Linkage map construction using InDel and SSR markers and QTL analysis of heading traits in Brassica oleracea var. capitata L. View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-06

AUTHORS

Honghao Lv, Qingbiao Wang, Yangyong Zhang, Limei Yang, Zhiyuan Fang, Xiaowu Wang, Yumei Liu, Mu Zhuang, Yan Lin, Hailong Yu, Bo Liu

ABSTRACT

Cabbage heading traits are important quantitative traits that greatly affect both quality and yield of cabbage. However, the genetic control of these traits remains unclear. To detect quantitative trait loci (QTLs) associated with heading traits, a double haploid (DH) population with 196 lines was created from a cabbage hybrid 01–20 × 96–100. A genetic map with insertion–deletion and simple sequence repeat markers was constructed based on the DH population, with a total length of 934.06 cM and average interval length of 2.3 cM between adjacent markers. Field experiments in three seasons were carried out to evaluate the heading traits, including head mature period (Hm), head weight (Hw), core length (Cl), head vertical diameter (Hvd), and the ratio of Cl to Hvd (Cl/Hvd). Using the map and the trait data, 13 reliable QTLs in total were identified and 5 were found in more than one season based on the adjusted means of three seasons. Major QTLs were identified for Hm (R2 = 40.4, LOD = 14.84), Hw (R2 = 28.6, LOD = 9.83), Cl (R2 = 38.8, LOD = 15.73), Hvd (R2 = 19.2, LOD = 9.26), and Cl/Hvd (R2 = 38.8, LOD = 12.75). The most significant QTLs were Hm3.1, Cl3.1, and Cl/Hvd3.1, which were detected in three seasons with the maximum contribution rate of almost 40 %. Six active regions that harbored more than one QTL were identified on five chromosomes, and one of them contained major QTLs associated with five traits. The QTLs obtained in this study should be useful for marker-assisted selection in cabbage breeding and for understanding the genetic control of these traits. More... »

PAGES

87-98

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    URI

    http://scigraph.springernature.com/pub.10.1007/s11032-014-0019-1

    DOI

    http://dx.doi.org/10.1007/s11032-014-0019-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1036276471


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