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.
Environment-specific quantitative trait loci (QTL) refer to QTL that express differently in different environments, a phenomenon called QTL-by-environment (Q 3 E) interaction. Q 3 E interaction is a difficult problem extended from traditional QTL mapping. The mixture model maximum-likelihood method is commonly adopted for interval mapping of QTL, but the method is not optimal in handling QTL interacting with environments. We partitioned QTL effects into main and interaction effects. The main effects are represented by the means of QTL effects in all environments and the interaction effects are represented by the variances of the QTL effects across environments. We used the Markov chain Monte Carlo (MCMC) implemented Bayesian method to estimate both the main and the interaction effects. The residual error covariance matrix was modeled using the factor analytic covariance structure. A simulation study showed that the factor analytic structure is robust and can handle other structures as special cases. The method was also applied to Q 3 E interaction mapping for the yield trait of barley. Eight markers showed significant main effects and 18 markers showed significant Q 3 E interaction. The 18 interacting markers were distributed across all seven chromosomes of the entire genome. Only 1 marker had both the main and the Q 3 E interaction effects. Each of the other markers had either a main effect or a Q 3 E interaction effect but not both.tion is a very important phenomenon in quantitative genetics. With the advanced molecular technology and statistical methods for quantitative trait loci (QTL) mapping (Lander and Botstein 1989;Jansen 1993;Zeng 1994), G 3 E interaction analysis has shifted to QTL-by-environment (Q 3 E ) interaction. In the early stage of QTL mapping, almost all statistical methods were developed in a single environment (Paterson et al. 1991;Stuber et al. 1992). Data from different environments were analyzed separately and the conclusions were drawn from the separate analyses of QTL across environments. These methods do not consider the correlation of data under different environments and thus may not extract maximum information from the data. Composite interval mapping for multiple traits can be used for Q 3 E interaction if different traits are treated as the same trait measured in different environments ( Jiang and Zeng 1995). This multivariate composite interval mapping approach makes good use of all data simultaneously and increases statistical power of QTL detection and accuracy of the estimated QTL positions. However, the number of parameters of this method increases dramatically as the number of environments increases. Therefore, the method may not be applied when the number of environments is large. Several other models have been proposed to solve the problem of a large number of environments ( Jansen et al. 1995;Beavis and Keim 1996;Romagosa et al. 1996). These methods were based on some special situations and assumptions. One typical assumption was independent errors or constant va...
Genotype by environment interaction is a phenomenon that a better genotype in one environment may perform poorly in another environment. When the genotype refers to a quantitative trait locus (QTL), this phenomenon is called QTL by environment interaction, denoted by Q×E. Using a recently developed new Bayesian method and genome-wide marker information, we estimated and tested QTL main effects and Q×E interactions for a well-known barley dataset produced by the North American Barley Genome Mapping Project. This dataset contained seven quantitative traits collected from 145 doubled-haploid (DH) lines evaluated in multiple environments, which derived from a cross between two Canadian two-row barley lines, Harrington and TR306. Numerous main effects and Q×E interaction effects have been detected for all seven quantitative traits. However, main effects seem to be more important than the Q×E interaction effects for all seven traits examined. The number of main effects detected varied from 26 for the maturity trait to 75 for the heading trait, with an average of 61.86. The heading trait has the most detected effects, with a total of 98 (75 main, 29 Q×E). Among the 98 effects, 6 loci had both the main and Q×E effects. Among the total number of detected loci, on average, 78.5% of the loci show the main effects whereas 34.9% of the loci show Q×E interactions. Overall, we detected many loci with either the main or the Q×E effects, and the main effects appear to be more important than the Q×E interaction effects for all the seven traits. This means that most detected loci have a constant effect across environments. Another discovery from this analysis is that Q×E interaction occurs independently, regardless whether the locus has main effects.
Chromosome segment substitution lines (CSSLs) are powerful tools to combine naturally occurring genetic variants with favorable alleles in the same genetic backgrounds of elite cultivars. An elite CSSL Z322-1-10 was identified from advanced backcrosses between a japonica cultivar Nipponbare and an elite indica restorer Xihui 18 by SSR marker-assisted selection (MAS). The Z322-1-10 line carries five substitution segments distributed on chromosomes 1, 2, 5, 6 and 10 with an average length of 4.80 Mb. Spikilets per panicle, 1000-grain weight, grain length in the Z322-1-10 line are significantly higher than those in Nipponbare. Quantitative trait loci (QTLs) were identified and mapped for nine agronomic traits in an F 3 population derived from the cross between Nipponbare and Z322-1-10 using the restricted maximum likelihood (REML) method in the HPMIXED procedure of SAS. We detected 13 QTLs whose effect ranging from 2.45% to 44.17% in terms of phenotypic variance explained. Of the 13 loci detected, three are major QTL (qGL1, qGW5-1 and qRLW5-1) and they explain 34.68%, 44.17% and 33.05% of the phenotypic variance. The qGL1 locus controls grain length with a typical Mendelian dominance inheritance of 3:1 ratio for long grain to short grain. The already cloned QTL qGW5-1 is linked with a minor QTL for grain width qGW5-2 (13.01%) in the same substitution segment. Similarly, the previously reported qRLW5-1 is also linked with a minor QTL qRLW5-2. Not only the study is important for fine mapping and cloning of the gene qGL1, but also has a great potential for molecular breeding.
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