Theoretical and Applied Genetics

, Volume 127, Issue 1, pp 85–96 | Cite as

Association mapping of six yield-related traits in rapeseed (Brassica napus L.)

  • Dongfang Cai
  • Yingjie Xiao
  • Wei Yang
  • Wei Ye
  • Bo Wang
  • Muhammad Younas
  • Jiangsheng Wu
  • Kede LiuEmail author
Original Paper


Yield is one of the most important traits for rapeseed (Brassica napus L.) breeding, but its genetic basis remains largely ambiguous. Association mapping has provided a robust approach to understand the genetic basis of complex agronomic traits in crops. In this study, a panel of 192 inbred lines of B. napus from all over the world was genotyped using 451 single-locus microsatellite markers and 740 amplified fragment length polymorphism markers. Six yield-related traits of these inbred lines were investigated in three consecutive years with three replications, and genome-wide association studies were conducted for these six traits. Using the model controlling both population structure and relative kinship (Q + K), a total of 43 associations (P < 0.001) were detected using the means of the six yield-related traits across 3 years, with two to fourteen markers associated with individual traits. Among these, 18 markers were repeatedly detected in at least 2 years, and 12 markers were located within or close to QTLs identified in previous studies. Six markers commonly associated with correlated traits. Conditional association analysis indicated that five of the associations between markers and correlated traits are caused by one QTL with pleiotropic effects, and the remaining association is caused by linked but independent QTLs. The combination of favorable alleles of multiple associated markers significantly enhances trait performance, illustrating a great potential of utilization of the associations in rapeseed breeding programs.


Plant Height Inbred Line Seed Weight Association Mapping AFLP Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research was supported by the National Natural Science Foundation of China (No. 31071452) and the Doctoral Fund of Ministry of Education of China (No. 20100146110019).

Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dongfang Cai
    • 1
  • Yingjie Xiao
    • 1
  • Wei Yang
    • 1
  • Wei Ye
    • 1
  • Bo Wang
    • 1
  • Muhammad Younas
    • 1
  • Jiangsheng Wu
    • 1
  • Kede Liu
    • 1
    Email author
  1. 1.National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina

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