结合辅助性状的玉米全基因组选择预测力评估  

Predictability of maize genome-wide selection combined with auxiliary traits

作　　者：焦宇馨 张宇翔 杨文艳 经思宇 尹玉琳 刘畅 王欣 徐辰武[1,2] 徐扬 JIAO Yu-xin;ZHANG Yu-xiang;YANG Wen-yan;JING Si-yu;YIN Yu-lin;LIU Chang;WANG Xin;XU Chen-wu;XU Yang(Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genetics and Physiology,Agricultural College of Yangzhou University,Yangzhou 225009,China;Jiangsu Co-innovation Center for Modern Production Technology of Grain Crops,Yangzhou University,Yangzhou 225009,China)

机构地区：[1]江苏省作物基因组学和分子育种重点实验室/植物功能基因组学教育部重点实验室/江苏省作物遗传生理重点实验室,扬州大学农学院,江苏扬州225009 [2]江苏省粮食作物现代产业技术协同创新中心,扬州大学,江苏扬州225009

出　　处：《江苏农业学报》2023年第2期313-320,共8页Jiangsu Journal of Agricultural Sciences

基　　金：国家自然科学基金项目(32170636、32061143030);江苏省重点研发计划项目(BE2022343);江苏省种业振兴揭榜挂帅项目[JBGS(2021)009];江苏省高等学校大学生创新创业训练计划项目(202111117029Z)。

摘　　要：多性状联合全基因组选择能够有效利用性状间的遗传相关和环境相关,有望提高表型预测的准确性。本研究提出了结合辅助性状的全基因组选择策略,以来源广泛的342份玉米自交系为试验材料,对其进行基因分型测序(GBS)并分析其农艺性状,对每个目标性状均基于辅助性状及其组合进行预测,利用五倍交叉验证法评价其预测力。结果表明,利用与目标性状相关性较高的辅助性状可较大程度地提升预测力,尤其是对于低遗传力性状;随着辅助性状个数的增加,预测力也随之增加。进一步比较了5种统计模型结合辅助性状的全基因组选择的表型预测力,总体而言,再生核希尔伯特空间(RKHS)模型和贝叶斯B(BayesB)模型的预测效果较优,而极端梯度提升(XGBOOST)模型的预测效果较差。本研究结合辅助性状有效提高了玉米全基因组选择的预测准确性,为玉米的全基因组选择育种提供新的思路和参考。Multi-trait genomic selection can use genetic and environmental correlations between traits,which holds great promise to improve the prediction accuracy.This study proposed a genomic prediction strategy using auxiliary traits.A total of 342 maize inbred lines from a diversity panel were used as test materials.Genotyping by sequencing(GBS)was performed and six agronomic traits were measured in the field.Each target trait was predicted based on auxiliary traits and their combinations.The predictability was evaluated using five-fold cross-validation.The results showed that the use of auxiliary traits highly correlated with target traits greatly improved predictability and low-heritability traits could benefit more from auxiliary traits.As the number of auxiliary traits increased,the predictability also increased.We also compared the prediction performance of five different models combined with auxiliary traits.Overall,reproducing kernel Hilbert space(RKHS)model and BayesB model performed well,while extreme gradient boosting(XGBOOST)model performed worst.This study improves the accuracy of genomic prediction and provides new ideas and references for genomic selection breeding of maize.

关 键 词：玉米 全基因组选择 辅助性状 预测力 

分 类 号：S513[农业科学—作物学] Q943[生物学—植物学]