Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection |
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Authors: | Xiaohong Yang Jianbing Yan Trushar Shah Marilyn L. Warburton Qing Li Lin Li Yufeng Gao Yuchao Chai Zhiyuan Fu Yi Zhou Shutu Xu Guanghong Bai Yijiang Meng Yanping Zheng Jiansheng Li |
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Affiliation: | 1. National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Yuanmingyuan West Road, Haidian, Beijing, 100193, China 2. International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico D.F., Mexico 5. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, Andhra Pradesh, India 3. USDA-ARS Corn Host Plant Resistance Research Unit, Box 9555, Columbia, MS, 39762, USA 4. Agronomy College, Xinjiang Agricultural University, Urumqi, 830052, Xinjiang, China
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Abstract: | Association mapping based on the linkage disequilibrium provides a promising tool to identify genes responsible for quantitative variations underlying complex traits. Presented here is a maize association mapping panel consisting of 155 inbred lines with mainly temperate germplasm, which was phenotyped for 34 traits and genotyped using 82 SSRs and 1,536 SNPs. Abundant phenotypic and genetic diversities were observed within the panel based on the phenotypic and genotypic analysis. A model-based analysis using 82 SSRs assigned all inbred lines to two groups with eight subgroups. The relative kinship matrix was calculated using 884 SNPs with minor allele frequency ≥20% indicating that no or weak relationships were identified for most individual pairs. Three traits (total tocopherol content in maize kernel, plant height and kernel length) and 1,414 SNPs with missing data <20% were used to evaluate the performance of four models for association mapping analysis. For all traits, the model controlling relative kinship (K) performed better than the model controlling population structure (Q), and similarly to the model controlling both population structure and relative kinship (Q + K) in this panel. Our results suggest this maize panel can be used for association mapping analysis targeting multiple agronomic and quality traits with optimal association model. |
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