| Title |
Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population
|
|---|---|
| Published in |
Molecular Genetics and Genomics, September 2015
|
| DOI | 10.1007/s00438-015-1121-8 |
| Pubmed ID | |
| Authors |
Zhanhui Zhang, Xiangyuan Wu, Chaonan Shi, Rongna Wang, Shengfei Li, Zhaohui Wang, Zonghua Liu, Yadong Xue, Guiliang Tang, Jihua Tang |
| Abstract |
Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection. |
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| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
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| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 6% |
| Unknown | 17 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 33% |
| Researcher | 5 | 28% |
| Other | 2 | 11% |
| Student > Doctoral Student | 1 | 6% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 72% |
| Biochemistry, Genetics and Molecular Biology | 3 | 17% |
| Earth and Planetary Sciences | 1 | 6% |
| Unknown | 1 | 6% |
Attention Score in Context
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Outputs of similar age from Molecular Genetics and Genomics
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