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Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

ABSTRACT: Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.

SUBMITTER: Muvunyi BP

PROVIDER: S-EPMC9257107 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

Project description:In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Project description:BackgroundBacterial grain rot (BGR), caused by the bacterial pathogen Burkholderia glumae, is a destructive disease of rice. Because BGR tends to be highly affected by environmental conditions such as temperature and humidity, it is difficult to evaluate BGR resistance of diverse cultivars with different heading dates by using field inoculation. Molecular tagging of genes involved in BGR is an important objective for rice breeding.ResultsIn this study, we mapped a quantitative trait locus (QTL) for BGR resistance by a modified cut-panicle inoculation method. First, we assessed the levels of BGR resistance in 84 cultivars by a standard cut-panicle inoculation technique, in which panicles are harvested and inoculated in the laboratory under controlled conditions. For the genetic analysis, we selected two cultivars: Kele, a resistant traditional lowland cultivar (indica) that originated in India, and Hitomebore, a susceptible modern lowland cultivar (temperate japonica) from Japan. Second, by comparing the susceptibility of Kele and Hitomebore spikelets before and up to 3 days after anthesis, we found a dramatic decline in susceptibility at 1 day after anthesis in Kele but not in Hitomebore. Thus, we applied a modified method by inoculating spikelets at 1 day after anthesis for further analysis. To search for QTLs associated with BGR resistance, we measured the ratio of diseased spikelets (RDS, an index reflecting both quantity and severity of infection) and the ratio of diseased spikelet area (RDSA) in 110 backcrossed inbred lines (BILs) derived from a cross between Kele and Hitomebore. One major QTL associated with both RDS and RDSA was detected on the long arm of chromosome 1. This QTL explained 25.7% and 12.1% of the total phenotypic variance in RDS and RDSA in the BILs, respectively, and the Kele allele increased BGR resistance.ConclusionsWe mapped a major QTL for BGR resistance on the long arm of chromosome 1. These results clearly demonstrated that genetic analysis of BGR resistance in rice can be effectively performed and that this trait could be a target of marker-assisted selection in rice breeding programs.

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Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

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