In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa. The modern study of domestication of plants and animals is multidisciplinary, and relevant contributions come from botany, zoology, archeology, genetics, ethnobiology, biogeography, and linguistics . Modern Given that domestication is an evolutionary process, genetics emerged as a powerful tool to understand the domestication of plants and animals, revealing the demographic history of the domesticated taxa and the genetic variants that underlie their domesticated phenotypes . The advtop or up when referring to a specific phenotype, while we refer to bottom or down when referring to the underlying genotype responsible for that trait. Thus, top-down approaches start by studying a particular phenotype and searching for its genetic basis. Huge advances in the genetic study of domestication traits have been made using classic top-down approaches  and domesticated individuals are sequenced, because evolution is a population-level process, and in consequence population data is necessary to address most of the evolutionary questions in domestication . PopulatAll population-level sequencing techniques share common pitfalls that should be known and avoided before investing any money on sequencing. Population sampling should be planned carefully, as the sampling scheme has a stronger impact over sequencing to obtain reliable results in any analysis . Also, dOnce adequate genomic population data is gathered, we need to analyze the demographic processes that shaped the genetic variation and the population structure of contemporary populations during the domestication process. This data is necessary to perform tests to detect natural and artificial selection, which are required to understand the genetic base of domestication syndromes . There aAfter assembling a reference genome, one of the next possible strategies to understand domestication is to sequence the complete genome of several individuals. This approach requires the alignment of the sequencing reads back to a reference genome, in order to infer the variable sites between individuals and know the genetic elements  associated to those sites. The main benefit of this approach is its potential to retrieve all the variant sites within an individual’s genome that are structurally represented in the reference genome. Whole-genome sequencing can be used in almost any population-level test of interest . Common Other approaches aim to reduce the sequencing cost per samples by pooling the DNA of several individuals into a single sequencing library  or by rePool sequencing (Pool-seq) is a promising alternative to whole-genome sequencing with a much lower cost . As the Pool-seq has several limitations that should be considered based on the objectives of the research project. It is difficult to discard a low-frequency allele from a sequencing error, but this problem is potentially fixed by either establishing a minor allele frequency threshold for SNP calling or by using pool replicates . One imppriori selection of regions of interest in order to design probes  is another useful approach to obtain population-level data from the transcriptionally active elements within genomes . RNA-seqde novo  and the de novo .However, transcription profiles are dependent on the sequenced tissues and organs, the development stage of the organism, and the influence of external stimuli, capturing just the transcripts that are active at the moment of RNA extraction . This code novo (Restriction site-associated DNA sequencing (RAD-seq), which may also be referred to as genotyping by sequencing (GBS), has been one of the most popular options for cost-affordable population genomics in the last decade . The tecde novo , making de novo approaches for RAD-seq data can lead to fewer predicted SNPs due to errors in the definition of loci and treatment of sequencing errors  in grapevine individuals  are common within plant and animal populations . Thus, tividuals . Despiteividuals , into thividuals .The concept of pan-genome rests on the idea that the genomes of individuals within a population or species share a core set of genes that unifies them , but also contain a fraction of genes that are absent from one or more individuals , which altogether give rise to the pan-genome of such population or species .de novo genome assemblies, the iterative assembly of several genomes from an initial reference or the use of de Bruijn graph assemblers to jointly assemble several genomes , observed heterozygosity (HO), expected heterozygosity (HE), nucleotide diversity (π), number of segregating sites (S) and number of private alleles . These summary statistics can reveal the level of genetic erosion in domesticated plants and animals when compared to the ancestral wild population, which is expected due to severe bottlenecks, selective sweeps and inbreeding  of domesticated taxa and of their wild relatives, as it can reveal the influence of historical events that shaped the genetic diversity of the organisms  in the populations throughout time  by calculating the distribution of the time of coalescence between all the heterozygous loci in complete diploid genomes  between two genomes given a specified mutation rate, recombination rate and generation time  individuals (as obtained from exome sequencing or RAD-seq), namely the extended Bayesian skyline plots  and the imate Ne . The infimate Ne . Furtherimate Ne .i.e., the genetic ancestry of an individual for an specific chromosomal position; Ancient gene flow and local ancestry , which suggest introgression events  in order to detect the ancestry of chromosomal segments e.g., . For exaa priori hypotheses  method compares the summary statistics of several simulated scenarios against the observed data to accept or reject certain demographic hypotheses . This meOther methods based on diffusion approximation can help us infer the demographic history of multiple populations and their interaction through migration and admixture using biallelic SNP data . DemograDemographic processes are important to understand the general history that led to the domestication of plant and animal taxa, but many studies are specially interested in finding the selected genes that explain the phenotypic differences between domesticated taxa and their wild counterparts . Indeed,i.e., selection favoring the fixation of a new allele), the adjacent alleles  also increase their frequency in a process known as hitchhiking  can reveal the overall genetic structure between populations, a local FST statistic calculated for each locus or SNP along the genome can evaluate whether particular regions of the genome are more differentiated from what is expected due to demographic processes, which can be interpreted as signals of a selective sweep , the intensity of the selective pressure, the selfing or allogamous nature of its sexual reproduction, and the migration patterns and genetic structure among populations .i.e., D test was used alongside other methods to detect selective sweeps associated to the domestication of yaks  test is another popular SFS-based method to detect selective sweeps that has been particularly useful for the study of domestication . ROD come genome . The RODe genome , watermee genome , cucumbee genome , common e genome , and chie genome , to nameGiven that selective sweeps remove the variation in regions adjacent to the locus under selection, they can form haplotype blocks that extend in strong LD compared to other haplotypes in the same locus because they reached a medium-to-high frequency in the population swift enough so they are not yet disrupted by recombination . This pai.e., the part of the haplotype that is shared by all the individuals carrying it, such as the allele under positive selection) in the population are identical by descent , as one looks to a specified distance farther away from the core region  test, sometimes named the relative EHH (rEHH) test, which controls for local recombination rates by comparing the EHH of several haplotypes localized within the same locus . Other Ei.e., assignation of alleles in an individual to their corresponding maternal and paternal haplotypes), which may or may not be possible depending on the sequencing depth and type of data available for the analysis  test, which rely on individuals that are homozygous for any given SNPs to look for LD differences between alleles in a population  or the ωSome examples of LD-based methods used to explore the domestication process includes an analysis using LRH to detect signatures of selection associated to dairy and beef cattle breeds , a studyOther important tests include the XP-CLR test  and the Genome-wide association studies have been used extensively to uncover the genetic variants that underlie domestication traits . The domAn important advantage of the GWAS over the bottom-up approaches is its ability to detect polygenic effects on single traits of interest, which is commonplace considering that genes interact between them and the environment to generate phenotypes .A prerequisite before preforming a GWAS is to have large sample sizes in both the number of sequenced genetic variants and the number of individuals included in the study, as they are necessary to obtain the statistical power to detect variants with small effects and to reduce the risk of false positives .Some recent examples include the use of a GWAS to identify candidate genes with unknown functions involved in several agronomic traits, including drought and heat tolerance in chickpea ; a GWAS Extant domesticated taxa lack the information of ancient genetic diversity that was lost through bottlenecks, selection and genetic drift . Howevere.g., An important limitation of paleogenomic analyses is the level of preservation of the ancient DNA itself, as well as the total yield of extracted DNA . The DNAe.g., Given the above difficulties and the uniqueness of the biological material retrieved from archeological sites, it is crucial to extract and sequence as much ancient DNA as possible while avoiding DNA contamination . Major eSorghum bicolor from different time periods (ranging from 1800 to 100 years ago) were compared to extant individuals of the species, revealing that this crop did not suffered an initial domestication bottleneck, but rather that the reduction in genetic diversity, and its associated mutational load, occurred gradually throughout time , the patterns of differentially expressed genes that do not have clear signs of selective sweeps, or even connect the causality between the genetic variation that was selected for during domestication and the resulting phenotypes .Epigenetic variation can be inherited from one generation to the next in a process known as trans-generational epigenetic inheritance, which has been documented in plants and animals , even thIn a similar fashion to GWAS, the use of epigenome-wide association studies (EWAS) can also reveal the association of an epigenetic variant to a trait of interest in domesticated taxa . The samThe most studied epigenetic mark is DNA 5-methylcytosine, which refers to the DNA methylation in cytosines which are usually associated to transcriptional gene silencing . CytosinReduced representation bisulfite sequencing (RRBS) is a high-throughput technique with a similar rationale to RAD-seq that enriches the sequencing of CG rich regions of the genome after the digestion of restriction enzymes . This maBesides cytosine methylation, adenine has also been shown to be methylated in both plants and animals (N6-methyldeoxyadenosine), which cannot be detected using bisulfite sequencing . HoweverHistone modifications refers to either posttranslational covalent modifications in histones  or the substitution of canonical histones by histone variants with different amino acid composition . These hChromatin immunoprecipitation sequencing (ChIP-seq) can be used to assess the genome-wide association between DNA regions and specific histone modifications . ChIP-seThe current epigenomic analyses regarding domestication have focused on DNA methylation patterns , but somOther studies focused on plant domestication have found differentially methylated sites associated to domestication syndromes . A studyin vitro systems, knock-out, knock-down or knock-in experiments that validate the involvement of such genes in the observed phenotypes (Once we have evidence of candidate genes involved in the domestication syndrome, the necessary next step to understand the genetic basis of domestication is to design enotypes . This caenotypes . As an eenotypes .e.g., in vivo which are accountable for the emergence of phenotypes (Previous knock-out and knock-in experiments were restricted to model organisms, but nowadays experimental validation of candidate genes can be supported via knock-out and knock-in experiments, using novel genome editing tools e.g., . Genome-enotypes .e.g., Regardless the genome-editing tool of choice , genome Of course, the above studies will hardly reproduce a complete domesticated or wild phenotype, since genetic elements interact in complex regulatory networks, including other elements within the genome as well as epigenetic and environmental components , but nonde novo by inserting the desired domestication alleles into their wild relatives, generating crops with the desired domestication phenotypes but without the problems of low genetic variation and accumulation of deleterious mutations that are an inevitable consequence of regular domestication processes (Once the candidate genes are validated, genome-editing tools can also become useful to introduce desirable traits from wild relatives to its domesticated counterparts, a goal of great interest for crop improvement  and currrocesses .e.g., Plant and animal domestication can be studied using genomic, transcriptomic and epigenomic strategies, revealing the action of evolutionary, ecological and anthropogenic processes . These tNonetheless, the modern study of domestication of plants and animals should still be multidisciplinary, since genetics only tells us part of the story . An exteJB-R, DP, and LE wrote the manuscript. All authors contributed to the article and approved the submitted version.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.


The particle size distribution, residual drug volume, and nebulization time remained similar across four weeks of use . Mesh was inspected using optical microscopy and showed that approximately 50% of mesh pores were obscured after 84 runs, and light penetration through the aperture plate was significantly reduced after the 21st use (p < 0.001) with no correlation to nebulizer performance. We conclude that the vibrating-mesh nebulizer delivered doses of salbutamol solution effectively over four weeks without cleaning after each use even though the patency and clarity of the aperture plate were reduced by the first week of use.This in vitro study evaluates the performance of a disposable vibrating-mesh nebulizer when used for 28 days. A lung model was used to simulate the breathing pattern of an adult with chronic obstructive pulmonary disease. The vibrating-mesh nebulizer was used for three treatments/day over 28 days without cleaning after each test. Results showed that the inhaled drug dose was similar during four weeks of use ( The administration of aerosolized drugs using a vibrating-mesh nebulizer has gained popularity because of its high delivery efficiency, low residual dose, and lack of requirement for external gas power ,2. A vibThe performance of a vibrating-mesh nebulizer can be affected by numerous factors, including viscosity and electrolyte conductivity of the solution, and clogging of the mesh plate by drug crystallization ,10,11. BClinicians are concerned that a vibrating-mesh nebulizer may malfunction owing to clogging or occlusion of the mesh plate. Previous research has suggested that suspensions or viscous drugs could clog the pores of a mesh nebulizer without making a noticeable difference to the nebulizer output . RottierAccording to the manufacturer, the Aerogen Solo single-patient, a multiple-dose disposable vibrating-mesh nebulizer can be used for up to 28 days without cleaning between treatments. A consistent performance of the nebulizer is crucial for patients receiving aerosol therapy. Therefore, the aim of this in vitro study was to evaluate the performance of this vibrating-mesh nebulizer when repeatedly used over time.2O, and airway resistance of 20 cm H2O/L/sec. A collecting filter was placed distal to the trachea of a teaching mannequin to capture aerosols, referred to as the inhaled dose.A dual-compartment passive test lung  consisting of two compartments connected with a rigid bar and a breath-simulation module, which inflates one compartment which lifts the other compartment, simulating a spontaneous breathing pattern . The modTM, Aerogen Ltd., Dangan, Galway, Ireland) placed on the face of the adult teaching mannequin; 2 L/min of oxygen was delivered to the oxygen port of the reservoir [A vibrating-mesh nebulizer  was selected for our experiments because of its label claim for extended use, ready availability, and use in hospital settings. The nebulizer was connected to a valve mask, and valve reservoir . Additionally, the nebulization time was recorded.The mass median aerodynamic (MMAD) was measured using an Anderson Cascade Impactor  during the 1st, 21st, 42nd, 63rd, and 84th run of each nebulizer, representing the performance after each week of use. The MMAD is defined as the diameter at which 50% of the particles by mass are larger and 50% are smaller, whereas the FPF corresponds to the fraction of drugs carried in particles with a diameter of <4.7 µm. The MMAD and geometric standard deviation (GSD) were calculated using the Copley Inhaler Testing Data Analysis Software .R2 = 0.9999). Salbutamol drug mass was calculated from the absorption-concentration standard curve.The aerosolized drug deposited on the collecting filters distal to the trachea was eluted with 10 mL of distilled water with gentle agitation for 2 min. The collection plates of the Anderson Cascade Impactor were disassembled and eluted with 10 mL of distilled water. The absorbance of each drug sample was measured using an ultraviolet spectrophotometer  at a wavelength of 276 nm. There was a linear relationship between the absorption and concentration of salbutamol between 2.0 and 250 µg/mL with a slope of 0.0061 . Image analysis was performed using ImageJ software. The patency of the aperture plates was determined by the penetration of the hole size and number of holes.p values < 0.05 were considered to be statistically significant.The amount of drug deposited on the filter was expressed as the total fraction of the charged dose placed in each nebulizer. Descriptive statistics, including the mean and standard deviations, were calculated. Data were analyzed using Statistical Package for the Social Sciences version 24.0 . The performance of the nebulizer over time was assessed using repeated-measures analysis of variance of inhaled drug dose, particle size, nebulization time, and residual volume. Changes in light penetration among the three conditions were analyzed using analysis of variance. p = 0.157). Compared to week 1, the inhaled drug dose was significantly increased at week 3 (p = 0.016) and week 4 (p = 0.045).The average inhaled drug doses of 5 nebulizers were between 13.9% and 19.3% and showed a low correlation with the number of uses . Resultsp = 0.110). The geometric standard deviations (GSD) were 2.0 ± 0.1 at baseline, 2.04 ± 0.09 at the first week, 2.02 ± 0.08 at the second week, 2.02 ± 0.04 at the third week, and 2.04 ± 0.05 at the fourth week (p = 0.626). The fine particle faction (<4.7 μm) was 65.6 ± 5.4% at baseline, 64.7 ± 4.7% at the first week, 64.7 ± 4.5% at the second week, 58.4 ± 2.4% at the third week, and 60.5 ± 1.9% at the fourth week (p = 0.712). The residual volumes measured gravimetrically were similar across results obtained from 0.05 ± 0.02 µL at baseline, 0.05 ± 0.03 µL at week 1, 0.04 ± 0.03 µL at week 2, 0.05 ± 0.03 µL at week 3, and 0.04 ± 0.03 µL at week 4 (p = 0.763). Lastly, the nebulization times were 7.37 ± 2.38 min at baseline, 7.01 ± 1.53 min at the first week, 6.74 ± 1.24 at the second week, 6.9 ± 1.21 min at the third week, and 6.74 ± 1.12 min at the fourth week (p = 0.573).t week 4 C, withourth week D. The ne2 for the new aperture, 79 pixel2 for 21 runs, and 76 pixel2 for 84 runs (p < 0.001). We measured the total penetrated light intensity to represent mesh penetration, reflecting the number of holes and open area of holes. p < 0.001).The images of aperture plates nebulizers unused and after 21 and 84 runs are shown in Our study evaluated the performance of a repeatedly used vibrating-mesh nebulizer for 84 administrations of salbutamol over 28 days without rinsing or washing between treatments. Performance tests showed device reliability and performance consistency over 28 days of use regarding the inhaled dose, residual volume, particle size, and nebulization time. The images of aperture plates were partially occluded by the first week of use, but no further occlusion, was observed by the end of the 28-day use, with no direct correlation to reduced performance.The effectiveness of a vibrating-mesh nebulizer has been extensively studied compared to other types of nebulizers. Only a few studies have assessed the reliability of vibrating-mesh nebulizers. Gowda et al., determined the reliability of a vibrating-mesh nebulizer (Aerogen solo™) using 3 mL normal saline by measuring the residual volume and nebulization time of 20 nebulizers each used with two controllers . They reRottier et al., evaluated the performance of the eFlow vibrating mesh nebulizer device administering tobramycin for six months and reported that the average nebulization time was longer when the nebulizer was not cleaned after use . The volThe Solo nebulizer aperture plate consists of 1000 funnel-shaped apertures. Microscopy images showed apparent occlusion or obscuration of apertures after 21 doses administered. From images with lower magnification, we observed heterogeneous obscuration patterns. Obscured apertures appeared aggregated, with a diameter of several hundred-micron areas—which suggests that clogging may alter local aerosol-flow distribution. At a higher magnification, more apertures showed irregular peripheries and appeared to be partially clogged or obscured, presumably with solute crystals, after extended use. We found that the obscuration level increased with usage time, but there was no obvious correlation between obscuration levels and inhaled doses, MMAD, and time of administration. This observation seemed consistent with previous reports that clogging did not significantly change the dose output of a mesh nebulizer . Only a As the vibrating-mesh nebulizer is considered relatively expensive compared to jet nebulizers, the ability to use for extended periods is critical for care providers to assess the cost/benefit for its use with their patients.According to the manufacturer, the mesh nebulizer has approximately 1000 apertures. Not all apertures actively generate aerosol at the same time. It is unclear whether the perception of diminishing hole size is due to blockage of the apertures by crystals formed by drying out of residual albuterol and saline. This residual is possibly reconstituted with the addition of the next dose. With the low residual drug volume of the nebulizer, these reconstituted “crystals” would add a small amount of active drug to the subsequent dose. We calculated a maximum dose of 5.102 mg at dose 84. Additionally, while inhaled doses between each week appeared statistically significant, the differences would be too small to be of practical importance. The variation in MMAD, and inhaled dose would likely not impact clinical response.Although the images of aperture plates appear to document partial occlusion after one week of use when not cleaned between treatments, the inhaled drug dose and nebulizer performance was similar across the four weeks of repeated use. This suggests that the staff, when administering aerosol treatment, can place the mesh nebulizer in the ventilator circuit without removing between doses to clean or add medication, avoiding “breaking the circuit”, which interrupts ventilation and can generate aerosols of contaminated condensate with explosive depressurization of the ventilator circuit, which may transmit infectious agents to care providers and others in the vicinity of the patient . During While our results demonstrated that the performance of the mesh nebulizer was consistent over 28 days of repeated use, we did not address potential contamination of the nebulizer over this period of time and use without cleaning. There are several implications for contamination with repeated nebulizer use . PrimaryOur experiments were performed using just one brand and model of vibrating-mesh nebulizer, and our findings should not be extended to other mesh nebulizers without additional testing. We chose this particular brand of vibrating-mesh nebulizer because it is widely used to treat acutely ill and intubated patients during mechanical ventilation. The inhaled drug dose is influenced by various factors during mechanical ventilation; therefore, further study on drug delivery in a ventilator system is warranted. The particle size testing was based on salbutamol solution, which is the most commonly administered drug used with nebulizers ,18. FurtThis study illustrates that the nebulization time, residual volume, and particle size of the vibrating-mesh nebulizer used without cleaning were consistent and reliable after repeated use over 28 days, despite the appearance of obscuration or partial clogging of apertures. Further studies on the repeated use of various liquid medications, such as antibiotics or budesonide suspension, are warranted.


Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert–Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain. In recent years, neural decoding research has progressed along with the expansion of machine learning (ML). Neural decoding has also been applied for interpreting mental states , 2 and fexpMSE). Consequently, we used the expanded MSE (expMSE) of decomposed signals as inputs for neural decoding.However, despite remarkable progress in ML, some room exists for improving preprocessing before ML. At present, raw data and band power are used mainly as inputs in E/MEG-based neural decoding. Comparably to raw data, band power improves decoding performance by decomposing the frequency information in advance , 16. HowMental imagery is a behavior with various modalities, such as visual, auditory, and motor, that has a multifaceted association with psychiatric disorders. For example, the mental imagery capabilities are altered in autism spectrum disorder (ASD) patients , concernIn summary, this study’s aims are two-fold: 1) achieve CNN decoding with the expMSE; and 2) achieve hierarchical task decoding to examine supramodal and modality-specific neural oscillations. For this study, we measured MEG in healthy participants performing hierarchical multimodal tasks , calculated the expMSE, and applied it to CNN decoding.This study examined eight healthy participants . All participants were native Japanese speakers reporting no prior or existing psychiatric, neurological, or medical illness. Participants were screened with a structured clinical interview for Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV-TR  to confiAll participants underwent MEG examination while performing several tasks. To obtain a diverse distribution of brain activity data, we defined 12 multimodal tasks: visual perception ; visual imagery ; auditory perception ; auditory imagery ; motor execution ; motor imagery ; visual imagery 2 ; motor imagery 2 ; auditory perception 2 , auditory perception 3 ; visual perception 2 ; and visual perception 3 . They completed eight trials × 3 sessions. To acquire a stable index, the tasks were performed successively in a fixed order as described above. Each trial included 6 s of ‘rest’ and 6 s of the ‘task’. Each task was started and stopped in conjunction with an acoustic stimulus. In each of the three sessions, a total of eight trials × 12 tasks were performed. The first session was performed as a practice session. The second and third sessions were analyzed. Sufficient breaks were given between the sessions to prevent fatigue. During each task, the participants opened their eyes and looked at a display. In addition, the first of the eight trials for each task was excluded from analyses because there was no pre-task break. The number of trials for the analyses was 14  per task.Magnetic fields were measured using a whole-head system for adults at the Laboratory of Yokogawa Electric Corp. in Japan. This system  consisted of 160 channels. Magnetic fields were sampled at 2,000 Hz per channel (bandpass 0.16–500 Hz). The T1-weighted magnetic resonance imaging (MRI) images were acquired . All participants had pointed spherical lipid markers placed at the five MEG fiduciary points to enable superposition of the MEG coordinate system on the MRI. The MRI consisted of 166 sequential slices of 1.2 mm, with resolution of 512 × 512 points in a field of view of 261 × 261 mm. Individual cortex envelopes were extracted using FreeSurfer 5.1 for cortical surface-based analysis  , 54.Preprocessing of the MEG data presented in this section was conducted using software . The MEm+1)-th time point from 1 to m time points. As described by Porta et al. -dimensional vector obtained by concatenating nx to nx given past values p(xn) and m+1)-dimensional and m-dimensional vectors described earlier are xn and To assess the temporal changes of the complexity, we modified existing complexity measures. ApEn and SampEn are well known to represent the temporal complexity of the time series. One report of the literature by Porta et al.  proposeda et al. , ApEn, STherein, <∙> represents the average over time. They can be summarized briefly as follows: ApEn represents the entropy computed at each time point and then averaged; SampEn represents the average of each probability before computing entropy; and LSampEn is a value representing the entropy after averaging the probability distribution. For the present study, expSampEn is defined as the time evolution of complexity without performing averaging over time.m = 2 and r = 0.2 to calculate the entropy  per trial. Consequently, 12 tasks × 14 trials × 126 crops were obtained both for the expMSE and the band power time series. However, 134 crops were obtained for the raw data with a sliding 2 s window (400 time points).Additionally, we adopted a cropping strategy , 63, 64.CNN decoding in this section was conducted using Keras 2.3.1 .For this study, we adopted a CNN model: a deep neural network that can learn local patterns in data using convolutional filters. Although CNN has made remarkable progress, especially in computer vision tasks, it has also been successful in recent years for neural decoding , 14, 66.a priori feature extraction, shallow CNN showed high decoding accuracy comparable to that of deep CNN. Because we applied EEMD, HSA and complexity analysis to obtain the inputs, we adopted a shallow CNN . This led to multiple label predictions per trial. The average of these predictions was used as the final prediction for the trial during the test phase. During training, we computed a loss for each prediction.As explained in We performed decoding with intra-subject and inter-subject designs. For intra-subject decoding, 14 trials were divided into four groups . The decoding performance was examined in 12 combinations of train:validation:test = 2:1:1. For inter-subject decoding, all 14 trials of one subject were used as test data. Seven trials of the remaining subjects were assigned to training and validation data.t-tests (two-tailed).Differences in decoding performance were examined using paired cS(x) be the score of the class c computed by the classification layer of the CNN for an input x, then the final classification class(x) can be represented asIn addition to identification of the features used for the classification, we calculated a gradient-based sensitivity map of the model for each feature map , 69. ThicM(x) asThen, we define a sensitivity map Sc(x) represents the derivative of cS. Therefore, cM(x) represents the amount of change in the class score when input x is perturbed. We used gradients function from keras.backend. This gradient of the class score with respect to input x elucidates which features are influential for the final classification (where ∂fication –70. Intup(band power – raw) < 0.001; p(expMSE – raw) < 0.001; p(expMSE – band power) = 0.003; p = 0.048; We first decoded the VP, VI, ME, and MI tasks, i.e., performed four-class classification. In intra-subject decoding, the expMSE showed higher performance than raw data and band power  = 0.019; p(expMSE – raw) < 0.001; p(expMSE – band power) = 0.006; p = 0.27; Furthermore, we decoded modality-independent imagery behavior: we applied two-class classification (imagery vs. non-imagery). For intra-subject decoding, the expMSE showed higher performance than band power . For intra-subject decoding, the decoding performance was higher such that the expMSE > band power > raw data, with significant differences between conditions, in two classes of imagery and non-imagery and four-class  classification (t-test (p = 0.048) but not by a Wilcoxon signed rank test (p = 0.078). However, the accuracies exceeded chance levels in all cases except for one condition  × time scale. For this study, this information was reshaped as a two-dimensional map of the 68 regions × 30 oscillatory properties. It was processed by shallow CNN Figure 2fication –77. In afication , used a fication , 79. Thet p = 0.0. HoweverMental imagery-related brain regions have been discussed in terms of modality-specific regions and supramodal imagery-related regions . To examWe first consider intra-subject decoding with the expMSE. Practically, in the four-class classification shown in We consider a sensitivity map with band power. In all conditions, sensitivity maps with band power were similar to that with expMSE, but few characteristic features were found in supramodal regions  and used these methods to examine modality-specific and supramodal imagery-related neural oscillations. Results indicate the usefulness of CNN with the expMSE for neural decoding and support the possible common imagery-related mechanism proposed in earlier studies demonstrating that the modulated activity pattern in the modality-specific regions and the additional activity in the supramodal imagery-related regions might be involved in imagery behaviors.furutaninaoki@gmail.com.The datasets presented in this article are not readily available because of a non-disclosure agreement. Requests to access the datasets should be directed to Naoki Furutani, The studies involving human participants were reviewed and approved by the ethics committee of Kanazawa University Hospital. The patients/participants provided their written informed consent to participate in this study.NF designed the study. NF, YY, HH, CH, and TI conducted data curation. NF and YN conducted data analyses. TT and MK supervised the research. NF and HI wrote the first draft of the manuscript. TT and MK revised the manuscript. All authors contributed to the article and approved the submitted version.https://www.coistream.osaka-u.ac.jp/en). The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.This work was supported by the Center of Innovation Program and CREST (Grant Number JPMJCR17A4) from the Japan Science and Technology Agency (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.


Dioscorea alata, one of the oldest edible tubers, remain unclear. Here, we address yam demographic and dispersal history using a worldwide sample.Inferring the diffusion history of many human-dispersed species is still not straightforward due to unresolved past human migrations. The centre of diversification and routes of migration of the autopolyploid and clonally propagated greater yam, We characterized genome-wide patterns of genetic variation using genotyping by sequencing 643 greater yam accessions spanning four continents. First, we disentangled the polyploid and clonal components of yam diversity using allele frequency distribution and identity by descent approaches. We then addressed yam geographical origin and diffusion history with a model-based coalescent inferential approach.Diploid genotypes were more frequent than triploids and tetraploids worldwide. Genetic diversity was generally low and clonality appeared to be a main factor of diversification. The most likely evolutionary scenario supported an early divergence of mainland Southeast Asian and Pacific gene pools with continuous migration between them. The genetic make-up of triploids and tetraploids suggests that they have originated from these two regions before westward yam migration. The Indian Peninsula gene pool gave origin to the African gene pool, which was later introduced to the Caribbean region.Our results are congruent with the hypothesis of independent domestication origins of the two main Asian and Pacific gene pools. The low genetic diversity and high clonality observed suggest a strong domestication bottleneck followed by thousands of years of widespread vegetative propagation and polyploidization. Both processes reduced the extent of diversity available for breeding, and this is likely to threaten future adaptation. The present geographical distribution of flora and fauna is often the result of a long process of human-mediated dispersal, which had profound effects on the genetic diversity and demographic histories of plant and animal populations  belongs to the family Dioscoreaceae. Unlike other edible yam species, its pantropical distribution can mainly be explained by its ease of cultivation and broad tolerance to different environments   . It has atedness , or D. nD. alata, has a Laurasian origin placed in East Asia in the Late Oligocene with D. alata speciation dated, using plastid markers, to the late Miocene around 7 Mya (Colocasia esculenta (L.) Schott), banana (Musa sp.) and greater yam’, during the westward expansion of Austronesian-speaking seafarers while colonizing Madagascar through a central Indian Ocean corridor ; Africa: Benin, Burkina Faso, Congo, Côte d’Ivoire, Equatorial Guinea, Ghana, Madagascar, Nigeria, Sierra Leone and Togo (141); the Caribbean: Cuba, Dominican Republic, Guadeloupe, Haiti, Jamaica, Martinique, Puerto Rico, Saint Lucia and Saint Vincent (157); and the Pacific: New Caledonia, Papua New Guinea and Vanuatu (123)  on 200 ng of genomic DNA for eacch sample, followed by ligation with a barcode adapter and a common Illumina sequencing adapter. Amplified multiplexed libraries were purified and verified to ensure that most of the DNA fragments were between 150 and 300 bp. Sequencing was conducted on an Illumina HiSeq 3000 system  at the GeT-PlaGe platform in Toulouse, France.We collected 600 accessions of greater yam, tu (123) . As we htu (123) . To set D. rotundata reference genome  . First, Different parameters/thresholds  were tested to filter the GBS data and barplots were drawn using 100, 250 or 500 classes . All comN = 93) using the fitdist function of the fitdistrplus R package . Finally, a network visualization based on significant kinship was drawn up using the prefuse force directed layout implemented in the Cytoscape 3.4.0 software (The genotyping matrix (643 genotypes × 6017 SNPs) was converted into a 012 incidence matrix with 0, 1 and 2 corresponding to homozygote for the reference allele, and heterozygote and homozygote for the alternate allele, respectively. Three matrices were produced, the first with the whole dataset, the second after removal of accessions with unknown ploidy (487 genotypes), and the third including the 93 independent diploid accessions  as well as the triploid and tetraploid accessions. Missing data were imputed using the mean allele frequencies. Kinship between samples was calculated as 1 minus the pairwise genetic distance derived from the Manhattan function and thus assessed as a percentage of common alleles. Significant kinship between samples was detected fitting a normal law on the kinship distribution between independent diploids ( package . The sigR) was calculated as described by Fis. We then generated a sample with unique diploid genotypes and randomly selected one MLG representative from each MLL.To identify multi-locus lineages (MLLs) represented by distinct multi-locus genotypes (MLGs), kinship coefficient, π, and identity-by-descent (IBD) probabilities for all possible pairs among the 347 diploid accessions were calculated using PLINK software , and theD , Indian Peninsula and Pacific gene pools. Migration was then added between the Indian Peninsula and the Pacific in the two best topologies obtained from previous analyses. After analysing the relationship between these three populations, Africa was included in the best model obtained previously and three further models were tested. The Caribbean sample was not included in this analysis as yam was introduced to the Americas relatively recently, primarily from Africa via the slave trade . Each moD. rotundata V1 reference genome  and by the negative inbreeding coefficients (excess of heterozygosity) within continents . The other 249 landraces had at least one clone and were spread over 40 MLLs, containing between two and 19 accessions . The 302ntinents .Among the 40 MLLs, only one MLL had clones shared between the four continents while 13 had an intercontinental distribution . Most of−5 and 1.26e−5 respectively) and the lowest for the Caribbean and Africa   with lower values obtained for MSEA and the Pacific , indicating a weak but generally significant differentiation between the gene pools  . This gectively) . A negatne pools . The lowFirst, we assessed the most likely tree topology for the MSEA, Indian Peninsula and Pacific gene pools in the presence and absence of gene flow. First, three simple demographic models were tested, featuring the MSEA, Indian Peninsula and Pacific gene pools. Migration was added between the Indian Peninsula and the Pacific in the two best topologies obtained from previous analyses. After analysing the relationship between these three populations, an AIC-based model comparison supported a scenario in which the Pacific lineages split early from an Asian ancestral population. Asian lineages split later into MSEA and IndP, and continuous migration occurred between Indian and Pacific gene pools . We thenDioscorea alata is a challenging task for multiple reasons. Greater yam is unknown in the wild and its wild relatives have yet to be identified. The origin of greater yam domestication and its dispersal are closely tied to three main historical events that are still under investigation, i.e. the Sunda or Sahul  domestication origin of many crops, human migrations from Asia to Africa via the Indian Ocean and the more recent Colombian exchange. We advanced our understanding of D. alata diversity and evolution by analysing the widest and most comprehensive sample of greater yam to date, spanning the four main continents where the species is cultivated.Investigating the evolutionary history of a crop such as We showed that worldwide diversity of greater yam is characterized by a high extent of clonality, corroborating findings from our previous studies . While African rice (Oryza glaberrima), probably introduced concomitantly with yams, has now been supplanted by Asian rice (O. sativa) (We gathered different lines of evidence supporting that the Caribbean gene pool mainly originated from Africa. Genetic differentiation was almost null between African and the Caribbean diploid gene pools. Most of the accessions were assigned to the Indian Peninsula and African gene pools. Moreover, most of the clonal lineages were shared with Africa. Greater yam was most probably introduced to the Americas during the Colombian exchange. The slave trade is known to have been a factor of introduction of African crops to the tropical Americas during Colombian exchange . ReportsIn the present study, our genetic analysis and demographic inference supported an early divergence of greater yam between Mainland Southeast Asia and the Pacific, probably followed by two independent domestication events. The species would then have reached the Indian Peninsula, subsequently Africa and from there the Caribbean. We also revealed high clonality and low nucleotide diversity, which are indicators of a strong domestication bottleneck and a diversification process achieved mainly via somaclonal accumulation. The narrow diversity raises concerns about the scope for genetic improvement of traits of interest. Future research efforts should have the two-fold aim of exploring the adaptation of worldwide-distributed clonal lineages under different cropping systems and environmental constraints, and identify useful alleles within the untapped diversity of greater yam wild relatives.https://academic.oup.com/aob and consist of the following.Fig. S1: Geographical origin of the 643 accessions of Dioscorea alata. Fig. S2: Bar plot of the allelic frequency distribution and the corresponding number of chromosomes after chromosome counting for diploid, triploid and tetraploid accessions. Fig. S3: The density distribution of identity by descent for all possible pairs of accessions, full-sibs and half-sibs, and the frequency distribution of all possible pairs of accessions with a defined window. Fig. S4: Distribution of the 6017 SNPs along the Dioscorea alata genetic map. Fig. S5: Network visualization of the genetic relationships between the 643 accessions according to their ploidy level and their geographical origin. Fig. S6: Cluster assignment of 93 diploid greater yam genotypes estimated using ADMIXTURE. Fig. S7: The different scenarios of domestication simulated with FASTSIMCOAL2.6. Table S1: Description of the optimal set of parameters used on the whole dataset and defined in the model training for inferring the ploidy level from GBS data. Table S2: Dioscorea alata accessions used in this study. Table S3: Geographical distribution of diploid genotypes showing estimated proportion of membership of their genome in each of the four gene pools. Method S1: PolynomPloidy.R script. Method S2: Description of the demographic models tested.Supplementary data are available online at mcaa122_suppl_Supplementary_MaterialClick here for additional data file.


Geographic patterns in human genetic diversity carry footprints of population history and provide insights for genetic medicine and its application across human populations. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure do not incorporate geography directly, and it must be considered post hoc alongside a visual summary of the genetic structure. Here, we estimate “effective migration” surfaces to visualize how human genetic diversity is geographically structured. The results reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, the relationship is often subtly distorted. Overall, the visualizations provide a new perspective on genetics and geography in humans and insight to the geographic distribution of human genetic variation. In many regions of the world, human genetic diversity “mirrors” geography in the sense that genetic differentiation increases with geographic distance  data combined from 27 different data sets comprising a total of 6,066 individuals from 419 locations across Eurasia and Africa  online.a and 3supplementary figs. 2, b and 4supplementary figs. 2, We organize our applications in seven analysis panels: an overview Afro-Eurasian panel (AEA), four continental-scale panels, and two panels of Southern African KhoeSan and Bantu speakers. In all cases, the inferred EEMS surfaces are “rugged,” with numerous high and low effective migration features a and 2 tn = 4,697 samples; 370 locales; FST = 0.071) we see that 19 out of 25 troughs visually align with plausible topographical obstacles to migration, such as deserts , seas , marine straits  and mountain ranges  or a combination thereof . Many of these features, such as the Sahara desert . Although, PCA-biplots typically reflect large-scale gradients of diversity in a panel, EEMS emphasizes local distortions, such as troughs features that are often imperceptible in the PCA-biplots  online. The maps we present provide compact summaries of the complex relationship of genes and geography in human populations. Most of the clearest features in these maps  between diverged populations , EEMS moSecond, decisions regarding which samples to include will affect the outcome of any analysis. When there is a feature inferred in a region with few samples, the exact positioning of the inferred change on the map will be imprecise , with the exception of SEA, where nine  failed and were excluded.The remaining SNPs were then merged using successive plink –bmerge commands into a single master data set with 9,003 individuals and 1.9 M SNPs but a total genotyping rate of only 20.6%. Forty six SNPs were removed because different studies reported different alternative alleles. We used a relationship filter of 0.6 using the “–rel-cutoff 0.6” flag in plink to remove 667 closely related individuals or duplicates. After merging, each analysis panel had missingness rates <0.5% . In all panels, all SNPs passed a one-sided HWE-test  to filter SNPs that did not map onto hg19 and we removed strand-ambiguous AT and GC polymorphisms.POPRES data were obtained under dbGAP study accession phs000145 to John Novembre, and we used the data as processed in http://www.biologiaevolutiva.org/dcomas/? p=607. We used version 32 of the annotation file GenomeWideSNP_6.na32.annot.csv” obtained on June 19, 2015 from affymetrix.com to map SNPs onto hg19, remove strand-ambiguous SNPs and to flip SNPs that were on the minus-strand.Data from The data were obtained on July 14, 2015 from Mark Stoneking in three different source files . After mhttp://drineas.org/Maritime_Route/RAW_DATA/PLINK_FILES/MARITIME_ROUTE.zip  sampling coordinates. However, the level of accuracy varied between sources, with some providing specific ethnicities, some (such as POPRES) only providing country information and others just providing city- or state-level information. For POPRES-derived data, and most countries, we assigned individuals to the country’s centerpoint, with the exception of Sweden and Finland, which were assigned their capital. For the Estonian Biocentre data, sampling location data were highly heterogeneous. Samples that could not be confidently assigned to a region with an accuracy of 100 km were excluded. For populations with samples from multiple studies, the most accurate source location was used. For locations covered with different accuracy, only the most accurate samples were retained. For example, we dropped all Spanish individuals from POPRES (only country level data), as the Human Origins data provided higher resolution, with samples from eleven different regions in Spain. The resulting table is given as To validate troughs correlating with presumed language barriers, we cross-referenced the genetic data with linguistic data from the Glottolog 3.2 database . To do sn = 74), we removed a small number of samples that would violate some assumptions of the EEMS model. In particular, we excluded all Jewish samples (n = 379), due to complexity of the diaspora and subsequent local admixture (n = 170), who both are recent migrant population to those locales. To avoid any possible distortion due to uneven sampling, we downsampled all single locales to at most 50 individuals, drawn independently for different panels. This resulted in a total of 6,066 individuals used in at least one panel . All configuration variables are stored in json and yaml config files. We perform EEMS and PCA for each panel independently. Structural variants are a potential confounding factor for genome-wide SNP based analysis. In PCA, these variants may result in a number of neighboring SNP in high LD to have very high loadings, thus overemphasizing the effect of these variants. For this reason, it is advisable to remove regions containing SNP that have extremely high loadings on some principal component. Thus, for each panel, we perform a preliminary PCA analysis using flashpca (We developed a second pipeline using snakemake  to perfoflashpca . The loahttps://github.com/dipetkov/eems), we must choose a grid size and boundaries. Choosing a coarse grid results in faster computation, but only produces a map with broad-scale patterns. A finer grid, on the other hand, is able to reveal more details, but at a steep increase in computational cost and with an increased danger of introducing patterns that are harder to interpret. Grid density and sizes are given in FST calculated using plink, and FST based on the mean migration rate inferred by EEMS and equilibrium stepping stone model theory  using dggrid v6.1  and inteFor EEMS, the posterior samples imply an expected distance matrix between populations. For PCA, the components and their loadings provide an approximation to the genetic distance matrix between individuals. We use the median PCA values of individuals across two, ten, or 100 PC components to produce an expected genetic distance matrix between populations. For each method, the expected genetic distance matrices are compared with the observed matrices using a simple linear correlation computed between all pairwise distances.msz280_Supplementary_DataClick here for additional data file.


Ho) = 0.23, and unbiased diversity index (uh) = 0.34. The present results will not only help breeders to understand the genetic diversity of wheat germplasm on the Eurasian continent between the latitudes of 30° and 45°N, but also provide valuable information for wheat genetic improvement through introgression of novel genetic variation in this region.Obtaining information on the genetic diversity and population structure of germplasm facilitates its use in wheat breeding programs. Recently, with the development of next-generation sequencing technology, genotyping-by-sequencing (GBS) has been used as a high-throughput and cost-effective molecular tool for examination of the genetic diversity of wheat breeding lines. In this study, GBS was used to characterize a population of 180 accessions of common wheat originating from Asia and Europe between the latitudes 30° and 45°N. In total, 24,767 high-quality single-nucleotide polymorphism (SNP) markers were used for analysis of genetic diversity and population structure. The B genome contained the highest number of SNPs, followed by the A and D genomes. The polymorphism information content was in the range of 0.1 to 0.4, with a mean of 0.26. The distribution of SNPs markers on the 21 chromosomes ranged from 243 on chromosome 4D to 2,337 on chromosome 3B. Structure and cluster analyses divided the panel of accessions into two subgroups (G1 and G2). G1 principally consisted of European and partial Asian accessions, and G2 comprised mainly accessions from the Middle East and partial Asia. Molecular analysis of variance showed that the genetic variation was greater within groups (99%) than between groups (1%). Comparison of the two subgroups indicated that G1 and G2 contained a high level of genetic diversity. The genetic diversity of G2 was slightly higher as indicated by the observed heterozygosity ( Triticum aestivum L.) is an important staple food crop for more than one-third of the world’s population and provides about 20% of calories consumed by humans , which can reduce complexity to a greater extent and achieve a more unified sequencing library than a one-enzyme protocol . GBS libraries were constructed following the protocol of Evolutionary relationships among the 180 wheat accessions were determined using the unweighted pair group method with arithmetic mean (UPGMA) based on genetic distances computed with TASSEL. Dendrograms were constructed using the dendrogram function, and then customized using the dendextend package  and circFST), and He    describeHo) (Na), number of effective alleles (Ne), observed heterozygosity (Ho), diversity index (h), unbiased diversity index (uh), and Shannon’s information index (I) were calculated. The AMOVA and estimation of genetic indices were performed using GeneAlEx 6.41 , polymorphism information content (PIC), MAF, and observed heterozygosity (Ho) . The SNPlEx 6.41 .A total of 24,767 SNPs were identified in the A, B, and D genomes. The highest number of SNPs were identified in the B genome , followed by the A genome , and the D genome had the lowest number of polymorphic markers with 2,998 . In the K values obtained were used to determine the optimal number of subpopulations. To determine the optimal k value, the number of clusters (K) was plotted. At k = 2 (He) in each subpopulation (Fst = 0.40), whereas the lowest value was observed in Group 1 (Fst = 0.13). On the other hand, the lowest He was observed in Group 2 and the highest He was detected in Group 1. The results of STRUCTURE analyses  and effective (Ne) allele numbers were 2.00 and 1.52 for the two subpopulations, respectively. The value of Ne in Group 2 (1.52) was higher than that in Group 1 (1.51). The mean values of I, Ho, h, and uh were 0.51, 0.23, 0.33, and 0.34, respectively. However, the Group 2 population showed slightly higher genetic diversity (Ho = 0.23 and uh = 0.34)  .Nm was observed (28.12), suggesting extremely high gene flow among subpopulations  than among the populations (1%). High haploid ulations . These rulations  indicateT. aestivum and its tetraploid progenitor T. turgidum (AABB) but not between the hexaploid and Aegilops tauschii (DD)  lines and found a QTL (QYR-2B.2) which was associated with yellow rust resistance. Similarly, in the 680–690 Mb of chromosome 1B (QTgw.ipk-1B-FS4) which was associated with TKW was identified  . This cohii (DD) . Furtherosome 1B , a QTL  when PIC > 0.5, the marker is considered to be highly polymorphic, (2) when 0.25 < PIC < 0.5, the marker is a moderately informative, and (3) when PIC < 0.25, the marker is a low-information marker . Lopes eNm (haploid) values are less than 1, gene exchange among subpopulations is limited. In the present study we observed an extremely high Nm value (28.12), indicating that high gene flow led to low genetic differentiation among subpopulations. The results of this study will not only help breeders to understand the genetic diversity of wheat germplasm on the Eurasian continent between the latitudes of 30° and 45°N, but also provides valuable information for genetic improvement of wheat through inclusion of novel genetic variation from China and certain other countries.In the present study, we obtained meaningful information on genetic diversity indices in each subpopulation. High levels of genetic diversity were detected in Groups 1 and 2. The results of AMOVA showed that a high level of genetic diversity was observed within subpopulations, whereas the variation among subpopulations was extremely low (1%). This result may be caused by breeders selecting for specific traits, such as yield, stripe rust resistance, and herbicide tolerance. However, the low genetic variability among subpopulations is explained by the high gene flow . Wright The PCA revealed a degree of broad geographic partitioning of the accessions. A previous study by In this study, a GBS protocol was used to investigate the population structure and genetic diversity of wheat accessions originating from the Eurasian continent between the latitudes of 30° and 45°N. The panel of 180 accessions was divided into two subgroups, which could be identified by their parentage and selection history. Group 1 principally consisted of European and a portion of Asian accessions, and Group 2 predominantly comprised Middle East and a portion of Asian accessions. Groups 1 and 2 showed high values for genetic diversity indices, which were higher for Group 2. The present results demonstrated that the 180 accessions represent high genetic diversity and can be used for future breeding programs to develop new wheat cultivars with desirable characteristics such as high yield potential, tolerance to biotic and abiotic stress, and good end-use quality, while being well-adapted to diverse environments in China and other countries.https://figshare.com/s/157a9613b935814cc2d5).We have uploaded our SNP sequencing data to FigShare repository (HK and YZ conceived and designed the research. XY, BT, and HL conducted the experiments. WZ, LX, YW, XF, LS, JZ, DW, YJ, and XH participated in the preparation of the reagents and materials. HZ and GC analyzed the data. XY and HK wrote the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Capsella bursa‐pastoris (Brassicaceae) as expressed in allozymic (within‐locus) diversity and isozymic (between‐locus) diversity. Results are based on a global sampling of more than 20,000 C. bursa‐pastoris individuals randomly taken from 1,469 natural provenances in the native and introduced range, covering a broad spectrum of the species’ geographic distribution. We evaluated data for population genetic parameters and F‐statistics, and Mantel tests and AMOVA were performed. Geographical distribution patterns of alleles and multilocus genotypes are shown in maps and tables. Genetic diversity of introduced populations is only moderately reduced in comparison with native populations. Global population structure was analyzed with structure, and the obtained cluster affiliation was tested independently with classification approaches and macroclimatic data using species distribution modeling. Analyses revealed two main clusters: one distributed predominantly in warm arid to semiarid climate regions and the other predominantly in more temperate humid to semihumid climate regions. We observed admixture between the two lineages predominantly in regions with intermediate humidity in both the native and non‐native ranges. The genetically derived clusters are strongly supported in macroclimatic data space. The worldwide distribution patterns of genetic variation in the range of C. bursa‐pastoris can be explained by intensive intra‐ and intercontinental migration, but environmental filtering due to climate preadaption seems also involved. Multiple independent introductions of genotypes from different source regions are obvious. “Endemic” genotypes might be the outcome of admixture or of de novo mutation. We conclude that today's successfully established Capsella genotypes were preadapted and found matching niche conditions in the colonized range parts.We analyzed the global genetic variation pattern of  Capsella bursa‐pastoris) has been traced by molecular markers for certain regions. However, a worldwide consideration of the entire range of the species is still missing. Here, we provide the worldwide distribution patterns of genetic variation that can be explained by environmental filtering due to climate preadaptation.In the current literature, the colonization history of Shepherd’s Purse ( The use of a diverse array of neutral molecular markers, for example, isozymes, RAPDs, AFLPs, microsatellites, and finally DNA sequences and next‐generation sequencing, has greatly enhanced the ability to reconstruct the evolutionary history of colonization processes and to assess the magnitude of genetic bottlenecks and founder events  glacial times , and its putative parental lineages are ancestral to present‐day Capsella orientalis  and Capsella grandiflora (distributed mainly in western Greece) . Informative value increases along this sequence. Finally, to assess the structure of allele frequency variation in the allozyme data set, we used clustering approaches to assign multilocus genotypes into clusters and species distribution modeling to predict potential distribution of the clusters in relation to macroclimatic data.The meta‐data set comprises >20,000 individuals from an array of provenances covering large distribution areas in the native and the introduced ranges. We evaluated the data in rising complexity, from allozyme frequencies via single‐locus up to multilocus genotypes : Portugal and Spain; British Isles (BRT): Great Britain and Ireland; Middle and Western Europe (M + WE): Andorra, Austria, Czech Republic, France, Germany, Hungary, Liechtenstein, Netherlands, Poland, Slovakia, and Switzerland; Mediterranean areas (MED): Egypt, Greece, Israel, Italy, Morocco, Turkey, and former Yugoslavia; Scandinavia (SCN): Denmark, Finland, Iceland, Norway, and Sweden; Eastern Europe (EEU): Bulgaria, Estonia, and European Russia; Asia (ASIA): Afghanistan, Armenia, Asian Russia, China, Iran, Japan, Kazakhstan, Kyrgyzstan, Mongolia, Nepal, and Sri Lanka; California (CAL); North America (NAM): Canada and USA ; and Middle and South America (M + SA): Argentina, Bolivia, Chile, Costa Rica, Ecuador, Falkland Islands, Mexico, and Venezuela; and Australasia (AUS): Australia and New Zealand; and Africa (AFR): Republic of South Africa.Individual seed samples from ns Table : Iberiann = 21,812 identified as C. bursa‐pastoris, n = 263 as Capsella thracica Velen., n = 109 as C. orientalis, n = 3,141 as Capsella rubella Reuter, and n = 1,998 as C. grandiflora , glutamate dehydrogenase , and leucine aminopeptidase . For buffer systems and other experimental details, see Hurka et al., . Following isozyme systems were assayed: aspartate aminotransferase , we quantified population genetic diversity of C. bursa‐pastoris using an analysis of molecular variance (AMOVA)  by calculating the population‐wise AMOVA sums of squares divided by n − 1  between 1 and 10 were performed using an MCMC length of 106 generations following a burn‐in of 105 generations. For each K value, we used clumpp v. 1.1.2 software  and averaged assignment probabilities for each individual. We applied the Greedy algorithm for K = 1–10, using 1,000 random input orders. The most probable K value was chosen by examining the log probability of the data [ln Pr(X|K)] and plots of ∆K  one variable was excluded, respectively. This procedure retained seven variables . Since the obtained clusters are similar in population samples, no resampling was necessary.To test whether independent macroclimatic data would support the cluster structure derived from isozyme pattern, classification tree analyses were performed. Specifically, we tested whether and which bioclimatic variables supported the distinction of the found population groups for rpart package in R  approach was used to predict and analyze the potential distribution of the two clusters derived from the population genetic clustering approach (structure). To this end, the cluster affiliation probabilities from the population genetic structure analysis were binarized at the threshold of 0.5 probability for the respective cluster. We used the software MaxEnt v. 3.3.4, which provides a machine learning SDM algorithm  and precipitation metrics (Bio12‐19) at a spatial resolution of 2.5 arc minutes (approximately 4.5 km2 at the equator). The iteratively self‐optimizing MaxEnt algorithm inherently identifies variables that contribute most to increasing predictive success. Thus, an a priori variable exclusion via cross‐correlation or PCA approaches is not mandatory, and one of two potentially correlated variables will be down weighted in the prediction process. The R package ENMeval  and accuracy (AUC) was selected for each cluster, respectively. Accordingly, we performed multiple runs, with random fivefold cross‐validation between test and training data. Linear, quadratic, and polynomial functions  were used as single and combined response options, and the number of background samples was set to 50,000 to enable the worldwide climatic background to be sampled. A regularization value of 1.1 was obtained as the best option to avoid overfitting. Finally, the resulting model solutions were compared in terms of complexity (AICc) and accuracy (AUC), and the best resulting models were selected for each cluster, respectively.From the 33.1Allele frequencies in different regions are pictured in Figure 3.23.2.1r = 0.62, p = .03).We analyzed genotype frequencies at single loci and at different associations in total and in the geographical regions Table . Altoget3.2.2Aat1, Aat2, Aat3, Gdh1, Gdh2, and Lap3. A total of 8,076 individuals recorded in the native and non‐native ranges provided this complete multilocus combination , endemism calculated as the ratio of endemic to overall genotypes was unrelated to sampling intensity .In nearly all geographical regions, some of the recorded genotypes were “endemic” which means they were not recorded in any other region Figure . No “end3.3H0 was zero in AFR or near zero in BRT, M + SA, and AUS (0.001). H0 over all loci was 0.003 , was high, 0.980 across populations varying from 0.947 to 0.99 , a measure of population differentiation, revealed population differentiation between regions , and AFR and NAM (0.132) and lowest between AUS and IBE (0.014), and CAL and IBE (0.016) , and the percentage of polymorphic loci per population ranged from 30 to 100, mean 81.7. Observed heterozygosity r = 0.484, p = .001) and EEU , and lowest in IBE  and BRT  .A Mantel test calculated on partitioned data sets for each region, revealed significant, but low correlation between genetic and geographic distances Table . The reg3.4X|K)] and plots of ∆K obtained by using the software structure harvester  and are provided in Figure K = 3 and K = 4  point to an increase in between‐run differences in population assignments to the respective cluster groups. These between‐run correlation values decrease from 0.988 for K = 2 to 0.798 and 0.785 for k = 3 and K = 4, respectively. Mapping the K = 2 solution revealed genetic affiliation of populations of warm–temperate, semiarid versus cool–temperate, semihumid to humid origin to different genetic clusters and the highest admixture of populations in regions with cool temperate, yet xeric climate  is predominantly distributed in cool and temperate, more continental climatic regions, and Cluster 2 (orange) in hot to warm and dry mediterranoid climate regions  and in the identification of the most discriminating macroclimatic variables. The rpart approach obtained 79.6% proportional agreement with the structure bipartitioning, while the ctree approach in party and the randomForest approach both revealed 82.8% agreement, respectively. With a mean agreement of 81.77% (SD 1.84%), the K = 2 solution was distinctly better supported eco‐geographically than the K = 3 and K = 4 solutions .The different classification approaches revealed similar results, both in classification agreement with the genetic bipartitioning  in combination with winter coldness (Bio6: CL‐01 < 1.9°C < CL‐02) and moist quarter temperature (Bio8: CL‐01 < 7.9°C < CL‐02) as most distinguishing variables. Conditional inference classification in party revealed again isothermality (Bio3: CL‐01 < 36.3% < CL‐02) as most important, accompanied by humidity (Bio13: mean precipitation of wettest month [CL‐01 < 89 mm < CL‐02]) and summer moisture (Bio18: CL‐01 > 172 mm > CL‐02) as discriminatory predictors. Winter coldness (Bio6: CL‐01 < 1.7° < CL‐02) was identified with nearly the same threshold value in this approach. Similarly, the randomForest approach identified Bio3, Bio6, and Bio18 as the most important variables for a successful classification of the samples into the genetically derived cluster partitioning.The 3.6For the SDM, the feature combination of L + Q + P and a regularization value of 1.1 was obtained as the best configuration option to avoid overfitting. With AUC test values of 0.94 and 0.95, respectively, both cluster distributions could be modeled with high accuracy based on macroclimatic data. The distribution for the two clusters solution resulted in a quite distinct geographical pattern Figure , overlap4Capsella bursa‐pastoris might be a “role model” for the history of worldwide weed dispersal by European colonists. We here analyze the global genetic diversity patterns of C. bursa‐pastoris and discuss the invasion process in terms of colonization history and adaptation.For about 1,000 years, human activities transported vascular plant species in large numbers and at increasing rates between and within continents .Measurements of genetic variation and H0 was near zero  was near + 1 (0.986) , t = (1 − F)/(1 + F)  Table . The outn et al.  revealedC. bursa‐pastoris  may indicate the main source regions. This is in good agreement with weed introduction history  indicating considerable gene pool exchange between the regions. Remarkably, the genetic differentiation between the Iberian Peninsula (IBE) and all other Eurasian regions except the circum‐Mediterranean region (MED) is more pronounced  and more or less half of that of the other Eurasian regions Figure . This is4.2.3R2 are very low. Less than 6% (0.06 > R2 > 0.00) of the genetic distances among populations within the geographical regions are explained by the geographical distance. Exception is Scandinavia with 23% (Table Isolation by distance is another source of genetic variation between populations (Mantel's test). A statistically significant linear correlation between genetic and geographic distances exists for all regions Table . The cor3% Table .4.3C. bursa‐pastoris is split into two lineages, or clusters, one occurring in Mediterranean climate regions and the other occurring in temperate climate regions. The two lineages are robust across methods ranging from Bayesian clustering to simple distance measures and clustering approaches for binary data. This bipartition is also corroborated by independent macroclimatic data  occidentalis, which, however, was never recognized let alone accepted. Plants belonging to this taxon are early flowering and display some leaf characteristics varying from other Capsella provenances. Shull recorded it from Arizona, California, Hawaii, Peru, Chile, Argentina and Uruguay, and noted, “how closely the range of this species […] agrees with the region occupied by the Spanish settlers in America. It seems probable that there is a causal relation between these two distributions.” Apparently, Shull's C. occidentalis belongs to the Mediterranean lineage shown here. Crossing experiments between populations with typical Mediterranean and temperate genotypes indicated that the success rate of crossing is restricted and even failed in the case where the mother plant was of the Eurosiberian type , leading to the assumption that there is some incompatibility between the two lineages.Shull  describeC. bursa‐pastoris: a Europe/Russia cluster (termed “EUR”) extending from Western Europe to the Russian Far East, a cluster with main distribution in eastern and southern Mediterranean regions , and a cluster in southeastern China (termed “ASI”). The term “Middle East” for the mainly Mediterranean distributed cluster is misleading. By far, the largest part of what is traditionally defined as Middle East is not sampled. The six “true” Middle East sample sites of Cornille et al.  and the most western locality in the ASI cluster  is a sampling gap of ca. 5.400 km, and within the EUR cluster is a sampling gap of ca. 4.300 km between the most eastern European site (Voronezh) and the most western Asian site (near Irkutsk). This sampling gap calls for caution in inferring demographic histories of the three clusters as discussed by Cornille et al. , sampling bias seems unlikely. The generally relatively low number of (frequent) genotype endemism in regions of post‐Columbian colonization points to a limited importance of new genotypes. It seems that natural selection in invaders relies mainly on standing variation  explains the geographical patterns in the introduced ranges, which mirror the history of weed introduction into the continents. Multiple independent introductions of genotypes from different sources and climate regions are obvious. Since we detected nonrandom, predictable niche properties of the two lineages, we suggest involvement of environmental filtering and hypothesize that most of the successfully colonizing Capsella genotypes were preadapted and found their respective matching niches in the colonized ranges. These assumptions need to be tested experimentally, since our macroscale approach is not sufficient to draw clear causal or mechanistic conclusions.The global biogeography of genetic variation of The authors have no conflicts of interest to declare.Christina Wesse: Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Erik Welk: Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Herbert Hurka: Conceptualization ; Data curation ; Investigation ; Methodology (lead); Resources ; Supervision ; Writing‐original draft ; Writing‐review & editing . Barbara Neuffer: Conceptualization ; Data curation ; Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Resources ; Supervision (lead); Writing‐original draft ; Writing‐review & editing .Supplementary MaterialClick here for additional data file. Click here for additional data file.


Simple Sequence Repeats (SSRs) represent the easiest and cheapest markers for olive genetic fingerprinting and have been the tool of choice for studying the genetic diversity of this crop in Tunisia, to resolve cases of homonymy and synonymy among the commercialized varieties, to identify rare cultivars, to improve knowledge about the genetic variability of this crop, to identify a hot spot of olive biodiversity in the Tunisian oasis of Degache, and to enrich the national reference collection of olive varieties. The present review describes the state of the art of the genetic characterization of the Tunisian olive germplasm and illustrate the progress obtained through the SSR markers, in individuating interesting genotypes that could be used for facing incoming problems determined by climate changes.Among the countries of the Mediterranean Basin, Tunisia is located at the crossroad for the immigration of several civilizations over the last two millennia, becoming a strategic place for gene flow, and a secondary center of diversity for olive species. Olive is one of the principal crop species in Tunisia and now it strongly characterizes the rural landscape of the country. In recent years, collecting missions on farm and  Olea europaea L.). The most iconic tree of the Mediterranean Basin has origins linked to the most ancient civilizations, about six millennia ago [Tunisia was at the crossroads different civilizations for several millennia becoming a reservoir for gene exchange and flow of genetic resources for numerous Mediterranean crops for which it shows a large genetic variation including in olive  is an evergreen, high longevity, predominantly allogamous, vegetatively propagated tree  on an arAlthough the olive germplasm in Tunisia is represented by more than 200 cultivars and genotypes  Figure , only feThe production of Tunisian olives is almost dominated by only two varieties, “Chetoui” in the north, and “Chemleli” in the center and south of the country , while sIn addition to the 56 cultivars identified by Trigui and Msallem , many “eex situ and on farm conservation strategies to sustainable management of local agrobiodiversity and use. The conservation of the genetic resources in the area where they have evolved (on farm conservation) is integrating strategies based on genebanks (ex situ conservation), especially for wild relatives of cultivated plants. When on farm conservation is adopted, the genetic diversity of cultivated native varieties is preserved within traditional farming systems. This diversity is guarded by custodian farmers who safeguard genetic resources for their families and communities. In general, smallholder farmers contribute to the conservation of genetic diversity by maintaining traditional varieties and minor genotypes adapted to local conditions. A growing awareness about the importance of preserving genetic resources from erosion as source of genes of agronomic interest, has prompted government authorities in several countries, to focus on saving agrobiodiversity. Additionally, programs have been implemented to help farmers manage and conserve their plant genetic resources which are crucial to food security and sustainable agriculture for present and future generations . Tunisiaex situ conservation has made it possible to improve the techniques and means used to preserve genetic resources. Ex situ conservation corresponds to the conservation of the biological components of diversity outside their natural habitats [ex situ collections of the International Treaty on Plant Genetic Resources for Agriculture and Food. According to the World Information and Early Warning System on Plant Genetic Resources (WIEWS), it is estimated that around 7.5 million entries are actually preserved worldwide with around 6.6 million held in national government genebanks, the 45% of which is in just seven countries, with a downward trend if compared to the 12 countries reported in 1996 [The development of the concept of habitats . This co in 1996 .Genomic DNA-based markers have enormously empowered our ability to characterize genetic variation in crop plants, in particular simple sequence repeats (SSRs) or microsatellites. SSRs are hypervariable short (1–6 bp) repeat motifs that show a high level of length polymorphism due to insertion or deletion mutations of one or more repeat types, highly distributed throughout the genome . IntroduO. europaea L. genetics, such germplasm diversity [de novo next generation transcriptome sequencing of O. europaea, Dervishi et al. 2018 [Olea taxon, also highlighting their potential application as functional markers. The increasing economic value of the olive tree has increased the use of SSRs also for oil traceability and authenticity, sometimes in combination with Single Nucleotide Polymorphism (SNP) markers [In olive, microsatellite regions were sequenced only in the early 2000s ,22. Theyiversity ,26,27,28iversity ,30, pateiversity ,32,33, civersity ,35. All iversity ,37,38,39al. 2018  identifial. 2018  to selec markers ,43. Despite the high use of throughput omics data for olive species, very few studies describing the state of art exist. This paper will focus on the contribution of molecular markers, particularly SSRs, in their utility and their use for a better exploration of Tunisian olive germplasm.So far, we number more than 100 collections of olive genetic resources at international, regional and national levels, with an increasing expansion of their number. The International Olive Council, with the aim to protect olive patrimony, created a network of 23 olive germplasm banks, housing over 1700 varieties. This network is composed of 3 international banks—Cordoba (Spain), Marrakech (Morocco) and Izmir (Turkey)—and 20 national banks, including one in Tunisia ,45,46. SThe Worldwide Olive Germplasm Bank of Córdoba (WOGBC) (Spain) conserves 900 accessions from 25 countries that were characterized through morphological and SSR markers . It inclTo guide conservation decisions for an effective protection of the endangered species as sources of valuable alleles for the future needs, optimizing the conservation of a species while reducing the cost, it is crucial to establish reduced collections which represents, in limited size, the genetic diversity of the crop with the minimum similarity between its entries ,48. To iFrom the whole collection of the WOGBC, using agronomical traits and molecular markers ), a core collection was established, composed of 68 accessions suitable for genetic conservation, and including 36 accessions with potential desirable traits for olive breeding . Gómez-RSSR markers were used to establish core collections from the world Olive Germplasm Bank of Marrakech, that encompasses the whole Mediterranean genetic diversity, holding 561 accessions from 14 Mediterranean countries , as wellex situ olive collections have been established, mainly by the Olive Tree Institute. The plant material has been characterized mainly by morphological and biochemical markers [In Tunisia, several  markers ,66, but  markers ,68,69,70The Boughrara Collection, implemented at the Olive Tree Institute, is considered the National collection in Tunisia, and it holds more than 201 varieties, 147 of which are indigenous (73.13%) .For their characterization, eight SSR markers and endocarp morphological characters were initially used in a study by Fendri et al.  which leThese genotypes are conserved at the national field of the National Gene Bank of Tunisia (NGBT) and they serve as a safety duplicate collection. This work allowed also the implementation of a national database of SSR data, and the genotyping of the 12 main commercialized cultivars to be proposed for the national varietal certification of Tunisian olive germplasm, and to be used to guarantee the genetic authenticity of commercial varieties .Due to the growing economic importance of the olive tree, studies aimed at characterizing the main varieties marketed have multiplied. In the meantime, in the light of the impact of climate change on crop performance and productivity , the neeUsing SSRs markers, numerous works of olive genotyping were carried out in Tunisia, allowing the tracing of an overview of the results achieved on the conservation of olive genetic resources in this country.Many researches focused on the main cultivated varieties. Taamalli et al.  investigSSR were applied to distinguish “Chemlali” samples originating from different parts of Tunisia, from the “Zarrazi” variety, typically cultivated in the south of Tunisia. The polyclonal cultivar “Chemlali” is a mixture of closely related genotypes, and it seems to provide evidence for the exchange of olive cultivars throughout different regions and of gene flow between genotypes . Among TConsidering the overall Tunisian olive germplasm, Taamalli et al.  observedOlea europaea L. in Tunisia is highly influenced by the empirical selection achieved by spontaneous crossing and the exchange of propagation material among growing areas.The classification of Tunisian olive varieties based on SSR molecular markers is highly correlated with the form and the weight of the fruits and the endocarps, thus demonstrating the efficiency of using qualitative morphological markers to discriminate olive varieties . LikewisThe richness of the Tunisian olive gene pools was demonstrated by Saddoud Debbabi  in a colCollection, characterization, conservation and evaluation of genetic resources are crucial steps to preserve them from genetic erosion and to offer plant material available for future use in breeding programs .The development of technologies for next generation sequencing (NGS) is producing a high amount of data, offering the opportunity to explore the relationships between genetic and phenotypic diversity in the olive tree. In addition, the recent advances in omics  are providing new tools for a better understanding of the molecular mechanism involved in the development of traits of interest, thereby facilitating their use in selection programs.Alongside molecular characterization, high-throughput phenotyping platforms are being developed, which make it possible to rapidly phenotype a large number of plants at a reduced cost and time compared to traditional techniques, therefore accelerating the timing of breeding .In the last twenty years, olive breeding has been focused on the productivity and quality of the oil, but following the climate changes taking place worldwide, the attention is now turning rather towards greater tolerance of the trees to abiotic stresses, in particular drought. More than 90% of Tunisian olive trees are non-irrigated, leaving Tunisia particularly vulnerable. This has led to the initiation of olive breeding programs focusing on the use of local olive germplasm and intraspecific crossbreeding of cultivars of well-known value to combine their advantageous qualities . Thus, wBy combining molecular with phenotypic data in meta-analyses and applying new approaches such as Quantitative Trait Loci mapping, genome-wide association studies, and genomic selection, it will be possible to explore the potential of cultivars and ecotypes statically stored in gene banks and other collections, accelerating olive breeding programs for achieving the desired improvements in olive cultivars.in situ and ex situ conservation are currently quite unplanned and uncoordinated, and to streamline and strengthen our efforts, we need effective and permanent support mechanisms that are in place for optimal utilization. Research is needed to identify gaps in existing ex situ collections in order to ensure that the entire gene pool is adequately represented, for broadening the exploration of crop diversity, and reducing redundancy by eliminating duplicates. At the same time, it is necessary to act at management level, to ensure accessibility to all information and facilitate their use through a further development of specific subsets of varieties that can be actively used in breeding programs for a genetic enhancement and broadening the genetic base aims.However, Finally, it will be important a further effort to promoting the use and commercialization of marginal varieties, by enlarging networks and information systems, and by strengthening the public awareness about the importance of plant genetic resources in developing a sustainable agriculture.