QTL mapping of yield component traits on bin map generated from resequencing a RIL population of foxtail millet (Setaria italica).
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2020
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Abstract
Foxtail millet (Setaria italica) has been developed into a model genetical system for deciphering architectural evolution, C photosynthesis, nutritional properties, abiotic tolerance and bioenergy in cereal grasses because of its advantageous characters with the small genome size, self-fertilization, short growing cycle, small growth stature, efficient genetic transformation and abundant diverse germplasm resources. Therefore, excavating QTLs of yield component traits, which are closely related to aspects mentioned above, will further facilitate genetic research in foxtail millet and close cereal species.Here, 164 Recombinant inbreed lines from a cross between Longgu7 and Yugu1 were created and 1,047,978 SNPs were identified between both parents via resequencing. A total of 3413 bin markers developed from SNPs were used to construct a binary map, containing 3963 recombinant breakpoints and totaling 1222.26 cM with an average distance of 0.36 cM between adjacent markers. Forty-seven QTLs were identified for four traits of straw weight, panicle weight, grain weight per plant and 1000-grain weight. These QTLs explained 5.5-14.7% of phenotypic variance. Thirty-nine favorable QTL alleles were found to inherit from Yugu1. Three stable QTLs were detected in multi-environments, and nine QTL clusters were identified on Chromosome 3, 6, 7 and 9.A high-density genetic map with 3413 bin markers was constructed and three stable QTLs and 9 QTL clusters for yield component traits were identified. The results laid a powerful foundation for fine mapping, identifying candidate genes, elaborating molecular mechanisms and application in foxtail millet breeding programs by marker-assisted selection.
| Reference Key |
liu2020qtlbmc
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| Authors | Liu, Tianpeng;He, Jihong;Dong, Kongjun;Wang, Xuewen;Wang, Wenwen;Yang, Peng;Ren, Ruiyu;Zhang, Lei;Zhang, Zhengsheng;Yang, Tianyu; |
| Journal | BMC genomics |
| Year | 2020 |
| DOI |
10.1186/s12864-020-6553-9
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