In animal genetics, linear mixed models are used to deal with genetic and environmental effects. The variance and covariance terms of these models are usually estimated by restricted maximum likelihood (REML), which provides unbiased estimators. A strong hypothesis of REML estimation is the multi-normality of the response variables. However, in practice, even if the marginal distributions of each phenotype are normal, the multi-normality assumption may be violated by non-normality of the cross-sectional dependence structure, that is to say when the copula of the multivariate distribution is not Gaussian. This study uses simulations to evaluate the impact of copula miss-specification in a bivariate animal model on REML estimations of variance components.Bivariate phenotypes were simulated for populations undergoing selection, considering different copulas for the dependence structure between the error components. Two multi-trait situations were considered: two phenotypes were measured on the selection candidates, or only one phenotype was measured on the selection candidates. Three generations with random selection and five generations with truncation selection based on estimated breeding values were simulated. When selection was performed at random, no significant differences were observed between the REML estimations of variance components and the true parameters even for the non-Gaussian distributions. For the truncation selections, when two phenotypes were measured on candidates, biases were systematically observed in the variance components for high residual dependence in the case of non-Gaussian distributions, especially in the case of a heavy-tailed or asymmetric distribution when the two traits were measured. Conversely, when only one phenotype was measured on candidates, no difference was observed between the Gaussian and non-Gaussian distributions in REML estimations.This study confirms that REML can be used by geneticists to evaluate breeding values in the multivariate case even if the multivariate phenotypes deviate from normality in the situation of random selection or if one trait is not measured for the candidate under selection. Nevertheless, when the two traits are measured, the violation of the normality assumption may lead to non-negligible biases in the REML estimations of the variance-covariance components.The online version contains supplementary material available at 10.1186/s12711-022-00729-3. Multivariate mixed models are widely used in animal genetics to deal with genetic and environmental effects . Even tA strong assumption of REML estimations is the multi-normality of the vector of phenotypes. However, this assumption may be violated even if the marginal distributions are normal. Indeed, marginal distributions are not sufficient to characterize the multivariate distribution of a random vector. The copula of a random vector is a (multivariate) cumulative distribution function that links the marginal distributions of the random vector to the multivariate distribution, and characterizes the dependence structure of the random vector. In recent years, copulas have been extensively used in many fields including hydrology , actuariThe aim of the present study was to use simulations to evaluate the impact of a copula miss-specification in the bivariate animal model on REML estimations of variance-covariance components and on estimated breeding values (EBV) when each of the two traits of the bivariate model are Gaussian but the set of bivariate traits is not.F and let continuous. According to Sklar’s theorem [unique function C characterizes the dependence structure of vector As mentioned above, marginal distributions do not enable the characterization of the multivariate distribution of a random vector. Let  theorem , there imentclasspt{minimaf Nelson . The welnd Favre  for an ementclass2pt{minimN and F copulas are symmetric, whereas both Cl and J copulas are asymmetric .In a univariate setting, skewness and kurtosis measure the asymmetry of the distribution and the heaviness of the distribution tails, respectively, and make it possible to quantify the distance from normality. An extension of these measures for the multivariate case is proposed in Mardia . For thementclass2pt{minimOther standard measures in a multivariate setting are lower , can be graphically evaluated by fitting a bivariate normal contour plot to the plot of the two phenotypes . DeviatThe first illustrative dataset consists in n of the phenotypes and the contour plot of the bivariate normal distribution are plotted in Fig. The quantiles of the ranks over n of the phenotypes are plotted in Fig. The second illustration is the average daily gain of mentclass2pt{minimmentclass2pt{minimUsing the VineCopula package e.g.  in R 1919, it isMardia proposed a normality test based on the distance between the measured skewness and kurtosis and the theoretical values for the Gaussian case . For theFor the lamb example, the respective skewness and kurtosis values were 0.03 and 8.67 and the p-values of the respective Mardia tests were 0.14 and In this study, the impact of a non-normal dependence structure between phenotypes on the REML estimations of the variance components in the genetic models was evaluated by simulating a simplified pig breeding scheme.The populations were generated following the same schema as Gonzales et al. . FounderThe first situation is standard case, where all progeny had two observed phenotypes. In the simulations, each female produced 12 offspring: 2 males and 10 females . Thus, each generation comprised 2448 animals. All progeny were candidates for selection. The total number of animals over eight generations, including founders was The second multi-trait situation mimicked a second phenotype that can only be measured after culling (such as carcass traits). In the simulations, half of the animals have a missing value on the second phenotype. Only the animals with a missing value are candidates for breeding. Each female produced 24 offspring: 4 males and 24 females with 2 males and 12 females with both traits and 2 males and 12 females with only one trait, the latter being the candidates for selection. Thus, each generation comprised 4896 animals, and over eight generations, In both situations, male and female breeders were randomly mated to produce the next generation, but in such a way, that the full/half siblings were not mated with each other to limit inbreeding.In generations ith animal and Genetic values were simulated as follows. For the founder generation, given a genetic covariance matrix Performances were then simulated according to different residuals. All the simulated residuals mentclass2pt{minimConditional copula approaches were used to generate the residuals for non-Gaussian distributions [To be sure that the comparisons would be meaningful, the copula parameter was chosen in such a way that Kendall’s tau, Note that, thus, the Pearson’s residual correlations sin(π2τ) .jth trait . The associated design matrices are denoted We considered three categorical fixed effects with three, two and two modalities for each trait and denoted Finally, the bivariate phenotypes For each generation ow sense ) were thmentclass2pt{minimquations  again ussoftware .Residual variances First, we present the results of the estimated heritabilities, genetic correlations and residual correlations in the situation with no missing phenotype. Then, we summarize the situation in which some phenotypes on the second trait are missing.At generation In Additional file At generation The differences between the estimated heritabilities and the true heritabilities were never significant for the F copula. For the Cl and J copulas, when the residual dependence was positive, the differences were significant for the trait with a moderate heritability when the residual dependence was high and the heritability of the two traits differed, with an underestimated heritability for Cl and an overestimated heritability for J. The maximum of the absolute bias was 0.059 for the J copula and 0.052 for the Cl copula. When the residual dependence was negative, with some rare exceptions, the moderate heritabilities estimates were systematically biased, whereas the heritability estimate of the traits with a low heritability only differed significantly from its true value when the negative residual dependence was strong.Figure For this set of parameters, we observed for the trait with the highest heritability marked changes in the estimated biases between At generation In Additional file At generation For non-normal copulas, when the residual dependence was positive, the genetic correlation was systematically biased upward and the residual correlation of data simulated with the F or Cl copulas was biased downward. With some rare exceptions, the reverse was observed for the data simulated using the J copula. When the residual dependence is negative, the general trend is underestimation of the genetic correlation and overestimation of the residual correlation for all non-normal copulas, but, for the genetic correlation, the values were never significantly different from 0.In the case of a positive residual dependence, for both the residual and genetic correlations, the highest values of the absolute bias were for a high residual dependence and heritability for both traits. In these cases, the differences between estimated and true parameters for the non-Gaussian distributions were all significant. The maximum absolute bias was reached for the J copula, i.e. 0.161 for the genetic correlations and 0.06 for the residual correlations, followed by the Cl copula, 0.140 for the genetic correlations, and 0.056 for the residual correlations.Figures For these Kendall correlations, we observed marked differences in the estimated biases between The genetic gains for generation mentclass2pt{minimThe genetic gains were higher for traits with a moderate heritability than a low heritability and higher when the residual correlation was negative compared to when it was positive. The SE were high for the whole set of parameters considered, ranging from 0.33 to 0.60, higher for positive residual correlations (average 0.43) and lower for negative residual correlations (average 0.35).Figure Biases and SE of heritabilities and estimated genetic and residual correlations are in Additional file For the sake of simplicity, we do not present the case of entclass1pt{minimaAs above, the genetic gains for the non-normal copulas were similar to the genetic gains for the N copulas. The maximum absolute difference relative to the Gaussian case corresponded to a loss of 2.2%.A symmetric dependence structure (F copula) and asymmetric distribution (Cl and J copula).A case with lower (left) tail dependence (Cl copula) upper (right) tail dependence (J copula) and without tail dependence (F copula).Note that in the sense of Mardia’s kurtosis, the higher the residual correlation, the heavier the tail of the distribution.We performed simulation analyses with the bivariate animal model to qualify the robustness of the REML in the presence of deviations from normality due to misspecification of the dependence structure of the residual part. Thus, we looked for classes of non-normal parametric copulas for the residual part that deviate from normality because of the asymmetry and/or because of the tails of the distribution. Hence, we considered:Villanueva et al.  reportedWith random selection of the reproducers, the REML is robust to a strongly asymmetric and heavy-tailed distribution on the residual part.When the phenotype for one trait is missing for half the population, and selection of the reproducers is carried out among the animals with only one trait by truncation from a combination of their EBV, the REML is also robust to a strongly asymmetric and heavy-tailed distribution on the residual part.With no missing phenotypes and with truncation selection of the reproducers from a combination of their EBV, both the asymmetry of the residual part and the heaviness of the tails of the distribution can lead to significant differences between the theoretical parameters and values obtained by REML, when the correlation of the residual part is sufficiently large. When both the genetic and residual correlations are positive, the differences between the estimated and theoretical heritabilities are larger in the case of unbalanced heritabilities and largest for the trait with the highest heritability. Conversely, the differences between the estimated and theoretical correlations are larger in the case of balanced heritabilities.As already mentioned above, for the generations with random selection  suggest that the observed deviations from normality in the case of truncation selection and two observed traits are due both to the selection process carried out in the upper right tail of the distribution  and indirectly to the selection intensity , anWhen the heritability of the two traits is the same, the truncation selection process combined with high (respectively low) dependences in the upper right tail of the residual distribution will lead to overestimation (resp. underestimation) of the residual correlations and consequently to underestimation (respectively overestimation) of the genetic correlations. In the case of a negative correlation, the considered residual distributions do not have a right upper tail, hence the impact on the estimated genetic and residual correlations was globally lower . Nevertheless, in this case, the selection had more impact on the REML estimations of the heritabilities , particularly when the heritability of the two traits was moderate.The largest differences in genetic gain compared with the Gaussian case were for the Cl copula with unbalanced heritabilities, for which the variability in the upper left part of the distribution was the highest among the copulas considered here.Copulas do not enable characterization of the full dependence structure between random vectors, except for a few copulas . See Hofert et al.  for an eWe limited our study to two correlated traits. The case of d Ristig .In this study, our aim was to exacerbate differences between the Gaussian and non-Gaussian case by considering high residual (and phenotypic) correlations and heavy-tailed distributions for the residual part. Of course, in practice, differences between the true distribution and the Gaussian distribution are not always so pronounced and any errors in the estimation of the genetic components can be difficult to evaluate. For bivariate data, the scatter plot of the ranks  can allow to identify the copula, and in particular, it can be viewed as a graphical tool to check the normality of the copula, especially the asymmetry or the tail dependence of the data. Mardia’s skewness and kurtosis allow to indicate deviations from the normality and can be evaluated before the selection process to anticipate estimation bias. Other measures can be used to evaluate the deviation from the normality. For example, lower/upper tail dependencies can be evaluated below/above a cut-off parameter according to Schmid and Schmidt .To reduce biases due to the selection process and the miss-copula specification in the REML estimations, an inference copula-based method, which assumes that the copula family of the residual part is known, could be developed to estimate more accurately the parameters of the genetic model. Nevertheless, this assumes prior knowledge on the residual dependence structure ; the choice for the parametric copula on the residual part could also be guided by the scatter plot of the ranks. An interesting perspective would be to propose an efficient algorithm to evaluate the parameters of the mixed model when the copula of the residual part is not normal.In this paper, we used simulations to assess the impact of a non-Gaussian distribution of the residuals for the bivariate animal model on the REML estimation of the variance-covariance terms. We considered one situation in which two traits are observed for each animal and another situation in which the second trait is only observed in half of the population . In the two situations, when male and female breeders are chosen at random, our results show that even in the case of a non-Gaussian distribution for the residual part, REML estimations based on Gaussian assumptions provide robust estimates of variance-covariance components. For a truncation selection based on the summation of the EBV, in the case of missing phenotypes for the second trait, we observed no impact of the miss-specification on the REML estimations of the variance-covariance components. Thus, in this situation, REML can be used even when phenotypes deviate from the multi-normal distribution. Nevertheless, in the case of two observed phenotypes, one must pay attention to possible errors due to the residual dependence structures, particularly in the case of high correlations and asymmetric or heavy-tailed distributions.Additional file 1: Table S1. Bias and SE of estimated heritabilities, for positive residual dependence,with no missing phenotypes. Table S2. Bias and SE of estimated heritabilities, for negative residual dependence,with no missing phenotypes. Table S3. Bias and SE of estimated genetic and residual correlations, for positive residual dependence, with no missing phenotypes. Table S4. Bias and SE of estimated genetic and residual correlations, for negative residual dependence, with no missing phenotypes. Table S5. Mean value and SE of the genetic gains for G8, for positive residual dependence, with no missing phenotypes. Table S6. Mean value and SE of the genetic gains for G8, for negative residual dependence, with no missing phenotypes.Additional file 2: Table S7. Bias and SE of estimated heritabilities, with missing phenotypes. Table S8. Bias and SE of estimated genetic and residual correlations, with missing phenotypes. Table S9. Mean value and SE of the genetic gain for G8, with missing phenotypes.


Mendelian randomisation analysis suggests a risk-decreasing causality of educational attainment, intelligence, and other cognitive traits on PUD and GERD, but not IBD, and a putative association of GERD with cognitive function decline. Gene-based analysis reveals a significant gene-level genetic overlap of cognitive traits with AD and GIT disorders . Our study supports the protective roles of genetically-influenced educational attainments and other cognitive traits on the risk of GIT disorders and highlights a putative association of GERD with cognitive function decline. Findings from local genetic correlation analysis provide novel insights, indicating that the relationship of IBD with cognitive traits (and AD) will depend largely on their local effects across the genome.Emerging observational evidence suggests links between cognitive impairment and a range of gastrointestinal tract (GIT) disorders; however, the mechanisms underlying their relationships remain unclear. Leveraging large-scale genome-wide association studies’ summary statistics, we comprehensively assessed genetic overlap and potential causality of cognitive traits and Alzheimer’s disease (AD) with several GIT disorders. We demonstrate a strong and highly significant inverse global genetic correlation between cognitive traits and GIT disorders—peptic ulcer disease (PUD), gastritis-duodenitis, diverticulosis, irritable bowel syndrome, and gastroesophageal reflux disease (GERD), but not inflammatory bowel disease (IBD). Further analysis detects 35 significant ( Aβ) accumulation, inflammatory processes, vagal nerve stimulation, and lipid metabolism have been suggested  = 269,867) [n = 257,828) [n = 318,526) [n = 766,345) [n = 253,580) [n = 125,935) [n = 124,929) [n = 68,065) [n = 96,994) [n = 99,934) [Well-powered GWAS summary statistics sourced from international research consortia or publicly available databases/repositories were analysed in this study. We utilised GWAS summary data for 10 cognition-related traits, including intelligence  , cogniti257,828) , educati318,526) , educati766,345) , age of 253,580) , and flu125,935) . Other m124,929) , FI chai 68,065) , FI cond 96,994) , and FI  99,934)  were alshttps://www.leelabsg.org/resources; accessed on 14 June–26 September 2022), including gastritis-duodenitis  and diverticular disease  were similarly included in the present study. Furthermore, we analysed GWAS summary data of 6 GIT disorders, including PUD , IBD , and irritable bowel syndrome , all of which were sourced from a recently published GIT GWAS . The GWALastly, we utilised one of the largest publicly available AD GWAS, comprising 71,880 cases and 383,378 controls, made up of both clinically diagnosed AD and AD-by-proxy . A summa2SNP for each of the traits included in our study and, second, to assess the global (genome-wide) genetic correlation of GIT disorders with AD and cognitive traits. LDSC [https://github.com/bulik/ldsc; accessed on 14 June–26 September 2022). In the present study, we used the standalone version of the software and followed the scripts provided by the program developers . SNP-based heritability (univariate LDSC) was estimated on the observed scale, and in all analyses, we used the precomputed European population LD scores from the 1000G for HapMap 3 SNPs. We performed LDSC regression analyses, first, to estimate hts. LDSC  is a welp < 0.005 (0.05/10), a Bonferroni adjustment for testing 10 cognitive traits, and nominally significant at p < 0.05.We performed a cross-trait genetic correlation between GIT disorders  and each of the 10 cognitive traits and AD, using the bivariate LDSC analysis approach. LDSC can adjust for sample overlap when the genetic covariance intercepts are not constrained . AlthougGlobal genetic estimates, obtained, for example, using the LDSC method, capture the average genetic correlation of two traits across the entire genome. However, this approach can mask the local genetic effects and may not detect a significant correlation if effect directions are discordant. To address this limitation and gain insight into regions contributing disproportionately to the genetic correlation of cognitive traits and GIT disorders, we also performed local genetic correlation analyses. In the present study, we utilised a newly developed method for assessing local genetic correlation—the LAVA  . This anIn brief, we implemented LAVA using the R statistical platform (in the Unix environment) and estimated bivariate local genetic correlation between GIT disorders , AD, and 10 cognitive traits across the genome. First, as part of the input files required in LAVA , we estip < 5 × 10−5. Third, we performed pairwise bivariate local genetic correlation analysis across the genome for all the traits included in our study but prioritised the relationship of cognitive traits and AD with GIT disorders, the focus of the present study. Applying a Bonferroni correction for the number of bivariate tests performed, we selected significant bivariate loci at p < 4.37 × 10−5 (that is 0.05/1144).To prevent false positive results, the direction of effect needs to be consistent across all phenotypes. Hence, LAVA first extracts SNPs common to the GWAS summary data and aligns their effect alleles to the reference data before commencing analysis . SNPs thWe performed bidirectional 2SMR analyses to assess the potential causal effects of genetic predisposition to GIT disorders on cognitive traits and vice versa. Mendelian randomisation (MR) is an instrumental variable-based analytic method that incorporates genetics into epidemiological study designs, thereby, mimicking randomised control trials (RCTs) and providing a reliable approach for estimating causality between one trait and another—considered as ‘exposure’ and ‘outcome’ variables . The methttps://mrcieu.github.io/TwoSampleMR/; accessed on 14 June–26 September 2022), implemented in R (version 4.0.2). We first assessed GIT disorders as the exposure against each of the cognitive traits as the outcome. In a reverse analysis, we also assessed each of the cognitive traits as an exposure against GIT disorders as outcome variables. Linkage disequilibrium (LD) independent (at r2 < 0.001) genome-wide significant (p < 5 × 10−8) SNPs, robustly associated with the exposure data, were utilised as instrumental variables (IVs). This stringent threshold corresponds to an F-statistics of about 30 [p < 1 × 10−5) for these traits. In the present study, we conducted MR analyses using the standalone 2SMR package  approach, it being the most powerful model for detecting causal association in MR analysis when all instruments are valid . In thisTo ensure that our findings were not driven primarily by individual influential variants, we assessed the possibility of this occurrence using the ‘leave-one-out’ approach and performed individual MR analyses. Furthermore, we applied two additional MR models, the weighted median and the MR-Egger regression methods, for sensitivity testing and to complement the IVW model in causality assessment. The weighted median and the MR-Egger models are more robust to genetic heterogeneity and can provide valid estimates even when up to 50% (the weighted median) or all (the MR-Egger) of the IVs are invalid ,50. We aFinally, we estimated causality by excluding likely pleiotropic variants (where present), using another MR model, the MR-PRESSO  analysis . The MR-We extended our study beyond the SNP level to gene-based analysis, thereby furthering the assessment of the genetic overlap of cognitive traits with GIT disorders. Genes are more closely related to biology than SNPs and, thus, can provide more interpretable results while enhancing insights into the relationship between cognitive traits and GIT disorders. To estimate gene-level genetic overlap, we performed gene-based analyses and assessed whether the observed proportion of genes overlapping GIT disorders and the corresponding cognitive traits was more than expected by chance, similar to practices in previous studies ,55,56,57Firstly, we conducted gene-based analysis separately for cognitive traits and GIT disorders using the multi-marker analysis of genomic annotation (MAGMA v1.08) software   with the observed proportion of gene overlap using the exact binomial statistical test. Secondly, using the respective outputs of the gene-based analysis for GIT disorders and cognitive traits, we extracted genes associated with each of the traits at pgene < 0.05 divided by the total number of genes obtained in the gene-based analysis for the GIT disorder  [pgene < 0.05 [The expected proportion of gene overlap was defined as the number of genes associated with GIT disorders  at nstance) ,55,56,57e < 0.05 ,55,56,57Our study identified highly significant risk-decreasing global and local genetic correlations of cognitive traits with GIT disorders, including PUD, GERD, gastritis-duodenitis, IBS, and diverticulosis , providing new insights into the relationships of these traits. Findings from MR analysis highlight causally protective roles of cognitive traits on GIT disorders (PUD and GERD but not IBD). Together, these findings support the protective roles of cognitive traits such as intelligence and higher educational attainments in the risk of GIT disorders (PUD and GERD), indicating they could contribute to a reduced risk of the disorders. On the other hand, GERD was putatively associated with decreased cognitive functions and may, thus, be a risk factor for cognitive impairment. Further analysis, based on local genetic correlation, reveals discordant local effects of IBD with one or more cognitive traits, providing new insights into its relationships and suggesting that the relationship of IBD with cognitive traits (and AD) will depend largely on their local effects across the genome. Overall, current findings support educational attainments as a potential avenue for reducing the risk of GIT disorders. Hence, encouraging higher educational attainment and/or cognitive training may be beneficial in reducing the risk of GIT disorders.


As the world’s population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future. Consequently, it will be necessary to increase agricultural production by more than 70% . In addiIn the second half of the 20th century, a backcrossing breeding method was commercialized. Marker-assisted backcrossing (MABC) allows rapid introgression of key genes representing superior traits into elite cultivars or breeding lines, resulting in a cultivar containing both the transgene and the preferred alleles . MAS genHumans have bred crops to introgress useful traits and increase yields. Breeding methods have changed from domestication to traditional phenotypic selection, molecular breeding, and phenotype prediction . The rec22.1Plant breeding developed through efforts to establish better-performing varieties by crossing between cultivars with the traits desired by the breeder . Breeder2.1.1In 1903, Johannsen’s research confirmed that a population mixed with self-pollinated species could be classified as a genetically pure line. Pure lines are suitable for applications that require trait uniformity because of small genetic differences and similar phenotypes . Further2.1.2Mass selection applies to self-pollination and cross-pollination, but the genetic results differ . The con2.1.3Pure line selection and mass selection methods focus on genetic variation, whereas pedigree selection methods create variation through hybridization. A pedigree selection method can be used for cross-pollinating species, but it is mainly used for breeding self-pollinating species. Pedigree selection uses a pedigree with accurate records of breeders to keep the ancestors by knowing who the parents of the F2 generation are in order that F2 individuals can be identified through the descendants of subsequent generations . As a re2.1.4The bulk population method improves crops by putting off artificial selection until later generations and influencing variation through natural selection at the early generation stage . In othe2.1.5SSD is a compromised breeding method between the bulk population and pedigree selection. This breeding method randomly selects ‘only one seed’ from each plant of the F2 population with the desired trait created by artificial hybridization of F1 to shorten the breeding time and reduce genotype loss in the segregated generation . This ad2.1.6Backcross breeding aims to transfer one or more specific genes of interest from an ineffective breed to an excellent breeding line while preserving all other characteristics in the already excellent breeding line. It is a breeding method achieved over a relatively short time through repeated crosses and selection of superior breeding lines, i.e., recurrent parents . Althoug2.2Traditional breeding, with a history of nearly 100 years, liberated humanity from starvation with groundbreaking achievements. Despite these outstanding achievements, it stagnated due to a long breeding time and the limited number of crops that could be cross-bred. Amid these difficulties, a new concept emerged that applied a molecular-based selection strategy that replaced the existing phenotype-based breeding strategy. Until then, breeders visually made selections for breeding. However, after the advent of molecular breeding, it became possible to determine whether the genes of the parent generation were passed on to the next generation through gene analysis technology . Next-ge2.2.1Molecular markers with DNA-based polymorphisms can be used for genetic improvement by allowing for the selection of useful traits . In gene2.2.2Another method of selection to identify loci involved in the inheritance of complex traits is association mapping, also called LD mapping. This methodology is more efficient than QTL mapping, as it explores diversity using existing natural populations or germplasm collections with diverse cultivars, as opposed to QTL analysis, which uses bi-parental populations constructed using contrasting parents . Correla2.2.3With the development of NGS technology, computational methods using information from the genetic analysis have been improved, and GWAS search for key genes underlying important traits, contributing to the production of genetically improved plants. The first published GWAS was a study on humans in the early 2000s. Since then, several active studies in animals and plants have been conducted . This me2.2.4Recently, the amount of molecular markers for traits of interest in plant breeding has been gradually increasing. QTL mapping to identify genetic loci quantitatively associated with traits of interest is the basis for developing molecular markers used in MA) . MAS can2.3Complex quantitative traits are regulated by genome-wide minor effect alleles. Most economically useful traits, such as yield, fruit quality, and stress tolerance, are mostly complex quantitative traits . There aZea mays L.) and two times higher for wheat (Triticum aestivum L.)  . TherefoGS combines phenotypic and genotype data of the training population to construct a model. Then, based on the learned model, the genomic estimated breeding values (GEBVs) of individuals in the breeding population are predicted Figure 2Although GS is a useful tool in plant breeding, there is limited information on setting up an optimized statistical model. Incorrect data imputation, unexpected responses, and environmental constraints limit the performance of GP. Although attempts were made to consider and overcome these limitations in several statistical prediction models, it is still the most challenging problem in multidimensional genomic data . The preABLUP- Traditional pedigree- Best Linear Unbiased Predictor; GBLUP-Genomic Best Linear Unbiased Predictor; ssGBLUP-Single-step Genomic Best Linear Unbiased Predictor; RRBLUP-Ridge Regression Best Linear Unbiased Predictor; rrGBLUP- Ridge Regression Best Linear Unbiased Predictor; Bayes-Bayesian method RF-Random Forest; RR-Ridge Regression; LASSO-Least Absolute Shrinkage and Selection Operator; SVM- Support Vector Machine; RF-Random Forest; ANN-Artificial Neural Networks; MLP-Multi-Layer Perception; CNN-Convolutional Neural Networks; DBN-Deep Belief Network; RKHS- Reproducing Kernerl Hillbert Space.2.3.1GS tries to avoid biased marker prediction effects by using the entire marker. Ordinary least squares (OLS) is a simple method for measuring the influence of markers. However, OLS only makes predictions within a sample and does not allow the weighting of markers. In the case of a high density of markers, the number of markers (p) exceeds the sample size (n). Therefore, it is not appropriate to obtain an estimate of the marker effect through OLS . In ordeRR estimates the regression coefficient using parameter shrinkage, giving the ℓ2-norm penalty. The ℓ2 penalty of RR tends to shrink the coefficient to zero. In particular, RR is advantageous for non-zero coefficients. In the case of k identical predictor variables, when the predictor variables are fitted alone, each coefficient is equally reduced to the size of 1/k. Moreover, RR is effective when many predictors have small effects, especially coefficients with many correlated variables. Therefore, RR cannot eliminate coefficients and cannot select only a relevant subset of predictors. Similar to RR, LASSO is advantageous for processing large amounts of data quickly and efficiently . However2.3.2Estimating the value of breeding requires optimizing the estimation of the regression coefficients but also optimizing the information in the phenotypic data. There is a method that combines these two factors. There is a method of obtaining a phylogenetic effect by simultaneously modifying the phenotype and a method of estimating the breeding value using an additional genetic relationship between plants. This method is called best linear unbiased prediction (BLUP) . BLUP st2.3.3Bayesian analysis is named after the British mathematician Thomas Bayes. This analysis is a statistical method that combines the information in the sample with the information on the parameters in the population in advance to explain the statistical reasoning process . The firMany Bayesian methods for GS have been developed. Similar sampling models have been shared, and a new concept of the Bayesian alphabet has emerged, including Bayes A and Bayes B in different analytical methods . In animDirect approaches such as GBLUP first construct the relationship between SNPs and molecules and then utilize mixed model equations to determine genetic merit directly [89]. The Bayesian approach is more effective than other QTLs and genetic value prediction models, as many SNPs are preferentially merged into ineffective ones . In addiAlthough the Bayesian model is widely used in animal and plant breeding, it has several drawbacks, including those previously described. First, SNPs treated as close to one are assigned an ineffective ratio. Second, the degree of freedom of the independent data dictionary is used for distribution. Only one degree of freedom can be added regardless of subsequent phenotypes and genotypes. In order to overcome these shortcomings, Bayes Cπ and Bayes Dπ were developed and sampled by replacing parameter π or scale parameter S with variables rather than data information .Bayesian methods in this context have mechanisms to combine prior probability distributions with sample data information for later probability distributions in the natural state. Because of this, it can also be used to make better decisions in posterior probabilities.2.3.4Parametric models such as MLM and Bayesian models, widely used in GS, use a prior effect size distribution determined by several parameters. The parameters used in predictive models limit the amount of information the model can use. Therefore, the small number of parameters determined in the predictive model limits the flexibility of the model. On the other hand, in the semi-parametric model, there is no assumption that data follows a specific distribution, and the number of parameters is determined according to the amount of training data. The semi-parametric model can be used when there is no prior information about the data . PredictConversely, semi-parametric models involve epistasis effects and have been used in several plant prediction studies. Epistasis plays a vital role in explaining the occurrence of genetic variation, and considering epistasis in predictive models can obtain good predictive accuracy of plant breeding in quantitative traits . EpistasFor this reason, several models have been devised for genetic prediction. RKHS is designed for genetic prediction in non-linear models. It makes inferences about functions without prior information in a semi-parametric method. RKHS reflects independent variables in a finite-dimensional space into infinite-dimensional Hilbert spaces. This method assumes that distances in Euclidean space can be expressed through a kernel matrix that reflects the distances between objects in Hilbert space . RKHS ob33.1Comparison of GS methods is evaluated by prediction accuracy. The prediction accuracy is measured by the correlation between the measured GEBVs and the actually measured phenotype data. Therefore, improvement of prediction accuracy is important for GS applications.3.1.1Among them is the density of the marker. Since GS estimates the effects of markers using LD between quantitative loci and markers, a high density of markers is advantageous for GS . Even foTo take advantage of GS while lowering the marker density, highly correlated duplicate markers can be removed from the LD block. This approach reduces multicollinearity and does not interfere with the marker effect. It can also be a good alternative because it reduces the possibility of overfitting . The low3.1.2The accuracy of genetic predictions is also determined by the design of the training population. Well-established training populations are important in GS. The training population consists of breeding lines with phenotype data for target traits and genotype data. After training a predictive model with markers, the prediction of GEBVs in the test population is performed using the trained model . As NGS 3.1.3Difficulties in making genetic predictions should apply to GEBVs in various environments. For accurate predictions, predictive models must consider terms that interact with various environments and environment x genotype interactions. These changed prediction models are divided into two types. The first is a non-informed model, which includes the environment as the main random effect. Also, the interaction between a genotype and environment is specified as a random effect. These prediction models report improved accuracy compared to traditional prediction models that specify only a genotype and environment as the main effects . On the 3.1.4With the development of NGS and large-scale marker information, the application of GS increases in plants. However, one of the challenges that breeders face in training GS models is inaccurate phenotypic data. The sophistication of phenotypic information is as important as genomic information. Inaccurate phenotypic data make the predictive ability of the GS model decline. In addition, the existing phenotype measurement method has disadvantages because it is labor-intensive and requires a lot of time and costs. Recently, high-throughput phenotyping (HTP) has been used for accurate phenotypic information measurements. HTP is a non-destructive, fast, and accurate phenotypic measurement method that accurately captures traits of interest . HTP has3.2ML uses statistical techniques to allow systems to learn from data without explicitly programming them. ML takes a sample and then builds a model to explore algorithms that learn from current data and make predictions on new data. ML-based methods can be effective in improving prediction accuracy compared to conventional GS . The maiGS statistical methods use ML for more accurate predictions Table 2.Most ML involves supervised learning, such as Bayes nets, rule-based learning decision trees, naive Bayes, and nearest-neighbors, and applies to GS in the form of RF, ANN, and SVM . RF is aAnother ML-based model, SVM, is advantageous for classification and regression, similar to other ML models. The difference from other ML models is that SVM is specialized in identifying subtle patterns in complex and large amounts of information data. SVM makes a decision boundary with various feature vectors to achieve predictions. SVM can flexibly handle data using kernel-based functions. Furthermore, SVM can improve the non-linear form of phenotypes and genotypes using kernel functions . ML modeRecently, the ML method, a specific type of ANN, has been considered to increase the performance of GS. ANN takes into account patterns in data and makes predictions about complex functions as universal approximations . In GS, 3.3Although studies on GS applied with DL are still lacking, several advanced studies exist Table 4.An artificial neural network (ANN), the most fundamental concept in DL, is a network structure created by mimicking the neuron connection structure of a human neural network. A structure in which three or more ANNs are superimposed is called a deep neural network (DNN), and ML using this is called DL . PopularIn MLP, in an artificial neural network, data moves in one direction from the input node through the hidden node to the output node Figure 4CNNs are used in visual recognition tasks involving images or video data. CNNs reduce the size of input and parameter sharing because the inputs are only partially connected to some neurons. Therefore, it is efficient because it reduces the parameters that need to be estimated. Most CNNs include three operations: convolution, non-linear transformation, and pooling Figure 5via synaptic connections. At least one feedback loop exists because the signal travels in both directions. Although the training parameters are reduced by sharing parameters across multiple steps, the short-term memory or latency of the network improves the performance, so training requires a lot of computational resources  because these traits are greatly influenced by the interaction between genes and the environment. Moreover, traditional breeding methods are time-consuming, laborious, and ineffective for cost and land use. In addition, low reliability and accuracy make these breeding methods less efficient. Accordingly, a new breeding method is required to quickly and accurately breed crops with high yield, good quality, and climate resilience. Researchers successfully established biotechnology-based molecular breeding technology to overcome the limitations of traditional breeding technology. They are now putting genome breeding technology into practice, also called predictive breeding. In summary, the history of plant breeding developed from traditional to molecular breeding. Predictive breeding will be available in the near future.Rapid development in advanced technologies such as biotechnology, genomics, and phenomics improves progress in plant breeding. Breeders in the 21st century create mutations that directly correct target genes and expand the limits of genetic resources indefinitely by transcending the category of sexual reproduction with transformation technology. Selection in a population or lineage can rapidly and accurately fix the desired genotype using genomic information.Integrating novel digital tools will be valuable and helpful in enhancing breeding progress, particularly for difficult-to-breed, quantitative traits. Vast genetic, genomic, phenotypic, and environmental data must be integrated and handled based on digital technology to fulfill the breeding technologies. In the current article, we first defined the term digital breeding, including breeding technologies from molecular to predictive breeding. For digital-based breeding technology to be commercialized, gene and genome information related to traits for each crop must be identified. It is expected that digital breeding can make it possible to reflect complex climates, geography, quantitative traits, and multiple traits in the breeding process. In a broad sense, therefore, digital breeding is not a single but a convergence technology. It can be defined as various attempts to actively utilize advanced technologies such as big data and artificial intelligence for agricultural breeding. However, as common confusion with new concepts in the early days of establishment, each researcher may have different ideas about digital breeding depending on their research field, research experience, and breeding resources. Particularly, some researchers think that DL-based techniques in breeding can become a game changer based on the results that DL has shown in various fields so far, so only DL-based techniques may be considered digital breeding. The different perceptions of digital breeding by researchers may cause confusion in the planning and promotion of related R&D. To address the problem, we first listened to the opinions of many researchers working with various breeding technologies. Consequently, we could classify the breeding level based on the intensity of digitalization. As a solution, we suggest that if digital breeding is divided like the autonomous driving levels in automobiles, it will be possible to easily organize the differences in thinking about digital breeding. Accordingly, we divided digital breeding into six phases. Phase 0 is traditional breeding that does not use digital technology and includes cross-breeding and mutation breeding. Phase 1 refers to identifying and processing substantial marker data generated by the development of genome sequencing technology. In Phase 2, markers are developed using bioinformatics tools. It is advantageous to select markers associated with qualitative traits using GWAS. Marker selection using GWAS has been actively carried out, but it is difficult to apply it to breeding directly. On the other hand, Phase 3 shows the potential to be practically applied to breeding. In this phase, the basis for practical application to breeding was laid by calculating the GEBVs using genomic information. Research based on GS started digital breeding. Although GS research has been actively conducted on livestock, it is still lacking in plants. Predictive statistical models for GS have been used in BLUP, LASSO, Bayesian, and ML-based models have also been applied. In Phase 4, breeding mainly uses DL to predict the phenotype by considering factors that affect plants, such as the environment. Some studies have been carried out by applying DL to plants, but it is still incomplete due to accuracy and technical problems. It looks like digital breeding, in the narrow sense, refers to this phase. In Phase 5, all processes from breeding design to phenotype prediction are automatically performed using DL. It has not yet been studied and is the ultimate goal for digital breeding techniques to evolve Table 4.CK, T-HL, and DJ designed and structured the review, DJ, YK, SL, SC, and YS collected the information, organized the figures and tables, and wrote the original draft. CK and T-HL reviewed the manuscript. DJ edited and wrote the manuscript. All authors contributed to the article and approved the submitted version.


An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV  or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy.Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality.For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.The online version contains supplementary material available at 10.1186/s12711-022-00722-w. Selecting animals for a better feed efficiency is one of the key levers to improve farm profitability while reducing the environmental impact of livestock farming , 2. ThanThanks to new genotyping technologies that render the genotyping of numerous single nucleotide polymorphisms (SNPs) cost-effective, genomic prediction (GP) has been implemented in multiple livestock species –28. For Thus, our goal was to propose and test operational solutions that could facilitate the implementation of selection for longitudinal traits, using the example of feed efficiency. Using the LR method, the objectives of the present study were to evaluate the quality of the predictions of SBV for longitudinal RFI in pigs, as well as the benefit of adding genomic information. In addition, for practical selection on longitudinal profiles, we evaluated the effect of using summarized phenotypes instead of longitudinal phenotypes on the quality of predictions of SBV.Phenotypes on feed intake (FI) and production (metabolic body weight (MBW), average daily gain (ADG), and backfat thickness (BFT)) records measured weekly on 2397 growing French Large White pigs over a 10-week period from ~ 13 to ~ 22 weeks of age were used in this study  were used to compute longitudinal RFI by a phenotypic regression of FI on production and maintenance traits, using the following RR model of degree 2 for the genetic and permanent environmental effects:Reml 4.0  and wereth SBV) The LR method applied to the SBV of a group of focal individuals  consists in comparing the estimated SBV (ESBV) of these focal individuals based on less information to their ESBV based on more information, considering that more information provides a reasonable reference. ESBV of the focal individuals based on less information  are referred to as Reverter , were usmentclass2pt{minim(1) SBV predicted for the group of focal individuals (group_FI(2) SBV predicted for the group of focal individuals (group_FI(3) SBV predicted for the group of focal individuals using pedigree information  breeding values. To compare estimates of breeding values obtained in different situations, we applied the LR method proposed by Legarra and Reverter . The usesampling .additional phenotypic records would have lower impact on GEBV (compared to EBV) because they would contribute with less information than the direct genomic value” [The bias . As expc value” . The lacc value” ) that isc value” . Consequ of GEBV .Breeding values that summarized the trajectory of RFI over time using eigenvectors of the estimated variance–covariance structure of the longitudinal data were obtained using two approaches: entclass1pt{minima in pigs , using tUsing the LR method, we evaluated, the quality of prediction of breeding values for candidates without own phenotypes in the study of longitudinal data, by using two different approaches , and evaluated the benefit of adding genomic information for prediction. Predictions were of similar quality with the two approaches, meaning that, in this population design, the use of summarized phenotypes would be of interest for routine evaluation of longitudinal traits. We did not highlight any benefit of genomic information for prediction in this study, which is certainly due to the number of genotyped animals with accurate estimation being too small in this dataset, contrary to the usual expectation.Additional file 1: Table S1. Descriptive statistics of the data. Table S2. Numbers of records, animals with phenotype, sires, and dams per line and generationAdditional file 2: Figure S1. Ls means of the line effect on the LR statistics Figure S2. Ls means of the type of SBV on the LR statistics


The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean. Glycine max (L.) Merr.] plays a multifaceted role in the global agricultural trade, economy, and food security due to its unique seed composition glycine) has high water solubility and vapor pressure of 1.9×10−7 mm HG at 25°C . SNPs were converted to numerical format , and were excluded based on minor allele frequency (MAF)< 0.05 resulting in 31,957 SNPs. The across-genome SNP density on a chromosome basis was 1,598, ranging from 1,186 (Chr. 11) to 2,619 (Chr. 18).A total of 382 genetically diverse soybean accessions with maturity groups (MG) ranging from MG 3 to 5 were used in this study. These comprise a subset of the USDA Soybean Germplasm Collection and originated from 15 countries, including Algeria (2), China (226), Costa Rica (1), Georgia (2), Indonesia (1), Japan (38), Nepal (1), North Korea (20), Russia (5), South Africa (1), South Korea (32), Taiwan (3), United States (40), and Vietnam (5). Five accessions have unknown origins. The USDA Soybean Germplasm Collection was genotyped with the SoySNP50K BeadChip  and the Field trials were conducted in three environments for two years (2020-2021) using a two-replicate randomized complete block design at the Lee Farm in Portageville, MO  and Rhodes Farm in Clarkton, MO . Each plot consisted of a single 2.13 m long row spaced 0.76 m apart. The Lee Farm and Rhodes Farm have been exposed to prolonged and homogeneously distributed off-target dicamba damage since 2017, where significant yield losses due to off-target dicamba exposure have been reported between non-DT and DT soybean genotypes . Off-tarEach year, genotypes were visually assessed for off-target dicamba damage once in the early reproductive stage between R1 to R3 (approximately 100 to 130 DAP) . Lines wls_means’ of the R  with Bayesian Information Content (BIC) in a fixed-effect model to boost computing speed. The detailed methodology can be found in Two models have been implemented to detect significant marker-trait associations. The Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model  was condGAPIT’ . It is aGAPIT’ . In summyijk represents the kth  response of the ith  genotype in the jth  environment, the GWAS was conducted using the following linear mixed model in matrix form:To account for the variable patterns of genotype responses to off-target dicamba in different environments, a model that allows the inclusion of the population structure in interaction with environments was considered. The model was fitted with ASREML‐R . Considering that E corresponds to the main effect of the environments; R:E represents the effect of the replicates nested within environments; PC1:10 are the first 10 principal components (PC) derived from decomposing the G matrix using a principal component analysis (PCA) and are included in the model for correcting for population structure; the interactions between the first 10 components PC1:10 and environments were also included with the  E×PCj term; the xk term corresponds to the kth molecular marker associated with βk (marker effect). All of the previous model terms were considered fixed terms. The random effect L is associated with the main effect of the genotypes, and e corresponds to the error term which captures the unexplained variability.Where eff) was derived by considering the eigenvalue decomposition of the matrix of correlations between markers. The resulting test was adjusted using the Meff with the following correction  was implemented. The effective number of independent tests (Mrrection :αp is the comparison-wise error rate and αe corresponds to the experiment error-wise (αe=0.05) .Where Across the three testing environments, the frequency of off-target dicamba damage scores was consistent and normally distributed with over 45% of the observations between scores of 2 and 3 (moderately tolerant) and 39% between 3 and 3.5 (moderately susceptible) Figure 1Across environments, scores were consistent with an overall Cronbach’s alpha (α) score of 0.89 (C.I 0.87 to 0.91). Within environments, scores ranged from 0.85 (2021-Clarkton) to 0.89 (2021-LeeFarm), indicating minor error variance and discrepancy across replications Table 1.eff) was 575, which returned a threshold of marker-trait association significance of logarithm of odds (LOD) of approximately 4.0. Using the proposed model that allows the inclusion of the population structure in interaction with environments (Eq. 2), three significant marker-trait associations were detected in chromosomes 10 (LG O), 15 (LG E), and 19 (LG L)  had the highest LOD (4.5) with a favorable allele frequency of 13.9%. This SNP is located within the gene Glyma15g07710 which encodes a copper-containing oxidoreductase enzyme, tyrosinase. These copper containing enzymes can oxidize a wide range of aromatic compounds, including the oxidation of o-diphenols to their corresponding o-quinones  had the second-highest LOD (4.2) with a favorable allele frequency of 8.0%. It is located within the gene Glyma10g01700 which encodes a multidrug resistance protein (MRP). MRPs are essential in phase III of plant herbicide detoxification by facilitating the transport of glucose- or glutathione-herbicide conjugates into the vacuole  had a LOD of 4.1 with a favorable allele frequency of 28.3% -dependent glycosyltransferase gene. This genomic region contains additional five UDP-glycosyltransferase genes. The conjugation of Phase I-hydroxylated herbicides to endogenous sugar molecules such as glucose is catalyzed by UDP-dependent glycosyltransferases and represents an important phase II reaction of plant herbicide detoxification  Figure 2quinones . Phase Iquinones . Given t vacuole . Lastly,fication .ss715635349 located at 44,580,800 bp of chromosome 19 had the highest LOD (6.4) followed by the SNP ss715605561 located at 1,227,933 bp of chromosome 10 (LOD of 6.1). The SNP ss715609879 had a LOD of 4.8 with a favorable allele frequency of 24.0%. It is positioned at 15,740,804 bp of chromosome 11 (Genome assembly version Wm82.a2) and is located within Glyma11g29391, a lipid phosphate phosphatase gene. Lastly, the SNP ss715632413 located at 57,025,570 bp of chromosome 18 (Genome assembly version Wm82.a2) had a LOD of 4.4 with a favorable allele frequency of 19.4% (Glyma18g291800 and Glyma18g291700) are located within 50-kb of ss715632413 .Four significant marker-trait associations were detected in chromosomes 10 (LG O), 11 (LG B1), 18 (LG G), and 19 (LG L) using the BLINK model Figure 2ss715605561 (Chr. 10), ss715635349 (Chr. 19), and ss715632413 (Chr. 18). Favorable alleles were represented as 1 whereas unfavorable alleles were represented as 0. For instance, SNP: 0,0,0 represents the allelic combination where ss715605561, ss715635349, and ss715632413 are unfavorable, and SNP: 1,1,1 represents all favorable alleles. The mean score of dicamba damage in genotypes carrying all three favorable alleles was 1.58, whereas the mean score of genotypes carrying all three unfavorable alleles was 2.90  and ss715635349  significantly reduced the overall damage from off-target dicamba as compared to all non-favorable alleles, while genotypes carrying only the favorable allele for ss715632413  did not show significant differences to the genotypes carrying only unfavorable alleles , moderate (2.5< score =< 3.5), and susceptible (score > 3.5). The classification distribution based on the allelic combination of these SNPs showed a substantial reduction in the susceptible class with the inclusion of the favorable alleles of 15635349 Figure 4dicamba monooxygenase (DMO) from Pseudomonas maltophilia (Strain DI-6) encoding the enzyme dicamba O-demethylase allows DT plants to metabolize dicamba to 3,6-dichlorosalicylic acid (DCSA), inactivating its herbicidal activity before it accumulates to toxic levels when expressed from either the nuclear genome or chloroplast genome of genetically engineered plants  and susceptibility (scores > 4). No differences in off-target dicamba damage were observed across MG . ToleranHeterodera glycine Ichinohe), is susceptible to off-target dicamba (average damage of 3.5). In general, soybean [Glycine max (L.) Merr.] shows a moderate response to off-target dicamba, and yield losses are expected when prolonged exposure occurs (Meloidogyne spp.)  (Plant introduction (PI) 424005 , PI 424038-B , PI 561701 , PI 603497 , and PI 342434  were the five most tolerant accessions . For inse occurs . Furthere occurs . This hae occurs , root-knne spp.) , foliar ne spp.) , and brogregata) .ss715635349 (Chr. 19) and ss715605561 (Chr. 10) and the response to off-target dicamba. The BLINK model identified additional significant marker-trait associations on Chrs. 11 (ss715609879), 14 (ss715619759), and 18 (ss715632413), while the G×E Model identified an additional significant marker-trait association on Chr. 15 (ss715622838). The significant SNPs identified by both models are located within candidate genes with annotated functions involved with different phases of herbicide detoxification in herbicides.Two models were used to identify marker-trait associations regulating the response of soybean to off-target dicamba. BLINK minimizes false-positive associations and greatly improve computational efficiency in larger datasets . To accoss715635349 is located within the gene Glyma19g37108, a uridine diphosphate (UDP)-dependent glycosyltransferase gene. Within a 30-kb window from ss715635349  there are another five UDP-dependent glycosyltransferases genes . Given the frequency of the favorable allele of ss715635349, we hypothesize that the ability to complete phase II detoxification of dicamba is relatively common (28.3%) and may explain the overall moderate response of soybean to off-target dicamba. Phase III of herbicide detoxification involves the active transport (ATP-dependent) of non-phytotoxic herbicide conjugates into the vacuole by proteins in the multidrug resistance-associated protein (MRP) family or by other transport mechanisms  could explain the rare occurrence of highly dicamba-tolerant soybean phenotypes. Based on these results, we hypothesize that most soybean genotypes conduct phase I ring hydroxylation and phase II detoxification of dicamba with glucose but have a rate-limiting step in the final phase III transport of non-phytotoxic dicamba-glucose conjugates into the vacuole. However, further metabolite profiling and subcellular transport studies are warranted to test this hypothesis directly.Phase I typically involves oxidation by cytochrome P450s or hydrolysis by carboxylesterases. These reactions introduce a reactive functional group suitable for subsequent metabolism and detoxification since this initial oxidation step may not lead to complete detoxification . Phase Ichanisms . The SNPss715605561 (Chr. 10), ss715635349 (Chr. 19), and ss715632413 (Chr. 18) can accurately distinguish among tolerant to susceptible genotypes. Accessions carrying the favorable alleles for these SNPs showed the lowest average off-target dicamba damage and the highest frequency of tolerant and moderate classes. Future plant breeding research will utilize and apply these alleles in marker-assisted selection programs targeting identification and development of genotypes with higher tolerance to off-target dicamba. Additionally, molecular physiology research is currently underway to investigate expression patterns and functional roles of the alleles and encoded proteins identified by our GWAS analysis.Although the response to off-target dicamba appears to be a highly complex trait regulated by multiple genes involved in several biochemical pathways, the combination of Widespread adoption of DT production systems has frequently resulted in yield losses in non-DT soybean genotypes from off-target dicamba exposure. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba in non-DT soybean genotypes may sustain and improve other non-DT soybean production systems, including the growing niche markets of organic and conventional soybean. Herein, we report several genetically diverse accessions that can be used as genetic sources for improved tolerance to off-target dicamba. Two genomic regions on Chrs. 10 and 19 were identified that may be directly associated with the ability of soybean to detoxify dicamba and/or transport non-phytotoxic dicamba metabolites into the vacuole. Three significant SNPs accurately distinguished between tolerant and susceptible genotypes. Intensive breeding that targeted single economically important traits may have caused selective sweeps where alleles conferring tolerance to off-target dicamba were lost after many breeding cycles. With the advancements in targeted gene-editing techniques, our results may facilitate identifying and developing conventional soybean cultivars with improved tolerance to off-target dicamba as well as other synthetic auxin herbicides. Further research investigating the physiological mechanisms underlying natural tolerance to dicamba in soybean and dose-response studies in controlled environments can help to determine the threshold at which tolerant genotypes maintain minimal symptomology following off-target dicamba exposure.https://doi.org/10.5061/dryad.gmsbcc2s4.The datasets presented in this study can be found in online repositories. The name of the repository and link to the data can be found as follows: Dryad; CC, DJ, JFZ, BD, DR, DX, GS, PC, and HN contributed to conception and design of the study. CC, BD, DR, and PC contributed to funding resources of the study. CC, EdN, DL, and SS contributed to collection of data used in this study. CC, DJ, and JZ contributed to the statistical analysis of this study. CC wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


The stem and leaves of fresh corn plants can be used as green silage or can be converted to biofuels, and the stalk sugar content and yield directly determine the application value of fresh corn. To identify the genetic variations and candidate genes responsible for the related traits in fresh corn, the genome-wide scan and genome-wide association analysis (GWAS) were performed. A total of 32 selective regions containing 172 genes were detected between sweet and waxy corns. Using the stalk sugar content and seven other agronomic traits measured in four seasons over two years, the GWAS identified ninety-two significant single nucleotide polymorphisms (SNPs). Most importantly, seven SNPs associated with the stalk sugar content were detected across multiple environments, which could explain 13.68–17.82% of the phenotypic variation. Accessions differing in genotype for certain significant SNPs showed significant variation in the stalk sugar content and other agronomic traits, and the expression levels of six important candidate genes were significantly different between two materials with different stalk sugar content. The genetic variations and candidate genes provide valuable resources for future studies of the molecular mechanism of the stalk sugar content and establish the foundation for molecular marker-assisted breeding of fresh corn. The stem and leaves of fresh corn plants are often applied to green silage and converted to biofuels. Fresh corn, mainly comprising sweet corn and waxy corn types, is an important staple food consumed as a vegetable or fruit, and is also used for livestock feed and fuel . The steSugar supply is crucial for plant growth and development. Calcium signaling-mediated transcriptional regulation might play an important role in glucose metabolism. Calcium-dependent protein kinases can phosphorylate and regulate sucrose synthase and sucrose phosphate synthase, thus influencing sucrose accumulation ,10. ReceThe stalk sugar content of corn is a complex quantitative trait controlled by hereditable factors despite its complex phenotype . The staLinkage analysis is limited by parental differences and population size, and the number of localized QTLs is limited . In receTo uncover the molecular regulation mechanism of the stalk sugar content and yield in fresh corn, the identification of the genetic variations and candidate genes responsible for the stalk sugar content and agronomic traits is the key problem. In this study, based on one hundred and eighty-eight sweet, waxy, and hybrid corn accessions differing in genetic background, the stalk sugar content and other agronomic traits  were investigated in four seasons of two years during the spring and autumn sowing. A GWAS analysis was performed to detect the significant single-nucleotide polymorphisms (SNPs) associated with stalk sugar content and the agronomic traits, and the candidate genes were screened, which provides significant insights into the genomic footprints of the stalk sugar content in fresh corn and facilitates the breeding of corn cultivars with higher stalk sugar content.In this study, 188 sweet corn, waxy corn, and hybrid accessions were genotyped. Among them, 41 were sweet corn inbred lines, 74 were waxy corn inbred lines, and 73 were hybrid accessions . After cr2 = 0.2 at about 50 kb  and nucleotide diversity between the sweet corn (group A) and waxy corn (group B) accessions (πA/πB). In total, 18 and 20 significant selective regions were detected from the FST and πA/πB values, respectively , proline-rich receptor-like protein kinase (Zm00001d033104), and zinc finger protein (Zm00001d052883). A gene ontology (GO) analysis of all selective genes revealed that 53 GO terms were enriched  encoded a calcium-dependent protein kinase (CDPK). Fifteen significant SNPs associated with WWP were identified  differed in WWP  and mixed linear model (MLM) models were constructed for the association analysis, but the GLM model showed poor correlations for most traits except the ear node (EN). Therefore, the more reliable MLM model was adopted for most traits and the GLM model was used only for EN . Signifientified . Among tentified . Importad in WWP E. Twentyd in WWP . The reld in WWP . The relZm00001d034759, which encoded an acyl-CoA-binding domain-containing protein. Through phenotypic analysis of the accessions with different genotypes, the accessions carrying AX-116875096-CC were shown to have higher stalk sugar content than the accessions carrying AX-116875096-GG  encoded the receptor protein kinase TMK1. In addition, the sugar content of the accessions with different genotypes of the significant SNPs AX-86246392, AX-86279495, AX-86312905, and AX-86312908 also be analyzed, which shows that the accessions carrying AX-86246392-CC, AX-86279495-GG, AX-86312905-GG, and AX-86312908-CC had higher stalk sugar content  in two fresh corn materials with significantly different stalk sugar content. The average stalk sugar content in multiple environments of the high-sugar content material was 15.5%, while the average stalk sugar content of the low-sugar content material was 3.7%. Based on the qRT-PCR results, we found that the expression levels of 1d050244 . Furtheraterials . Sugars are the primary products of photosynthesis and accumulate in sinks, including the stalk. Studying the sugar content and yield-related traits in corn is helpful to improve the yield and quality of fresh corn, which is beneficial to the breeding of crosses with higher stalk sugar content and yield. The stalk sugar content also directly affects the application value of fresh corn as a biofuel feedstock. Research on the stalk sugar content of fresh corn may improve the efficiency of corn utilization and expand its application for green silage and biofuel production. The stalk sugar content may also influence the sustainability features of the processes of green silage and biofuels, using advanced sustainability assessment tools is beneficial to evaluate the application value of fresh corn as biofuel production . In receZm00001d020297 encodes a CDPK, which was identified in two environments and might be a crucial candidate gene regulating plant height. Moreover, the alleles of AX-86252871 had differential effects on WWP, the accessions carrying the allele AX-86252871-AA had a higher WWP  [Zm00001d009466 encoding a PHD finger protein. Additionally, we also identified a candidate gene (Zm00001d009156) encoding an ATP-binding cassette (ABC) transporter through significant SNP AX-91358539. The study in Arabidopsis has shown that ABC transporters are involved in the transport of lipids, glycosides, hormones, and so on [The QTLs that contribute to the stalk sugar content also affect the leaf area of corn . In addigher WWP e. AX-862ripening . Ethylenripening . A previ(AGPase) . Our stuZm00001d052289, which encodes a pyruvate dehydrogenase involved in energy production and conversion. Pyruvate dehydrogenase is a crucial enzyme required for glucose metabolism, which converts pyruvate to acetyl CoA to complete the tricarboxylic acid cycle [Zm00001d052289 in the high-sugar content material was significantly higher than in the low-sugar content material  is required for the transferal of glucosylceramide [Zm00001d034759, which encodes an ACBD protein. The accessions carrying AX-116875096-CC had higher sugar content in the stalk  and multiple environments, which had higher credibility. We found that the accessions carrying AX-86257654-AA had higher talk sugar content than the accessions carrying AX-86257654-GG  encoding an MYB transcription factor was identified in multiple environments, and the SNP AX-86263525 was located in the exon of Zm00001d042287. The alleles of AX-86263525 had different effects on stalk sugar content and explained 15.16%–16.57% of the phenotypic variance. The accessions carrying AX-86263525-TT had higher stalk sugar content than the accessions carrying AX-86263525-CC  in the spring and autumn of 2020 and 2021. A completely randomized design with three replications was used in this study. Each replicate comprised two rows of each accession of 3 m in length, and the plant density was 0.75 m × 0.3 m. Field management  was applied uniformly throughout the growing period.Before tasseling, 10 plants of each accession exhibiting normal growth and development were selected for measurement of stalk sugar content and agronomic traits. Artificial pollination was performed to prevent pollen mixing. In the harvest season for fresh corn, eight plants per growth period were selected for measurement of plant height (PH), whole weight per plant (WWP), ear weight with bract (EWB), ear node (EN), sugar content of the stalk (SCS), sugar content in the lower ear parts (SCLEP), three-node sugar content in the ear (TNSCE), and sugar content in the upper ear parts (SCUEP). For estimation of the sugar content, we extracted the whole stem juice from each plant, and after thorough mixing, the sugar content was determined with a hand-held sugar meter . To minimize measurement error, the stem juice of each plant was measured three times, and the average was taken as the sugar content of each individual. Frequency distribution analysis, correlation analysis, and variance analysis of the data were performed with SPSS software (version 22.0).® Maize56K SNP Array, which includes 56,000 SNPs in total. After comparison with the corn genome (B73 RefGen_v4), SNP markers with minimum allele frequency >0.05 and missing data <30% were filtered using TASSEL software [The 188 sweet corn, waxy corn, and hybrid accessions were genotyped with the Axiomsoftware . Annotatsoftware . A totalhttps://itol.embl.de/ (accessed on 27 October 2022)). Principal component analysis (PCA) was conducted using GCTA software (version 1.92.0) [A phylogenetic tree was constructed from a data set of 36,069 SNPs using the maximum likelihood method in FastTree 2.1.9 . The tre 1.92.0) . The fir 1.92.0) . The rel 1.92.0) . Linkage 1.92.0) . A and πB, respectively) was calculated using a 100 kb window and a step size of 10 kb. Then, πA/πB was calculated and the windows, with the top 5% largest πA/πB values were identified as selective sweeps. In addition, the genetic differentiation (FST) among sweet corn (group A) and waxy corn (group B) was determined using a 100 kb window and a step size of 10 kb, and the windows with the top 5% largest FST values were identified as selective sweeps. To explore the function of selected genes located in the selective regions, the AgriGO analysis toolkit [The selective sweeps between sweet corn (group A) and waxy corn (group B) were detected using VCFtools . First,  toolkit  and the p-values from the association analysis [n and 1/n, where n is the number of SNPs [Plant height (PH), whole weight per plant (WWP), ear weight with bract (EWB), ear node (EN), sugar content of the stalk (SCS), sugar content in the lower ear parts (SCLEP), three-node sugar content in the ear (TNSCE), and sugar content in the upper ear parts (SCUEP) recorded in four environments were used for GWAS analysis. For the association analysis, a generalized linear model (GLM) and mixed linear model (MLM) were generated with TASSEL . Manhattanalysis . The sig of SNPs .GAPDH was used as the reference gene for the qRT-PCR. The original data were processed by the 2−ΔΔCt method, where Ct is the threshold cycle [To verify the expression levels of important candidate genes, total RNA was extracted from two fresh corn materials with significantly different stalk sugar content using the RNAprep Pure Plant Kit : the high-sugar content material LQ5 from a domestic hybrid second cycle line and the high-sugar content material SH23 from a domestic inbred line. A total of 1 ug RNA was used to synthesize cDNA according to the instructions of a PrimeScript RT Reagent Kit . The qRT-PCR reactions were performed through the SYBR-Green PrimeScript RT-PCR Kit (Takara) following the manufacturer’s instructions. ld cycle , and theIn this study, genome-wide association analysis was used to detect significant SNPs and candidate genes associated with stalk sugar content and agronomic traits. A total of 92 significant associated SNPs were identified. Among them, 24 significant SNPs and 22 candidate genes were identified in multiple environments, including CDPK, MYB transcription factor, pyruvate dehydrogenase, and so on. Most importantly, accessions differing in genotype for several significant SNPs showed significant phenotypic differences, indicating that the genetic variations in candidate genes might be closely related to the phenotypic traits. The expression levels of six important candidate genes were significantly different between the high-sugar content material and the low-sugar content material. In general, the significant genetic variations might be useful in molecular marker-assisted breeding, and the investigation of candidate genes may provide important insights into the mechanism of stalk sugar content in fresh corn, which establishes the foundation for fresh corn breeding. Future research should focus on illustrating how the genetic variations and candidate genes regulate the stalk sugar content in fresh corn, and concentrate on breeding new varieties with higher stalk sugar content.


However, the apical plugs formed by the AC method had significantly higher microleakage than those formed using the MAP and MC methods (p < 0.05). Conclusion: Within the limitations of this study, the sealing ability of the apical plugs formed by the MC method is comparable to the MAP method and better than the AC method.The purpose of this study was to compare the sealing ability and time required for the formation of Biodentine and mineral trioxide aggregation (MTA) apical plugs, using three different delivery methods: an amalgam carrier (AC), the Micro Apical Placement (MAP) System or a novel tool using a modified cannula (MC). Materials and Methods: A total of 60 uniformed molar roots were divided into three main groups, according to the technique of apical plug formation: AC, MAP, and MC. Each group was divided into two subgroups, according to the filling material used: MTA and Biodentine. A timer was used to calculate the required time for apical plug formation. After setting the filling materials, the apical microleakage of the formed plugs was quantified using the dye extraction method and spectrophotometry. The differences between the groups were analyzed using the one-way ANOVA and LSD post hoc tests. The significance level was set at 0.05. Results: No significant differences were reported in the time required to form the apical plugs in all groups ( The dentin formation and root development processes stop when an immature tooth undergoes pulp necrosis, due to dental caries or traumatic injuries. In such cases, the canal walls remain thin, and the root apex remains open. Consequently, achieving an adequate apical stop during canal preparation or obturation in these teeth is impaired, and the risk of extrusion of irrigants, medicaments, and root canal fillings beyond the apex is increased ,2.Apexification is a commonly used technique to close the apex by artificially inducing the formation of a calcified barrier, to allow condensation of the root filling materials and improve the apical seal ,4. HistoThe success of the apexification procedure depends on the ability to create a homogenous and well-condensed apical plug, and this was introduced as an alternative of apexification for the treatment of open apices. The material used in creating the apical plug plays a vital role in the success of the apexification procedure. Ideally, the used material should be biocompatible with the periradicular tissues, impervious to dissolution or breakdown by the tissue fluids after setting, and capable of being adapted as closely as possible to the dentinal walls. Several materials have been used to form apical plugs, and, nowadays, calcium silicate-based materials, such as mineral trioxide aggregate (MTA and Biodentine), are among the most used in apexification procedures to form apical plugs ,6,7,8.Introducing the material into the canal orifice using an amalgam carrier (AC) and plugging it to the apical third with handle pluggers ,9, largeSpecialized carriers, such as the Micro Apical Placement (MAP) System, deliver the material to the apical third of the canal and condense it with handle pluggers ,13,14.Using modified Thermafil carriers to deliver and condense the material in the apical third .Using prefabricated apical plugs .Proper delivery and condensation of the plug material to the apical third of the canal is another essential requirement for creating an apical barrier. Several methods have been described in the literature, including the following: The ideal method for the delivery and application of apical plug materials into the canal is still controversial. Furthermore, the availability and cost of specialized delivery systems may preclude their use in general dental clinics. Some authors suggest that the success of the apical plug technique could be influenced by the intracanal delivery technique .Therefore, the study aimed to introduce a novel tool (modified cannula—MC) for the formation of apical plugs and to compare the performance of the MC method with two commonly used methods (AC and MAP), concerning the sealing ability of apical plugs of MTA and Biodentine and the time required to form them. This study was approved by the Ethics Committee of Damascus University, Faculty of Dentistry (ID: 1403-9-3-2020). This article reports the findings of an experimental, in vitro portion of the study. The study used 60 recently extracted, permanent human maxillary and mandibular molars with at least a single, straight root with one canal. The teeth included here were extracted for orthodontic or periodontal reasons. The excluding criteria were teeth with cracks, open apices, internal or external resorption, or previous endodontic treatment. The teeth were decoronated by a 0.3 diamond disc  to obtain 60 roots with a standard length of 13 mm. The sample size was set based on a study by Adel et al. . Apical patency was established with a #10 K-file  until it appeared from the apical foramen, and the working length was recorded. The root canals were instrumented with Fanta AF3  and irrigated with 1.3% sodium hypochlorite solution using a 27G needle . The apical size of the canals was standardized by manual instrumentation to size #60 K-files  using the balanced force technique. The canals were irrigated with normal saline, filled with 1 mL QMix  for 1 min as final irrigation, and then the canals were dried with paper points .n = 10) were established . Each group was then divided into two subgroups (MTA and Biodentine). Samples were given numbers and were randomized using a randomization site. Therefore, six experimental groups  powder was mixed with 0.34 g distilled water for 30 s on a glass board using a metal spatula. Each capsule of Biodentine  powder was poured with 5 drops of Biodentine pipette liquid, and then it was mixed with an amalgamator  at 4000 rotation/min. The plug material was loaded in a regular-amalgam carrier (2 mm diameter)  and injected at the canal orifice as a big batch. Then, it was down packed into the middle third with a size 3 hand plugger , and then with a size 2 hand plugger into the apical third. The process was repeated until the apical plug placement was of 4 mm thickness.The MAP One system  includes one NiTi tip with a 1.10 mm diameter. The plug material was loaded into the carrier, delivered into the apical third in small batches, and condensed vertically with a pre-fitted hand plugger (size 2) until a 4 mm apical plug was created.Cannulas consist of a needle, catheter, wings, injection part cap, and Luer lock plug.A 2 mm cannula needle was cut with a diamond bur mounted on a high-speed handpiece perpendicularly to the vertical axis of the needle B.The catheter was cut until 1 mm of the clipped needle appeared outside to push out the entire amount of cement inside the catheter when it reached the apical third of the canal C.The clipped needle tip was closed with a silver weld to push the cement inside the catheter and condense the apical plug D.Plastic rings were fixed to the cannula wings, and Luer lock plugs with silicon to make them easier to catch and use E.The modified cannula was immersed in glutaraldehyde solution 2% for 24 h, then dried by air and kept in plastic wraps until use.An 18-gauge size intravenous cannula A was modThe modified cannula was loaded with the appropriate amount of plug material, delivered into the apical third, injected in small batches, and condensed vertically with the closed needle of the modified cannula. The process was repeated until the apical plug placement was of 4 mm thickness. All procedures were performed by a single specialist. After formation, a radiograph was taken of all apical plugs to ensure their quality . FinallyA digital timer  was used to calculate the time needed to load, place and condense both materials.® Scientific, Inc., Ocala, FL, USA)  at 37 °C for 72 h. Then, the samples were washed under running tap water to remove the dye traces, dried, and the nail varnish was removed with an ultrasonic scalpel tip. The sample was immersed in a test tube containing 1 mL of freshly prepared 65% nitric acid for 72 h to dissolve the root. Then, the obtained remaining solution was transferred to Eppendorf tubes   and centp < 0.05.The data were analyzed by a statistician blinded to the type of apical plug material and the delivery method using the SPSS software . The normal distribution of data was evaluated using the Shapiro–Wilk test. Since the data were normally distributed, the mean times required to form the apical plug and the mean microleakage scores in all groups were compared using a one-way ANOVA test with the significance level set at p = 0.12).p = 0.004) between the different groups, an LSD post hoc test was used for pairwise comparisons. The groups that used the AC methods (group 1: MTA-AC and group 2: Biodentine-AC) reported the highest apical leakage scores and significantly differed from the remaining groups.Calcium silicate-based materials are used in apical plugs. The apexification therapeutic approach was first introduced with the advent of MTA and, recently, Biodentine . BiodentAccording to previous studies, many tools have been used to transfer calcium silicate cement into the root canal and plug it to form an apical plug. However, they may not be available in the dental clinic, due to their high price or the difficulty obtaining them, so the main idea of this research is to present a new tool (MC) for forming apical plugs and compare the sealing ability of the formed plugs with that formed with AC or MS.Based on the commercially available MTA carriers in their various forms and high prices, it was found that intravenous cannulas are readily available tools that are cheap, disposable, and have a structure that is somewhat similar to the MTA carriers, with a reasonable price, where it is possible to make a simple adjustment to the cannula and sterilize it to use as calcium silicate-based cement carriers in the apical third of wide canals.The plastic tube of the catheter is made of flexible reinforced plastic that remains protected, without bending, when the MC is inserted into the apical third of the canal, and its closed needle plays a plugger role when it condenses the apical plug.This study included 60 intact straight roots of permanent molars that were extracted for orthodontic or periodontal reasons, and after cutting and preparing the roots, they had a uniform length and apex size.The QMix irrigant has good smear layer removal ability , and it Clinical studies on apical leakage and sealing continue to focus on intention because clinical failures still occur, despite the advances in endodontics. Most failures are probably attributed to the proliferation of bacteria that remain after chemical–mechanical preparation and cause apical lesions .Different methods were suggested to evaluate the sealing ability of the apical plug, such as dye penetration, fluid filtration, glucose penetration, bacterial leakage, and dye extraction ,26.The dye extraction method has reliable results in microleakage studies , where aA tight apical seal of the root canal system is an essential factor in the success of endodontic treatment . Both MTThe results of this study showed that there are differences in the microleakage of apical plugs when forming with different techniques. When forming apical plugs using handle pluggers, they transfer the compressed calcium silicate cement mass directly to the apical third, with minimal compacting, which reduces the fragmentation and deformation of the total mass of the material, which keeps it coherent and free of voids and gaps. The clinical work that used this technique demonstrates its value when working with children ,31 in clThe groups that showed the most microleakage around the apical plugs were the AC, whether MTA or Biodentine. The reason for this may be that the material does not reach the apical third directly, and the compressed calcium silicate cement mass emerging from the amalgam carrier may be subjected to deformation, fragmentation, and void and gab formation as it rubs against the rough surface of the dentin while plugging the material from the orifice to the apical third.The main limitation of this study is that its results cannot be compared with other in vitro studies results because this in vitro study is the first that studied the effect of tools that form apical plugs on its sealing ability; more clinical studies about this novel tool, to assess its quality, are warranted.Within the current study’s limitations, the material delivery system affects the sealing ability of the apical plugs. The MC delivery method is comparable to the MAP method and better than the AC method.