Location: Plant, Soil and Nutrition ResearchTitle: An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
|LIU, HONGJUN - Sichuan Agricultural University|
|NIU, YONGCHAO - Beijing Genome Institute|
|GONZALES-PORTILLO, PEDRO - Iowa State University|
|ZHOU, HUANGKAI - Cold Spring Harbor Laboratory|
|WANG, LIYA - Cold Spring Harbor Laboratory|
Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/24/2015
Publication Date: 12/21/2015
Citation: Liu, H., Niu, Y., Gonzales-Portillo, P.J., Zhou, H., Wang, L., Ware, D. 2015. An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize. Biomed Central (BMC) Genomics. 16:1078.
Interpretive Summary: To safeguard the food supply for the growing human population, it is important to understand and exploit the genetic basis of quantitative traits. Next-generation sequencing technology performs advantageously and effectively in genetic mapping and genome analysis of diverse genetic resources. Hence, we combined re-sequencing technology and a bin map strategy to construct an ultra-high-density bin map with thousands of bin markers to precisely map a quantitative trait locus.
Technical Abstract: In this study, we generated a linkage map containing 1,151,856 high quality SNPs between Mo17 and B73, which were verified in the maize intermated B73'×'Mo17 (IBM) Syn10 population. This resource is an excellent complement to existing maize genetic maps available in an online database (iPlant, http://data.maizecode.org/maize/qtl/syn10/). Moreover, in this population combined with the IBM Syn4 RIL population, we detected 135 QTLs for flowering time and plant height traits across the two populations. Eighteen known functional genes and twenty-five candidate genes for flowering time and plant height trait were fine-mapped into a 2.21–4.96 Mb interval. Map expansion and segregation distortion were also analyzed, and evidence for inadvertent selection of early flowering time in the process of mapping population development was observed. Furthermore, an updated integrated map with 1,151,856 high-quality SNPs, 2,916 traditional markers and 6,618 bin markers was constructed. The data were deposited into the iPlant Discovery Environment (DE), which provides a fundamental resource of genetic data for the maize genetic research community.