Data from: Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping

High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.

高通量表型鉴定（high-throughput phenotyping）可随时间序列获取多组测量数据，亟需开发既能灵活量化植物生长与蒸腾过程、又兼具计算经济性的新型分析方法。本研究开发了此类分析方法，并将其应用于经700k SNP高密度阵列完成基因分型的水稻群体。本研究针对两个水稻多样性群体——籼稻（indica）与奥斯稻（aus），共计553个基因型，在淹水条件下开展表型鉴定。本研究采用三次平滑样条函数估算植物生长与蒸腾速率，据此确定了四个可表征水稻对盐胁迫早期响应的关键时间区间。本研究通过一种可考量处理组（对照组与盐胁迫组）与遗传标记间交互效应的新型关联分析模型，对相对生长速率、蒸腾速率与蒸腾利用效率（transpiration use efficiency, TUE）进行解析。借助该模型，本研究在第11号染色体上鉴定出此前未被报道的影响蒸腾利用效率的基因位点，为解析水稻对盐胁迫的早期响应机制，特别是盐胁迫对植物生长与蒸腾过程的调控效应提供了全新认知。