Abstract
Background: Sunflower (Helianthus annuus L.) is an important oil crop only after soybean, canola and peanuts. A high-quality genetic map is the foundation of marker-assisted selection (MAS). However, for this species, the high-density maps have been reported limitedly.
Objective: In this study, we proposed the construction of a high-density genetic linkage map by the F7 population of sunflowers using SNP and SSR Markers.
Methods: The SLAF-seq strategy was employed to further develop SNP markers with SSR markers to construct the high-density genetic map by the HighMap software.
Results: A total of 1,138 million paired-end reads (226Gb) were obtained and 518,900 SLAFs were detected. Of the polymorphic SLAFs, 2,472,245 SNPs were developed and finally, 5,700 SNPs were found to be ideal to construct a genetic map after filtering. The final high-density genetic map included 4,912 SNP and 93 SSR markers distributed in 17 linkage groups (LGs) and covered 2,425.05 cM with an average marker interval of 0.49 cM.
Conclusion: The final result demonstrated that the SLAF-seq strategy is suitable for SNP markers detection. The genetic map reported in this study can be considered as one of the most highdensity genetic linkage maps of sunflower and could lay a foundation for quantitative trait loci (QTLs) fine mapping or map-based gene cloning.
Keywords: Sunflower (Helianthus annuus L.), Genetic linkage map, High-density, SLAF-seq, SNP, SSR.
Graphical Abstract
[http://dx.doi.org/10.1038/nature22380] [PMID: 28538728]
[http://dx.doi.org/10.3390/biology3020295] [PMID: 24833511]
[http://dx.doi.org/10.1016/j.energy.2018.02.033]
[PMID: 9475757]
[http://dx.doi.org/10.1007/BF02702092]
[http://dx.doi.org/10.1038/hdy.1993.41]
[http://dx.doi.org/10.1007/s00122-002-0989-y] [PMID: 12582890]
[http://dx.doi.org/10.2135/cropsci2003.3670]
[http://dx.doi.org/10.1007/s11032-011-9585-7]
[http://dx.doi.org/10.1007/s13313-013-0265-4]
[http://dx.doi.org/10.1016/j.plantsci.2005.03.016]
[http://dx.doi.org/10.1371/journal.pone.0098628] [PMID: 25014030]
[http://dx.doi.org/10.1016/S0378-1119(99)00219-X]
[http://dx.doi.org/10.1016/j.gpb.2014.09.001] [PMID: 25462152]
[http://dx.doi.org/10.1038/nrg3012] [PMID: 21681211]
[http://dx.doi.org/10.1371/journal.pone.0019379] [PMID: 21573248]
[http://dx.doi.org/10.1371/journal.pone.0003376] [PMID: 18852878]
[http://dx.doi.org/10.1371/journal.pone.0058700] [PMID: 23527008]
[http://dx.doi.org/10.1186/s12870-016-0741-4] [PMID: 27067834]
[http://dx.doi.org/10.1270/jsbbs.18051] [PMID: 30697121]
[http://dx.doi.org/10.1093/bioinformatics/btn025] [PMID: 18227114]
[http://dx.doi.org/10.1093/bioinformatics/btp324] [PMID: 19451168]
[http://dx.doi.org/10.1038/ng.806] [PMID: 21478889]
[http://dx.doi.org/10.1093/bioinformatics/btp352] [PMID: 19505943]
[http://dx.doi.org/10.3724/SP.J.1006.2017.00019]
[http://dx.doi.org/10.1371/journal.pone.0098855] [PMID: 24905985]
[http://dx.doi.org/10.1017/S0016672311000279] [PMID: 21878144]
[http://dx.doi.org/10.1007/s001220000489]
[http://dx.doi.org/10.1007/s00122-005-0124-y] [PMID: 16258753]
[http://dx.doi.org/10.1038/ng.1018] [PMID: 22138690]
[http://dx.doi.org/10.1093/dnares/dsv003] [PMID: 25776277]
[http://dx.doi.org/10.1038/srep24070] [PMID: 27040179]
[http://dx.doi.org/10.1016/j.scienta.2017.02.015]
[http://dx.doi.org/10.1016/j.plantsci.2006.12.007]
[http://dx.doi.org/10.1007/s11032-016-0558-8]
[http://dx.doi.org/10.1534/g3.112.002659] [PMID: 22870395]
[http://dx.doi.org/10.2135/cropsci2014.11.0752]
[http://dx.doi.org/10.2174/1574893613666181113131415]
[PMID: 31157855]
[http://dx.doi.org/10.1016/j.omtn.2019.05.028] [PMID: 31299595]
[http://dx.doi.org/10.1534/g3.116.030031] [PMID: 27342736]
[http://dx.doi.org/10.1038/s41598-017-16006-z] [PMID: 29180623]