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利用回交导入系群体发掘水稻种质资源中的有利耐盐QTL
引用本文:孙勇,藏金萍,王韵,朱苓华,徐建龙,黎志康.利用回交导入系群体发掘水稻种质资源中的有利耐盐QTL[J].作物学报,2007,33(10):1611-1617.
作者姓名:孙勇  藏金萍  王韵  朱苓华  徐建龙  黎志康
作者单位:1 中国农业科学院作物科学研究所/农作物基因资源与遗传改良国家重大科学工程,北京100081; 2 International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
基金项目:引进国际先进农业科技计划(948计划)
摘    要:以中等感盐籼稻IR64与粳稻Tarom molaii培育的85个BC2F8回交导入系为材料,定位苗期在140 mmol L-1 NaCl胁迫下影响叶片盐害级别、幼苗存活天数、地上部和根部的K+、Na+浓度等6个耐盐相关性状的QTL。幼苗存活天数与地上部Na+浓度呈极显著负相关,与地上部K+浓度呈显著正相关,与根部K+、Na+浓度无关,表明叶片盐害是由于地上部Na+积累过多造成的。根部K+浓度与Na+浓度高度正相关,但与地上部的K+、Na+浓度均无关,表明根对K+、Na+的离子吸收与向地上部运输存在不同的机制。检测到影响6个耐盐相关性状的23个QTL,包括影响叶片盐害级别的5个、幼苗存活天数的6个、地上部K+浓度的4个、地上部Na+浓度的4个、根部K+浓度的1个和根部Na+浓度的3个。影响地上部K+、Na+浓度与影响根部K+、Na+浓度的QTL分布在不同基因组区域,进一步表明根和茎对K+、Na+的吸收存在不同的遗传机制。通过比较图谱,发现影响耐盐相关性状的23个QTL中有12个(占52.2%)与以往不同群体中影响耐盐相关性状的QTL定位在同一或相邻的染色体区域。其中在第2染色体RM240~RM112区间检测到1个影响地上部所有4个耐盐相关性状的主效QTL,其增加耐盐性的有利基因来自供体Tarom molaii,适宜用作标记辅助选择耐盐性的遗传改良。对从种质资源中发掘“隐蔽”耐盐QTL进行了讨论。

关 键 词:水稻  耐盐性  回交导入系  基因发掘
收稿时间:2007-02-12
修稿时间:2007-02-12

Mining Favorable Salt-tolerant QTL from Rice Germplasm Using a Backcrossing Introgression Line Population
Fotokian Mohammadhosein,SUN Yong,ZANG Jin-Ping,WANG Yun,ZHU Ling-Hua,Fotokian Mohammadhosein,XU Jian-Long,LI Zhi-Kang.Mining Favorable Salt-tolerant QTL from Rice Germplasm Using a Backcrossing Introgression Line Population[J].Acta Agronomica Sinica,2007,33(10):1611-1617.
Authors:Fotokian Mohammadhosein  SUN Yong  ZANG Jin-Ping  WANG Yun  ZHU Ling-Hua  Fotokian Mohammadhosein  XU Jian-Long  LI Zhi-Kang
Institution:1.Institute of Crop Sciences/ The National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China;2.International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
Abstract:Knowledge of the genetics of salt tolerance and mining of favorable alleles from germplasm should help develop rice varieties with high salt tolerance.QTLs affecting six salt-tolerance related traits including score of salt toxicity(SST),survival days of seedlings(SDS),shoot K concentration(SKC),shoot Na concentration(SNC),root K concentration(RKC),root Na concentration(RNC) were detected using 85 backcrossing introgression lines derived from a indica cultivar IR64 and a japonica upland cultivar Tarom Molaii from Iran under salt stress with the concentration of 140 mmol L-1 NaCl at the seedling stage.Continuous variation and transgression for all six traits were observed in the BIL population although there were only significant differences in SDS and SNC between the parents.Correlation analysis indicated that SDS had highly negative correlation with SNC and positive correlation with SKC but no correlations with RKC and RNC,suggesting that salt toxicity of leaves resulted from over-accumulation of Na in shoots.RKC highly positively correlated with RNC while both of them had no correlations with SKC and SNC,respectively,indicating different mechanisms in uptake of K and Na in roots and their transport from roots to shoots.Twenty-three QTLs for the six traits on the ten chromosomes except chromosomes 5 and 10 were identified by single-marker ANOVA using SAS PROC GLM,including 5 for SST,6 for SDS,4 for SKC,4 for SNC,1 for RKC,and 3 for RNC.Among them,the region of RM240-RM112 on chromosome 2 simultaneously affected SST,SDS,SKC,and SNC and the allele associated with improvement of salt tolerance was from Tarom Molaii.This QTL could be useful for improvement of salt tolerance through marker assisted selection.The QTLs affecting SKC and SNC didn't share the same genomic region with the QTLs for RKC and RNC,further confirming the view that different genetic mechanisms involved in uptake of K and Na between roots and shoots.By comparative mapping,12(52.2%) QTLs for the six related traits located in the same or near genome regions on chromosomes 1,2,3,7,and 9 with the QTLs previously identified in different mapping populations.The advantages of mapping QTLs using BILs and strategy of mining 'hidden' salt-tolerant main-effect QTL from rice germplasm were discussed.
Keywords:Rice  Salt-tolerance  Backcrossing introgression lines  Gene mining
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