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Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map
Authors:Zhaoming?Qi,Junbo?Pan  author-information"  >  author-information__contact u-icon-before"  >  mailto:@qq.com"   title="  @qq.com"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,Xue?Han,Huidong?Qi,Dawei?Xin,Wei?Li,Xinrui?Mao,Zhongyu?Wang,Hongwei?Jiang,Chunyan?Liu,Zhenbang?Hu,Guohua?Hu,Rongsheng?Zhu,Qingshan?Chen
Affiliation:1.College of Agriculture,Northeast Agricultural University,Harbin,People’s Republic of China;2.Crop Research and Breeding Center of Land-Reclamation of Heilongjiang Province,Harbin,People’s Republic of China
Abstract:The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 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. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. 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 protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.
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