کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8408727 | 1545076 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Identification of stably expressed QTL for resistance to black shank disease in tobacco (Nicotiana tabacum L.) line Beinhart 1000-1
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم زراعت و اصلاح نباتات
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Identification of stably expressed QTL for resistance to black shank disease in tobacco (Nicotiana tabacum L.) line Beinhart 1000-1 Identification of stably expressed QTL for resistance to black shank disease in tobacco (Nicotiana tabacum L.) line Beinhart 1000-1](/preview/png/8408727.png)
چکیده انگلیسی
Cigar line Beinhart 1000-1 has effective durable resistance to black shank (BS) and is considered one of the most resistant sources in tobacco (Nicotiana tabacum L.). To investigate the inheritance and identification of stable quantitative trait loci (QTL) for BS response, F2, BC1F2 individuals and BC1F2:3 lines were produced from a cross between Beinhart 1000-1 and Xiaohuangjin 1025. Two major quantitative trait loci (M-QTL) named qBS7 and qBS17 were repeatedly detected under different conditions. QTL qBS7 was mapped to the region between PT30174 and PT60621 and explained 17.40%-25.60% of the phenotypic variance under different conditions. The other QTL qBS17 in interval PT61564-PT61538 of linkage group 17 was detected in a BC1F2 population in the field and in BC1F2:3 in both the field and at the seedling stage, explaining 6.90% to 11.60% of the phenotypic variance. The results improve our understanding of the inheritance of resistance to BS and provide information that can be used in marker-assisted breeding.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: The Crop Journal - Volume 6, Issue 3, June 2018, Pages 282-290
Journal: The Crop Journal - Volume 6, Issue 3, June 2018, Pages 282-290
نویسندگان
Yusheng Zhang, Xuan Guo, Xingxing Yan, Min Ren, Caihong Jiang, Yazeng Cheng, Liuying Wen, Dan Liu, Yu Zhang, Mingming Sun, Quanfu Feng, Aiguo Yang, Lirui Cheng,