ArticleJournal of genetics2011
Mapping of shoot fly tolerance loci in sorghum using SSR markers.
Article in Journal of genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Recent advancements in the breeding of sorghum crop: current status and future strategies for marker-assisted breeding.Frontiers in genetics · 2023Review
- QTL Mapping of Traits Associated with Dual Resistance to the African Stem Borer (International journal of genomics · 2021Article
- Identification of genomic regions associated with shoot fly resistance in maize and their syntenic relationships in the sorghum genome.PloS one · 2020Article
- Mating Design and Genetic Structure of a Multi-Parent Advanced Generation Intercross (MAGIC) Population of Sorghum (G3 (Bethesda, Md.) · 2018Article
- Introgression of Shoot Fly (Frontiers in plant science · 2017Article
- Article
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Authors and funding
6 authors.
Funding
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Abstract
Sorghum (Sorghum bicolor (L.) Moench) is one of the most important crops in the semiarid regions of the world. One of the important biotic constraints to sorghum production in India is the shoot fly which attacks sorghum at the seedling stage. Identification of the genomic regions containing quantitative trait loci (QTLs) for resistance to shoot fly and the linked markers can facilitate sorghum improvement programmes through marker-assisted selection. A simple sequence repeat (SSR) markerbased skeleton linkage map of two linkage groups of sorghum was constructed in a population of 135 recombinant inbred lines (RIL) derived from a cross between IS18551 (resistant to shoot fly) and 296B (susceptible to shoot fly). A total of 14 SSR markers, seven each on linkage groups A and C were mapped. Using data of different shoot fly resistance component traits, one QTL which is common for glossiness, oviposition and dead hearts was detected following composite interval mapping (CIM) on linkage group A. The phenotypic variation explained by this QTL ranged from 3.8%-6.3%. Besides the QTL detected by CIM, two more QTLs were detected following multi-trait composite interval mapping (MCIM), one each on linkage groups A and C for the combinations of traits which were correlated with each other. Results of the present study are novel as we could find out the QTLs governing more than one trait (pleiotropic QTLs). The identification of pleiotropic QTLs will help in improvement of more than one trait at a time with the help of the same linked markers. For all the QTLs, the resistant parent IS18551 contributed resistant alleles.
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