CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Key Laboratory for Zhejiang Super Rice Research, China National Rice Research Institute, Hangzhou, China.
PLoS One. 2023 Apr 5;18(4):e0283989. doi: 10.1371/journal.pone.0283989. eCollection 2023.
Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection/prediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding.
直播已被广泛应用于水稻生产中，是一种经济且省力的技术，但存在着出苗率低、出苗不整齐和抗倒伏能力差等问题。目前，通过增加播种量可以部分克服这些问题，但对于杂交稻来说，由于种子成本高，这种方法是不可接受的。通过育种来提高直播稻的性能被认为是解决这些问题的最终方法。对于杂交稻的育种，通过表型评价从杂交的大量父本和母本群体中鉴定出优良的杂交种是繁琐且昂贵的。相比之下，基因组选择/预测（GS/ GP）可以利用基因组数据有效地检测出优良的杂交种，这在植物杂交种的育种中具有很大的潜力。在这项研究中，我们利用 402 个水稻自交系和 401 个杂交种来研究 GS 对水稻中胚轴长度的有效性，中胚轴长度是直播适宜性的一个代表性指示性状。研究了几种 GP 方法和训练集设计，以寻求杂交种预测的最佳方案。结果表明，使用半同胞杂种作为训练集，并将所有亲本系的表型作为协变量进行拟合，可以最优地预测中胚轴长度。根据全基因组关联研究，将分子标记分为与性状相关和不相关的两组，可以进一步提高预测精度。本研究表明，GS 可以成为一种有效的水稻直播杂交种育种方法。
