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氮胁迫条件下玉米籽粒和秸秆品质及N、P、K含量的QTL分析

QTL Analysis of Nutrient and N、P、K Components in Maize under Nitrogen Stress

【作者】 王艳朋

【导师】 刘宗华;

【作者基本信息】 河南农业大学 , 作物遗传育种, 2008, 硕士

【摘要】 本研究以玉米杂交种农大108及其F2:3和F2:4家系为材料,研究了施氮和不施氮两种处理对玉米籽粒和秸秆品质以及N、P、K含量的影响,并对有关基因进行了QTL分析,本研究主要结论如下:1)构建了包含194个SSR标记,覆盖玉米10条染色体的遗传连锁图谱,图谱总长度2100.9cM,平均间距为10.82cM。2)利用近红外反射光谱(NIRS)定量分析模型测定了F2:5家系的蛋白质、淀粉、油分和赖氨酸等4个籽粒品质性状以及F2:4群体的酸性洗涤纤维(ADF)、中性洗涤纤维(NDF)、粗蛋白(CP)和粗脂肪(CF)等4个秸秆品质性状含量。利用鲍士旦介绍的植物常量元素的测定方法测定玉米籽粒和秸秆中的氮、磷、钾含量。3)利用复合区间作图法共定位了11个籽粒品质相关性状的QTL,其中在缺氮条件下检测到7个QTL,施氮条件下共检测到4个QTL,分布在1, 3, 7,9染色体上。其中,与蛋白质含量相关的QTL 2个,在7,9染色体上,单个QTL贡献率为9.50%~14.60%;与淀粉含量有关的2个QTL分别位于3,9染色体上,单个QTL贡献率为8.77%~9.90%;与油分含量有关的3个QTL位于第3染色体上,单个QTL贡献率为9.82%~17.66%;与赖氨酸含量有关的4个QTL分别位于1, 3, 7,9染色体上,单个QTL贡献率在8.05%~10.25%之间;部分显性、超显性、加性对籽粒品质的遗传均起着主要的作用。4)共检测出19个有关秸秆品质性状的QTL,在缺氮条件下两个地点共检测到5个,施氮条件下共检测到14个。其中,NDF的3个QTL,单个QTL贡献率为9.53%~12.79%;ADF的4个QTL,单个QTL贡献率为8.55%~14.05%;CF检测到4个QTL,单个QTL贡献率为10.03%~12.66%之间;CP检测到7个QTL,单个QTL贡献率为10.26%~15.04%之间;加性和部分显性对秸秆品质的遗传均起着主要的作用。5)共检测出22个有关籽粒中N、P、K含量的QTL,在缺氮条件下两地点共检测到10个,施氮条件下共检测到12个。其中,与N含量有关的8个QTL,单个QTL贡献率为7.80%~14.91%;与P含量有关的7个QTL,单个QTL贡献率为8.12%~13.03%;与K含量有关的7个QTL,单个QTL贡献率为8.85%~12.36%之间;部分显性和超显性是控制玉米籽粒中N、P、K含量基因的主要作用形式。6)共检测出25个有关秸秆中N、P、K含量的QTL,在缺氮条件下两地点共检测到12个,施氮条件下共检测到13个。其中,与N含量有关的9个QTL,单个QTL贡献率为7.30%~31.09%;与P含量有关的7个QTL,单个QTL贡献率为7.57%~16.65%;与K含量有关的9个QTL,单个QTL贡献率为7.23%~32.82%之间;超显性是控制玉米秸秆中N、P、K含量基因的主要作用形式。

【Abstract】 In this investigation,a elite hybrid maize Nongda108, it’s F2:3,F2:4 families were used to identify and analyz the QTL for nutrient components under nitrogen plus (N+)and no- nitrogen plus (N-) treatments. The results were as follows:1)The genetic linkage map with 194 marker loci was constructed on maize(Zea maysL.)and covered 2100.9cM on 10 chromosomes with an average interval length of 10.82cM2)Four nutrient components in kernel:protein,starch,oil and Lysine and four nutrient components in straw: Acid Detergent Fiber(ADF),Neutral Detergent Fiber(NDF), Crude Protein(CP)and Crude Fat (CF),were measured using near infrared reflectance spectroscopy (NIRS) method.Three components of kernel and straw:N,P and K, were measured using familiar element of plant mensuration method.3)A total of 11 QTL associated with nutrient components of kernel were detected using composite interval mapping method (CIM) in F2:5 population. The result showed that 7 QTL were detected under N- condition and 4 QTL were detected under N+ condition. The QTL were lie on chromosomes 1,3,7 and 9. There were 2 QTL associated with protein,mainly concentrating on chromosomes 7 and 9,each QTL could explain over 9.50%~14.60% of phenotypic variation. There were 2 QTL associated with starch lying on chromosomes 3 and 9. Each QTL could explain over 8.77%~9.90% of phenotypic variation. There were 3 QTL associated with oil locating on chromosomes 3. Each QTL could explain over 9.82%~17.66% of phenotypic variation. There were 4 QTL associated with Lysine locating on chromosomes 1,3,7 and 9. Each QTL could explain over 8.05%~10.25% of phenotypic variation, Partially dominant ,over-dominant and additive effects played main functions in the heredity of nutrient components of kernel.4)A total of 19 QTL associated with nutrient components of straw were detected in F2:4 population. The result showed that 5 QTL were detected under N- condition and 14 QTL under N+ condition. Three QTL were identified for NDF, Each QTL could explain over 9.53 % ~12.79 % of phenotypic variation.Four QTL were identified for ADF,each QTL could explain over 8.55%~14.05% of phenotypic variation. Four QTL were identified for CF, each QTL could explain over10.03%~12.66% of phenotypic variation.Seven QTL were identified for CP, each QTL could explain over 10.26%~15.04% of phenotypic variation,Partially dominant and additive effects played main functions in the heredity of nutrient components of straw.5)A total of 22 QTL associated with N、P and K components of kernel were detected in F2:4 and F2:5 population. The result showed that 10 QTL were detected under N- condition and 12 QTL under N+ condition. Eight QTL were identified for N, Each QTL could explain over 7.80%~14.91% of phenotypic variation.Seven QTL were identified for P,each QTL could explain over 8.12%~13.03% of phenotypic variation. Seven QTL were identified for K, each QTL could explain over 8.85%~12.36% of phenotypic variation ,which indicate that partially dominant and additive effects are main gene action for N、P and K components of kernel.6)A total of 25 QTL associated with N、P and K components in straw were detected in F2:3 and F2:4 population. The result showed that 12 QTL were detected under N- condition and 13 QTL under N+ condition.Nine QTL were identified for N, Each QTL could explain over 7.30%~31.09% of phenotypic variation.Seven QTL were identified for P,each QTL could explain over 7.57%~16.65% of phenotypic variation. Nine QTL were identified for K, each QTL could explain over7.23%~32.82% of phenotypic variation,which indicate that over-dominant effect is main gene action for N、P and K components in straw.

【关键词】 玉米低氮胁迫品质性状QTL分析
【Key words】 MaizeLow nirogen stressQuality traitQTL analysis
  • 【分类号】S513
  • 【被引频次】1
  • 【下载频次】182
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