Genome-wide identification and characterization of Puccinia striiformis-responsive lncRNAs in Triticum aestivum

Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici (Pst) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines—rust resistance line FLW29 and rust susceptible line PBW343—which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst-resistant and Pst-susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease

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Not AvailableThe Double Ranked Set Sampling (DRSS) procedure has been extended to the case of estimation of finite population mean under classical approach of survey sampling. In view of the complexity of the theoretical framework, the sample sizes are restricted to ‘2’. Using real data, it is empirically demonstrated that an estimator based on DRSS procedure performs better than estimators based on the Ranked Set Sampling (RSS) procedure and Simple Random Sampling (SRS) respectively.Not Availabl

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Not AvailableIt is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.Not Availabl

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Not AvailableIt is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.Not Availabl

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Not AvailableBioinformatics plays a significant role in the development of many fields of biological science and plant genetic resources (PGR) is one of them. With the advent of high throughput sequencing technology, bioinformatics continues to make considerable progress in biology by providing scientists with access to the genomic information. There are many areas of plant genetic resources such as development of core set, trait associated gene discovery, genetic diversity analysis, Genome Wide Association Studies (GWAS), phylogenetic and evolutionary analysis, database development and its management etc. where bioinformatics plays important roles. Main role of bioinformatics is to provide computational algorithm and software tools to accelerate the research of PGR. Bioinformatics is a new paradigm in the genomic research, which provides PGR research a great thrust. However, there are many areas such as pan genomics, multi locus GWAS, Genomic Selection with epistasis effects where bioinformatics can play better role.Not Availabl

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Not AvailableIt is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.Not Availabl

Estimation of Error Variance in Genomic Selection for Ultrahigh Dimensional Data

Estimation of error variance in the case of genomic selection is a necessary step to measure the accuracy of the genomic selection model. For genomic selection, whole-genome high-density marker data is used where the number of markers is always larger than the sample size. This makes it difficult to estimate the error variance because the ordinary least square estimation technique cannot be used in the case of datasets where the number of parameters is greater than the number of individuals (i.e., p > n). In this article, two existing methods, viz. Refitted Cross Validation (RCV) and kfold-RCV, were suggested for such cases. Moreover, by considering the limitations of the above methods, two new methods, viz. Bootstrap-RCV and Ensemble method, have been proposed. Furthermore, an R package “varEst” has been developed, which contains four different functions to implement these error variance estimation methods in the case of Least Absolute Shrinkage and Selection Operator (LASSO), Least Squares Regression (LSR) and Sparse Additive Models (SpAM). The performances of the algorithms have been evaluated using simulated and real datasets

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Not AvailableGenomic selection is a very recent area of study in case of molecular breeding of livestock or crop species. There are various statistical models available for genomic selection. The performances of these models depend on several factors like sampling population, genetic architecture of target species, statistical models as well as missing genotypes. Missing genotype is very common problem in high throughput sequencing data. These missing genotypes are necessary to be imputed in order to implement the genomic selection models. Different statistical models of genomic selection behave differently in imputed data. So, it is highly imperative to evaluate the performances of statistical models under different levels of imputations to know the behavior of the models. In this article, performance of three statistical models viz. Sparse Additive Models (SpAM), Hilbert-Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator (HSIC LASSO) and Integrated Model for genomic selection are compared after incorporating the various degree of imputation (0, 2, 5 and 10%) in the real data. Results indicate that integrated model is found to be more robust against the level of imputation of the genotypic data.Not Availabl

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Not AvailableGenomic selection is a modified form of marker-assisted selection in which the markers from the whole genome are used to estimate the genomic-estimated breeding value (GEBV). Several estimators are available to estimate GEBV. These estimators are able to capture either additive genetic effects or nonadditive genetic effects. However, there is hardly any procedure available that could capture both the effects simultaneously. Therefore, this study has been conducted to develop an integrated framework that is able to capture both additive and nonadditive effects efficiently. This integrated framework has been developed after evaluating existing additive and nonadditive models for marker selection. Furthermore, two efficient additive and nonadditive methods, that is, sparse additive models (SpAM) and Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO), have been combined to select both additive and nonadditive genetic markers for estimation of GEBV. The performance of the proposed framework has been evaluated on the basis of prediction accuracy, fraction of correctly selected features, and redundancy rate, along with standard error of mean for estimation of GEBV, compared with the individual performances of SpAM and HSIC LASSO separately. The newly developed framework is found to be satisfactory in terms of its performance and found to be robust for estimation of GEBV.Not Availabl

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Not AvailableGenomic Selection is a modern technique used to improve the genetic gain through breeding program. Genomic selection can be done both on single trait and multi trait data. Multi-trait genomic selection is better than single-trait genomic selection if genetic correlation between the traits is observed. However, multi-trait genomic selection has little been explored in the view of non-linearity problem between responses (traits) and explanatory variables (markers). In this study, problem of non-linear relation between markers and traits has been taken care in Multi Trait Genomic Selection (MTGS) framework. First of all, a comprehensive comparison between methods of single trait and multi-trait genomic selection has been made by using real data set. It has been observed that MTGS methods are better than STGS methods. In Genomic Selection, number of markers are very large which creates the large p and small n problem (n<<p). Many feature selection techniques are proposed to select the important markers associated with the traits. Among these techniques penalized regression like Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net etc. are most popular as they deal with n<p problem. But being a linear technique, they are unable to handle the problem of non-linear input-output dependency. Kernelised LASSO resolves the issues of n<<p as well as non-linearity problem simultaneously. We have used this technique in Multi-trait Genomic Selection (MTGS) and demonstrated it with real genomic and phenotypic data. Performance of the proposed method has been found to be fairly impressive. We have also developed web based software which has been named as “Genomic Selection Tool (GST)”. This on-line tool has been implemented the proposed method for prediction of genome estimated breeding value (GEBVs). This tool requires genotypic and phenotypic data belongs to different traits. This tool, predicts genome estimated breeding values for single trait as well as multi-traits. We have incorporated some of existing single trait and multi-traits methods along with our proposed method in this tool. GEBVs predicted by this tool may provide useful postulate for breeders for selection of important variety or breed