SSR-based association mapping of fiber quality in upland cotton using an eight-way MAGIC population

  • Cong Huang
  • Chao Shen
  • Tianwang Wen
  • Bin Gao
  • De Zhu
  • Xiaofang Li
  • Muhammad Mahmood Ahmed
  • Dingguo Li
  • Zhongxu Lin
Original Article

Abstract

The quality of fiber is significant in the upland cotton industry. As complex quantitative traits, fiber quality traits are worth studying at a genetic level. To investigate the genetic architecture of fiber quality traits, we conducted an association analysis using a multi-parent advanced generation inter-cross (MAGIC) population developed from eight parents and comprised of 960 lines. The reliable phenotypic data for six major fiber traits of the MAGIC population were collected from five environments in three locations. Phenotypic analysis showed that the MAGIC lines have a wider variation amplitude and coefficient than the founders. A total of 284 polymorphic SSR markers among eight parents screened from a high-density genetic map were used to genotype the MAGIC population. The MAGIC population showed abundant genetic variation and fast linkage disequilibrium (LD) decay (0.76 cM, r2 > 0.1), which revealed the advantages of high efficiency and power in QTL exploration. Association mapping via a mixed linear model identified 52 significant loci associated with six fiber quality traits; 14 of them were mapped in reported QTL regions with fiber-related or other agronomic traits. Nine markers demonstrated the pleiotropism that controls more than two fiber traits. Furthermore, two SSR markers, BNL1231 and BNL3452, were authenticated as hotspots that were mapped with multi-traits. In addition, we provided candidate regions and screened six candidate genes for identified loci according to the LD decay distance. Our results provide valuable QTL for further genetic mapping and will facilitate marker-based breeding for fiber quality in cotton.

Keywords

Cotton MAGIC population Fiber quality SSR Association mapping 

Notes

Acknowledgements

We would like to thank Dingguo Li and Xiaofang Li for developing the MAGIC population as well as thank Wu Li, Chao Shen, Tianwang Wen, Bin Gao, De Zhu, and Muhammad Mahmood Ahmed for their assistance with collection of trait data. This research was supported by the National Natural Science Foundation of China (Grant No. 31371674) and the Special Fund for Agro-scientific Research in the Public Interest (Grant No. 201303008).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The authors declare that this study complies with the current laws of the country in which the experiments were performed. This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

438_2018_1419_MOESM1_ESM.tiff (258 kb)
Fig. S1 Histogram frequency distribution of pairwise relative kinship coefficients estimated in MLs (TIFF 258 KB)
438_2018_1419_MOESM2_ESM.tiff (910 kb)
Fig. S2 Principal component analysis of 8 PMs and 960 Mls. The top two principal components, PC1 and PC2, are illustrated in the bottom panels (TIFF 910 KB)
438_2018_1419_MOESM3_ESM.tif (1.6 mb)
Fig. S3 Phenotype histogram and the Q–Q plots in association analysis. (a)–(f) The phenotype histogram of fiber upper half mean length (FUHML), fiber strength (FS), fiber elongation (FE), fiber uniformity (FU), micronaire value (MV), and short fiber content (SFC). (g)-(l) The Q–Q plots of GLM and K models for FUHML, FS, FE, FU, MV, and SFC (TIF 1600 KB)
438_2018_1419_MOESM4_ESM.xlsx (57 kb)
Supplementary material 4 (XLSX 56 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and TechnologyHuazhong Agricultural UniversityWuhanChina
  2. 2.Institute of Crop Genetic and BreedingYangtze UniversityJingzhouChina

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