Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality. A current challenge in neural prosthetic development is how to artificially activate the central nervous system to restore touch and proprioceptive sensation to tetraplegic patients . DevelopRecently, human intracortical microstimulation (ICMS) has been successful in eliciting minimal naturalistic tactile and proprioceptive sensations . In one One aspect requiring further investigation is whether other targets on the somatosensory neuraxis might offer advantages over the cortex for restoring somatosensory function. The complexity of neural networks in the cortex makes it a difficult region in which to target microstimulation. There has been better success in restoring somatosensory percepts in amputees by interfacing with peripheral nerves where the labeled line arrangement of afferent fibers has led to effective artificial recreation of somatosensory signals . Users oSpinal cord injury sufferers require somatosensory signals to be recreated in the central nervous system above the site of damage, so peripheral interfaces are not appropriate for this purpose. In our view, the dorsal column nuclei  and its complex , may be an ideal alternative target to the cortex as they are easily accessible, being located in a supraspinal position in the dorsal aspect of the brainstem medulla , and areHere, we review recent work on the functional organization and somatosensory-evoked signals of key contributors to the DCNc. We suggest that the DCNc show promise as a target for a somatosensory neural prosthetic device. We discuss some of the potential limitations of the DCNc as a neural prosthetic target and propose future directions that are necessary before development of a DCNc neural prosthesis can begin.Effective activation of the DCNc to elicit somatosensory percepts will require precise knowledge of its functional organization. The key components of the DCNc necessary to appreciate its potential use for neuroprosthetics are the gracile and cuneate nuclei, which are recipient of tactile (and other) inputs from lower and upper body afferents, respectively, and the external cuneate nuclei, nuclei X, and nuclei Z, which are key regions of proprioceptive inputs  and floating microelectrode arrays in the cuneate nuclei of macaques, which were able to obtain stable recordings up to about 140 days post-implantation, and awake behaving macaques could detect amplitude-dependent DCN stimulation at 100 Hz . These sThe HF and LF DCN activity we have investigated is either directly recorded volleys of action potentials arriving in the DCN from afferent fibers (HF activity), or from the subsequent activation of DCN neurons (HF and LF activity). Therefore, to ensure that neural activity giving rise to both the HF and LF features is restored, consideration should be given to whether the best approach is to activate the cuneate and gracile fasciculus fibers of the dorsal column (DC), rather than DCN neurons, or perhaps both. Some interesting recent studies show that rats and monkeys can detect differences in frequency and location of epidural DC stimulation , 2020, w2), facilitating access to large body regions. One of the challenges faced in ICMS is the large cortical surface area dedicated to processing somatosensory information from the human hand  . HoweverFinally, spinal cord or peripheral nerve injury has been shown to cause changes in DCN somatotopy and is thought to be a crucial driver of some cortical somatotopic reorganization . This wiWe believe that a DCNc somatosensory neural prosthesis is a goal worth pursuing and may provide advantages over cortical somatosensory neural prostheses. However, there are a number of concerns that need be addressed, regarding the safety and efficacy of placing microelectrode arrays and electrically stimulating in the DCNc, before a brainstem somatosensory neural prosthesis can be considered feasible. Compared to peripheral nerves and the somatosensory cortex, there is also a dire lack of knowledge about how somatosensory information is coded in the DCNc, which demands future efforts directed toward the understanding of how tactile and proprioceptive features are represented in DCNc neurons. The next frontier will then be to determine how to implement neural codes using a biomimetic approach to artificially stimulate the DCNc which is already connected to multiple sensorimotor systems, including conscious (likely involving the cortex) and unconscious  pathways. Such an approach could enable the subject to receive tactile and proprioceptive sensations from an anthropomorphic robotic limb for complete sensorimotor integration into multiple systems.Both authors wrote 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.


P = 0.0007). To illustrate application in fine-mapping studies, we applied our MGW-prioritization approach to three non-coding regions associated with systemic lupus erythematosus. Together, this study presents the first usage of sequence-dependent DNA topology as a prioritization metric in genomic association studies.In genomic fine-mapping studies, some approaches leverage annotation data to prioritize likely functional polymorphisms. However, existing annotation resources can present challenges as many lack information for novel variants and/or may be uninformative for non-coding regions. We propose a novel annotation source, sequence-dependent DNA topology, as a prioritization metric for fine-mapping. DNA topology and function are well-intertwined, and as an intrinsic DNA property, it is readily applicable to any genomic region. Here, we constructed and applied Minor Groove Width (MGW) as a prioritization metric. Using an established MGW-prediction method, we generated a MGW census for 199 038 197 SNPs across the human genome. Summarizing a SNP’s change in MGW (ΔMGW) as a Euclidean distance, ΔMGW exhibited a strongly right-skewed distribution, highlighting the infrequency of SNPs that generate dissimilar shape profiles. We hypothesized that phenotypically-associated SNPs can be prioritized by ΔMGW. We tested this hypothesis in 116 regions analyzed by a Massively Parallel Reporter Assay and observed enrichment of large ΔMGW for functional polymorphisms ( Genetic association studies have successfully identified thousands of loci associated with a broad range of phenotypes . HoweverTo prioritize potential causal variants, association results can be weighted by functional information  from publicly available resources . This apFrom chromatin conformation to selective protein binding , DNA is This study presents using sequence-dependent DNA topology as a prioritization metric in genomic association studies. Here, we focused on minor groove width (MGW), which measures the distance  between the sugar phosphate backbone of the forward and reverse strands. For each SNP, we analyzed its change in minor groove width (ΔMGW) to evaluate whether the SNP’s alleles created similar or divergent MGW profiles. MGW has been implicated in numerous protein binding studies and used in transcription factor binding prediction algorithms ,37,40,41We specifically hypothesized that highly correlated SNPs in a phenotype-associated region can be functionally prioritized using each SNP’s magnitude of ΔMGW. We evaluated this hypothesis in four stages. First, using an established MGW-prediction algorithm , we genehttps://bioconductor.org/packages/release/bioc/html/DNAshapeR.html), available through Bioconductor (The predicted MGW for a given sequence was obtained using the DNAshapeR package (onductor . DNAshaponductor .9 (262 144) 9-mer sequences. From this dataset, all possible bi-allelic pairings  were created on the fifth nucleotide of each sequence (‘SNP position’) while holding the flanking nucleotides constant, generating 393 216 9-mer pairings. These 9-mer pairings represent every possible sequence combination that could be observed for a bi-allelic SNP  for a ‘BDNA nucleic acid’ (https://www.cgl.ucsf.edu/chimera/)  .The NCBI hg19 dbSNPs150 data file (snp150.txt.gz) was downloaded via UCSC GoldenPath (hgdownload.cse.ucsc.edu) on July 6, 2018 . InsertiFor dbSNP 150 data, each SNP’s flanking sequence of four nucleotides was retrieved from the Human Reference Genome   using SAet al. (n = 740) were removed to retain the one with the most significant allelic skewing. Insertion-deletions were also excluded. Any remaining variants that did not meet the filtering criteria for ΔMGW analyses of the dbSNP150 data (as previously described) were pruned, leaving bi-allelic SNPs without ‘unusual mapping conditions’ by the UCSC Table browser. The final dataset of MPRA data contained 2819 SNPs.MPRA data was downloaded from et al. . The tabP-value and all (filtered) SNPs tested within 500kb upstream and downstream of the top variant. To test for global trends of enrichment, genomic regions were required to have at least 5 SNPs (n = 136 regions) and were retained only if there was at least one SNP in the region that met FDR significance for allelic skewing as defined by Tewhey et al. . For the final dataset, ΔMGW values were obtained based on rsID lookups in the SNP 150 data, as previously described.Genomic regions were defined by the SNP with the most significant MPRA allelic-skewing Genomic data for fine-mapping analyses came from the published trans-ancestral SLE Immunochip study; genotype calling and genomic quality control methods were previously described . This daP < 5 × 10−8) in at least two ancestry-specific analyses (FAM167A-BLK (8p23), STAT4 (2q32) and TNIP1 (5q33) met these a priori criteria. Quality controlled genomic data for these regions were extracted using a 250 kb window around the previously reported top association from the Immunochip analysis  to retrieve the four flanking nucleotides. Each SNP’s strand information (based on Illumina Infinium Immunochip manifest file) was utilized to ensure that the corresponding alleles appropriately aligned with the reference genome. Using the 9-mers created by the SNP’s alleles and flanking nucleotides, the ΔMGW was calculated using DNAshapeR, as previously described .Single-SNP associations were previously reported and described in the transancestral SLE Immunochip study .https://CRAN.R-project.org/package=SKAT). For each genomic region, the model parameters and residuals were calculated for SKAT using SKAT_Null_Model for a dichotomous outcome (case/controls status) and previously described (n = 5 SNPs), we expect a large proportion of each window to contain non-causal SNPs, further supporting our selection of SKAT. For comparison, we also applied SKAT-O but noted minimal differences on the final outcome. To localize the top association signals to each SNP, SNP-window P-values were treated as a SNP prioritization metric by generating the geometric mean of −log10 across windows containing each SNP. That is, the prioritization metric was calculated using the P-value for each SKAT analysis window (pi) that contained the kth SNP . With the exception of the first and last five SNPs in a region, each SNPk was included in five analysis windows (n = 5). Thus, for each SNP k, we calculated its prioritization metric as:The previous single-SNP logistic regression analyses  did not escribed  populatiP-values are not without limitations . Bayesian methods offer an alternative approach; here, Bayes factors are used, capturing the ratio of probabilities between the null and alternative hypotheses.Frequentist approaches, such as those implemented SKAT or single-SNP logistic regression analyses are widely utilized; however, their resulting itations . For onek, were then calculated using method published by the Welcome Trust Case Control Consortium , using the score test and additive genotype modeling . Posterinsortium . For SNPa priori probabilities that the causal SNP is any one of the analyzed SNPs  SNP weights .r) between ΔMGW and the absolute value of log2 fold change by MPRA (‘LogSkew_Comb’ from downloaded data) was computed for each of the 116 genomic regions (r > 0). To test for enrichment of r > 0, we compared positive and negative r counts at iterative thresholds of |r| in increments of 0.1 magnitude using the two-sided binomial test. To illustrate the resulting mixture distribution , we fit a normal curve with mean of zero and a variance estimated by the 6σ method (i.e. range divided by 6) applied to the negative correlations  (n = 116 SNPs). We computed a chi-squared goodness-of-fit test under the null hypothesis of no relationship between ΔMGW and log2 fold change.For each of the 116 genomic regions, SNPs were ranked from smallest to largest according to ΔMGW and these ranks were converted to percentiles. These ΔMGW ranks were summarized across each region's top MPRA SNP  (cis-Regulatory Element (ccRE). ccREs are designated based on evidence from DNase hypersensitivity sites, H3K4me3 and H3K27ac histone activity, and CTCF-binding data. SCREEN contains 1.31 million ccREs, correlating to 20.8% of the mappable human genome (http://screen.encodeproject.org). For both, GTEx and SCREEN, functional searches were agnostic to tissue type. Genomic variants were also evaluated for evidence of long-range DNA interaction via Hi-C data (hg19) available through the Yue Lab 3D Genome Browser (http://promoter.bx.psu.edu/hi-c/) , the Genotype-Tissue Expression (GTEx) dataset, version 8 was queried at gtexportal.org . SNPs weect.org) ,59. Builu/hi-c/) . SimilarIn the complete sample space of ΔMGW, ΔMGW values ranged from 0.00 to 3.16 Å, with a mean of 0.77 Å and a median of 0.68 Å  and yielded ΔMGW means of 0.69 and 0.56 Å, respectively.We compared the ΔMGW sample space statistics to the observed frequencies of ΔMGW across the human genome using dbSNP data. The hg19 download of NCBI dbSNP150 contained 234 104 110 entries. After pruning to high quality, bi-allelic SNPs, 199 038 197 polymorphisms remained. For these SNPs, there was a mean ΔMGW of 0.68 Å with a standard deviation of 0.43. In comparison to the ΔMGW sample space, SNPs across the genome exhibited a stronger, right-skewed distribution of ΔMGW Figure ,62, and While changes in MGW have been implicated in a number of targeted functional studies, we aimed to identify patterns between ΔMGW and function on a global scale. The Massively Parallel Reporter Assay (MPRA) for finding expression-modulating variants presents an ideal framework for evaluating patterns between intrinsic ΔMGW and functionality. Allelic skewing is identified by comparing reporter assay activity between two oligonucleotides which are identical except at the selected SNP locus. As such, MPRA can be used to experimentally prioritize variants that are in high LD. We utilized a published MPRA dataset  to test 2 fold change (r) shows a mixture distribution with a subset of the regions not showing an enrichment of larger, positive correlations and another subset showing enrichment of a large positive correlation between ΔMGW and log2 fold change  for increasing r thresholds , with an enrichment of larger ΔMGW among top MPRA-identified SNPs for each genomic region  with SLE in at least two ancestries  and the association signals are not refined to a single SNP, due in part to strong LD. Furthermore, neither the SNPs nor their LD proxies are protein-coding variants, leaving DNA topology as a potential functional mechanism. For each region, we first describe the previous SNP association results ; within the AA data, the top associations were refined to a smaller 14 kb window containing 7 highly correlated SNPs  Figure . Insteadtransition SNPs (0.50 Å), indicating a considerable departure from the expected value and thus we would hypothesize a greater likelihood of functional relevance. We explored  functional data resources for evidence of biological relevance, in comparison to the top SNP signals from the single-SNP analyses . All three SNPs are in high LD (R2 > 0.95) with one another in both EUR and AFR 1000 genomes data. Thus, it is not surprising that all three SNPs yielded similar eQTL results via GTEx (data not shown). Despite the high LD, these three SNPs are physically separated by several kilobases. Both rs2061831 and rs13277113 mapped to Candidate cis-Regulatory Elements (cCRE) . Of note, the cCRE mapping to rs2061831 showed high CTCF activity with supportive TF binding , which we previously showed to have the smallest (on average) ΔMGW  mapped to six SNPs in high LD, spanning 29 kb. Five of these SNPs also comprised the LD block of strongest associations in the HA data (P < 1 × 10−13), in a slightly narrower 26 kb region.The single-SNP SLE associations at 2q32 span the e Figure . SNP asse Figure . In bothThe mean ΔMGW for SNPs in this region was 0.72 Å in EA and 0.73 Å in HA and both cohorts had a median ΔMGW of 0.56 Å. While these average ΔMGW were slightly higher than what was observed across the entire bi-allelic dbSNP dataset (mean = 0.68 Å), the EA and HA medians were of the same magnitude (dbSNP ΔMGW median = 0.56). The ΔMGW for SNPs within the 110 kb association window exhibited similar means as the 500 kb region .P-value in the single-SNP association analysis, but in the ΔMGW-weighted approach, its prioritization distinctly increased relative to the other SNPs in the LD block.We again applied the two ΔMGW-weighted approaches using SKAT and Bayesian credible sets in the region. In EA, the ΔMGW-weighted SKAT analyses shifted the top signal upstream to rs11889341, which markedly increased its priority Figure . This SN64), that the influence of ΔMGW was largely diminished in the analysis. However, despite the predominant rs7568275 signal, the derived credible set still detected rs11889341, the SNP identified by the ΔMGW-weighted SKAT approach. In the HA data, rs11889341 yielded the largest posterior probability in the ΔMGW-weighted derived credible set. This SNP also had the largest posterior probability in the unweighted credible set. Unlike the EA analysis, where the magnitude of the Bayes factor dominated the impact of the ΔMGW-weighting, in the HA data, the ΔMGW strongly increased the posterior probability of rs11889341 from 58.6% to 73.5%. This limited the derived 95% credible set to only 3 SNPs: rs11889341 (73.5%), rs8179673 (16.6%) and rs7574865 (4.8%)  .64) over other SNPs (bf ≤ 1.79 × 1063) largely negated the impact of ΔMGW in this analysis. Considering the evidence for rs11889341 in the other three analyses, due to its strong combination of SLE association and ΔMGW, we would hypothesize that rs11889341 would be a candidate functional polymorphism. Like rs2061831 in FAM167A-BLK, rs11889341 is also a transition SNP (purine/purine). While transition SNPs are more frequent across the genome  in EA and HA data at 5q33  in high LD, spanning 3 kb of a TNIP1 intron. These three SNPs are also encompassed by the associated LD block in the HA data, where four, highly correlated SNPs  yielded P-values < 5 × 10−8. As completed in the FAM167A-BLK and STAT4 regions, we again applied ΔMGW-weighted fine-mapping strategies to prioritize these non-coding SLE-associated SNPs.Previous single-SNP association analyses  identifiTNIP1 region, the lists of high-quality genotyped SNPs were largely the same between the EA and HA datasets. Consequently, the statistics for ΔMGW in this region were very similar between the two cohorts. Across the 500 kb window of high quality SNPs, the mean ΔMGW was 0.67 Å (median = 0.55 Å) in both EA and HA  and HA (rs10036748) were the same as those identified in the single-SNP logistic regression analysis  across the genome. We showed the observed genomic ΔMGW was lower than the complete ΔMGW sample space. These findings were consistent with the relative frequencies of transversion (∼33%) and transition (∼66%) mutations in the human genome , the observed patterns importantly document a potential role between ΔMGW and functionality.Sequence-dependent DNA topology could provide important functional context for associations, especially for polymorphisms in non-coding regions and/or in regions not covered by currently available functional databases. We explored ΔMGW, a specific sequence-dependent measure of DNA topology, as a weighting metric in fine-mapping analyses. In a sample of 300k SNPs, Wang e common . Here, wn genome ,62. We hn genome . We obseFAM167A-BLK, STAT4, and TNIP1) with well-established SLE associations. In FAM167A-BLK and STAT4, we successfully reduced the number of potential functional SNPs to a single, transancestral SNP in each region. For both of these ΔMGW-identified SNPs, we identified external evidence supporting their functional roles.To illustrate an application of our approach, we applied two ΔMGW-weighted fine-mapping approaches, across three genomic regions  . As an intrinsic variable it is also not ancestry specific, tissue specific, or sample size dependent. Limitations in external (non-intrinsic) data may down-weight potentially causal SNPs due to a lack of available functional data. While publicly available functional resources continue to expand, these challenges remain, especially for rare or novel variants. This is particularly relevant for diverse study populations where annotation resources based on European data may offer limited information for regions of interest . For exae genome . While abrowser) ,58,60, bbrowser) . We idenSTAT4 showed that rs11889341 altered HMGA1 binding, functional work is still needed to evaluate the rs2061831 genotype in FAM167A-BLK. Here, the biological implications of rs2061831 could involve transcription factor binding, and/or, given its apparent location within a long-range chromatin interaction hotspot (A second benefit of using DNA topology in fine-mapping is that DNA topology (e.g. MGW) can potentially impact an array of biological functions such as transcription factor binding, chromatin remodeling, or methylation ,31,32,36 hotspot , chromat hotspot . LeveragFAM167A-BLK and STAT4 resolved the association signal to the same SNP in each ancestry via the frequentist approach, followed by largely corroborating evidence via the derived credible sets in the Bayesian approach. Notably, rs2061831 was not the top-associated SNP in either the ancestry-specific analyses; however, it was previously identified via the SLE Immunochip trans-ancestral meta-analysis, where combining association signals across ancestries identified it as the top SNP , a SNP’s impact on intrinsic DNA topology is consistent across ancestries, highlighting the potential utility of DNA topology as a means of resolving association signals across ancestries. Here, we showed that ΔMGW-weighted analyses of  top SNP .TNIP1 region. First, the functional variants may not have been genotyped in the study. Analyses that utilize SNP-specific weights decouple associations from LD. Thus, a weighted metric performs best when the functional SNP is included in the analysis set. For this reason, we propose application of this prioritization technique when there is high confidence that the functional variants have been included through dense genotyping or sequencing. We note this limitation exists for any statistical association method.There are several considerations and limitations to using sequence-dependent topology as a weighting metric in fine-mapping analyses. Notably, some of these limitations could result in inconclusive and/or insignificant results, as observed in the FAM167A-BLK and STAT4 regions, the highest prioritized SNPs, rs2061831 and rs11889341, did not have the largest magnitude of ΔMGW in the regions . The focus on MGW was motivated by the breadth of study on MGW and function , future studies could also explore weighting SNPs by particular topological profiles . For instance, our function ,32,34,36STAT4 data, the magnitudes of the Bayes factors were so large that weighting by ΔMGW yielded minimal impact. Future work should consider approaches to scale weighting schemes by a constant derived from the magnitude of signal across a genomic region. In the SKAT approach, for the sliding analysis window, we used five SNPs, which should yield a region that is neither too wide nor too unstable. Additional testing could potentially improve optimization of parameters for this analysis. Furthermore, we emphasize that our evaluation of the SKAT results by summarizing each SNP as the geometric mean of SKAT-analysis P-values should be regarded as a metric for prioritizing SNPs, not an association analyses, as these values do not have the statistical properties of a P-value. Overall, these limitations should be carefully considered when applying these specific methods; but they also highlight opportunities to further explore the relationship between sequence-dependent DNA topology and phenotype associations.Fourth, there are some limitations to the methods implemented through ΔMGW-weighted SKAT and the derived credible sets approach. Here, we assumed that the majority of variants in the region are non-causal, which is why we selected SKAT over a combined burden test. However, we note that the results from SKAT and SKAT-O were largely similar. Similarly, in case of the applied Bayesian approach, a limitation is its assumption that a single causal SNP exists in a region . In the ad hoc weighting of the posterior probabilities by ΔMGW. While there are existing Bayesian methods that can incorporate functional annotations, we note these require binary categorical values and assume agnostic application of annotations and thus adjust prioritization based on enrichment of the annotation in associations  may show large magnitude of ΔMGW. For this reason, we did not apply these existing methods within this manuscript. While we showed success with the two methods applied here, we also propose incorporating intrinsic DNA topology (e.g. ΔMGW), as an annotation resource in other statistical methods as they develop.We note that, at present there are limited options for incorporating continuous values for SNP-specific weights. In our frequentist approach, we utilized SKAT for its flexibility in accepting user-specified weights. In our Bayesian method we used derived credible sets based on ciations ,69. ThatIn summary, weighting SNP associations by functional data can greatly improve identification of potentially causal SNPs; however, existing annotation resources can negatively affect these outcomes when SNP information is unavailable in public datasets, especially in novel genomic regions ,10,11,14Data used within this manuscript was obtained from publicly available resources (with relevant links noted in-text) and previously published references  and 44)44).gkaa877_Supplemental_FilesClick here for additional data file.


Vigna radiata L.) quality is dependent on seed chemical composition, which in turn determines the benefits of its consumption for human health and nutrition. While mung bean is rich in a range of nutritional components, such as protein, carbohydrates and vitamins, it remains less well studied than other legume crops in terms of micronutrients. In addition, mung bean genomics and genetic resources are relatively sparse. The objectives of this research were three-fold, namely: to develop a genome-wide marker system for mung bean based on genotyping by sequencing (GBS), to evaluate diversity of mung beans available to breeders in the United States and finally, to perform a genome-wide association study (GWAS) for nutrient concentrations based on a seven mineral analysis using inductively coupled plasma (ICP) spectroscopy. All parts of our research were performed with 95 cultivated mung bean genotypes chosen from the USDA core collection representing accessions from 13 countries. Overall, we identified a total of 6,486 high quality single nucleotide polymorphisms (SNPs) from the GBS dataset and found 43 marker × trait associations (MTAs) with calcium, iron, potassium, manganese, phosphorous, sulfur or zinc concentrations in mung bean grain produced in either of two consecutive years’ field experiments. The MTAs were scattered across 35 genomic regions explaining on average 22% of the variation for each seed nutrient in each year. Most of the gene regions provided valuable candidate loci to use in future breeding of new varieties of mung bean and further the understanding of genetic control of nutritional properties in the crop. Other SNPs identified in this study will serve as important resources to enable marker-assisted selection (MAS) for nutritional improvement in mung bean and to analyze cultivars of mung bean.Mung bean ( Vigna radiata) is an important pulse crop for most of the highly populated countries of Asia, such as Bangladesh, China, India, Indonesia, Laos, Malaysia, Myanmar, Pakistan, Thailand and Vietnam with growing presence in other parts of the world too  markers is limited and few applied SSR markers have been developed  because An alternative to single marker assays for SNPs, is Genotyping by Sequencing (GBS) based on the fast development of next generation sequencing (NGS) technology . CompareIn summary, mung bean can effectively contribute to the alleviation of iron, zinc and protein deficiency in human populations of Asia , but so This study began in 2015 with the evaluation of a core collection of 408 genotypes of mung bean provided by the United States Department of Agriculture (USDA), Plant Genetic Resources Conservation Unit, in Griffin, GA, United States. As not all genotypes were adapted, a smaller set of 140 genotypes were grown in 2016. From these a subset of 95 higher yielding genotypes were selected for molecular and GWAS analyses. The subset had the following geographical representation: 3 genotypes were from China, 6 from the Philippines, 7 from South Korea, 2 from Thailand, 37 from India, 32 from Iran, 2 from Pakistan, and one each from Afghanistan, Argentina, Mexico, Turkey, United States, and Uzbekistan. Therefore, the total from each continent skewed toward Asia, the center of origin for mung bean, over those from the Americas. The genotypes tended to have sub-tropical rather than temperate adaptation because they were selected for growth ability in the hot summer season in the Tennessee State University (TSU) field station as described below.The experiments were carried across two consecutive summer seasons of 2015 and 2016 at the Agricultural Research and Education Center (AREC) field station of TSU in Nashville TN, United States of America which is situated at 36° 10′ 1″ N/86° 49′ 45″ W with an elevation of 54.3 m above sea level. The climate conditions were hot and humid . The experiments were conducted in an open field and only watered once to induce germination. Thereafter, the crop relied upon rainfall for water supply during both years. An augmented design was used in 2015 and a randomized complete block design with two repetitions was used in 2016. A total of 30 seeds per genotype were sown into each plot, and the plots were 2.4 m long with l.0 m row centers and blocks were separated by 0.8 m alleys.The soil was a Byler type Silt Loam with 2–5 percent slope toward the West in the direction of the Cumberland River which surrounds the farm of which the field is part of the valley. Water carrying capacity was estimated to be 50 mm of available water per cubic foot. The natural fertility and micronutrient content of the site was tested with a soils analysis at the University of Tennessee – Extension Soil, Plant and Pest Center  based on soil collected in the late winter prior to planting of the mung bean experiments. In addition to macronutrients nitrogen (N), phosphorus (P) and Potassium (K), the soils test evaluated Boron (B), Calcium (Ca), Iron (Fe), Magnesium (Mg), Manganese (Mn), Sodium (Na), Sulfur (S), and Zinc (Zn) along with organic matter and pH readings. No fertilizer was used as that could bias micro-nutrient accumulation.–1 (ppm).The seeds from each genotype were harvested from pods, cleaned and dried at room temperature. To analyze mineral concentrations, 20 g of seeds were ground using zirconium oxide grinding balls and Teflon coated contained using a Retsch mix mill 400  to grind each sample. A 0.5 g sub-sample for each genotype was measured out for each genotype were digested in CEM tubes containing 10 mL of ultra-pure nitric acid using a MARS 5 Microwaving Digestion Oven  set at 200°C for 15 min. Reagent blanks were included in each series of digestions. Subsequently, the digested samples were diluted in 50 mL volumetric flasks by using ultrapure water and filtered before analysis. The sample solutions were stored in polyethylene vials at 4°C until analysis. Each sample was then analyzed for seven different elements, Ca, Fe, K, Mn, P, S, and Zn, using inductively coupled plasma optical emission spectroscopy (ICP-OES) with a CIROS Model FCE12  housed in the Soils and Tissue Analysis laboratory of TSU. Mineral concentration was calculated by multiplying the average sample elemental concentration and reported in μg g2O. Sterilized seeds were then transferred into individual magenta box  with a sterilized paper tower and placed in a growth chamber  for 7 days until seeds had sprouted. The ground tissue (approximately 50 mg) was enough for DNA extraction with a DNeasy 96 Plant Kit . Quantity and quality verification for DNA was made with a FLUOstar Omega spectrometer . A threshold value of 1.8 for 260/280 nm absorbance ratio was considered a threshold to select for high DNA purity and was confirmed by electrophoresis on a 1% agarose gel run at 110V in TAE buffer. A lyophilized aliquot consisting of 1.5 μg of DNA was prepared for each accession and was placed in a 96 well plate for GBS.High molecular weight DNA that was also of high quality and purity was needed for the GBS method. In this study, we used the shoot apex and young leaves of mung bean sprouts to obtain soft etiolated leaf tissue that were easily ground to a powder in the Retsch mill using stainless steel balls and grinding chambers cooled to below freezing with liquid Nitrogen. In this method, seeds were sterilized in bleach for 5 min followed by 2 5-min rinses in autoclaved ddHApeKI , followed by ligation with a 95-plex barcode adaptor and a common Illumina adaptor. DNA fragments selection was done after digestion/ligation using magnetic beads for a size range of 300–400 nt. (ApeKI sticky end restriction cut sites (CWGC) were trimmed to 64 bases and stored in the database. Reads that had no matching barcode or cut site overhangs were excluded, as well as reads containing unidentified bases (N) and reads with adapter dimers. Subsequently, the bar-coded and tagged sequence reads were sorted and collapsed into unique sequence tags that were aligned with the Vigna radiata genome v1.0 from Legume Information System (LIS) at https://legumeinfo.org/genomes, using the Burrows-Wheeler Aligner (BWA) algorithm (Vigna radiata (SVR) identifying its chromosome and position.GBS library construction was done at the Cornell University Biotechnology Institute according to –400 nt. . Single-–400 nt. . The FASlgorithm  for confP-values at alpha = 0.05, leading to a significance threshold of 7.7 × 10–6  or −log10 (7.7 × 10–6) = 5.11.Histograms and correlation coefficients for mineral concentrations measured in 2 years were calculated and plotted using customized codes in the software R v.3.3.1 (R Core Team). During the marker-based diversity analysis, we examined subpopulation structure with all the SNP markers found across all the mung bean accessions using principal coordinate analysis (PCoA) carried out and diagramed with the same program. Analyses of variance (ANOVAs) were conducted for each experiment independently, distribution normality was checked and correlations between means across years and amongst minerals were calculated also in R v.3. TASSEL was also used to perform association analyses between the SNP markers and the seed minerals Ca, Fe, K, Mn, P, S, and Zn across the mung bean panel in 2 years. Generalized linear models (GLMs) and mixed linear models (MLMs) were compared for each of the nutrients for both years. Combinations of co-variables were used within each model as follows: (1) model with the country of origin and the first two PCoA axes as covariates; (2) model with the first two PCoA axes scores as covariates; (3) model with the country of origin as covariate; and (4) model without covariates. All four MLMs used a centered IBS kinship matrix as a random effect to control for genomic background implementing the EMMA (Efficient Mixed-Model Association) and P3D  algorithms to reduce computing time . QQ-plotGWAS evaluations were done on a per year basis with construction of customized Manhattan diagrams carried out with the R v.3.3.1 software. For genome diversity inspection in the light of associated regions, we used a sliding window approach to determine patterns of overall variation across the genome, using a window size of 1 Mb and a step size of 200 kilo base pairs (Kb). We computed per-window averages of SNP density, Tajima’s D  and nuclThe entire USDA mung bean core collection consisting of 408 genotypes was the starting point for our work and was planted in an augmented design in 2015 after a winter rye grass/summer corn rotation. Early planting was done in April of that year to accommodate the variable maturity dates, photoperiod responses and differential growth habits of the diverse set of genotypes. Most genotypes were harvested by September, but some never produced seed. The best adapted genotypes from the initial trial were re-planted with multiplied seed from that year and put into another section of the same field to avoid diseases or pest carry over. The 2016 trial was implemented from June to October with 140 genotypes. Soils analysis based on a sampling and mixing topsoil from across the fields, showed that pH varied only slightly from 6.0 to 5.9, Na salt was low and average organic matter content was 2%. The soil concentration of minerals showed that no liming was necessary and as a result no extra Zn or Fe micro-fertilization was required.n = 408) compared to the second year (n = 95) and the many higher yielding genotypes of the second year compared to the low yielding genotypes which were screened during the first year.The seed mineral concentrations were measured across both years and resulted in normally distributed data for each year and for most of the seven minerals analyzed for the genotypes used in this GWAS study . NotablyBy comparison, the means for the seed macronutrient concentrations for K and P were higher in 2016 than in 2015 primarily due to the within field locations used since the experiments were planted in the same field across years; however, the smaller RCBD experiment in 2016 was planted in the lower half and higher nutrient section of that field while the augmented design experiment occupied the entire field in 2015. The mean concentrations of the seed minerals Ca Mn and S were similar in both years given that the sections of the field used for the two trials were of the same soil series and had the same overall agronomic management regime. The variability in concentration for these minerals was still high with ten-fold differences for Ca and Mn found among genotypes’ seed mineral concentration, although S concentrations were less variable.R2) calculated for the seven minerals within year showed different patterns depending on the mineral. Some correlations were high as with K and P measured in 2016 (R2 = 1) or P and S (R2 = 0.84), Mn and Ca (R2 = 0.78), and Mn and K (R2 = 0.75) measured in 2015 . We used the shoot tips of sprouts that not only contained more cells than expanded leaves, but also reduced the time and space for genetic experiment preparation. ApeK1 was found to be an ideal enzyme for GBS library preparation and after filtering, a total of 6,486 SNP markers were validated for the analyses of the mung bean accessions  and for 6 bp, step size = 200 kb) was conducted to understand the patterns of genome wide diversity. Average nucleotide diversity as measured by π was found to be 0.33 per Mb  and two on chromosome Vr08 , explaining on average 30% of the variation. However, four different SNP markers over two other regions, one on chromosome Vr01 (with 3 SNPs) and the other on chromosome Vr05 (with 1 SNP), were associated with K concentration measured in 2016, explaining on average 27% of the variation. The same four SNP markers and chromosomal regions were also associated with seed P measured in 2016, agreeing with the high correlation between K and P measured that year.On the other hand, 4 different MTA loci were located in two other genomic regions, both on chromosome Vr07 , were associated with seed P concentration measured in the previous year, 2015, explaining on average 23% of the variation. In comparison, 3 MTA loci in only two regions, one on chromosome Vr06 (with 2 SNPs) and the other in chromosome Vr09 (with 1 SNP), were associated with seed P measured in 2016, explaining on average 22% of the variation.In terms of one of the most important micronutrients for biofortification, 5 SNP markers were associated with seed Fe measured in 2016. These were in a single region in chromosome Vr06 and explained on average 13% of the variation. Interestingly, this MTA region coincided with values of lower nucleotide diversity (π) and Tajima’s D profiles , suggestFor seed Mn concentration in 2016, 5 SNP markers were associated with three genomic regions, one MTA on chromosome I (with 1 SNP), another MTA on chromosome Vr08 and a further MTA on chromosome Vr09 (both with 2 SNPs each) explaining on average 39% of the variation. Interestingly, the associated region for seed Mn concentration on chromosome Vr08 coincided with the peak in the SNP density profile  of 200 SFor seed S concentration as measured in 2015, there were associations with 4 additional SNP markers found in four different regions, three on chromosome Vr07 and one on chromosome Vr09, explaining on average 19% of the variation. In addition, five different SNP markers were associated with S concentration in 2016. These were found in two other regions, both on chromosome Vr08 , but only explained very low percent the variation despite having LOD values above 8.5. Four of the five associated markers fell within the same 1 Mb region on Vr08, suggesting that the explanatory power in this region emerges from various SNPs together rather than single marker associations.Finally, seed Zn concentration in 2015 was associated with 4 markers over three regions, one on chromosome Vr01 (with 1 SNP), one on chromosome Vr07 (with 2 SNPs) and the other one on chromosome Vr08 (with 1 SNP), and explained on average 19% of the variation. One further associated region at the end of chromosome Vr09, though not involving the same associated markers, was associated with both Mn and Zn measured in 2016 .R2 = 0.84, Overlap of significant SNP for different mineral concentrations was observed for all 4 markers that were associated with both K and P measured in 2016 (explaining on average 27% of the variation), 1 marker at the end of chromosome Vr07 was associated with both P and S measured in 2015  and associated with high correlation for these two elements in that year  and one on Vr05 (Vradi05g16350) for K and P concentrations measured in 2016, respectively. Two genes (Vradi07g26320 and Vradi07g26340), both on chromosome Vr07, were near SNPs associated with P concentration in 2015. Three genes , were identified as associated with K in 2015. These genes that identified with macronutrients were involved in cell proliferation regulation, oxidation-reduction process, ATP biosynthesis pathways, vacuolar transporting pathway, and transcription and translation regulation during plant development. Two genes, Vradi05g16350 and Vradi08g15760, were uncharacterized genes with unknown protein function.A total of 35 regions were identified within the windows of 43 significant SNP loci associated with the different mineral concentrations. Of these, 11 regions were associated with seed macronutrients, 12 with micronutrients and 12 with other elements . Totals Vradi06g09900), metal translocation (Vradi06g10020), uptake of minerals in plants (Vradi06g10060), membrane transport (Vradi06g10120) and ATP binding activities (Vradi06g10210). Meanwhile, of the four genes identified for Zn measured in 2015, some were found on chromosome Vr01 (Vradi01g05570) and others on chromosome Vr07 (Vradi07g05950 and Vradi07g06200).For the micronutrients, a total of 12 regions were found to be associated with Fe and Zn, five with the former and seven with the latter. Genes related to Fe accumulation were all located on chromosome Vr06 and participate in various biological process such as metal iron binding (Vradi01g05570), metal translocation (Vradi07g05950), metal iron binding (Vradi07g06200), and membrane signal transduction pathways (Vradi06g02380). One additional gene (Vradi06g02380) was identified on chromosome Vr06 that was associated with Zn in 2016; while more were identified for S over two years. S concentration might reflect the accumulation of sulfur containing amino acids such as cysteine and methionine, another nutritionally important trait for legumes. Therefore, we were interested in the loci associated with this mineral even if they explained moderate and low average variability (R2) in 2015 and 2016, respectively. A total of six genes  with three each per year were identified. As is evident from the gene numbering that reflects the chromosome base position, the last of these three genes, all on Vr08 were very close together in position and this might explain the lower R2 values as the genes might have been conferring a string association for the region as a whole more so than for each marker individually. One of the genes had unknown function while the other two had cell functions. Finally, a total of four SNPs was associated with Mn measured in either of the two years but only one gene (Vradi01g11650) was found in their vicinity. This gene was involved in a carbohydrate metabolic process unrelated to Mn accumulation.These four genes were involved in gene regulation . Four markers were found to be associated with both P and K, one marker was found to be associated with both P and S. In total, 17 SNPs were found to be associated with micronutrients Fe, Zn, and Mn. In term of SNP locations for the MTAs within the mung bean genome, macronutrient-associated SNPs were located on chromosomes Vr01, Vr05, Vr07, Vr08, and Vr09, while micronutrient-associated SNPs spanned chromosomes Vr01, Vr06, V07, V08, and Vr09.The multigenic control of nutrient accumulation in mung bean seeds agrees with previous of Among the candidate genes found in this study of mung beans, those MTAs associated with macronutrients were involved with biological process and plant development. This would be expected since Ca, P, K, and S are essential elements for cell proliferation and differentiation. However, candidate genes for micronutrients tended to be more specific.YSL) gene, which has often been implicated in Fe uptake and transport as well as tolerance to Fe-deficient soils  were identified to be associated with seed Zn accumulation  for higher Fe concentration in mung bean breeding programs. This would be important for South and Southeast Asian diets which are mostly cereal and legume based, and where mung bean can be a very important food for the balanced nutrition of people in these regions. The genotyping by sequencing (GBS) technology is highly efficient for producing large numbers of SNP markers across the genome. Together with the available whole genome information of mung bean, the new loci discovered could greatly facilitate genome-wide association studies in this crop.The major achievements of our study were finding variability for micronutrient and macronutrient traits and a total of 6,484 high-quality SNPs by GBS of 95 mung bean accessions. The SNP loci could be placed on all eleven chromosomes throughout the genome and the overall cost was economical especially as the modified extraction protocol provided enough quality DNA for sequencing. Given the benefits of using GBS in our study, we expect this technology to become widely adopted in mung bean research for marker discovery and application in marker-assisted breeding. Association of genotypic and phenotypic data in our GWAS models and based on our GBS dataset, identified a total of 43 SNP markers that were found to be highly associated with mineral concentration. These SNP loci were found in 20 different 1 Mb regions and explained on average 22% of the overall variation. Further variation could be explained in the future, by additional genotype x environment tests needed both locally and in other climates to evaluate the stability of the GWAS associations found. For biofortification one of the most important associations was for one SNP related to the https://doi.org/10.5061/dryad.k0p2ngf5s.The datasets generated for this study can be found in Dryad repository MB, XW, and AI planned the study. XW, AI, and LM did field harvesting and laboratory work. AI, NL, JM, and XW analyzed the seed mineral concentrations. XW, AC, and AI organized the data and prepared graphs.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.


Populus szechuanica var. tibetica, distributed from low  to high altitudes  of Sejila Mountain on the Qinghai–Tibet Plateau. Population structure analyses allow clear classification of two groups according to their altitudinal distributions. However, in contrast to the genetic variation within each population, differences between the two populations only explain a small portion of the total genetic variation (3.64%). We identified asymmetrical gene flow from high‐ to low‐altitude populations. Integrating population genomic and landscape genomic analyses, we detected two hotspot regions, one containing four genes associated with altitudinal variation, and the other containing ten genes associated with response to solar radiation. These genes participate in abiotic stress resistance and regulation of reproductive processes. Our results provide insight into the genetic mechanisms underlying high‐altitude adaptation in Tibetan poplar.Plant adaptation to high altitudes has long been a substantial focus of ecological and evolutionary research. However, the genetic mechanisms underlying such adaptation remain poorly understood. Here, we address this issue by sampling, genotyping, and comparing populations of Tibetan poplar,  Populus szechuanica var. tibetica, distributed at low  and high altitudes  of Sejila Mountain on the Qinghai‐Tibet Plateau. We identified clearly classification of these two populations according to altitudinal distribution, and we also detected low population divergence and robust asymmetry gene flows between them. Finally, we identified a hot spot region under selection and associated with environmental variables through scanning of the whole Tibetan poplar genome.In this article, we address this issue by sampling, genotyping, and comparing populations of Tibetan poplar,  Trimmed reads were mapped to the P. trichocarpa genome version 3.0 (https://genome.jgi.doe.gov/) using BWA  at the Novogene Bioinformatics Institute  in 2015. Raw reads were trimmed to remove (a) adapter sequences, (b) raw reads containing more than 15 N bases (10% of 150 nt) in a single read, and (c) raw reads containing more than 75 nt low‐quality bases  was set from 1 to 5. The number of iterations for convergence for each K is given in Figure K = 3 versus K = 4, we also pruned the dataset by randomly selecting one SNP each from 5‐, 15‐, 20‐, and 25‐Kbp window sizes and repeating the population structure analysis. Individuals collected from one sample site but clustering with other sites based on the Q‐value measured by Admixture with K = 4 were treated as admixtures  were performed using the package GCTA  using Arlequin version 3.5  were used as the outgroup. The migration event parameter was set from 1 to 4. Pruned SNPs  were used to estimate migration events.According to the continual distribution of Tibetan poplar on Sejila Mountain and lack of a geological barriers separating our four sample sites, gene flow might be a driving force for the population structure and genetic diversity. Therefore, we inferred the population divergence and gene flow by reconstructing the maximum likelihood phylogenetic tree based on allele frequency data using Treemix software version 1.12  were used for calculating these genetic parameters.FST). The population fixation index (FST) was calculated for all six pairwise comparisons of four sample sites using a sliding window method implemented in VCFTools with a window size of 80 Kbp and a step size of 10 Kbp. A multiple regression on matrices (MRM) test was performed using the package ecodist implemented in R software (https://www.r‐project.org/) with 1,000 permutations to test whether genetic distance was correlated with geological distance for the four sample sites.Population fixation index (π). Sliding window estimates of nucleotide diversity (π) were calculated for all four sample sites with the same parameters used in the FST calculation. We also compared the nucleotide diversity of different genomic regions, such as intergenic, exon, intron, UTR, upstream, and downstream regions.Nucleotide diversity estimation (r2) was calculated for LD of SNPs in a 100‐Kb window for all four sample sites using Plink software (‐‐ld‐window‐kb 100). The general pattern of LD decay was then estimated using the software LD decay with default parameters .Linkage disequilibrium (LD). The squared correlation coefficient  and a population‐specific component (β). Selective signatures can be detected when α is extremely different from zero. The minimum false discovery rate at which the locus may be under significant selection was calculated as a q‐value. Prior odds of the selection model were set at 10,000 to reduce false‐positive results under a variety of demographic events  were considered as putative SNPs under selection (selSNPs). These selSNPs were retained for the subsequent environmental association analysis.To investigate the mechanisms underlying adaptation to altitudinal gradients, whole‐genome scanning  was performed with a 2.7http://worldclim.org) version 2.0 , corresponding to recent historical conditions (1970–2000) with a spatial resolution of 2.5 arc‐min. Since there was a high degree of correlation among environmental factors, we chose only alt, srad, and tavg as independent factors in environmental association analyses. The yearly standard average value of each climate variable was used.Seven altitudinal climatic variables were examined as possible factors explaining loci under selection: solar radiation (srad), precipitation (prec), wind speed (wind), water vapor pressure (vapr), average temperature (tavg), minimum temperature (tmin), and maximum temperature (tmax). All climatic variables were acquired from the WorldClim global climate database . The variance–covariance matrix was then used to control for evolutionary history during the process of BF calculation using Bayenv2. BF was calculated using normal environmental correlation analysis for each SNP. Additionally, a nonparametric test, which excluded the covariance structure among populations, was performed for calculation of the nonparametric Spearman's rank correlation coefficient ρ using the parameter ‐c. Those loci that ranked in the top 1% of BF and the absolute value of ρ were identified as putative loci associated with a certain environmental factor. Due to the limitation imposed by the small number of sample sites in this study (n = 4), we further performed a permutation test by reassigning individuals to sample sites and repeating the Bayenv2 analysis 1,000 times. The possibility that the recalculated values of BF and absolute ρ were greater than the original values was defined as the p‐value. Putative environment‐associated SNPs (eaSNPs) would be identified as such under a threshold .Here, we applied a Bayesian linear mixed‐based model in Bayenv2  using a custom Python script. We then converted gene IDs to gene ontology (GO) IDs using the bitr function and the toTable function to extract GO terms in the clusterProfiler package  file of the 33.1P. trichocarpa genome, with an average 15 × (10.08×–19.7×) depth of sequencing. A total of 490,363 SNPs were retained for subsequent analyses after SNP filtering. Among those SNPs, 39,428 were out of Hardy–Weinberg equilibrium (HWE) under a threshold p‐value (10–4). Approximately 48.54% of 490,363 SNPs were located in intergenic regions, and 12.88% were detected in exon regions  were mapped onto 394 Mb, providing approximately 88% (84.53%–91.78%) coverage of the ns Table .3.2K value by selecting the minimum CV error using 31,793 SNPs pruned by 10‐Kbp random windows  to low‐altitude (PL) sites was detected in all inferred trees. Figure . Another3.4FST value of the four sampling sites was 0.050, ranging from 0.006 (PL against TM) to 0.083 (LL against TM), indicating that there was little genetic differentiation among the four sampling locations (Table The average ns Table . Almost ns Table .π) was 7.26 × 10–5. The maximum π value was detected at sample site PL (7.57 × 10–5), and the minimum was detected at sample site LL (6.95 × 10–5). The four sample sites exhibited significant differences in nucleotide diversity . In addition, the average π of the intergenic region was 2.2 × 10–4, which was significantly greater than that of the genic region (π = 4.37 × 10–5). The results of ANOVA indicated that π values significantly differed among divergent genetic regions  than that for the high‐altitude group . Approximately 62.8% of outliers  were out of HWE (p = 1 × 10–5), and these loci were located in 531 unique genetic regions. The average population index (FST) of these selSNPs was 0.181 (range: 0.150–0.271). The locus‐specific component (α) ranged from 1.116 to 2.072, indicating that these outlier loci were undergoing continuous directional (divergent) selection. The highest FST (0.271) was harbored in gene LOC18098853, which encodes a disease resistance protein and is homologous with At4g27220 in Arabidopsis. This gene is involved in resistance to Verticillium wilt in Arabidopsis  were identified based on the 3.6ρ. Most of these (n = 69) were retained after permutation tests. Similarly, 82 selSNPs were associated with variation in solar radiation (sradSNPs) under the criteria of permutation (p < .01). However, only 6 selSNPs were associated with average temperature  based on the hierarchical distribution of BF and absolute s Figure a.r2) for 284 SNPs from this region was 0.501, forming three main LD block regions , catabolic processes , and signal transduction  and modification of proteins , which is involved in citrate efflux transportation and sustained microspore development during pollen tube growth in A. thaliana , and extensive LD. However, no selective signatures for other peGPX genes have been identified in the desert poplar. GPX also reportedly plays complex roles in diverse developmental processes. In Arabidopsis, extremely high expression levels of AtGPX1 were detected in leaf cell cultures, in mesophyll protoplast cultures, and in shoot apical meristems, indicating its physiological importance during shoot development  are a group of proteins that protect cells from oxidative damage generated by a reactive oxygen species  and landscape genomic (Bayenv2) methods, we detected two hotspot regions with robust signals of natural selection associated with altitude and solar radiation. These regions comprised 14 genes that are mainly involved in abiotic stress resistance and sustaining successful reproduction. Therefore, we hypothesize that Tibetan poplar adapted to high altitude partially through sustaining successful reproduction under conditions of environmental stress. The interaction between gene flow and natural selection drives local adaptation in this population of Tibetan poplar. This paper will be useful for understanding how various evolutionary forces, including natural selection and environmental factors, drive local adaptation to altitudinal differences in Tibetan poplar.This is the first attempt to elucidate the intricate genetic structures associated with high‐altitude climate adaptation in the QTP endemic woody plant Tibetan poplar, The authors state that there is no conflict of interest.Chenfei Zheng: Formal analysis ; resources (lead); writing – original draft ; writing – review and editing . Lizhi Tan: Formal analysis (supporting); software (supporting). Mengmeng Sang: Formal analysis (supporting); software (supporting). Meixia Ye: Formal analysis ; software . Rongling Wu: Conceptualization (lead); funding acquisition (lead); supervision (lead); writing – original draft ; writing – review and editing (lead).Appendix S1Click here for additional data file.


Whiteflies feed on plant sap and cause many problems on agricultural crops around the world. Whiteflies have endosymbiotic bacteria in cells inside their bodies that help them to feed on plants. Therefore, the sweetpotato whitefly feeds on a wide range of plants including many vegetable crops, and there are different types of these bacteria which do not all occur in the same whitefly population. In this paper, we focus on endosymbiotic bacteria that are associated with different biotypes of the sweetpotato whitefly with emphasis on their biological characteristics, diversity, and their interactions with whitefly-transmitted plant viruses. This information will be useful to the scientific community for the development of strategies to disrupt these bacteria and provide better whitefly control.Candidatus Portiera aleyrodidarum and have a variable number of secondary endosymbionts. The secondary endosymbiont complement harbored by the cryptic whitefly species Bemisia tabaci is particularly complex with various assemblages of seven different genera identified to date. In this review, we discuss whitefly associated primary and secondary endosymbionts. We focus on those associated with the notorious B. tabaci species complex with emphasis on their biological characteristics and diversity. We also discuss their interactions with phytopathogenic begomoviruses (family Geminiviridae), which are transmitted exclusively by B. tabaci in a persistent-circulative manner. Unraveling the complex interactions of these endosymbionts with their insect hosts and plant viruses could lead to advancements in whitefly and whitefly transmitted virus management.Whiteflies (Hemiptera: Aleyrodidae) are sap-feeding global agricultural pests. These piercing-sucking insects have coevolved with intracellular endosymbiotic bacteria that help to supplement their nutrient-poor plant sap diets with essential amino acids and carotenoids. These obligate, primary endosymbionts have been incorporated into specialized organs called bacteriomes where they sometimes coexist with facultative, secondary endosymbionts. All whitefly species harbor the primary endosymbiont  Bemisia tabaci (Gennadius) is of particular concern due to its highly polyphagous and prolific nature, its composition as a cryptic species, and its transmission of plant viruses [Bemisia tabaci is a natural vector of persistently transmitted begomoviruses (family Geminiviridae) as well as some semi-persistently and non-persistently transmitted plant viruses [Trialeurodes vaporariorum Westwood, the bandedwinged whitefly, Trialeurodes abutilonea , and the castor bean whitefly, Trialeurodes ricini (Misra) [Whiteflies (Hemiptera: Aleyrodidae) are globally significant agricultural pests and virus vectors causing direct and indirect damage to crops with estimated losses totaling billions of dollars (US) annually worldwide . The not viruses ,3,4. Bem viruses . Other kB. tabaci and other whiteflies, the maternally inherited primary (obligate) endosymbiont is Candidatus Portiera aleyrodidarum [B. tabaci harbors highly diverse communities of these endosymbionts [B. tabaci, including Hamiltonella [Rickettsia [Wolbachia [Arsenophonus [Cardinium [Fritchea [Hemipteriphilus [Sap-feeding insects, including whiteflies, subsist on inferior plant diets enabled by their evolutionary incorporation of endosymbiotic bacteria ,7. Obligodidarum . This enymbionts . Seven gltonella , Rickettckettsia , Wolbacholbachia , Arsenopnophonus , Cardiniardinium , FritcheFritchea , and Hemriphilus . These sriphilus .B. tabaci, in which the roles of endosymbionts are complex and complicated by the diversity of secondary endosymbionts and the cryptic nature of the host species. Untangling the intricate relationships between symbiont and host and understanding their roles in plant virus transmission should offer novel insights for whitefly management and virus control.In this review, we discuss the evolution and biology of whitefly endosymbionts and their interactions with plant viruses. We focus on the primary and secondary endosymbionts of the species Candidatus Portiera aleyrodidarum (hereafter referred to as Portiera), the primary endosymbiont of all whitefly species, provides essential amino acids, carotenoids, and other metabolites that its host is unable to produce on its own and does not receive in its diet or from other symbionts [Portiera is a gammaproteobacterium with a highly reduced genome  may be more the latter type given its involvement in essential amino acid biosynthesis pathways and production of B vitamins [Portiera for host derived resources [Arsenophonus, another enterobacterium, may also have a more mutualistic type of relationship with B. tabaci as it too is suggested to help provide essential nutrients [Hamiltonella and Arsenophonus appear to be fixed within B. tabaci populations [Rickettsia and Wolbachia, class Rickettsiales, are generally not fixed in populations and may represent more dynamic, parasitic-like associations with B. tabaci [Rickettsia genus have been found associated with B. tabaci: Rickettsia Bellii, which includes many pathogenic strains [Rickettsia Torix [B. tabaci populations, Ca. Hemipteriphilus asiaticus (hereafter Hemipteriphilus), is closely related to a mite-borne human pathogen in the genus Orientia [Hemipteriphilus so far appears to be restricted to the bacteriocytes, but its function and effect on host fitness is unknown. Candidatus Cardinium hertigii (Bacteroidetes) has a reduced and dynamic genome that lacks robust cofactor and amino acid biosynthesis capability and, therefore, is not suspected to be important to host nutrition metabolism [Fritschea) , occurs in the bacteriocytes, but little is known about its function in B. tabaci [B. tabaci.In addition to its primary endosymbiont, riphilus  have beevitamins ,27. Howeesources . Arsenoputrients ,28. Both. tabaci ,14,29. T strains ,31,32, aia Torix . AnotherOrientia ,33. Hemitabolism ,34. The . tabaci . AltogetB. tabaci and T. vaporariorum [T. vaporariorum. These results were supportive of earlier 16S rDNA sequencing of whitefly bacteriomes [B. tabaci. A follow up study comparing populations of B. tabaci MEAM1  and NW (New World) group whiteflies  demoons; see . Bemisiaa et al. , the coct organs ,40. The t organs  laid thet organs .Portiera within the bacteriome; however, secondary endosymbionts exhibit more varied localization patterns [B. tabaci secondary endosymbiont genera in different life stages of MEAM1 and MED (Mediterranean) populations. Hamiltonella was identified in both MEAM1 and MED populations, and it was found in patches inhabiting only the bacteriome in all observed life stages . Similarly, in the MED population Arsenophonus was located only in the bacteriome, but its distribution was more often surrounding the nuclei of bacteriocytes. Typically, Arsenophonus is observed exclusively in the bacteriome; however, Rana et al. [B. tabaci Asia II. In eggs, nymphs, and adults of the MED population, Wolbachia was detected inside the bacteriome and concentrated along its circumference [Cardinium in the MED population and Rickettsia in MEAM1 and MED had wider distribution patterns than the other symbionts [Cardinium was detected in the bacteriocytes in all stages, in higher concentrations in the abdomen and head in nymphs, in abdominal cells suspected by the authors to be fat cells in adult females, and widely distributed throughout the abdomen of adult males, excluding the rectal sac. Rickettsia interestingly had two very different distribution patterns, either strictly within the bacteriome concentrated around the circumference (confined phenotype) or, as previously observed [Wolbachia to have both confined and scattered phenotypes in B. tabaci AsiaII7. Similar localization patterns have been demonstrated for endosymbionts in other whitefly species, including Arsenophonus and Hamiltonella in T. vaporariorum [Rickettsia, Wolbachia, and Cardinium in T. vaporariorum [Fritschea in MEAM1 and NW2 [Hemipteriphilus in China 1 [Portiera in the bacteriome and that Hamiltonella, Fritschea, and Hemipteriphilus are restricted to the bacteriocytes, while Arsenophonus, Rickettsia, Wolbachia, and Cardinium can occur in other whitefly tissues.Localization studies consistently place patterns . Using fpatterns  localizea et al.  reportedmference . In someymbionts . Cardiniobserved , distribobserved . The speobserved  found Worariorum  and Rickrariorum . Thus fa and NW2  and Hemi China 1 . AltogetB. tabaci endosymbionts are vertically  transmitted [B. tabaci, a single bacteriocyte cell is transferred to the plasma of an individual developing oocyte [Rickettsia), the endosymbiont will temporarily colocalize with Portiera and any other endosymbionts within the bacteriocyte [Rickettsia and Wolbachia; [B. tabaci has been supported by phylogenetic incongruence between host and endosymbionts [Wolbachia and B. tabaci and partial congruence for Arsenophonus and Cardinium with B. tabaci, suggesting that Wolbachia may be more adapted to horizontal transmission events. Plant-mediated horizontal transmission has been demonstrated for Rickettsia, which established in phloem cells after whitefly feeding and was acquired by Rickettsia-free whiteflies feeding on the same cotton leaf [Wolbachia [B. tabaci species (MEAM1 and MED) for Rickettsia [Wolbachia and Rickettsia has been documented. Wolbachia was stably acquired by whiteflies after non-lethal probing by Eretmocerus sp. nr. furuhashii (Hymenoptera: Aphelinidae) contaminated with Wolbachia from whiteflies harboring the bacterium [Wolbachia via the parasitoid had enhanced fitness. Other work has revealed that the Rickettsia and Wolbachia detected in field collections of B. tabaci nymphs and the emerging Eretmocerus parasitoids were identical haplotypes, suggesting that parasitoid-mediated horizontal transmission may be occurring between these species in the field [All nsmitted . Costa ensmitted  describeg oocyte ,48. Thisg oocyte . When seeriocyte ,49. As tlbachia; ,51,52) olbachia; ,54. Evidymbionts . Ahmed eymbionts  demonstrton leaf . Host placterium . WhiteflB. tabaci, particularly given the species’ cryptic diversity and complex endosymbiont composition. To study the effects of specific endosymbionts on whitefly fitness, colonies that differ only in the presence or absence of a particular endosymbiont are ideally needed. In this respect, antibiotics have been used to obtain endosymbiont-free colonies. Endosymbiont elimination studies using antibiotic treatments of whiteflies have provided some clues as to the effects of endosymbionts on host fitness. Costa et al. [B. tabaci without identification of the specific bacteria reduced or eliminated demonstrated that antibiotics with different modes of action had differing effects on whitefly fitness. For example, antibiotics with action against bacterial protein synthesis (tetracycline and rifampicin) reduced host growth and development, while antibiotics with action against bacterial cell walls  did not have an observed effect on fitness [Theoretically, secondary endosymbionts should provide some benefit to or increase the fitness of their hosts in return for the cost of harboring said endosymbiont. While studies have reported beneficial effects , are syntenic, indicating evolutionary stasis, the genome of B. tabaci Portiera has undergone extensive rearrangements [The symbiosis of whiteflies with their primary endosymbiont iteflies ,83. Phyllogenies , a pattelogenies ,85,86,87logenies . This pa repeats ,90. Compd repair . While tB. tabaci and other phytophagous hemipterans, several of these genes have been lost or pseudogenized, therefore requiring complements of those genes to be expressed by either the host or another symbiont [B. tabaci Portiera genomes revealed that approximately half of their metabolism genes are involved in amino acid biosynthesis [Hamiltonella in MEAM1), and other bacterial genes [Portiera-Hamiltonella pathway complementarity was demonstrated for B. tabaci MED, as well [Portiera appears to be both compensated for and facilitated by genomic complementation in the host and other symbionts [A counter-intuitive finding in many studies on the genomes of obligate intracellular endosymbionts of sap-feeding insects is the loss of genes involved in the biosynthesis of the 10 essential amino acids ,92,93, tsymbiont ,90,94,95ynthesis . Howeveral genes . Whitefl as well . The genymbionts ,27.B. tabaci in a later section), and by evidence of multiple acquisitions of secondary endosymbionts [Secondary endosymbionts can be intricately involved in the essential metabolic processes of its insect host ,27,28. Hymbionts ,96. The ymbionts ,25.Bemisia tabaci harbors the highest known diversity of secondary endosymbionts among phloem-feeding hemipterans [B. tabaci species. One of the earliest studies on endosymbiont composition in cryptic B. tabaci species demonstrated that US native NW populations had two distinct C-type (secondary) endosymbionts within their bacteriocytes whereas the invasive MEAM1 had only one [B. tabaci biotypes (now cryptic species) may have been facilitated by secondary endosymbionts and differences in their composition among the biotypes . Since e e.g., ,106,107),107Bemise e.g., ,109,110),110BemisB. tabaci endosymbionts showed that three of the seven described genera of facultative endosymbionts are widespread in B. tabaci cryptic species [Rickettsia, Wolbachia, and Cardinium were found in a wide diversity of B. tabaci species, including MEAM1, MED, and certain Asia, SSA (Sub-Saharan Africa), and NW cryptic species [Hamiltonella and Fritschea were associated with only MEAM1, MED, and NW species, Arsenophonus was not associated with MEAM1, and Hemipteriphilus was associated with China 1 only. Further, phylogenetic analyses of the secondary endosymbionts demonstrated evidence of more than one genetic group for some endosymbionts  and only one group of Hamiltonella, which was conserved among MEAM1, MED, and NW species.Recently, using 16S rRNA sequences with associated geographical information retrieved from GenBank, a study on the global diversity of  species . Rickett species . Of courRickettsia, 5 OTUs for Arsenophonus, and 2 OTUs for Wolbachia [Hamiltonella, Cardinium, Fritschea, Hemipteriphilus, and Portiera. Only one population of each B. tabaci cryptic species was sampled, however, Wang et al. [Hamiltonella and, notably, Hemipteriphilus associated with more cryptic species than Kanakala and Ghanim [B. tabaci cryptic species and populations, these studies highlight the improbability of secondary endosymbiont facilitated speciation of B. tabaci, the hypothesis proposed for cryptic B. tabaci early in the discovery of biotypes associated with different secondary symbionts.Another recent study, using PacBio sequencing of 16S rRNA bacterial genes from field populations of 23 cryptic species, also revealed wide genetic diversity in some secondary endosymbionts . This stolbachia . One OTUg et al.  found Had Ghanim  found. WBemisia tabaci transmits viruses in three distinct manners: non-persistently, semi-persistently, and in a persistent-circulative manner [Bemisia tabaci is notorious for its persistent-circulative transmission of begomoviruses (family Geminiviridae), for which this species is the only demonstrated vector. Bemisia tabaci also transmits viruses in the genera Crinivirus (family Closteroviridae), Ipomovius (family Potyviridae), Carlavirus (family Betaflexiviridae), and Torradovirus (family Secoviridae) in a non-circulative manner [For us, one of the more exciting avenues of research on hemipteran endosymbionts is the elucidation of the interactions of these bacteria with the plant viruses transmitted by their host. e manner ,111. A fe manner ,113,114.Polerovirus, family Luteoviridae) binds to symbionin  (Hemiptera: Aphididae). Additional studies with aphids and circulative luteoviruses further demonstrated this interaction, suggesting it as a means of virus protection from degradation in the hemolymph by the vector immune system and emphasizing the probable role of endosymbiont expressed GroEL-like protein in the virus transmission process [The first reported study to suggest a possible role of endosymbionts in transmission of plant viruses was van den Heuvel et al. . This stprotein; ) express process ,118,119.Buchnera, that was demonstrated to express the GroEL-like chaperonin in M. persicae, and Buchnera is unrelated to the primary endosymbiont of whiteflies, a secondary endosymbiont related to Buchnera is present in some whiteflies [B. tabaci B biotype (MEAM1), that TYLCV showed an affinity for GroEL, and that TYLCV transmission was significantly (>80%) reduced with the introduction of anti-GroEL antiserum. Both insect transmissible (TYLCV-Israel) and non-transmissible  begomoviruses interacted with GroEL in vitro, indicating the inability of whitefly transmission of AbMV-Is (and lack of AbMV-Is detection in whitefly hemolymph) is likely due to an inability to cross the gut epithelial barrier rather than a lack of affinity to B. tabaci GroEL [The differences between luteoviruses and geminiviruses are many, but begomoviruses share a similar persistent-circulative mode of transmission, with a key barrier to transmission being persistence in the vector hemolymph subsequent to passing through the gut tissue . While iiteflies . This leiteflies  demonstriteflies ,39) of Bci GroEL . Similarci GroEL ,123. AltB. tabaci MEAM1 and NW [B. tabaci cryptic species. Studies to determine which secondary endosymbionts and to what degree are responsible for expression of GroEL correlated with virus transmission began with looking at populations of B. tabaci MEAM1 and MED from Israel. At that time, Rickettsia was known to occur in the Israeli MEAM1 and MED, Hamiltonella was identified in MEAM1 only, and Wolbachia and Arsenophonus were identified in MED only [Portiera, expressed GroEL in the hemolymph of both MEAM1 and MED, only GroEL expressed by Hamiltonella in the MEAM1 biotype interacted with TYLCV resulting in transmission  and tomato chlorosis virus (genus Crinivirus) at higher rates. Therefore, the Hamiltonella-plant virus interaction is not restricted to B. tabaci MEAM1, and it has implications in the epidemiology of begomoviruses worldwide as well as some non-circulative viruses.Since the early work on GroEL–begomovirus interactions, the number of secondary endosymbionts discovered in whiteflies has increased from the two previously described C-types in 1 and NW ,39 to noMED only ,98,124. ctively; ,126). Inno acids . Given tn Brazil ,128. IntBemisia tabaci-harbored Rickettsia has also been implicated in virus interactions within the vector , in B. tabaci Asia II 7 [Begomovirus, family Geminiviridae) after a prolonged acquisition access period [e vector . Isolineficiency . When whmolymph; ), likelyacquired . Another. tabaci . More resia II 7 ,110 sligs period . RiTBt wHamiltonella GroEL was the only endosymbiont-expressed GroEL thought to interact with begomoviruses in B. tabaci, Rana et al. [Arsenophonus of an Asia II population devoid of Hamiltonella expressed GroEL which interacted with cotton leaf curl virus  in vitro and in vivo  population was negatively impacted by harboring Arsenophonus and Rickettsia (AR+) and that acquisition and retention of East African cassava mosaic virus-Uganda variant was lower in AR+ B. tabaci [Portiera or secondary endosymbionts Wolbachia, Cardinium, Fritschea, and Hemipteriphilus with plant viruses in B. tabaci transmission.Fewer studies have focused on the roles of the other secondary endosymbionts in virus transmission. While, previously, a et al.  demonstr in vivo . Unlike  process . Another. tabaci . We are B. tabaci complex with its numerous sibling species, several secondary endosymbionts inhabiting their hosts in different combinations, and transmission of plant viruses. Understanding all levels involved  and their activities in host interactions and virus transmission processes are key to developing novel pest management strategies targeting endosymbionts or their products. With the application of high throughput sequencing technology to the study of whitefly metagenomes, the issue of false negatives in PCR-based endosymbiont detection has been minimized, allowing a more complete picture of the microorganisms involved in the whitefly holobiont. While endosymbionts play important roles in whitefly–host plant interactions, other symbiotic bacteria, including gut bacteria, likely influence these interactions. It is central then to accurate research on endosymbiont roles in whitefly fitness and whitefly–host plant and -virus interactions to demonstrate the establishment of comparative populations whose microbiome compositions are, ideally, identical. Using antibiotic treatments to establish endosymbiont-free populations disrupts the microbiome; therefore, efforts to reestablish the microbiota (excluding the endosymbiont of interest) should be considered. Selective elimination of endosymbionts which are generally not fixed in populations, such as Rickettsia and Wolbachia, is possible without antibiotics; however, those which generally are fixed, such as Hamiltonella and Arsenophonus, present more of a challenge.Tri-trophic vector-endosymbiont-plant and vector-endosymbiont-virus interactions are indeed complex considering the cryptic B. tabaci or virus management. Disentangling these multitrophic relationships should unlock new ideas, leading to needed advancements in managing this global pest.While many questions regarding the effects of particular endosymbionts on whitefly behavior and fitness remain, the same is true for endosymbiont influence in virus transmission. Studies have demonstrated the involvement of endosymbiont expressed proteins, such as GroEL, in the virus transmission process. Given this interaction and that the GroEL chaperone is highly expressed by intracellular endosymbionts, the question of coevolved relationships arises. Perhaps the relationship is one of chance, or maybe the protection of begomovirus virions against the vector immune system by secondary endosymbiont GroEL is a coevolved relationship which benefits both whitefly/endosymbiont and virus. With improvements on the resolution of in vivo endosymbiont transcriptomes, studying the effects of virus acquisition on endosymbiont gene expression using comparative transcriptomics could address this question. Whitefly gene expression  and beha