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A High-Density SNP Map of Sunflower Derived from RAD-Sequencing Facilitating Fine-Mapping of the Rust Resistance Gene R12
Author(s) -
Zahirul I. Talukder,
Li Gong,
Brent S. Hulke,
Venkatramana Pegadaraju,
Qijian Song,
Quentin Schultz,
Lili Qi
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0098628
Subject(s) - biology , genetics , gene mapping , quantitative trait locus , association mapping , genetic marker , genetic linkage , computational biology , gene , single nucleotide polymorphism , chromosome , genotype
A high-resolution genetic map of sunflower was constructed by integrating SNP data from three F 2 mapping populations (HA 89/RHA 464, B-line/RHA 464, and CR 29/RHA 468). The consensus map spanned a total length of 1443.84 cM, and consisted of 5,019 SNP markers derived from RAD tag sequencing and 118 publicly available SSR markers distributed in 17 linkage groups, corresponding to the haploid chromosome number of sunflower. The maximum interval between markers in the consensus map is 12.37 cM and the average distance is 0.28 cM between adjacent markers. Despite a few short-distance inversions in marker order, the consensus map showed high levels of collinearity among individual maps with an average Spearman's rank correlation coefficient of 0.972 across the genome. The order of the SSR markers on the consensus map was also in agreement with the order of the individual map and with previously published sunflower maps. Three individual and one consensus maps revealed the uneven distribution of markers across the genome. Additionally, we performed fine mapping and marker validation of the rust resistance gene R 12 , providing closely linked SNP markers for marker-assisted selection of this gene in sunflower breeding programs. This high resolution consensus map will serve as a valuable tool to the sunflower community for studying marker-trait association of important agronomic traits, marker assisted breeding, map-based gene cloning, and comparative mapping.

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