Data from: Homoploid F1 hybrids and segmental allotetraploids of japonica and indica rice subspecies show similar and enhanced tolerance to N deficiency than parental lines
Sun, Yue (2022), Data from: Homoploid F1 hybrids and segmental allotetraploids of japonica and indica rice subspecies show similar and enhanced tolerance to N deficiency than parental lines, Dryad, Dataset, https://doi.org/10.5061/dryad.z612jm6bd
The datasets were generated to compare the phenotypic performance of two diploid rice subspecies indica and japonica (Oryza sativa L.) with the derived homoploid F1 hybrids (9N and N9) and segmental allopolyploids (99NN and NN99) under normal and low nitrogen conditions. Total gene expressions of 18 genes in critical growth-related pathways along with genotyping data by Sequenom MassARRAY of the plant groups were also involved to reveal the molecular rationale behind the morphological and physiological observations. The data were collected at the population level and falls into mainly 3 parts including i) phenotype (morphology and physiology) measurements for above and underground parts of the plants under normal and low-nitrogen conditions; ii) qRT-PCR quantification of the 18 involved genes by ΔΔCT method, and iii) genotyping dataset generated from Sequenom MassARRAY platform. The study shows that both hybridization and whole-genome duplication can confer rice better tolerance to low nitrogen conditions.
Regular phenotype measurement and qRT-PCR were carried out and genotyping data were obtained by the Sequenom MassARRAY platform. Detail can be seen in the M&M section of the study.
The total expression of the gene "Chl-7" by qRT-PCR in 9N and N9 under N-limiting conditions were failed to be quantified; some other data points in total gene expression and genotyping datasets are missing. The instruction of the datasets can be found in Readme.txt.
National Key Research and Development Program of China, Award: 2016YFD0102003
National Natural Science Foundation of China, Award: 31901076
China Postdoctoral Science Foundation, Award: 2019M651186