Rice Genomics and Genetics 2024, Vol.15, No.5, 287-308 http://cropscipublisher.com/index.php/rgg 298 This study attempts to comprehensively evaluate the genetic contributions of flood tolerance genes in rice and their impact on key agronomic traits, discuss the identification of stable QTLs and key genes that confer flood tolerance and their physiological mechanisms, and provide an overview of their effects on yield and other important traits. By synthesizing data from various studies, this analysis aims to generate knowledge that aids in the development of more resilient rice varieties, ultimately contributing to sustainable rice production and food security in flood-prone regions. 2 Methodology 2.1 Criteria for selecting studies included in the meta-analysis The selection criteria for studies included in this meta-analysis focused on identifying research that investigated the genetic basis of flood tolerance in rice. Studies were selected based on the following criteria: (1) they must have performed quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), or transcriptomic analyses related to flood tolerance; (2) they must have evaluated agronomic traits under flood conditions; and (3) they must have provided sufficient statistical data for meta-analysis. For instance, studies like those by which examined QTLs related to adventitious root formation under submergence (Lin et al., 2022), and which performed GWAS to identify loci contributing to flooding tolerance during germination (Zhang et al., 2017), were included. 2.2 Statistical methods used for synthesizing genetic data The genetic data from the selected studies were synthesized using meta-QTL analysis, which is a robust approach for consolidating QTL information across different studies and environments. This method refines the confidence intervals of QTLs, making it easier to identify stable QTLs and candidate genes. For example, the study by utilized meta-QTL analysis to identify stable QTLs for yield and root architecture traits under water deficit conditions (Khahani et al., 2021). Additionally, mixed linear models were employed in GWAS to detect significant single nucleotide polymorphisms (SNPs) associated with flood tolerance traits (Zhang et al., 2017; Thapa et al., 2022). 2.3 Agronomic traits considered in the analysis The agronomic traits considered in this meta-analysis included both primary and secondary traits related to flood tolerance. Primary traits included survival rate, plant height, and yield under flood conditions. Secondary traits encompassed root architecture traits such as root length, root thickness, and the formation of adventitious roots, as well as physiological traits like coleoptile elongation and leaf greenness. For instance, focused on the development of aquatic adventitious roots, while examined traits like stem length and panicle length under stagnant flooding conditions (Sitaresmi et al., 2019; Lin et al., 2022). 2.4 Tools and software used for analysis Various tools and software were employed to conduct the meta-analysis and bioinformatics pipelines. Meta-QTL analysis was performed using software like MetaQTL, which helps in refining QTL intervals and identifying candidate genes. GWAS was conducted using software such as TASSEL and GAPIT, which facilitate the identification of significant SNPs and their associations with phenotypic traits. For transcriptomic data analysis, tools like DESeq2 and edgeR were used to identify differentially expressed genes under flood conditions (De Oliveira-Busatto et al., 2022). Additionally, bioinformatics pipelines were utilized for sequence alignment, SNP calling, and haplotype analysis (Figure 1) (Zhang et al., 2017; Thapa et al., 2022). This section outlines the methodology used in the meta-analysis of flood tolerance genes in rice. Studies were selected based on their focus on genetic mapping and evaluation of agronomic traits under flood conditions. Statistical methods like meta-QTL analysis and mixed linear models in GWAS were employed to synthesize genetic data. The analysis considered primary and secondary agronomic traits related to flood tolerance, and various tools and software were used to perform the meta-analysis and bioinformatics pipelines. This comprehensive approach aims to identify stable QTLs and candidate genes that can enhance flood tolerance in rice (Volante et al., 2017).
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