Genetic Basis of Tiller Dynamics of Rice Revealed by Genome-Wide Association Studies
| Autor: | Dong Gwan Kim, Zhengxun Jin, Su Jang, Hee-Jong Koh, Yoon Kyung Lee, Shuyu Zhao |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
0301 basic medicine Candidate gene tiller number Single-nucleotide polymorphism Genome-wide association study Tiller (botany) Plant Science Biology 01 natural sciences Article heading date 03 medical and health sciences Linear regression Ecology Evolution Behavior and Systematics Genetic association Panicle genome-wide association study Ecology Botany food and beverages 030104 developmental biology Agronomy phase transition QK1-989 Grain yield productive tiller number rice tillering 010606 plant biology & botany |
| Zdroj: | Plants Volume 9 Issue 12 Plants, Vol 9, Iss 1695, p 1695 (2020) |
| ISSN: | 2223-7747 |
| DOI: | 10.3390/plants9121695 |
| Popis: | Background: Tiller number is the key determinant of rice plant architecture and panicle number and consequently controls grain yield. Thus, it is necessary to optimize the tiller number to achieve the maximum yield in rice. However, comprehensive analyses of the genetic basis of tiller number, considering the development stage, tiller type, and related traits, are lacking.Results: We sequenced 219 Korean rice accessions and constructed a high-quality single nucleotide polymorphism (SNP) dataset. The tiller number at different development stages and heading traits involved in phase transitions were evaluated. By a genome-wide association study (GWAS), we detected 20 significant association signals for all traits. Five signals were detected in genomic regions near known candidate genes. Most of the candidate genes were involved in the phase transition from vegetative to reproductive growth. In particular, HD1 was simultaneously associated with the productive tiller ratio and heading date, indicating that the photoperiodic heading gene directly controls the productive tiller ratio. Multiple linear regression models of lead SNPs showed coefficients of determination (R2) of 0.49, 0.22, and 0.41 for the tiller number at the maximum tillering stage, productive tiller number, and productive tiller ratio, respectively. Furthermore, the model was validated using independent japonica rice collections, implying that the lead SNPs included in the linear regression model were generally applicable to tiller number prediction.Conclusions: We revealed the genetic basis of tiller number in rice plants during growth by a GWAS and formulated a prediction model by linear regression. Our results improve our understanding of tillering in rice plants and provide a basis for breeding high-yield rice varieties with the optimum tiller number. |
| Databáze: | OpenAIRE |
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