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Identification of genetic variants associated with maize flowering time using an extremely large multi‐genetic background population
Author(s) -
Li Yongxiang,
Li Chunhui,
Bradbury Peter J.,
Liu Xiaolei,
Lu Fei,
Romay Cinta M.,
Glaubitz Jeffrey C.,
Wu Xun,
Peng Bo,
Shi Yunsu,
Song Yanchun,
Zhang Dengfeng,
Buckler Edward S.,
Zhang Zhiwu,
Li Yu,
Wang Tianyu
Publication year - 2016
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.13174
Subject(s) - biology , single nucleotide polymorphism , candidate gene , genetics , domestication , population , genetic association , quantitative trait locus , gene , genome , selection (genetic algorithm) , genotype , medicine , artificial intelligence , computer science , environmental health
Summary Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi‐ genetic background population that contained more than 8000 lines under multiple Sino‐United States environments. The population included two nested association mapping ( NAM ) panels and a natural association panel. Nearly 1 million single‐nucleotide polymorphisms ( SNP s) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One‐third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome‐wide association study of the three panels identified nearly 1000 flowering time‐associated SNP s, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNP s – one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNP s exhibited high accuracy for predicting flowering time.