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      Molecular Tools and Their Applications in Developing Salt-Tolerant Soybean (Glycine max L.) Cultivars.

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          Abstract

          Abiotic stresses are one of the significant threats to soybean (Glycine max L.) growth and yields worldwide. Soybean has a crucial role in the global food supply chain and food security and contributes the main protein share compared to other crops. Hence, there is a vast scientific saddle on soybean researchers to develop tolerant genotypes to meet the growing need of food for the huge population. A large portion of cultivated land is damaged by salinity stress, and the situation worsens yearly. In past years, many attempts have increased soybean resilience to salinity stress. Different molecular techniques such as quantitative trait loci mapping (QTL), genetic engineering, transcriptome, transcription factor analysis (TFs), CRISPR/Cas9, as well as other conventional methods are used for the breeding of salt-tolerant cultivars of soybean to safeguard its yield under changing environments. These powerful genetic tools ensure sustainable soybean yields, preserving genetic variability for future use. Only a few reports about a detailed overview of soybean salinity tolerance have been published. Therefore, this review focuses on a detailed overview of several molecular techniques for soybean salinity tolerance and draws a future research direction. Thus, the updated review will provide complete guidelines for researchers working on the genetic mechanism of salinity tolerance in soybean.

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          Most cited references125

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          Principal components analysis corrects for stratification in genome-wide association studies.

          Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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            An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

            Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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              Mixed linear model approach adapted for genome-wide association studies.

              Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.
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                Author and article information

                Journal
                Bioengineering (Basel)
                Bioengineering (Basel, Switzerland)
                MDPI AG
                2306-5354
                2306-5354
                Sep 22 2022
                : 9
                : 10
                Affiliations
                [1 ] College of Agronomy, Hunan Agricultural University, Changsha 410128, China.
                [2 ] Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou 350002, China.
                [3 ] Department of Agronomy, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan.
                [4 ] Research Center on Ecological Sciences, Jiangxi Agricultural University, Nanchang 330045, China.
                [5 ] Department of Biology, Al-Jumum University College, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
                Article
                bioengineering9100495
                10.3390/bioengineering9100495
                9598088
                36290463
                3b8f42d4-be16-44f2-ab8f-67a312de05d8
                History

                CRISPR/Cas9,QTL mapping,abiotic stress,climate change,genetic engineering,legumes,omics

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