Rice Genomics and Genetics 2024, Vol.15, No.4, 153-163 http://cropscipublisher.com/index.php/rgg 158 Additionally, the integration of three C4-specific genes (CA, PEPC, and PPDK) into rice plants led to a 12% increase in grain yield, showcasing the potential of combining multiple genes to enhance photosynthetic efficiency and yield (Sen et al., 2016). Figure 2 Overexpression of OsDREB1C in transgenic plants can increase grain yield (Adopted from Wei et al., 2022) Image caption: A: List of the top 13 genes up-regulated in response to nitrogen deprivation (adjusted P<0.05). The genes represent the overlap of previously reported RNA-seq datasets (17) and an expression analysis of a subset of 118 rice transcription factors (16), and were sorted by the fold change in low versus normal nitrogen supply. The color scale represents the log2-fold change of the FPKM (fragments per kilobase of transcript per million mapped reads) ratio under low- versus high-nitrogen conditions, with the FPKM value of each gene under high-nitrogen conditions set to 1.00; B: qRT-PCR analysis of Os06 g0127100 expression in 10-day-old O. sativacv. Nipponbare seedlings grown in soil in a growth chamber under long-day photoperiod (16 hours light/8 hours dark, 28°C, The white bar below the x-axis indicates the light period, and the black bar indicates the dark period. Data are presented as means±SD (n = 3 biological replicates). *P<0.05, **P<0.01 compared with the first time point (11:00 p.m.), Student’s t test. C: Phenotypes of WT and transgenic rice plants grown in Beijing in 2018. (D to H) Yield-related parameters including grain yield per plant; D: grain yield per plot; E: grain number per panicle; F: straw weight; G: and harvest index; H: The data were obtained from the field experiment shown in (C). Box plots in (D) and (F) to (H) show median (horizontal lines) and 10th to 90th percentiles, and outliers are plotted as dots (n = 138 biological replicates). Data in (E) are presented as means±SD (n=3 plots, 44 plants within a plot). *P<0.05, **P<0.01 compared with WT, Student’s t tests (Adopted from Wei et al., 2022) 4.2 High-throughput phenotyping High-throughput phenotyping technologies have revolutionized the assessment of yield traits in rice breeding programs. Advances include the use of remote sensing, imaging technologies, and automated phenotyping platforms that allow for rapid and accurate measurement of plant traits under field conditions (Swamy and Kumar, 2013). These technologies enable the collection of large datasets, facilitating the identification of phenotypic variations associated with yield-related genes (Su et al., 2021). High-throughput phenotyping has been applied to assess various yield traits, such as grain number per panicle, grain weight, and tiller number. For example, genome-wide association studies (GWAS) combined with high-throughput phenotyping have identified significant loci for yield component traits, providing valuable insights for breeding programs (Su et al., 2021). These technologies also support the evaluation of transgenic lines and QTL introgression lines, ensuring that the desired yield traits are accurately measured and selected (Guo and Ye, 2014).
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