Identification and validation of major QTLs and epistatic interactions for seed oil content in soybeans under multiple environments based on a high-density map
2017
Zhaoming, Qi | Xiaoying, Zhang | Huidong, Qi | Dawei, Xin | Xue, Han | Hongwei, Jiang | Zhengong, Yin | Zhanguo, Zhang | Jinzhu, Zhang | Rongsheng, Zhu | Zhenbang, Hu | Chunyan, Liu | Xiaoxia, Wu | Qingshan, Chen | Daidi, Che
The seed oil content in soybean is an important trait that drives successful soybean quality. A recombination of inbred lines derived from a cross between the ‘Charleston’ and ‘Dongnong594’ cultivars was planted in one location (HRB) across 13 years and two locations (HXL and JMS) across 6 years in China (25 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Twelve main-effect quantitative trait loci (QTLs) were identified on chromosomes Ch3, Ch4, Ch6, Ch13, Ch15, Ch17, Ch18,Ch19 and Ch20 and detected in more than two environments. Among the intervals of the main-effect QTLs, eleven candidate genes were screened for their involvement in seed oil content and/or fatty acid biosynthesis and metabolism processes based on gene ontology and annotation information, moreover, the main effect QTL were identified in a wild soybean chromosome segment substitution lines population, the phenotype and the QTL fragment showed the identity relationship significantly. Furthermore, an analysis of epistatic interactions showed that four epistatic QTL pairs were detected, and they could explain approximately 70% of the phenotypic variation interaction with environments in total. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean oil content and that the candidate genes can also be used as a foundation data set for research on gene functions.
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