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    王思齐, 李海洋, 李荣华, 夏岩石, 张振臣, 袁清华, 郭培国. 烟草青枯病抗病的动态QTL分析[J]. 中国烟草科学, 2020, 41(3): 1-8. DOI: 10.13496/j.issn.1007-5119.2020.00.003
    引用本文: 王思齐, 李海洋, 李荣华, 夏岩石, 张振臣, 袁清华, 郭培国. 烟草青枯病抗病的动态QTL分析[J]. 中国烟草科学, 2020, 41(3): 1-8. DOI: 10.13496/j.issn.1007-5119.2020.00.003
    WANG Siqi, LI Haiyang, LI Ronghua, XIA Yanshi, ZHANG Zhenchen, YUAN Qinghua, GUO Peiguo. Dynamic QTL Analysis for Bacterial Wilt Resistance in Tobacco[J]. CHINESE TOBACCO SCIENCE, 2020, 41(3): 1-8. DOI: 10.13496/j.issn.1007-5119.2020.00.003
    Citation: WANG Siqi, LI Haiyang, LI Ronghua, XIA Yanshi, ZHANG Zhenchen, YUAN Qinghua, GUO Peiguo. Dynamic QTL Analysis for Bacterial Wilt Resistance in Tobacco[J]. CHINESE TOBACCO SCIENCE, 2020, 41(3): 1-8. DOI: 10.13496/j.issn.1007-5119.2020.00.003

    烟草青枯病抗病的动态QTL分析

    Dynamic QTL Analysis for Bacterial Wilt Resistance in Tobacco

    • 摘要: 为检测控制烟草青枯病抗性动态变化的QTLs,以大叶密合×长脖黄建立的158份F6代重组自交系(RIL)群体为研究对象,利用SSR和InDel标记进行基因分型,并运用JoinMap 4构建一个含有24个连锁群、覆盖2269.3 cM的遗传图谱;该图谱含有546个SSR和80个InDel标记,平均标记密度达到了3.63 cM/标记。结合2016和2017连续两年不同调查期各株系的病情指数,使用WinQTLcart 2.5软件的复合区间作图法(CIM)进行QTL定位,在2016年的4个调查期中分别检测到4、4、6和4个青枯病抗病QTLs,其表型变异解释率在5.03%~13.07%之间;而在2017年的4个调查期分别定位到7、3、6和6个青枯病抗病QTLs,其表型变异解释率在4.63%~18.18%之间。两年共检测到28个青枯病抗病QTLs,其中有7个QTLs在不同调查期中被重复定位,但没有一个QTL可以在所有调查期中出现;另外,不同调查期检测到QTL的数目与表达效应存在较大差异。这些结果表明烟草在发病的不同阶段可能有不同的抗性基因发挥作用,且其表达具有一定的时序性。

       

      Abstract: In order to detect the QTLs controlling the dynamic change of tobacco bacterial wilt (TBW) resistance, 158 F6 recombinant inbred lines (RIL) derived from a cross between Dayemihe and Changbohuang were selected and genotyped with SSR and InDel markers, and a genetic map with 24 linkage groups was constructed by JoinMap 4 software. The map contained 546 SSR and 80 InDel markers and covered 2269.3 cM with a mean distance of 3.63 cM between adjacent markers. QTL mapping for TBW resistance was performed by WinQTLcart 2.5 software using the composite interval mapping (CIM) method based on TBW disease indexes at different survey time points in 2016 and 2017. 4, 4, 6 and 4 TBW resistance QTLs were detected at the four survey time points in 2016, respectively; the phenotypic variances of these QTLs ranged from 5.03% to 13.07%. At the four survey time points in 2017, 7, 3, 6 and 6 QTLs for TBW resistance were detected, respectively, and their phenotypic variances varied between 4.63% and 18.18%. A total of 28 QTLs were identified in two years, seven of them were detected repeatedly in different survey time points, but none could be found at all survey time points in both years. Moreover, the number and additive effect of TBW resistance QTLs had significant changes at four survey time points in both years. These results indicated that tobacco may utilize different TBW resistance genes at different stages during the pathogenesis process, and the expressions of these genes were time-dependent and sequential.

       

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