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Theoretical and Applied Genetics

, Volume 120, Issue 3, pp 665–678 | Cite as

Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize

  • Xiaohong Yang
  • Yuqiu Guo
  • Jianbing Yan
  • Jun Zhang
  • Tongming Song
  • Torbert Rocheford
  • Jian-Sheng LiEmail author
Original Paper

Abstract

High-oil maize is a useful genetic resource for genomic investigation in plants. To determine the genetic basis of oil concentration and composition in maize grain, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was phenotyped using gas chromatography, and genotyped with 228 molecular markers. A total of 42 individual QTL, associated with fatty acid compositions and oil concentration, were detected in 21 genomic regions. Five major QTL were identified for measured traits, one each of which explained 42.0% of phenotypic variance for palmitic acid, 15.0% for stearic acid, 27.7% for oleic acid, 48.3% for linoleic acid, and 15.7% for oil concentration in the RIL population. Thirty-six loci were involved in 24 molecular marker pairs of epistatic interactions across all traits, which explained phenotypic variances ranging from 0.4 to 6.1%. Seven of 18 mapping candidate genes related to lipid metabolism were localized within or were close to identified individual QTL, explaining 0.7–13.2% of the population variance. These results demonstrated that a few major QTL with large additive effects could play an important role in attending fatty acid compositions and increasing oil concentration in used germplasm. A larger number of minor QTL and a certain number of epistatic QTL, both with additive effects, also contributed to fatty acid compositions and oil concentration.

Keywords

Fatty Acid Composition Epistatic Interaction Epistatic Effect Recombinant Inbred Line Population Total Phenotypic Variance 
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.

Notes

Acknowledgments

Financial support was provided by the National Natural Science Foundation of China and Chinese High Technology Project. We gratefully acknowledge Prof. Jun Zhu’s guidance for using QTLNetwork Version 2.0 software and Professor R. A. McIntosh for language editing.

Supplementary material

122_2009_1184_MOESM1_ESM.doc (156 kb)
Supplementary material (DOC 156 kb)

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Xiaohong Yang
    • 1
  • Yuqiu Guo
    • 1
  • Jianbing Yan
    • 1
    • 2
  • Jun Zhang
    • 1
  • Tongming Song
    • 1
  • Torbert Rocheford
    • 3
  • Jian-Sheng Li
    • 1
    Email author
  1. 1.Beijing Key Laboratory of Crop Genetic Improvement, National Maize Improvement Center of ChinaChina Agricultural UniversityBeijingChina
  2. 2.International Maize and Wheat Improvement Center (CIMMYT)Mexico, D.F.Mexico
  3. 3.Department of AgronomyPurdue UniversityWest LafayetteUSA

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