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QTL mapping of general combining abilities of four traits in maize using a high-density genetic map

QTL mapping of general combining abilities of four traits in maize using a high-density genetic map

作     者:WANG Hai HE Yan WANG Shou-cai 

作者机构:National Maize Improvement Center of ChinaChina Agricultural UniversityBeijing 100193P.R.China 

出 版 物:《Journal of Integrative Agriculture》 (农业科学学报(英文版))

年 卷 期:2017年第16卷第8期

页      面:1700-1707页

核心收录:

学科分类:09[农学] 0901[农学-作物学] 

基  金:financially supported by the National Key Research and Development Plan of China(2016YFD0101200) 

主  题:maize QTL mapping general combining ability high-density genetic map 

摘      要:General combining abilities (GCAs) are very important in utilization of heterosis in maize breeding. However, its genetic basis is unclear. In the present study, a set of 118 doubled haploid (DH) lines were induced from F1 generations produced from the cross between the inbred line Zheng 58 and the inbred line W499 belonging to the Reid subgroup. Using the MaizeSNP50 BeadChip, a high-density genetic map was constructed based on the DH population which included 1 147 bin markers with an average interval length of 2.00 cM. Meanwhile, the DH population was crossed with three testers including W16-5, HD568, and W556, which belong to the Sipingtou subgroup. The GCAs of the ear height (EH), the kernel moisture content (KMC), the kernel ratio (KR), and the yield per plant (YPP) were estimated using these hybrids in three environments. Combining the high-density genetic map and the GCAs, a total of 14 QTLs were detected for the GCAs of the four traits. Especially, one pleiotropic QTL was identified on chromosome 1 between the SNP SYN16067 and the SNP PZE-101169244 which was simultaneously associated with the GCAs of the EH, the KR, and the YPP. These QTLs pave the way for further dissecting the genetic architecture underlying GCAs of the traits, and they may be used to enhance GCAs of inbred lines under the fixed heterotic pattern ReidxSipingtou in China through a marker-assisted selection approach.

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