Vitis vinifera ssp. sylvestris) is distributed from Switzerland in the north to Israel in the south. However, little is known about the ecogeographic constraints of this species and its genetic and phenotypic characteristics, especially at the southern edge of its distribution range in the Levant region. In this study, we explore the population structure of southern Levantine wild grapevines and the correlation between demographic and ecogeographic characteristics. Based on our genetic analysis, the wild grapevine populations in this region can be divided into two major subgroups in accordance with a multivariate spatial and ecogeographical clustering model. The identified subpopulations also differ in morphological traits, mainly leaf hairiness which may imply adaptation to environmental stress. The findings suggest that the Upper Jordan River population was spread to the Sea of Galilee area and that a third smaller subpopulation at the south of the Golan Heights may represent a distinguished gene pool or a recent establishment of a new population. A spatial distribution model indicated that distance to water sources, Normalized difference vegetation index, and precipitation are the main environmental factors constraining V. v. sylvestris distribution at its southern distribution range. These factors in addition to limited gene flow between populations prevent further spread of wild grapevines southwards to semi‐arid regions.The spatial distribution of plants is constrained by demographic and ecogeographic factors that determine the range and abundance of the species. Wild grapevine ( In this paper, genetic analysis, spatial, and eco‐geographical clustering models show there are two major subpopulations of wild grapevine in the south Levant, differing morphologically mainly in leaf hairiness. We also showed that closeness to water sources, medium‐high NDVI levels, and sufficient precipitation rates are the main effectors possibly preventing further distribution of wild grapevines southwards. Thus, pa) Hegi, . Yet, reV. v. sylvestris is a dioecious species where the male flower contains erect stamens and does not contain female organs, while the female flower contains an ovary, stylus, and degenerated stamens  markers  and species distribution models (SDM) : calculated from a digital elevation model (DEM) generated by the Survey of Israel (SOI). Slopes were calculated in degrees relative to a horizontal plane, and aspect was calculated as degrees relative to the North cardinal direction (0°).Distance to a water source: calculated from the streams layer generated by SOI, using the Euclidean distance tool in ArcGIS ESRI, .Normalized difference vegetation index (NDVI): generated for two dates (7 April 2019 and 12 July 2019) using Landsat 8 imagery. Two time points (summer and spring) were chosen because of the differences in vegetation density between these seasons; the spring period is characterized by higher vegetation compared to the dry summer period, when the vegetation remains mostly near water sources or at irrigated areas. The images were atmospherically corrected using the dark object subtraction method via the “RStoolbox” package in R : calculated based on the Landsat 8 thermal infrared (TIR) band (band 10) acquired on 12 July 2019  based on a 30‐year average as measured by the Israel Meteorological Service.Z‐score that is assigned to each point after standardization and indicates the degree of similarity between neighboring entities. Z‐score values far from zero signify strong spatial dependencies, and negative or positive values correspond to low or high values of the original variable, respectively  clustering algorithm was then applied with the Z‐score values of the different variables as input  are not always discrete; thus, assignment of an observation to a specific cluster is not definite   map: represented as a categorical variable and developed by the SOI. Nine LC categories were defined: bare soil, orchards, water, crop fields, forest, natural grove, general vegetation, bare rock, and urban areas  and test panel (25%). The accuracy of the final model was estimated by computing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve  standards adopted by the “COST Action GrapeNet FA1003” (2007). The list of 20 OIV descriptors that were used in this study is provided in Table A principal component analysis (PCA) was performed for the morphological characteristics using the “Gifi” package  protocol, described previously by Drori et al.  and DrorGenotyping of each accession was conducted using a panel of 22 standard SSR markers that were developed for genotyping grapevine  ranging from 1 to 9 using 50,000 iterations following a burn‐in length of 5,000 iterations. The most likely number of clusters was determined based on the log‐likelihood score of each K and the ΔK method  which were included as an outgroup. The dendrogram was generated from a Bruvo's genetic distance , the observed heterozygosity (oH), unbiased expected heterozygosity (eH), the number of effective alleles (eN), Hardy–Weinberg equilibrium test (HWE), and the number of private alleles, extent of gene flow (Nm), and genetic differentiation (STF)  and PCoA  was calculated as implemented in GenAlex v6.501  the northeastern Sea of Galilee shore (Betiha), a low‐altitude (approximately −200 m mean sea level (MSL)) flood plain, wherein several streams, including the Jordan River, flow into the lake and thick soils (mainly haploxerolls and vertisols) cover an extensive area, (b) the Upper Jordan River and its tributaries (circa 100 m MSL), where the grapevines are found mainly in basaltic haploxerolls, (c) the central part of the Golan Heights basaltic plateau (about 500 m MSL), and (d) the southern slops of the Golan Heights (around 300 m MSL), which are covered by haploxerolls, basaltic haploxerolls, and vertisols ; thus, these coordinates mark the global southern distribution edge of this species in accordance with previous survey conducted in this region  is the most probable number of clusters while at K = 3 (ΔK = 4.32) is the second‐best result  between the genetic and ecogeographic clustering indicating that the genetic differentiation closely corresponds to the environmental differences between regions.A McNemar's χThese results were further supported by a neighbor‐joining (NJ) dendrogram constructed across all accessions based on the 20 SSR markers Figure . The genSTF) between clusters which was found to be low (0.038), yet significant  indicating that the two clusters maintain some level of gene flow despite their clear split. To explore the structure and interactions between populations, genetic differentiation (STF) and gene flow (Nm) were calculated for the two main clusters and also for the south‐Golan subpopulation (Table STF = 0.037) presumably due to extensive gene flow (Nm = 6.5666). The south‐Golan subpopulation was significantly differentiated , and low gene flow was obtained  with both the Upper Jordan River and the Sea of Galilee clusters , compared to the variation within clusters (96%). We further calculated the level of genetic differentiation  and expected (eH) heterozygosity and inbreeding coefficients (F) were calculated. A high level of genetic diversity  was observed in the Upper Jordan River cluster compared with the Sea of Galilee cluster which was characterized by higher inbreeding coefficient (F = 0.091) and lower observed heterozygosity (oH = 0.677). Conducting the same analysis while separating into the three subpopulations mentioned above, the south‐Golan subpopulation showed the lowest heterozygosity (oH = 0.613) and fixation index (F = −0.16).  and Sea of Galilee (n = 29) regions using 20 OIV descriptors  and lower number of lobes  than that of the Sea of Galilee subpopulation  to wild grapevine spatial distribution across the studied area Figure . Next inV. v. sylvestris populations throughout the studied area. Outcropping lithology was found to affect the occurrence of wild grapevine only at the Upper Jordan River region, while the soil type was found to affect the occurrence only at the Sea of Galilee region, and the high occurrence probability was obtained in areas characterized by vertisols (rich clay soils) in both regions  and east (Syria). The Sea of Galilee population, which marks the southernmost local and global edge of wild grapevine distribution range ; project administration (supporting); resources (supporting); software (lead); validation (lead); visualization (lead); writing‐original draft . Noa Ohana‐Levi: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); investigation (supporting); resources (supporting); software ; validation (supporting); visualization ; writing‐original draft (supporting). Hodaya Brauner: Conceptualization (supporting); formal analysis (supporting); resources ; Visualization (supporting). Nimrod Inbar: Data curation (supporting); software (supporting); writing‐original draft (supporting). Sariel Hübner: Conceptualization ; investigation (supporting); methodology (supporting); resources (supporting); software (supporting); supervision ; writing‐original draft . Elyashiv Drori: Conceptualization (lead); data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation ; methodology ; project administration (supporting); resources (supporting); supervision (lead); validation (supporting); visualization (supporting); writing‐original draft .Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.


Populus nigra, we have shown that, although the dynamic was mainly determined by the trade‐off between genetic gain and genetic diversity, better formulations of the genomic relationship matrix, especially those fostering individuals carrying multiple heterozygous loci, can lead to better short‐term genetic gain and a higher selection plateau.In breeding, optimal contribution selection (OCS) is one of the most effective strategies to balance short‐ and long‐term genetic responses, by maximizing genetic gain and minimizing global coancestry. Considering genetic diversity in the selection dynamic—through coancestry—is undoubtedly the reason for the success of OCS, as it avoids preliminary loss of favorable alleles. Originally formulated with the pedigree relationship matrix, global coancestry can nowadays be assessed with one of the possible formulations of the realized genomic relationship matrix. Most formulations were optimized for genomic evaluation, but few for the management of coancestry. We introduce here an alternative formulation specifically developed for genomic OCS (GOCS), intended to better control heterozygous loci, and thus better account for Mendelian sampling. We simulated a multigeneration breeding program with mate allocation and under GOCS for twenty generations, solved with quadratic programming. With the case study of  Its simplicity has made it today one of the most successful strategies to address the problem of selection‐induced loss of diversity , and to account for dominant effects  was introduced by Meuwissen  as a comvs genealogical information , to show by simulations that managing heterozygous loci in a multigeneration breeding program can achieve higher performances than with a classical OCS, and reach a higher Pareto curve. Among all the possible ways of constructing the genomic relationship matrix, the best strategy was to foster the heterozygote × homozygote relationship between pairs, which appeared to be the best compromise between genetic fixation and diversity.In the present study, we will focus on heterozygote x homozygote relationships, and the objective is to exploit the impact of Mendelian sampling in the framework of genetic contribution , and assuming no epistasis, it is possible to formulate the expected future performance or inbreeding coefficient of the population as TYc (cY for simplicity) or as ½ TAcc, respectively . Deriving optimal selection decisions simultaneously accounting for future performance and future inbreeding is then possible through OCS, using genetic contributions as a decision variable. One of the possible formulations of such a problem is to solve min. λ cGc − cY, with λ a weighting parameter  represents the expected inbreeding assuming panmixia or, more precisely, uniting in a full diallelic way all parents while respecting each parental c. This corresponds to the best expectation for inbreeding when the mating regime is unknown or not under the control of the breeder, which is often the case when, for example, mating is allowed to follow an open pollination regime as in a seed orchard.With the knowledge of G, see Nejati‐Javaremi et al., As classical selection over cycles tends to accelerate the change of frequencies when they are intermediate, that is, when the variances are at the highest levels, the risks of loss of favorable alleles by hitchhiking the alternative detrimentals would also increase  subject to a constrained inbreeding coefficient (cAc). Likewise, it is also possible to minimize the inbreeding rate while constraining the genetic gain . In other words, the curve of Pareto optima is the parametric curve, as a function of λ, of solutions minimizing λ cGc − cY. It is therefore the set of , where c* is the optimal contribution vector for a given λ. Without loss of generality, we consider the scalarized problem parameterized by α such that the problem becomes:α ⋲  can be interpreted as the trade‐off value between coancestry and genetic gain (or the weight of coancestry compared to that of genetic gain).Following the approach of Akdemir et al. , we consα . Both formulations—with α or with an inbreeding constraint—are strictly equivalent, and choosing a value for α is as arbitrary and difficult as choosing a value for a constraint without any a priori. Here, we will develop the framework with α, and different selection scenarios will be expressed in terms of α.Each OCS solution given a constraint , and the sum of all contributions must be equal to 1 (1Tc = 1). The constrained scalarized problem for a particular value of α is a constrained quadratic programming and can therefore be solved deterministically with an interior point method, adequate to solve constrained convex optimization . Note that the elements of X are not corrected by minor allele frequencies, nor is the resulting G scaled by the expected heterozygosity, as is usually done in genomic evaluation. Therefore, the diagonal elements of G provide information on the number of homozygous loci per individual, while the off‐diagonal elements reflect the number of homozygous states shared by individuals across loci. Thus, for off‐diagonals, the same homozygous state at a given locus adds one unit to the count, while one unit is subtracted for opposite homozygous states  and N columns . The two homozygous states are encoded as −1 and 1, and the heterozygous state as 0 ; opposite homozygote × homozygote, which value is −1 (−1 × 1); heterozygote × homozygote, which value is 0 (0 × 1 or 0 × −1); and heterozygote × heterozygote, which value is also 0 (0 × 0). To distinguish heterozygote × homozygote (He × Ho), and heterozygote × heterozygote (He × He), we propose to change the value of He × Ho to β, which ranges from −1 to 1. A positive value of β means that few offspring will be generated from individuals with a large number of loci in He × Ho relationship with others. Hereafter, we denote G* the matrix constructed with β, where G* = G + β Q, Q being the matrix accounting for He × Ho loci, as G already account for homozygote loci  remained constant across generations within a given scenario. No introgression of external genetic material was considered here. As mentioned above, we estimated the allelic effects from the genotype data with a real phenotype (trunk circumference). The resulting allelic effects were considered “true” and constant across generations and used to obtain the true breeding value (TBV) of the newly simulated candidates. Phenotypes and genotypes of all simulated individuals were considered to be known at each generation.We considered different simulation scenarios, assuming different values of h2 = 0.5134, environmental deviation was simulated as a normally distributed perturbation of mean 0 and variance (1‐h2)σg2, where σg2 was the TBV variance. At each generation, as was done in Jannink (G (not G*). Genomic evaluation included all previous generations, so the reference population incrementally increased over time.When simulating multigeneration breeding programs, the question arises as to how genomic estimated breeding values (GEBVs) should be assessed at each new virtual generation. With a heritability of  Jannink  and De B Jannink , GEBV wa Jannink , using tG at generation t + 1 . All scenarios started with the same initial population: the initial average breeding value was equal to 27.7 ± 25.9 and the population coancestry was equal to 0.214 ± 0.086.OCS was then applied at each generation on the simulated GEBVs, and the resulting contributions were converted into a mating plan that fits the OCS solution. We can notice that once the genetic contributions are fixed, the mating plan will not change the average breeding value, nor the average coancestry in the selected candidates. The mating, however, can change the progeny homozygosity—the diagonal elements of the matrix ming see . Mate alhttps://github.com/mtiret/ocs.git).Every simulated replicate proceeded for 20 (nonoverlapping) generations and was devised without mutation. The simulation followed a grid of parameters: α ranged from 0.1 to 0.9 (with steps of 0.1), and β was equal to −0.5, 0, or 0.5. Each parametric setting was simulated 100 times. In order to compare the results from different β, we also computed for each simulation the “true” coancestry, which is the coancestry the population would have had if β was equal to 0 , α and β the simulation parameters treated as fixed effects, α2 as the fixed effect of the squared parameter of α, m as the fixed effect of mate allocation , and e the residual fitted to a normal distribution. We considered here a polynomial regression (quadratic term α2) as there were symmetrical boundary effects of α upon Y: adding the quadratic term resulted in a gain of up to 70% in terms of adjusted coefficient of determination (r2). Quadratic α increases with a slower rate than linear α; hence, the quadratic term can be interpreted as an effect that is constant for low values of α, but highly variable among high values of α. We will hereafter refer to this model as model (1). In some cases however, we focused on a given value of α, where we considered the following model:lm and anova from the base R package stats). We performed a type I analysis of variance, and the statistical significance of each factor was assessed with a Fisher's F test.Finally, in order to determine the relative importance of the factors α, β and mate allocation on genetic gain and true coancestry  at a given generation, we considered the following statistical model:33.1α (representing the weight of coancestry compared to that of genetic gain), and considering β  equal to 0—corresponding then to the classical formulation of OCS—as expected, the average value of genetic gain increased for the whole period of selection, especially for low α , a consistent decrease was obtained for the whole period of selection. In other words, when α is high enough, though genetic gain is lower, producing genetic gain has no perceptible cost in terms of coancestry. De Cara et al. , simulations showed a consistent decrease over time Table . Surprisα  and then to higher values of α .Different long‐term horizons of genetic gain were reached depending on the value of α Figure . In the α = 1) did not change across generations, that is, the same ranking of α according to coancestries occurred for all generations. The most relevant feature, however, is that the change in coancestry between extreme values of α was larger than those observed for gain. In the long term (at generation 20), coancestry was multiplied by 10.23 when α shifted from 0.9 to 0.1, whereas for the same shift of α, genetic gain was only multiplied by 1.28. Such a difference in scale of response between gain and coancestry is clear even at the first generation of application of OCS. This trend can also be observed in the Pareto curve, where a substantial reduction in coancestry can have a minimum cost in gain whenever α is set between zero and intermediate values  and coancestry , suggesting a strong explanatory power of the parameters α, β, and mate allocation. Overall, for both unshuffled and shuffled datasets, α was the most explanatory factor, with more than 84.6% of genetic gain variance explained, and more than 97.6% of coancestry variance explained , every bit of increase in α has a perceptible effect on genetic gain, whereas for a broken linkage disequilibrium (shuffled), low α does not have any effect on genetic gain, only high values do. Coancestry, on the opposite, is always fully explained by the linear term, suggesting that although the effect of α is not always perceptible for genetic gain, it always is for coancestry. Mate allocation did only explain 0.0877% of the genetic gain variance in the unshuffled dataset and was not significant in the shuffled dataset, suggesting that mating (defined as the minimization of progeny homozygosity) had only a small effect on genetic gain. Likewise, mate allocation only explained 0.00485% of the variance of coancestry in the unshuffled dataset, and only 0.00842% in the shuffled dataset.Statistical analyses of model (1) showed strong adjusted coefficients of determination for both genetic gain (unshuffled: β) increased both genetic gain and coancestry (Table β explained a slightly more important proportion of genetic gain variance for unshuffled dataset (6.49%) than for shuffled dataset (3.98%), suggesting that high linkage disequilibrium can benefit from a management of heterozygous loci. For coancestry, β only explained 0.434% (unshuffled) and 0.0470% (shuffled) of the variance. In the unshuffled dataset, when analyzing the effect of β on genetic gain with a fixed value of α (model (2)), β was not significant for α = 0.1 , but significant for α = 0.9 ; the same pattern was observed in the shuffled dataset. The fact that β cannot have an impact for low α was expected by construction, but its significance for high α pinpoints that managing heterozygous loci can be beneficial in terms of genetic gain when there is sufficient diversity. On the opposite, β had a significant effect on coancestry , for low and high values of α, in the unshuffled and shuffled dataset. Overall, genetic gain and coancestry were mostly determined by α, even if the parameter was defined at the OCS level, and so, being blind to mate allocation. This result highlights the strikingly consistency of predictions with the genetic contribution framework. Similar results have been shown in previous studies , both genetic gain and coancestry were strongly influenced by α, but only negligibly by β. At this horizon, the population is detached from the Pareto curve. However, for larger α , favoring He × Ho relationship (β < 0) resulted in best performances, that is, a higher genetic gain for a given value of true coancestry. In other words, favoring He × Ho relationship allows the population to be on a higher Pareto curve . For shuffled dataset however, the best strategy was to promote He × Ho relationship only for high values of α, as the breeding potential of β = −0.5 was significantly higher than other values of β only when α > 0.6 . The breeding potential of β = −0.5 and of a given α can almost reach that of a lower value of α. Therefore, favoring He × Ho relationship would increase short‐term genetic gain, while guaranteeing a high selection plateau—as its level is determined by α, not β.Considering an alternative way of assessing the potential of a breeding program by measuring the GEBV of the population from favorable alleles that are not fixed yet, namely the breeding potential, the best strategy for the unshuffled dataset was to promote He × Ho relationship, especially for high values of α Figure . Except 44.1α, where diversity among candidates is modeled through relatedness or coancestry. This weighted approach, or the constrained formulation, can enhance the selection plateau, but often at the cost of slowing down the rate of progress. When such a perspective is applied to late maturing perennials, the cost in time for slow progress becomes a heavy drawback.In a multigeneration breeding program, being able to select favorable alleles with little losses of favorable alleles on other loci would be the most desirable feature, that is, avoiding the Bulmer effect Bulmer, . Drift oAnother alternative to accelerate breeding without the drawback of drift is to minimize the uncertainties concerning the consequences of selection decisions, so that decisions can be based on sound predictions of the impact of selection on future generations. A way of doing this is to account for the He × Ho relationship between candidates, which is generally not considered in classical OCS. We have shown that optimizing mating, and modifying the genomic relationship matrix to account for He × Ho relationship, that is, gain control over the Mendelian sampling, has always resulted in better performances, for both genetic gain and coancestry. The mating optimization was particularly important in the first generations, as already reported by Toro and Varona .α), better controlling Mendelian sampling had a small effect, meaning that the effect of drift cannot be counterbalanced. However, when considering higher α, controlling Mendelian sampling, especially by favoring He × Ho relationship (lower β) had a strong effect on the Pareto curve. Increasing genetic gain usually means: (i) a lower genetic (and genic) variance after selection, (ii) a higher level of fixation of favorable alleles, which constitutes the matter making up genetic gain, and (iii) a higher level of negative linkage disequilibrium covariance due to the Bulmer effect. The extra gain obtained from β < 0 could come from using more efficiently the genic variance, resulting in more depletion compared to that of higher β levels, and thus converting this available variation into favorable allele fixation, or likewise unfavorable allele elimination.When focusing on genetic gain (low 4.2G*) showed a consistently lower accuracy than with G . In addition, when linkage disequilibrium was the highest (unshuffled dataset), our method performed best in terms of the Pareto curve.Several studies proposed alternative formulations of the realized genomic relationship matrix  could be important at the very short term to prevent losses of favorable alleles in low frequency, but once the Bulmer effect is absorbed by recombination, it could be safe to switch to a more aggressive strategy such as with lower values of α. Preliminary works showed indeed that a geometrically decreasing α along the generations with a rate of 0.99 brings better long‐term performances. Differential selection over generation is therefore a field worth investigating, and warrants further studies.Introducing variability in the parameter 5In this article, we have extended OCS by proposing an alternative formulation of the realized genomic relationship matrix that better accounts for the Mendelian sampling. In multigeneration breeding programs, it is important to account for diversity to reach a higher selection plateau, even though the speed at which it is reached can be slow. We have shown by and large that the population dynamic is overall dominated by the trade‐off value α between genetic gain and genetic diversity. However, better accounting for Mendelian sampling, even implicitly as proposed here by fostering individuals with multiple loci in He × Ho relationship with others, could minimize the speed problem, by “accelerating” the breeding while maintaining a high level of diversity and selective potential for future generations.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.Supplementary MaterialClick here for additional data file.


In this manuscript we take a closer look at the genetic tier of precision medicine. The volume and variety of data provides a more robust picture of health for individual patients and patient populations. However, this increased data may also have an adverse effect by muddling our understanding without the proper pedagogical tools. Patient genomic data can be challenging to work with. Physicians may encounter genetic results which are not fully understood. Genetic tests may also lead to the quandary of linking patients with diseases or disorders where there are no known treatments. Thus, physicians face a unique challenge of establishing the proper scope of their duty to patients when dealing with genomic data. Some of those scope of practice boundaries have been established as a result of litigation, while others remain an open question. In this paper, we map out some of the legal challenges facing the genomic component of precision medicine, both established and some questions requiring additional guidance. If physicians begin to perceive genomic data as falling short in overall benefit to their patients, it may detrimentally impact precision medicine as a whole. Helping to develop guidance for physicians working with patient genomic data can help avoid this fate of faltering confidence.Precision medicine is increasingly incorporated into clinical practice  As precision medicine proliferates in the provision of medical care , 2, a crThe legal liability of physicians is generally governed by the common law of negligence. In general, a physician is negligent or liable for challenged conduct when they fail to exercise reasonable judgment and departs from generally acceptable standards of practice. In other words, a physician is negligent when he or she fails to follow generally accepted practice in any particular medical domain. State law is especially important because state courts set the parameters of what kind of claims can be sought and on what basis. Some genetic legal cases have required explicit statutes , while oWilliams v. Quest/Athena , likely benign, and benign. The usage of “likely” is defined as “a >90% certainty that a specific variant is pathogenic or benign” . A 25-geAre physicians responsible for tracking reported VUS, in case they become classified as “significant” for both current and previous patients?Does the nature of the duty change when the doctor is the patient's “primary physician?”Do general practitioners or genetic specialists owe the same or a different duty based on their expertise in genetics?A further confounding factor is the gap, sometimes indefinite, between the capacity to diagnose using genetic tests and the capacity to treat due to the lack of effective therapies for any number of genetically linked conditions , 38.But the physicians' quandary does not stop here. As the ability to interpret genetic variations grows, the physician is faced with the fact that known variants that have the potential for disease may never lead to the disease, including patients with a genetic disease for which there is no known treatment . What duThe challenge of genetic knowledge is knowing what to do with most genetic information. For example, women who test positive for BRCA-1 or BRCA-2 pathogenic variants, may undergo prophylactic mastectomy because of their fear of developing an aggressive cancer. However, some of these patients may have been either misdiagnosed or do not fully appreciate the uncertain nature of the genetic mutations , 41. SomMoore v. Curry County Health  is becoming popular for diagnosing patients with complex disease. However, tests often return results outside of the condition for which it was originally ordered . Does thv. Burke , a courtIn addition to the aforementioned challenges, WES may not adequately cover all genetic regions  that are important to a certain pharmacogenomic result or secondary finding . What haIn confronting test results the physician must decide both what to disclose and who else to advise. For example, if a patient's test reveals a significant variant known to be linked to a disease that is potentially hereditary in nature, is the physician under any obligation to advise the patient's children or any other family member, e.g., the patient's siblings, of the variant, and their potential exposure to the disease? Here, the courts have reached different results.Pate v. Threlkel (In Threlkel , the couSafer v. Estate of Pack (In  of Pack , the cou of Pack . The Ame of Pack . The allWhat bearing does infrequent application of genetic knowledge impact a physician's duty to warn a patient's children and family members?What guidance should be provided to physicians in order to make such decisions when faced with a multitude of “variants of unknown significance” results?When does a physician have a duty to recommend genetic testing in face of a patient's history of disease generally associated with a genetic mutation?Downs v. Trias (While questions surrounding family consultations are numerous, the duty to recommend genetic testing is not a novel issue for courts to address. Case precedent exits extending as far back as the early 1980's holding that the failure to recommend genetic testing in appropriate circumstances, e.g., a family history of a genetically linked disease, constitutes a departure from the standard of care . State cv. Trias , the couIn summary, physicians face many challenges in the interpretation of genetic testing results given the current state of knowledge. The issue is only heightened because the field is changing rapidly with ever-increasing information. Physicians carry enormous responsibility when they enter the arena of genetic testing. The law is only beginning to articulate the duties each must fulfill. Even though, there are legal challenges that are presented with these new capabilities. As legal challenges associated with precision medicine are assessed, it is critical to not overlook the risk associated with avoiding new technology which may incur even larger liability. A recent review of genomic malpractice cases in the United States through the end of 2016 showed that most cases were not based on misinterpretation of genetic variants but were related to failure to perform genetic testing or failure to act on the results of genetic testing. In fact, it suggested that 57% of the genomic medical malpractice cases could have been avoided if genetic testing had been performed when the patient first presented with the condition . There iThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.NW and AP performed the primary research on the cases and constructed the initial draft manuscript. DW assisted with the legal reviews and contributed to the writing about legal cases. SM contributed to both the writing and research and led the revision and preparation of the final manuscript. All authors contributed to the article and approved the submitted version.AP was employed by the company Hooper, Lundy & Bookman, and DW was employed by the company Walton Legal PLLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The lines were genotyped using 2500 single nucleotide polymorphism (SNP) markers. Majority (85.4%) of the inbred lines exhibited wide pairwise genetic distances between 0.4801 and 0.600. Genetic distances were wider between yellow and orange endosperm lines and predicted high heterosis in crosses between parents of different endosperm colors. The unweighted pair group method with arithmetic mean (UPGMA) and the admixture model-based population structure method both grouped the lines into five clusters. The clustering was based on endosperm color, pedigree, and selection history but not on LN tolerance or tryptophan content. Genotype by trait biplot analysis revealed association of grain yield with plant height and ear height. TZEEQI 394 and TZEEIORQ 73A had high expressivity for these traits. Indirect selection for high grain yield among the inbred lines could be achieved using plant and ear heights as selection criteria. The wide genetic variability observed in this study suggested that the inbred lines could be important sources of beneficial alleles for LN breeding programs in SSA.Information on the genetic diversity, population structure, and trait associations of germplasm resources is crucial for predicting hybrid performance. The objective of this study was to dissect the genetic diversity and population structure of extra-early yellow and orange quality protein maize (QPM) inbred lines and identify secondary traits for indirect selection for enhanced grain yield under low-soil nitrogen (LN). One hundred and ten inbred lines were assessed under LN (30 kg ha  Striga and optimal growing environments. Suwarno et al. [Information on the genetic diversity and population structure of inbred lines in hybrid breeding programs enables breeders to develop inbred lines with high heterosis in hybrid combinations and enhanced adaptation to specific or broad environmental conditions ,2. Knowlo et al.  also repo et al.  reportedStriga and optimal growing environments. Similarly, [Genetic diversity in maize populations can be determined using molecular, biochemical or morphological markers. However, molecular markers are the preferred choice because they are faster and more reliable for diversity studies and are independent of the environment, plant growth and developmetal stages . DAlthoumilarly,  reportedmilarly, , identif-1 compared to the global average of 140 kg ha-1 [The present global challenge to increase food production while protecting the environment neccesitates the the development of nitrogen use efficienct genotypes. In Africa, particularly sub-saharan Africa (SSA), however, poor soil fertility especially low soil nitrogen (LN) remains a widespread constraint to maize production and productivity . Additio kg ha-1 . Moreove kg ha-1 . Quality kg ha-1 . TherefoStriga tolerant populations, 99 TZEE-Y Pop STR QPM and 2009 TZEE-OR2 STR QPM. To maximize the utilization of the germplasm for breeding LN tolerant QPM hybrids, it is imperative to determine the genetic diversity, population structure and important agronomic traits for selecting LN tolerant genotypes. Under the IITA- MIP, selection of Striga resistant/tolerant genotypes is carried out under low-N stress (30 kg N ha-1) conditions and has resulted in concomitant improvement of LN tolerance [Striga resistant/tolerant populations.IITA, through its breeding efforts over the years has developed extra-early (80–85 days maturity) yellow and orange inbred lines from two olerance . TherefoIn breeding for LN tolerance, although high GY is the primary goal, secondary traits have been invaluable for indirect selection for high GY due to the low heritability of GY under stress. Therefore, secondary traits such as ears per plant, stay-green characteristic, anthesis-silking interval , plant hA total of 110 extra-early QPM inbred lines recently developed under the IITA- MIP were used . These ioC to 31 oC. The second test site was the West Africa Centre for Crop Improvement (WACCI) Research Field-Legon , with an altitude of 97 m above sea level, a Coastal Savanna agro-ecological zone with annual rainfall of 809 mm and temperatures of 23 oC to 31 oC. The Legon test site was depleted of N by planting maize at very high population density and removing the biomass at harvest. The Fumesua test site required no initial depletion because it had been previously depleted of N and used for LN trials only. Soil samples from each location were analyzed with Kjeldahl digestion calorimetric procedure [Two test locations (Fumesua and Legon) in Ghana were used for the study during the minor planting season (September to November) of 2017. The first testing site was the Crop Research Institute-Fumesua , with an altitude of 286 m above sea level, a semi-deciduous agro-ecological zone with annual rainfall of 1500 mm and temperatures of 21 rocedure  to ensur-1 N, applied as urea (46% N) at two weeks after planting. Potassium  and phosphorus  were applied at 60 kg ha-1 each. Top-dressing was carried out using 15 kg N ha-1 at 4 weeks after planting, bringing the total N to 30 kg N ha-1. Hand weeding was done to control weeds when necessary.The inbred lines were planted using a 10 x 11 alpha lattice design with two replications at each test location. Plot size was 4 m long single rows, with planting distance of 0.75 x 0.4 m. Three seeds were initially planted and thinned to two seeds per hill at 2 weeks after planting, resulting in population density of about 66,666 plants per hectare. Each plot received 15 kg haSeed samples of 65 (55 yellow plus 10 orange lines) selected inbred lines were dispatched to the Maize Nutritional Quality and Plant Tissue Analysis Laboratory of CIMMYT, for tryptophan analysis using the calorimetric method . The 65 -1) in each plot was calculated using the grain weight and moisture content of shelled ears and 15% adjusted moisture content.Days to anthesis (DA) was recorded as number of days from planting to when 50% of the plants in a plot had shed pollen. Days to silking (DS) was obtained as the number of days from planting when 50% of the plants in a plot had silks. Anthesis silking interval (ASI) was the difference between DS and DA. The stay green characteristic was scored at 70 days after planting (DAP) based on leaf senescence using a scale on 1–9; where 1 = 0–10% dead leaf area and 9 = 91–100% dead leaf area, respectively. Plant aspect was scored on a scale of 1–9; where 1 = excellent plant phenotypic appearance and 9 = poor plant phenotypic appearance . Plant ahttp://www.lgcgroup.com/) for DNA extraction and genotyping. The targeted genotyping by sequencing service (SeqSNP) platform provided by LGC genomics was used (https://www.biosearchtech.com/services/sequencing/targeted-genotyping-by-sequencing-seqsnp). A total of 2500 SNP markers were selected from the 50 K maize SNP genotyping array by [https://www.maizegdb.org/) to enable selection of SNPs targeting the relevant QTLs. The schematic map of the QTLs used for the selection of SNPs for this study can be accessed from the cited references.Out of the 110 inbred lines studied, 103 were genotyped because seven lines did not germinate in the nursery. Leaf samples were taken from two-week-old plants and dispatched to LGC Company  was performed using the general linear model procedure (PROC GLM) of the Statistical Analysis System (SAS) software adopting a random statement with test option . Each loThe low nitrogen base index (LNBI) proposed by  that comPrior to the analysis, all traits were standardized using a standard deviation of 1 and mean of zero to reduce the effects of the different scales used for measuring the various traits. Therefore, a positive LNBI value indicated LN tolerance and the negative value susceptibility.http://www.rproject.org/).Genotype-by trait biplot analysis was done using the standardized means of the measured traits and GGE biplot procedure  in the RThe data was filtered in TASSEL version 5.2.53  to retaiCombined analysis of variance (ANOVA) revealed significant (P<0.01) differences among the inbred lines for all measured traits across the two LN environments . GenotypThe genotype-by-trait biplot (GT) analysis was performed to study the inter-relationships among the measured LN tolerance traits and to select parental lines and predict hybrid performance . The topThe genetic diversity among the newly developed extra-early yellow and orange QPM inbred lines was studied using 1691 informative markers to obtain information for parent selection in hybrid breeding programs. Major allele frequency of the SNPs ranged from 0.500 to 0.990 with a mean of 0.725. . Gene diThe UPGMA cluster analysis grouped the inbred lines into five main clusters using Nei’s genetic distances . Group 1ST) for each sub-population and indicated significant divergence between the five sub-populations. The FST values for populations 1–5 were 0.29, 0.51, 0.72, 0.43 and 0.41, respectively.In the population structure analysis, the inbred lines were grouped into five sub-populations  based onThe development of high yielding QPM and LN tolerant varieties is critical for addressing the present malnutrition and food security challenges in sub-Saharan Africa (SSA). The success of a stress tolerant hybrid breeding program depends largely on the performance of the inbred parents and the heritability of traits under stress . UnderstStriga tolerance that uses low N levels (30 kg ha-1), and probably induced concomitant improvement for LN tolerance. The result corroborated the previous findings of [Striga tolerant lines under LN and suggested that selection for Striga resistance under LN resulted in simultaneous improvement in LN tolerance. The degree of LN tolerance was higher for the orange lines compared to the yellow line suggesting that the orange lines had undergone more cycles of genetic enhancement for LN tolerance.Fifty-six out of the 110 inbred lines studied displayed positive LN base indices although the populations were not improved for LN tolerance. This is attributable to the screening method adopted to improve the populations for dings of  who repoThirty-eight percent out of 65 inbred lines analyzed for tryptophan content had optimum tryptophan contents although all the inbred lines were extracted from QPM populations. This is probably because early-generation selection for QPM alleles and desirable grain qualities was done using visual characterization under the light box and not through chemical analysis due to the large number of genotypes and the high cost of laboratory and molecular analysis. According to Vivek et al. , endospeThe fifteen extra-early yellow inbred lines that had high positive base indices, but low tryptophan content levels could serve as good germplasm resource for developing LN tolerant normal endosperm hybrids. Furthermore, favorable tryptophan alleles could be introgressed into these inbred lines to increase the levels of tryptophan while maintaining LN tolerance to develop LN tolerant QPM lines. The four inbred lines TZEEIORQ 73A, TZEEQI 392, TZEEQI 394 and TZEEQI 408 that combined high positive base indices with high tryptophan levels could provide good sources of alleles for improving tryptophan contents of the orange and yellow source populations.The genotype-by trait biplot (GT) analysis provides a graphical display of the inter-relationship among traits and helps to identify the best genotypes and traits for indirect selection. In this study, the GT analysis identified TZEEQI 394 and TZEEIORQ 73A as the best inbred lines for GY, PHT and EHT indicating that they possessed desirable genes for improving these traits and for hybrid production. The positioning of GY, PHT and EHT within the same sector containing the best two inbred lines TZEEQI 394 and TZEEIORQ 73A indicated that the traits were inter-related such that selection for tall plant and ear heights, and high EHT/PHT ratio could result in indirect selection for high GY. TZEEIORQ 4 was the best genotype for EPP and possessed favorable alleles for improving the trait although it was not very high yielding. Additionally, the results suggested that TZEEQI 368 may not have been the best genotype for improving earliness or for producing early maturing hybrids because it was late in maturity and possessed high ASI.Due to the low heritability of GY under stress, high selection efficiency in LN breeding programs can be achieved through indirect selection with secondary traits that are easy to measure, have high heritability and strong correlation with grain yield . In the Knowledge of the genetic diversity among inbred lines or within a population is important in selecting parental lines for maximizing heterosis without making all possible crosses among a set of inbred lines. Genetic diversity studies have been used to determine the phylogeny and genetic distances among lines extracted from the same source population for the purpose of initiating a hybrid program ,55. The Genetic distance is a useful parameter for estimating the genetic similarity among individuals in a population . In thisThe average heterozygosity of 7% observed among the inbred lines exceeded the 3.34%, 3.8% and 5.6%, reported by ,60,62, rIn the cluster analysis, majority of the lines were grouped based on the endosperm color, pedigree, and selection history. Even though most of the markers selected for this study were located within LN tolerance genomic regions, the pattern of clustering was not based on the performance under LN since both tolerant and susceptible lines were grouped together. Furthermore, no tryptophan content-based clustering pattern was observed. The failure of some lines to cluster based on selection history corroborated the findings of  who repoST values observed among the five sub-populations in the in the present study varied between 0.29 and 0.72 and were high and based on the interpretation by [ST value of above 0.15 as significantly different populations. The FST values were similar to those previously reported in other tropical inbred lines by [ST values indicated that the inbred lines from the different groups possessed unique alleles that could be beneficial for improving LN populations in tropical breeding programs. In this study, the grouping of the lines under five sub-groups were different between the population structure and the phylogenic tree. This is attributable to the differences in the models and clustering algorithms used by the two methods. Inconsistent grouping pattern by different clustering methods have been reported by [Population structure analysis also grouped the lines into five sub-populations largely based on pedigree and selection history. The Fation by  which colines by , of 0.33orted by ,12,69.The inbred lines exhibited wide pairwise genetic distances and expressed varying degrees of LN tolerance. The admixture model-based population structure and the unweighted pair group method with arithmetic mean placed the inbred lines into five groups each. The clustering patterns were neither according to tryptophan nor LN tolerance level, but were based on the endosperm color, pedigree, and selection history. Genetic distances were wider between the yellow and orange inbred lines compared to the distances between inbred lines of the same endosperm color. Most of the inbred lines selected for tryptophan analysis had low tryptophan content. TZEEIORQ 73A, TZEEQI 408, TZEEQI 392, TZEEQI 394 were LN tolerant and had optimal tryptophan contents. Fifteen of the inbred lines expressed LN tolerance but sub-optimal tryptophan contents. Inter-trait relationship existed between GY, PHT and EHT with TZEEQI 394 and TZEEIORQ 73A having high expressivity for these traits. The study indicated that the inbred lines are beneficial sources of alleles for QPM breeding programs for population improvement and developing LN tolerant varieties.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Bos j. javanicus) is a local breed originating in Indonesia, accounting for 32.3% of the total cattle population. To date, no studies of the genetic structure and demographic status of Bali cattle have been conducted, even though the breeding of Bali cattle has a long and unique history that is likely to have impacted its genetic diversity. Therefore, a study that used molecular breeding technologies to characterize the demography of Bali cattle would be timely. This study aimed to examine genome diversity in Bali cattle and estimate the linkage disequilibrium (LD) and effective population size (Ne) values in the cattle population.Bali Cattle  array  data.In this study, we explored the population structure and genetic diversity of Bali cattle using genomic-level analyses. Our study primarily studied cattle that had been bred in livestock breeding centers since these breeds had subsequently spread throughout Indonesia. We focused on characterizing the genetic structure, determining the level of LD present, and estimating the eN value of Bali cattle from the breeding center and farmers was 151 and 96, respectively.We found that the LD values of Bali cattle from the breeding center and those raised elsewhere were 0.48±0.43 and 0.39±0.40, respectively. Subsequently, the Our results suggest that the selection program of the breeding center is beneficial for maintaining the genetic diversity of Bali cattle. Bos j. javanicus) have a unique history and genetic background, as they are the only native Indonesian cattle that were domesticated by the Banteng [et al. [PLAG1 (a gene on chromosome 14 of the cattle genome) with Ongole cattle birth weight [Bali cattle  are a type of variation in nucleotide sequence found in DNA. SNPs are often stable and may also be directly related to protein function; therefore, they are one of the best genetic markers used by breeding programs . A projeeN) [eN is critically important for trying to maintain genetic diversity in a livestock population, since a high eN reflects a great deal of genetic variation within this population. In contrast, a low eN indicates that less genetic variation is present.Linkage disequilibrium (LD) is one of the important parameters in population genetics; it reflects the degree of correlation between allelic variants found at different loci in a given population. Therefore, LD values reflect the distance between genetic markers and the level of inbreeding (genetic proximity) of individuals in a population. LD values are smaller if the genetic distance is greater; this may reflect historical migration, mutation, recombination, or selection in a population ,11. LD vNe) ,12. UndeeN values in the cattle population. Specifically, we seek to examine the genomic effects of the selection and breeding programs of the Breeding Center of Bali Cattle Breeding; to do so, we will compare LD and eN values of cattle bred at the breeding center with other Bali cattle populations bred by private farmers. Estimates will be obtained using the Bovine 50SNP Beadchip (Illumina Inc.). The results of this study may be useful as an assessment of breed development in Indonesian Bali cattle and may also be used as a basis for future policies regarding Bali cattle.This study aimed to examine genome diversity in Bali cattle and estimate the LD and All animal procedures related to sample collection were approved by the Ethical Clearance Commission at National Research and Innovation Agency  No. 82/Klirens/X/2021.The study was carried out from March to November 2021. A total of 48 Bali cattle blood samples were obtained from the Breeding Center of Bali Cattle . The DNA isolation and related works were conducted at the Division of Biology, Integrated Laboratory of Universitas Sebelas Maret. In addition, genomic analysis was conducted at Macrogen (Korea).et al. [http://datadryad.org/), 18 samples from the appendix of a publication by Hartati et al. [We obtained genomic data from 102 samples of Bali cattle. Forty-eight of these samples came from DNA isolated from the blood of 48 Bali cattle bred at the Breeding Center of Bali Cattle . The other 54 samples included 20 samples uploaded by Decker i et al. , and 16 i et al. , which wi et al. . By contet al. [DNA isolation was carried out as per the method of Sambrook et al.  at the I−4, the SNP call rate was >90%, the minor allele frequency was <1%, and the proportion of empty SNP variants for each individual and the proportion of empty genotypes for each variant must not exceed 10% [Before analyzing the genomic structure and genetic diversity of Bali cattle, we filtered our genomic data to remove low-quality data using PLINK v1.07  . Qualityceed 10% . In addieH) and observed heterozygosity (oH) values and determined the value of the inbreeding coefficient (ISF) as well as the average genomic relationship matrix (GRM). The oH and eH values were calculated using the hierfstat package as implemented in R v.3.2.2  [Analysis of the genetic structure was performed in both Bali cattle populations. First, we calculated the expected heterozygosity (Austria) . These cAustria) , as follN: The number of samples testedNij1: Heterozygosity at each locusm: The number of loci tested2a:Allele frequency – a from all k allelepISF value was also calculated using the same R package  through the formula described by Weir and Cockerham [The ockerham , as follIH: The average oH of all samples in a populationSH: The average eH of all samples in a populationThe GRM value was calculated using Genome-wide Complex Trait Analysis v1.25.2  . This prr2 formula [Various statistics have been introduced to measure LD. In our study, we calculated the LD using the  formula , as follf(x) designates the frequency of the x allele. LD value estimation was performed using PLINK v1.07 (Purcell Lab) [-r2 command was used to obtain the LD value of SNP pairs. Next, the --ld-window-r2 command was used to report all SNP pairs [ell Lab)  and was ell Lab) . PLINK wNP pairs . LD valueN) was estimated using the LD value and Sved’s formula, as presented by Xu et al. [The effective population size  [eN was plotted following estimated times in horizontal ordinate, which was obtained by (2c)–1. We graphed the resulting relationship using the ggplot2 package in R to obtain usable plots.mputing) . Next, NThe SNP microarray we utilized in this study screened for 53,218 SNPs (Illumina Inc.). Our genotyping dataset showed that the average percentage of SNPs detected  in the Bali cattle genome was 97.8%. In addition, the average genotype score for each SNP was 0.7. These figures suggest that the level of reliability of the SNP data used in this study is high. Supplementary The number of SNPs from Bali cattle genome data from the breeding center obtained after the genome data quality control process was 49,439 variants (93%). Conversely, the number of clean SNPs in farmers’ Bali cattle genome data was 52,886 variants . The redFurthermore, the diagonal GRM value  of the Bali cattle from the breeding center was higher than that of Bali cattle reared by local farmers . This inThe uniformity of variants present in the genomes of Bali cattle from the breeding center can be verified by checking the average LD value. As per our knowledge, this the first study of LD in Bali cattle; our results show that the calculated LD value of the Bali cattle population from the breeding center (0.48±0.43) was higher than the LD value of Bali cattle bred by farmers (0.39±0.40) . This inet al. [Bos indicus cattle (0.39) but lower than for Bos taurus cattle (0.59). By contrast, Bali cattle bred by farmers showed average LD values that were equivalent to those of the B. indicus cattle. In general, LD values can differ between populations even within a single cattle breed, since large LD values are dependent on changes in genetic composition experienced by individuals in the population [B. indicus cattle in developing countries [et al. , the LD pulation ,23. LD vountries .eN values in the Bali cattle population. Relative to eN values from several generations ago, the effective population size of Bali cattle has experienced a sharp decline. However, in the more distant past  eN values were increased  [The eration) . Future B. indicus and just below B. taurus, and the effective population size remains high. The livestock selection program implemented by the Breeding Center of Bali Cattle may, therefore, be influential in helping to maintain the genetic diversity of Bali cattle. The results of this study can be used as a basis for developing and improving Bali cattle breeding policies in the future.Based on observed heterozygosity levels, inbreeding coefficients, and GRM values, we conclude that the Bali cattle population bred at the breeding center has a higher level of genetic diversity than the Bali cattle bred by farmers. Moreover, the LD value of Bali cattle falls just above PS and MC: Conceptualization, investigation and data curation, software, validation, writing original draft and review and editing. RYK: Investigation and data curation and Methodology. SDV: Investigation and data curation, methodology, validation, and review and editing. All authors read and approved the final manuscript.


The Yangtze River Basin (YRB) is an important area for China’s economic development and environmental governance. The aim of this paper is to analyze the total factor productivity across 97 cities in the YRB from 2005 to 2016. Based on the input and output indicators from 2005 to 2016, this paper selects the SE-SBM model to measure the environmental regulation efficiency (ERE) of 97 cities in the YRB and then uses the DEA–Malmquist index to measure the total factor productivity of the region. Results suggest that the overall ERE in the YRB is weakly ineffective, while ERE in the central and eastern coastal areas is relatively high. ERE matches the economic foundation and development of the city. YRB environmental regulation efficiency was in descending order in the middle stream, upstream, and downstream. The efficiency of regional environmental regulation shows an N-type development trend, with obvious characteristics of phased development. Moreover, the total factor productivity of the YRB has shown a downward trend. The scale efficiency index and the technical efficiency index have positively boosted the total factor productivity, while the technological progress index has dragged down the total factor productivity of the area. The contribution to the total factor productivity index is in order of scale efficiency, technological progress index, and technological efficiency index in the downstream. The overall inputs and outputs of the YRB have great development potential. The inputs have not been fully utilized, the outputs have not been maximized, and the regional differentiation is significantly observable. The Yangtze River Basin (YRB) contains a vast array of developed industries in densely populated cities and rich mineral, water, and agricultural resources; nearly half of the country’s heavy chemical, power, and steel companies are distributed along the river, and as such, environmental protection is an important facet of further development. As a result, the “Master Plan for the Protection, Development and Utilization of the Yangtze River Coastline” issued in September 2016 comprehensively analyzed the main problems in the development and utilization of both sides of the Yangtze River. China’s 14th Five-Year Plan in 2020 emphasizes the environmental issues and will strive to coordinate and integrate resources and promote the green development of both sides of the Yangtze River. Considering these realities, the aim of this paper is to study total factor productivity across 97 cities in the YRB from 2005 to 2016.In recent years, China has issued a series of environmental protection policy documents, and many scholars have begun to pay attention to the efficiency of environmental regulations. Yu and Wang  argued t2 and accounts for approximately 20% of China’s total land area. The Yangtze River is a hub of economic development, accounting for almost 50% of the country’s total economic output, covering three major economic regional urban agglomerations in China, including the Chengdu–Chongqing, Yangtze River middle reaches, and the Yangtze River Delta (YRD) urban agglomerations. Considering the availability of data, this paper finally selected 97 cities as the research objects (The Yangtze River Basin (YRB) refers to the vast area through which the mainstream and tributaries of the Yangtze River flows through three economic areas of eastern, central, and western China. The mainstream of the Yangtze River flows through 11 provinces, autonomous regions, and municipalities that are directly controlled by the Central Government and includes Qinghai, Tibet, Yunnan, Sichuan, Chongqing, Hubei, Hunan, Jiangxi, Anhui, Jiangsu, and Shanghai. In addition to the provinces mentioned above, there are the Guizhou, Zhejiang, Shaanxi, Gansu, and Guangxi provinces into which the Yangtze River’s first-level tributaries flow. The YRB also includes Henan, Fujian, and Guangdong, which the mainstream of the Yangtze River and the first-level tributaries do not flow through, but some areas belong to the YRB, which has a total of 17 provinces, autonomous regions, and two municipalities directly under the Central Government. The basin covers an area of 1.8 million km objects .n is the number of decision-making units in the input–output system, I,O represent the vectors of input and output, respectively, m, q represent the number of variables, and λ is the weight vector. j = 1,2 … n, i = 1,2 … m, r = 1,2 … q. The K decision-making unit, kI, kO) are the evaluated DMUs, and S represents the slack value. The DEA model is composed of multi-segment linear functions. If a single DMU projection falls within the parallel section, the problem of slack variables will occur. The measurement in the radial model does not consider the slack variables. The advantage of the SE-SBM non-radial model is that it can judge the impact of each input and output on the overall efficiency based on the slack value. In general, the slack value can be obtained by adding slack variables to the constraints of the model [The data envelopment analysis (DEA) method is a non-parametric technical efficiency analysis method based on the comparison between the evaluated objects. Using DEA for efficiency evaluation can obtain a lot of management information with economic connotation and background . DEA meahe model . If the I and O represent input and output, respectively, t represents time, and D represents distance function. If MI is greater than 1, the productivity will show an upward trend from t to t + 1. Conversely, MI less than 1 indicates that productivity tends to decrease. If it is equal to 1, it means the productivity remains unchanged from t to t + 1 [Originally proposed by Malmquist in 1953, this Malmquist index (MI) was originally suitable for measuring changes in production efficiency. Färe et al.  used theto t + 1 .ch) and the technological progress index (TPch). If the scale efficiency is variable, the technical efficiency index (EFFch) can be further decomposed into pure technical efficiency (PEch) and scale efficiency (SEch). The relationship is as follows:The environmental TFP index (MI) can be decomposed into the technical efficiency index  in YRB during 2005–2016 is 0.6239, which is weak and invalid. The areas with higher environmental regulation efficiency are mainly concentrated in the central and eastern coastal areas of the basin see b. HoweveFrom 2005 to 2016, we can see that Chongqing, Yingtan, Zhangjiajie, and Bazhong rank the top four among the cities in terms of environmental regulation efficiency, all of the four cities are located in the middle and upper reaches of YRB . The aveFrom 2005 to 2016, the overall productivity of YRB showed a downward trend based on geometric average, and TFP was 0.93. However, the arithmetic average result was 1.06, which showed an upward trend. The arithmetic average method made the Malmquist index result biased . Among tFrom 2005 to 2016, the productivity index of YRB showed a wave-like trend. From 2005 to 2007, 2009 to 2010, and 2013 to 2014, the productivity value increased. Displayed in ch of 1.00, a PEch of 0.93. The technical progress index of further decomposed into PEch of 0.94 and Sech of 1.07. The input of production factors in the entire region can basically be effectively used, and the resource allocation structure is relatively reasonable, but there are still insufficient innovations, serious regional differentiation, and uneven management levels. YRB lacks specificity in response to local conditions. Meanwhile, the YRB has a large gap in institutional guarantees, technological innovation, and management control and has not yet formed an efficient management pattern (see The overall TFP value of YRB is 0.93, which is specifically decomposed into a EFFch of upstream and middle stream has shown an upward trend, both approximately unity. SEch has also been improved, but the PEch and SEch are both less than 1, indicating the technological innovation, institutional system, and management level of both areas of YRB need to be improved. The downstream, which mainly includes the YRD, has achieved the factor productivity of 1.07, showing a clear upward trend. Decomposing the TFP, EFFch, TPch, and SEch are 1.01, 1.06, and 1.08, respectively. It shows that the overall technical efficiency of the lower reaches of the Yangtze River is relatively high, the resource allocation is relatively reasonable, and the city’s ability to coordinate development is relatively strong. Among them, the scale efficiency index contributes the most to total factor productivity. However, as shown in The environmental TFP indices of the upper and middle reaches of the Yangtze River were 0.89 and 0.90, respectively, which means that total factor productivity did not rise from 2005 to 2016 but showed a downward trend. The EFFLooking at the Yangtze River Basin as a whole, from 2005 to 2016, the efficiency of inputs and outputs was not high, and regional differentiation was large. There is much room for improvement in the input and output efficiency of environmental regulations in YRB. In terms of inputs, 19 cities made full use of labor input, including Pingxiang, Ganzhou, Wuxi, Changzhou, Suzhou, Zhenjiang, Shanghai, Nanping, Longyan, Maanshan, Tongling, Huangshan, Deyang, Ziyang, Nanyang, Zhumadian, Chongqing, etc. There are 25 cities in which capital is fully utilized, including Yueyang, Xinyu, Suzhou, Shanghai, Hangzhou, Quzhou, Chongqing, etc. There are 14 cities with the most efficient human capital input, including Changde, Xinyu, Ganzhou, Wuxi, Suzhou, Shanghai, Quzhou, etc. Overall, the seven cities where the inputs are most effectively used are Suzhou, Shanghai, Nanping, Longyan, Nanyang, Zhumadian, and Chongqing. In terms of outputs, 27 cities have the highest economic output efficiency, mainly including Zhuzhou, Xiangtan, Yueyang, Zhangjiajie, Nanjing, Nantong, Yangzhou, Zhenjiang, Huzhou, Nanping, Wuhu, Wuhan, etc. In terms of ecological indicators, there are 11 cities with high efficiency in artificial afforestation area and forestry output value, including Changde, Zhangjiajie, Xinyu, Yingtan, Suzhou, Shanghai, Quzhou, Longyan, Suizhou, Guangyuan, and Zhumadian. There are 14 cities with high efficiency in the treatment of industrial wastewater discharge, including Zhangjiajie, Shanghai, Fuzhou, Suizhou, etc., and 23 cities with higher efficiency in the treatment of agricultural land pollution, including Shaoyang, Zhangjiajie, Yiyang, Suzhou, Nantong, Taizhou, and other cities. In terms of climate governance, the overall efficiency is low, except for Enshi and Zhumadian .From the perspective of time development, in 2005, the labor input of 49 cities was maximized, the material capital input of 64 cities was effective, and the education input of 45 cities was fully utilized. In 2016, these three indicators were 60, 53, and 44, respectively. In 2005, there were 27 cities with effective overall input of factors. In 2016, the number rose to 31. In 2005, 65 cities maximized economic output and 63 in 2016, a slight decrease. In terms of ecological construction, 48 cities had the most efficient output of planted forests and 51 cities in 2016. In 2005, the forestry output value was the most effective, and there were 61 cities, but 63 cities in 2016. In 2005, there were 48 cities with the most effective industrial wastewater treatment and 52 cities in 2016; in terms of agricultural land pollution treatment, there were 58 cities in 2005 and 66 cities in 2016. As for climate governance, 42 cities were the most effective in 2005 and 38 cities in 2016. The difference gap between cities is obvious.The overall efficiency of environmental regulations in the YRB is weakly ineffective, while the efficiency of environmental regulations in the central and eastern coastal areas is relatively high. From 2005 to 2016, half of the urban areas improved environmental regulation efficiency, while the other half showed a downward trend. The efficiency of environmental regulation matches the initial economic conditions and development. Over the period of 2005 to 2016, environmental regulation efficiency in the middle stream of the Yangtze River, the upstream of the Yangtze River, and the downstream of the Yangtze River were ranked in descending order. The regional environmental regulations efficiency shows an N-shaped trend with obvious characteristics of phased development. The first high point appeared in 2007, and the second low point appeared in March 2012. The total factor productivity of YRB showed an overall downward trend from 2005 to 2016, but 28 cities showed an upward trend, and these cities were mainly located in the middle and downstream of the Yangtze River. During this period, the productivity showed a double M-shaped trend, among which the TFP value peaked at 2007, 2011, and 2015, respectively. The scale efficiency index and the technical efficiency index have positively promoted total factor productivity, while the technological progress index has dragged down the total factor productivity of the Yangtze River Basin. The contribution to the downstream total factor productivity index is in order of scale efficiency, technological progress index, and technological efficiency index. From 2005 to 2016, the overall input and output efficiency of the YRB had great development potential. The inputs have not been fully utilized, and the outputs have not been maximized. The input–output efficiency varies greatly among cities. In 2016, 31 cities’ inputs were fully utilized; only 18 cities maximized their output, and 17 cities achieved better results in pollution control and climate control. Compared with 2005, the number of cities that fully utilized labor input factors increased in 2016, but the cities that fully utilized education factors remained almost unchanged, while the number of cities that fully utilized physical capital decreased. In 2016, the number of cities that maximized economic output decreased slightly. Ecological construction and pollution control were effective, but climate control in the whole area was still ineffective.Based on the above conclusions, the following suggestions can be made. Firstly, investments in material and human capital for pollution control should be increased to improve high-quality economic development and promote green development. The expected outputs can be enhanced, and undesired output must be well controlled. The problem of “chemical encirclement of the river” should be solved as soon as possible to reduce the total amount and intensity of pollution. Secondly, environmental regulation policies should be tailored to local conditions. Based on the differences in environmental resource carrying capacity and economic conditions among these cities, the government should establish a top-level design and management mechanism for environmental policies. The ability of regional coordinated development and management mechanisms’ systemic control needs to be strengthened. Thirdly, enterprises should insist on scientific and technological innovation and make full use of big data, geographic information system, and high-tech environmental protection technology in pollution control and environmental management and also establish an early warning mechanism to improve the refined management efficiency.


Arabidopsis thaliana by studying a set of 500 whole-genome sequenced and over 2,800 RAD-seq genotyped individuals in the context of global diversity represented by Afro-Eurasian genomes. We use methods based on haplotype and rare-allele sharing as well as phylogenetic modeling to identify likely sources of introductions of extant N. American A. thaliana from the native range in Africa and Eurasia. We find evidence of admixture among the introduced lineages having increased haplotype diversity and reduced mutational load. We also detect signals of selection in immune-system-related genes that may impart qualitative disease resistance to pathogens of bacterial and oomycete origin. We conclude that multiple introductions to a nonnative range can rapidly enhance the adaptive potential of a colonizing species by increasing haplotypic diversity through admixture. Our results lay the foundation for further investigations into the functional significance of admixture.Large-scale movement of organisms across their habitable range, or migration, is an important evolutionary process that can shape genetic diversity and influence the adaptive spread of alleles. Although human migrations have been studied in great detail with modern and ancient genomes, recent anthropogenic influence on reducing the biogeographical constraints on the migration of nonnative species has presented opportunities in several study systems to ask the questions about how repeated introductions shape genetic diversity in the introduced range. We present an extensive overview of population structure of North American  Arabidopsis thaliana is predominantly a human commensal that is native to Africa and Eurasia. Its demographic history is filled with episodes of range expansions, bottlenecks, migrations, and admixture. Current models of A. thaliana’s population history highlight the recurrent theme of lineage migration and admixture with locally adapted genotypes in the native range of the species  markers has revealed the presence of a dominant lineage “Haplogroup1” (Hpg1) (” (Hpg1) . Pattern” (Hpg1) . So far,A. thaliana population as viewed through the lens of range-wide genetic diversity of the species. Using genomes of A. thaliana individuals collected from the Midwest, the Eastern Seaboard, and the North–East of the current United States, we infer possible sources of ancestry based on haplotype-sharing, phylogenetic tree-based modeling, and rare allele sharing (RAS) with the worldwide data set. We also describe how admixture in this predominantly selfing species is generating new haplotype diversity and how admixture affects the fate of deleterious mutations and allows selection on immunity-related loci. The work presented here shows that increased global connectivity through the past two centuries has made species invasions from across the species range common and could have accelerated invasion of N. American habitat by avoiding the genetic paradox of invasion. Further, our work highlights that processes such as admixture and selection that determine success of lineages in the native range play a similarly important role in the nonnative range.We present the fine-scale population structure of the N. American A. thaliana samples across an area of about 1,200 by 900 km in the Eastern United States during the spring seasons (mid-March to early June) of 2014, 2015, and 2016 . A PCA of 500 N. American individuals, including a subset of previously analyzed herbarium individuals , these data are not well suited for fine-scale, quantitative population genomic analyses . We therividuals  (supplemgroups) using a coancestry matrix derived using CHROMOPAINTER v2 and MCMC-based clustering in fineSTRUCTURE  and clustering individuals based on their shared ancestry that emerges after accounting for LD . TherefoTRUCTURE  . Relicts comprise highly diverged individuals from ice age refugia  of N. American groups to detect whether groupTest was admixed between groupA and groupB. There were several groupTest examples with negative f3 scores and Z scores below −3 in several trios (supplementary fig. S6A and groupA or groupB)  in the form of  to learn the extent of gene flow between Hpg1 and other N. American groups  than BABA sites, confirming the contribution of Hpg1 ancestry to the genetic makeup of these groups.Stochastic changes in allele frequency, as a result of the neutral process of drift, hold information about shared ancestry. We therefore estimated values for the tatistic  to under refugia  and there groups . We calc groupB)  online. A. thaliana to N. America. It is also unclear whether the observed haplogroups already existed in Eurasia, or whether they only formed by intercrossing in N. America. We therefore wanted to learn whether N. American extant haplogroups include ancestry from different geographic regions in Eurasia. We first excluded lineages that showed evidence of recent admixture , and we then applied statistical procedures based on shared haplotype chunks (fineSTRUCTURE), shared drift  and enrichment of rare alleles with respect to the AEA haplotype diversity to identify sources of ancestry in Eurasia based on WGS from AEA individuals (n = 928)  . We travregions (BritishIsles1 and BritishIsles2) also feature significantly across several groups  and building a maximum likelihood (ML) tree by fitting Treemix  in the form of NorthGermany, RelictsFs12; X, A. lyrata . Specifically, we tested whether a set of f4-statistics comprising two N. American groups across a set of layer1 outgroups  makes a matrix of rank 0 (same wave of ancestry) , indicating distinct streams of the ancestry among the test groups. We added an extra outgroup from additional regions of BritishIsles2, ItalyBalkanPeninsula, NorthGermany, RussiaAsia, and Upper/EastFranceBritishIsles one-by-one. Adding these putative source regions affected the symmetrical relationships observed with our original outgroup set. Except in the case of SouthIndiana4 and Ohio7, all the N. American group combinations showed asymmetric relationships (rejection of rank 0) with these extra outgroups , central Europe, and eastern Europe.We extended this analysis using qpWave  to test ncestry)  online. utgroups  online. groups. We found that N. American groups have accumulated rare alleles from different AEA subclusters , value of the statistic is proportional to the shared ancestry between the populations relative to the outgroup), using linear combinations of four environmental variables: average temperature (tavg), precipitation (prec), solar radiation (srad), and water vapor pressure (vapr). Arabidopsis thaliana shows significant local adaptation to climate  and water vapor pressure (kPa) revealed that environmental dissimilarity calculated by Euclidean distance between each N. American atistics . Althouggroups should be closer to their source AEA subclusters. To test this, we performed UMAP on the standardized values for environmental variables for N. American groups and AEA clusters together, followed by hierarchical clustering on the reduced environmental space  occupied similar space in the UMAP embeddings , N. American groups  obtained with Loter (groups into three different categories according to SIFT4G (group including the source groups (Hpg1 and non-Hpg1s). Derived alleles with frequency of 1 were considered as fixed, and based on the count we calculated fixed-to-total derived alleles ratios . The scaled non-syn-deleterious (Φ) is significantly lower  than for nonsynonymous tolerated mutations  (nSL (number of segregating sites-by-length) (xp-EHH (cross population extended haplotype homozygosity)  (Methods that track the decay of haplotype homozygosity in a population  can be uy score)  and nSL -length)  for indi-length)  online aygosity)  for compR <0.05)  online WR <0.05)  (supplemR <0.05)  online.RPP13 and BAR1, which confer resistance to the oomycete Hyaloperonospora arabidopsidis (Har)  and the H2/H1 ratio are calculated. At RPP13 and BAR1, we observed relatively low values for these two metrics and the presence of the selected alleles on multiple backgrounds, which together suggests soft sweeps at these loci   and bactudomonas . We measudomonas . The mosesponses  online.Har , central Europe , and Eastern Europe . We detected these introductions using methods based on haplotype sharing  and total derived allele count are elevated in a few N. American populations , Kentucky (1001 Genomes collection), and Georgia (herbarium collection). Alternative explanations such as short-term fitness benefits through heterosis    might plCapsella, it has been shown that dramatic losses of genetic diversity after extreme genetic bottlenecks can be tolerated at most genes in the genome, except for immunity loci  and sorted using samtools v1.3   to the Tols v1.3 . Reads fols v1.3  using deols v1.3  with a mols v1.3 . GATK toHaplotype phasing for estimation of recombination rate was performed with ShapeIt2 (v2.r837)  on samplPrincipal component analysis was performed using SmartPCA of EIGENSOFT version 6.0.1  package.Clustering of individuals based on shared ancestry from haplotype data was performed using fineSTRUCTURE on a coancestry matrix derived with the software CHROMOPAINTER v2 , which tAll N. American individual haplotypes were painted as a mosaic of all other N. American individuals’ haplotypes (self-excluding).subclusters, clusters (comprising subclusters), and regions .All non-N. American (Afro-Eur-Asian/AEA) haplotypes were formed as a mosaic of each other. Based on the haplotype sharing these individuals were then clustered and grouped into what we call All N. American haplotypes were then formed as a mosaic of AEA haplotype clusters. Detailed description of the analysis is in the groups and among these groups and AEA subclusters as inferred by fineSTRUCTURE using Treemix v1.13 (We determined the phylogenetic relationship among the N. American ix v1.13 . Detailsf3-outgroup tests as described ((i), AEA SubCluster(j): Relicts (Fs12_3)N. American configuration was used and implementation of the test was carried out using R package “admixr”  and N. American haplogroups, we used escribed . N. Amer“admixr” .subclusters defined by fineSTRUCTURE analyses), we used D-statistic and qpWave analyses from ADMIXTOOLS  and the analysis was performed by the pipeline available at . Minimum allele count of 2 and maximum allele count of 20 was used on the SNPs with less than 10% missing data. Alleles were polarized with the A. lyrata data.About 1,039 AEA individuals that formed the fineSTRUCTURE groups Hpg1 and SouthIndiana4. Details of the substitution model and prior used are in the Bayesian phylogenetic analyses were carried out using BEAST v.2.4.8  for grougroups are described in the We performed LAI for RAD-seq genotyped samples from population MISJ and WGS samples from MISJ, NJSC, OHML, and OHPR with Loter model . Exact pFST) between individuals that showed visible symptoms of infection by H. arabidopsidis . Environmental variables average temperature (°C), precipitation (mm), solar radiation (kJ m−2 day−1), and water vapor pressure (kPa) were used for further analysis. Pairwise Euclidean distances of all the environmental variables were calculated for each N. American haplogroup to AEA subclusters  and standardized values were used to model shared drift (measured by f3-outgroup statistics) among N. American haplogroups and AEA subclusters as a function of the environmental variables using Bayesian multilevel  linear regression. Description of the priors and hyper-priors is in the Historical climate data from 1970 to 2000 were downloaded from WorldClim2.0  at 2.5-mProjection of the N. American haplogroups in reduced dimension formed by standardized average temperature, precipitation, and vapor pressure was performed using uniform manifold approximation and projection (UMAP) . Two indA. thaliana and outgroups A. halleri and A. lyrata . The detailed strategy is described in the A. thaliana were obtained from https://sift.bii.a-star.edu.sg/sift4g/public//. Using these predictions, positions were divided into three different categories: 1) High-confidence deleterious mutations (score = 0–0.05), 2) Nonsynonymous tolerated mutations (score = 0.05–1) and, 3) synonymous mutations. We then calculated the ratio of fixed-to-total derived alleles .D  were calculated with scikit-allel (We performed haplotype homozygosity-based selection scans to detect recent and ongoing selection. e score)  and XP-Ee score)  were cale score) , details length) , Garud’s length)  (window it-allel . Nucleotit-allel .P values were calculated per SNP. P value less than 0.001 andariation , we usedon rates , overlapAgriGOv2  with PlaAgriGOv2  because Molecular Biology and Evolution online.msab268_Supplementary_DataClick here for additional data file.