-sense heritabilities in C0 base population ranged from 0.77 for CMD6i to 0.81 for CMD severity at 6 MAP (CMD6s) for C1  to 0.60 for CMD severity at 3 MAP (CMD3s) in the C) for C1 . Meanwhi0 and C1 populations, and their selected progenitors  than for the C1 (0.06 for CBSDRs and 0.13 for CBSDRi) population. On the other hand, narrow-sense heritability for foliar CBSD ranged from 0.47 for CBSD3s to 0.72 for CBSD3i in C0, and from 0.57 for CBSD3s to 0.68 for CBSD6i in C1 for selected progenitors (0 (0.54 for CBSDRs and 0.65 for CBSDRi) than those in C1 (0.21 for CBSDRs and 0.31 for CBSDRi) for selected parents.Estimates of narrow-sense heritability, also referred to as “SNP-based heritability,” for foliar CBSD ranged from 0.27 for CBSD3s to 0.53 for CBSD3i among the Cpulation . For CBSgenitors . Similar0 base population and from 0.44 for CMD6i to 0.59 for CMD3s for the C1 base population. Meanwhile, narrow-sense heritability varied between 0.72 for CMD6s and 0.82 for CMD3i in C0, and between 0.05 for CMD6i and 0.23 for CMD3i in C1 for selected parents from C1. We observed higher SNP-based heritability estimates for HI in the C0 base population (2h = 0.48) and their selected progenitors (2h = 0.67) than those for the C1 base population (2h = 0.18) and their selected parents (2h = 0.11). Generally, low (2h ≤ 0.36) SNP-based heritability estimates were recorded for DMC and RTWT for both the C0 and C1 populations, except for C1 clones selected as parents, where SNP-based heritability estimate was relatively high (2h = 0.79) for DMC  for the C for DMC .0 and C1 clones  than the C0 clones. Further, a t test of mean differences between C0 and C1 GEBVs for CBSD6s and CBSDRs, revealed highly significant differences (P ≤ 0.001) between the two populations. For CMD, C0 clones exhibited better performance (lower disease) than C1; however, the mean differences between the GEBVs for the two populations were nonsignificant for both CMD3s and CMD6s  higher average GEBVs than C0 clones  were recorded between seedling CBSDRs and CBSDRi , varying from rg = −0.14 to rg = 0.24  and CBSD3i (rp = 0.35 and rg = 0.30), and (ii) CBSDRs (rp = 0.39 and rg = 0.70) and CBSDRi (rp = 0.36 and rg = 0.77). We observed a similar trend for the phenotypic and genetic correlations between CBSDRi scored at seedling and other CBSD-related traits, with the highest correlation (rg= 0.80) observed between CBSDRi measured at seedling and CBSDRi at clonal stage (rp =0.08) observed between seedling and clonal stages. On the other hand, a negative genetic correlation (rg = −0.46) was observed between seedling plant vigor and HI measured at clonal stage.Results for phenotypic correlations between seedling and clonal evaluations are presented in al stage  and 4. U0 and C1 traits evaluated at clonal stage (rp = 0.13 and rg = −0.12), and between CBSD6s and CBSDRs (rp = 0.23 and rg = −0.03). Furthermore, we consistently observed negative genetic correlations ranging from −0.32 to −0.17 between disease traits and RTWT, similar to genetic correlations between DMC and CBSDRs (rg = −0.64). However, HI had a positive phenotypic and genetic correlations (rg = 0.4 and rg = 0.54) with root weight per plant. (rg) ≥0.2 in absolute values were significant (P ≤ 0.05) at an individual test level.Finally, we examined phenotypic and genetic correlations among Cal stage . We recor plant. . Importa0 and C1 populations. Indeed, majority of the total genetic variation (49%) in C0 and C1 populations was explained by the first PC, with 13% attributed to PC2 (F), were 0.904 in C0 and 0.708 for C1 , as well as their respective selected progenitors. Our primary traits of focus included diseases (CMD and CBSD) and yield components .In this paper, we highlight progress that has been made towards increasing the productivity of cassava for the benefit of communities that depend on it. Notable production obstacles addressed include susceptibility to CMD and CBSD, and low yields of both fresh root and dry matter. In response to these challenges, the cassava breeding program based at NaCRRI initiated efforts to implement GS to accelerate the breeding cycle of cassava  in both populations studied here. The broad-sense heritability estimates for CBSD in the present study were comparable with heritability estimates previously reported  observed in this study.Broad-sense heritability estimates for DMC were particularly low (0.00–0.18), except for clones selected as parents of Ce et al. . In parte et al. . Often, e et al.  uses 3- 1 clones. The precise reason for this seeming discrepancy between broad- and narrow-sense heritability estimates is not known. However, it has been shown both theoretically and with simulations on real data that one reason for the bias in heritability estimation using markers is variation in the amount of linkage disequilibrium (LD) between the markers and the causal loci. If the most important loci are in LD with many more markers than lesser causal loci, then the SNP-based heritability estimates can be upwardly biased. In contrast, if important causal loci are under-tagged by markers, the heritability estimates can be downwardly biased  in C0 and DMC (2h = 0.79) in C1 selected as parents, could be attributed to uneven LD between SNPs. Variation in LD has been previously reported in cultivated cassava  on chromosomes 1 and 4. These variations in LD patterns across the genome could therefore have led to over- or underestimation of SNP-based heritability observed in the present study.Thus, it is possible that high SNP-based heritability estimates observed for traits, such as CMD3s (rg = 0.70) between seedling and clonal CBSD root severities, suggesting the usefulness of seedling data in parental selection for recombination, training GS models, or in selection of clones for further evaluation, targeting cultivar release. Furthermore, CBSD has been reported to spread rapidly in the last two decades in Africa , as it is a trait known to be highly heritable  and genetic correlation (rg = 0.41) between HI and root weight with the large number of clones evaluated in the present study agree with the results of previous studies conducted in other breeding populations  and rice  and tropical Manihot selection clones. These introgressions are believed to have occurred at the time of pioneer CMD and CBSD breeding at the Amani breeding station in Tanzania  based on markers, was 0.904 and 0.708, respectively. These values should be interpreted to mean that the clones were less inbred than might be expected on the basis of the marker allele frequencies . Cassava is known to suffer from inbreeding depression (Rojas et al., 0 population might have removed clones that were inbred. For the C1 population, the priority given to between-, rather than within-, cluster crosses could also be expected to generate above average heterozygosity. Comparison of inbreeding levels in C0 and C1 populations indicated less inbreeding in C1 population than in C0. As indicated, we think the crossing strategy we designed accounts for this observation. Evident in the current study was the better average performance of the C1 population than the C0 population, an indication of overall genetic progress for most traits, which could be a result of less average inbreeding exhibited by C1, as indicated by comparing the mean diagonals of the kinship matrix.For both the C0 to C1. Given these results, we do not expect GS to cause rapid inbreeding as breeding populations are moved from one cycle of GS to the next.From the datasets presented, three major conclusions are drawn: first, seedling evaluation for CBSD, within limits, predicts CBSD clonal performance. This finding justifies selection for CBSD at the seedling stage; for this, the use of both incidence and average root severity can suffice. Second, we observed moderate to high genetic correlations between foliar assessments made for CBSD and CMD at 3 and 6 MAP. This finding justifies a single evaluation done at 6 mo; such a strategy could significantly reduce costs associated with data collection in multi-location trials. Third, selection on GEBVs did not erode the original genetic diversity and resulted in genetic progress for most traits as advances were made from CClick here for additional data file.Click here for additional data file.Click here for additional data file.