Optimal yield-related attributes of irrigated rice for high yield potential based on path analysis and stability analysis

Improvement of yield in rice(Oryza sativa L.) is vital for ensuring food security in China. Both rice breeders and growers need an improved understanding of the relationship between yield and yield-related traits. New indica cultivars(53 in 2007 and 48 in 2008) were grown in Taoyuan,Yunnan province, to identify important components contributing to yield. Additionally, two standard indica rice cultivars with similar yield potentials, II You 107(a large-panicle type) and Xieyou 107(a heavy-panicle type), were planted in Taoyuan, Yunnan province and Nanjing,Jiangsu province, from 2006 to 2008 to evaluate the stability of yield and yield-related attributes.Growth duration(GD), leaf area index(LAI), panicles per m2(PN), and spikelets per m2(SM) were significantly and positively correlated with grain yield(GY) over all years. Sequential path analysis identified PN and panicle weight(PW) as important first-order traits that influenced grain yield. All direct effects were significant, as indicated by bootstrap analysis. Yield potential varied greatly across locations but not across years. Plant height(PH), days from heading to maturity(HM), and grain weight(GW) were stable traits that showed little variation across sites or years, whereas GD(mainly the pre-heading period, PHP) and PN varied significantly across locations. To achieve a yield of 15 t ha-1, a cultivar should have a PH of 110–125 cm, a long GD with HM of approximately 40 days, a PN of 300–400 m-2, and a GW of 29–31 mg.

Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis

Significantly increasing crop yield is a major and worldwide challenge for food supply and security.It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide.Yet,the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery.Here,we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group.We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method,i.e.,dynamic cross-tissue(DCT)network analysis.We used one of the candidate genes,Os SPL4,whose function was previously unknown,for gene editing experimental validation of the high yield,and confirmed that Os SPL4 significantly affects panicle branching and increases the rice yield.This study,which included extensive field phenotyping,cross-tissue systems biology analyses,and functional validation,uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice.The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample.DCT can be downloaded from https://github.com/ztpub/DCT.