|GRASSA, CHRISTOPHER - University Of British Columbia|
|BOWERS, JOHN - University Of Georgia|
|BURKE, JOHN - University Of Georgia|
|TALUKDER, ZAHIRUL - North Dakota State University|
|RIESEBERG, LOREN - University Of British Columbia|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/18/2015
Publication Date: 6/9/2015
Citation: Hulke, B.S., Grassa, C.J., Bowers, J.E., Burke, J.M., Qi, L., Talukder, Z.I., Rieseberg, L.H. 2015. A unified SNP map of sunflower (Helianthus annuus L.) derived from current genomic resources. Crop Science. 55:1696-1702. doi:10.2135/cropsci2014.11.0752.
Interpretive Summary: Recently, two separate SNP genetic marker maps of moderate density (about 5,000 and 9,000 markers each) were published. Without common data between the maps, comparisons between them cannot be made. Since genetic maps are the basis of any genetic study for any plant trait, comparisons between studies using different maps would be impossible. In this work, we used information from the sunflower genome project to bring the two maps together into a common map, allowing geneticists and plant breeders to compare results across maps. In doing so, we increased SNP marker density, which may benefit trait mapping studies conducted in the near future. This work was completed at the request of the National Sunflower Association and its member plant breeders.
Technical Abstract: Dense genetic maps are critical tools for plant breeders and geneticists. While many maps have been developed for sunflower in the last few decades, most have been based on low-throughput technologies and include markers numbers in the hundreds. However, two maps with reasonably dense coverage of about 5,000 and 9,000 SNP loci each, have recently been produced using high-throughput genotypic methods. Unfortunately, no mapping population is common between the two maps, which makes joining them by linkage analysis a challenge. With genome sequencing and resequencing of mapping populations currently in progress, there will be opportunities in the near future to develop much more informative resources. In the meantime, there is much demand from the sunflower community, particularly plant breeders, to combine these two maps to develop a denser map. In this paper, we used an in silico approach to join the two SNP maps based on existing knowledge from sunflower genomics projects to place the markers on sequenced scaffolds from a mapping population. Genetic map positions of the markers were determined using the genetic map position of the scaffolds. In this way, we were able to directly place 10,307 SNP and Indel markers on a common linkage map, and also provide the ability to infer genetic position of a further 6736 markers from both previously published maps. These results will allow researchers to compare previous genetics research conducted on the separate maps, and facilitate collaborative work on marker-assisted breeding approaches in sunflower.