Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize |
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Authors: | Xiaohong Yang Shibin Gao Shutu Xu Zuxin Zhang Boddupalli M Prasanna Lin Li Jiansheng Li Jianbing Yan |
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Institution: | (1) National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, 100193 Beijing, China;(2) Maize Research Institute, Sichuan Agricultural University, 625014 Ya’an, Sichuan, China;(3) National Key Laboratory of Crop Improvement, Huazhong Agricultural University, 430070 Wuhan, Hubei, China;(4) International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico, Edo Mex, Mexico; |
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Abstract: | Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize (Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing
the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor
allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis
revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches
identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based
analyses. Marker–trait associations were performed to assess the suitability of different models for false-positive correction
by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K
and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear
height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand
the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal
statistical methods. |
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