外文摘要：Drought is the primary abiotic stress limiting wheat growth and yield. Transcriptomic analysis can unravel innovative mechanisms of drought tolerance and putative tolerance genes. The current study employed meta-analysis and supervised machine learning approaches, including attribute weighting algorithms (AWAs) and models of decision trees, to identify the most informative genes associated with drought tolerance and assess their expression patterns in drought-sensitive and drought-tolerant cultivars. First, a meta-analysis of microarray data was performed separately for drought-tolerant and drought-sensitive cultivars to identify differentially expressed probe sets in drought-tolerant and drought-sensitive cultivars under stress conditions. Subsequently, 10 AWAs were implemented on the meta-analysis results to specify probe sets that differentiate between drought-tolerant and drought-sensitive cultivars. We identified 261 and 300 probe sets by at least four AWAs in control and drought stress conditions, respectively, as the probe sets associated with tolerance. For validation, the expression profiles of two candidate genes, DAD1, and MYB80, were evaluated by real-time PCR. According to the expression analysis results, DAD1 and MYB80 exhibited opposing expression trends in drought-sensitive and drought-tolerant cultivars, thus indicating their functions in wheat drought tolerance. The identification of key genes associated with drought tolerance in wheat cultivars, which has been achieved through our study, has important implications for breeding drought-resistant wheat cultivars and potential applications in agricultural practices. These genes can be specifically targeted in future breeding efforts. Altogether, the integrated approach could provide great potential to identify key genes in drought tolerance responses and predict drought resistance and sensitivity in various wheat cultivars.
外文关键词：decision trees;real-time PCR;drought tolerance;Attribute weighting algorithms;Transcriptomic analysis
作者：Azimi, Niloufar;Ravash, Rudabeh;Zinati, Zahra
作者单位：Shahrekord Univ;Shiraz Univ
期刊名称：GENETIC RESOURCES AND CROP EVOLUTION
期刊影响因子：0.0
出版年份：2024
出版刊次：71（7）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台