Abstract
BackgroundHybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance.ResultsFrom the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively.ConclusionsThis study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.
Highlights
Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue
In order to determine the utility of using Genomic selection (GS) to guide hybrid breeding in rice, we evaluated the accuracy of genomic prediction for hybrid performance in a rice NC North Carolina II (II) population where 115 inbred varieties were crossed with five male sterile lines using six representative methods including genomic best linear unbiased prediction (GBLUP), least absolute shrinkage and selection operation (LASSO), BayesB, partial least square (PLS), support vector machine (SVM) and Reproducing kernel Hilbert space (RKHS)
The predictability is highly correlated to the heritability of the trait with thousandgrain weight (TGW) and plant height (PH) having the highest predictabilities across all methods, followed by traits panicle length (PL) and secondary branch number (SB), with trait grain yield per plant (GY) and PN being the worst predictable traits
Summary
Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. We used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. Xu et al (2014) provided a proof of concept for hybrid prediction using an immortalized F2 population in rice and found that selection of top 100 hybrids would lead to a 16% increase in yield, which indicated the potential of using GS to improve yield in rice. In order to overcome this limitation, we constructed a training population of rice for GS according to the NC II design where 115 inbred varieties were crossed with five male sterile lines. The 120 inbred parents were genotyped and 575 hybrids were measured for eight agronomic traits
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