[1]章慧敏,张舒钰,宋旭东,等.糯玉米茎秆穿刺强度QTL分析与基因组选择[J].江苏农业学报,2024,(07):1191-1198.[doi:doi:10.3969/j.issn.1000-4440.2024.07.005]
　ZHANG Huimin,ZHANG Shuyu,SONG Xudong,et al.QTL analysis and genomic selection of rind penetrometer resistance in waxy maize[J].,2024,(07):1191-1198.[doi:doi:10.3969/j.issn.1000-4440.2024.07.005]
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糯玉米茎秆穿刺强度QTL分析与基因组选择()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年07期
页码:
1191-1198
栏目:
遗传育种·生理生化
出版日期:
2024-07-30

文章信息/Info

Title:
QTL analysis and genomic selection of rind penetrometer resistance in waxy maize
作者:
章慧敏1; 张舒钰1; 宋旭东1; 张振良1; 陆虎华1; 陈国清1; 2; 郝德荣1; 冒宇翔1
(1.江苏沿江地区农业科学研究所,江苏南通226012；2.江苏省现代作物生产协同创新中心,江苏南京210095)
Author(s):
ZHANG Huimin1; ZHANG Shuyu1; SONG Xudong1; ZHANG Zhenliang1; LU Huhua1; CHEN Guoqing1; 2; HAO Derong1; MAO Yuxiang1
(1.Jiangsu Yanjiang Institute of Agricultural Science, Nantong 226012, China;2.Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing 210095, China)
关键词:
糯玉米; 茎秆穿刺强度; 数量性状位点; 基因组选择
Keywords:
waxy maize; rind penetrometer resistance; QTL(quantitative trait locus); genomic selection
分类号:
S513
DOI:
doi:10.3969/j.issn.1000-4440.2024.07.005
文献标志码:
A
摘要:
茎秆穿刺强度是衡量玉米茎秆机械强度和抗倒伏能力的重要指标之一,本研究以衍生于糯玉米自交系衡白522和通系5的198个重组自交系为试验材料,对茎秆穿刺强度进行数量性状位点(QTL)分析和基因组选择研究。单个环境QTL分析共检测到4个控制糯玉米茎秆穿刺强度的QTL,每个QTL的表型变异贡献率均小于10.00%,且仅在单个环境中被检测到；多个环境QTL分析共检测到8个QTL与环境互作,其加性效应总共可解释24.64%的表型变异,加性效应与环境互作贡献率为17.51%；上位性QTL分析共检测到4对QTL与QTL互作,可解释8.25%的表型变异。基因组选择中,当训练群体占群体总数的80%,随机选择500个标记即可获得较高的预测准确性；但是根据单个环境QTL分析结果,选择机率常用对数值排名前200的标记,即可大幅度提高基因组选择预测准确性。
Abstract:
Rind penetrometer resistance (RPR) is an important index that can be used to measure mechanical strength and lodging-resistance capability of maize stalk. In this study, a total of 198 recombinant inbred lines developed from waxy maize inbred lines Hengbai 522 and Tongxi 5 were used as test materials to perform quantitative trait locus (QTL) analysis and genomic selection (GS) study of RPR. Four QTLs for waxy maize PRP were detected by individual environmental QTL analysis, and contribution rate of phenotypic variation for each QTL was below 10.00% and was detected only in individual environment. Eight QTLs were detected to interact with the environment by multiple environmental QTL analysis, the additive effect could explain a total of 24.64% phenotypic variations, and the contribution rate of interaction between additive QTL and environment was 17.51%. Four pairs of QTL-QTL interactions were detected by epistatic QTL analysis, which could explain 8.25% of the phenotypic variation. In GS, when the training population size occupied 80% of the total population size, a relatively high prediction accuracy could be obtained by selecting 500 markers randomly. However, the genome selection prediction accuracy could be significantly improved by using top 200 markers for logarithm of odds values based on QTL analysis results of individual environmental.

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备注/Memo

备注/Memo:
收稿日期：2023-08-11基金项目：江苏省科协青年科技人才托举工程项目(TJ-2023-052)；江苏省种业振兴揭榜挂帅项目(JBGS
[2021]054)；江苏省农业科技自主创新基金项目[CX(23)1043]；江苏省重点研发计划项目(BE2022343)；江苏现代农业产业技术体系建设专项作者简介：章慧敏(1993-),女,安徽铜陵人,硕士,助理研究员,主要从事糯玉米遗传育种研究。(E-mail)2240114725@qq.com通讯作者：周广飞,(E-mail)gfzhou88@jaas.ac.cn
更新日期/Last Update: 2024-09-14