Dataset Information

Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa (Medicago sativa L.).

ABSTRACT: Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.

SUBMITTER: Medina CA

PROVIDER: S-EPMC7247575 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa (<i>Medicago sativa</i> L.).

Medina Cesar Augusto CA Hawkins Charles C Liu Xiang-Ping XP Peel Michael M Yu Long-Xi LX

International journal of molecular sciences 20200509 9

Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by g ...[more]

PMID: 32397526

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Project description:Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Project description:BackgroundSoil salinity is an important factor affecting growth, development, and productivity of almost all land plants, including the forage crop alfalfa (Medicago sativa). However, little is known about how alfalfa responds and adapts to salt stress, particularly among different salt-tolerant cultivars.ResultsAmong seven alfalfa cultivars, we found that Zhongmu-1 (ZM) is relatively salt-tolerant and Xingjiang Daye (XJ) is salt-sensitive. Compared to XJ, ZM showed slower growth under low-salt conditions, but exhibited stronger tolerance to salt stress. RNA-seq analysis revealed 2237 and 1125 differentially expressed genes (DEGs) between ZM and XJ in the presence and absence of salt stress, among which many genes are involved in stress-related pathways. After salt treatment, compared with the controls, the number of DEGs in XJ (19373) was about four times of that in ZM (4833). We also detected specific differential gene expression patterns: In response to salt stress, compared with XJ, ZM maintained relatively more stable expression levels of genes related to the ROS and Ca2+ pathways, phytohormone biosynthesis, and Na+/K+ transport. Notably, several salt resistance-associated genes always showed greater levels of expression in ZM than in XJ, including a transcription factor. Consistent with the suppression of plant growth resulting from salt stress, the expression of numerous photosynthesis- and growth hormone-related genes decreased more dramatically in XJ than in ZM. By contrast, the expression levels of photosynthetic genes were lower in ZM under low-salt conditions.ConclusionsCompared with XJ, ZM is a salt-tolerant alfalfa cultivar possessing specific regulatory mechanisms conferring exceptional salt tolerance, likely by maintaining high transcript levels of abiotic and biotic stress resistance-related genes. Our results suggest that maintaining this specific physiological status and/or plant adaptation to salt stress most likely arises by inhibition of plant growth in ZM through plant hormone interactions. This study identifies new candidate genes that may regulate alfalfa tolerance to salt stress and increases the understanding of the genetic basis for salt tolerance.

Project description:Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.

Dataset Information

Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa (Medicago sativa L.).

Publications

Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa (<i>Medicago sativa</i> L.).

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