Abstract
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.
Highlights
Alfalfa (Medicago sativa L) is the first most-important forage legume in the world, because of its high biomass yield (BY) and good nutritional quality
We investigated the impact of three Bayes statistical methods on the prediction accuracies of alfalfa agronomic and quality traits with genotypic data obtained by GBS
Since our previous works have described the phenotypic variations of some fiber-related traits (Wang et al, 2016) and crude protein and mineral elements (Jia et al, 2017), we will not describe them in this study
Summary
Alfalfa (Medicago sativa L) is the first most-important forage legume in the world, because of its high biomass yield (BY) and good nutritional quality. To meet the future demand of quantity and quality, the main objectives in alfalfa breeding programs are biomass related agronomic traits and nutrition value related quality traits. Though yield and quality of alfalfa have been improved by phenotypic selection, the genetic gain are relatively low compared to other crops, owing to many reasons, such as low heritability, complex genetic architecture, and high genotypeenvironment interaction (Annicchiarico et al, 2015a). It is emergent that new breeding strategies should be introduced into alfalfa breeding programs to accelerate the genetic gain of targeted traits and to meet the increasing demands of forage production. Many markers in linkage disequilibrium (LD) with QTLs contributed to targeted traits are needed to realize a relatively high prediction accuracy
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