Genetic analysis of phenotypic variations of traits under drought stress to identify associated loci will facilitate crop improvement and performance in marginalized drought-prone areas. To this end, we identified genomic regions/loci underlying variations among recombinant inbred lines (RILs) of WAB638-1/BRS-PRIMAVERA rice under drought stress and control environments using diversity array technology (DArT)-based markers in linkage analysis. The linkage map of 94 RILs and 581 DArT-Based markers (295 SNP and 286 SilicoDArT) covers all the 12 rice chromosomes. The total length of the linkage map was 3057.85 cM, with an average of 254.82 cM per chromosome. The average interval between two adjacent markers was 5.82 cM and ranged from 0.56 cM to 39.8 cM, with 429 (73.8%) markers less than 5.0 cM from their nearest neighbours. A total of 28 additive effect QTLs associated with seven agronomic traits across the two environments were localized on chromosomes 1, 2, 3, 8, 10, and 11. The QTLs individually explained 0.96% to 41.6% of the phenotypic variance (PVE), with 11 (40%) major-effect QTLs, each explaining more than 10% PVE. Four major-effect QTLs were mapped to regions consistent with other studies, while seven are newly reported. The consistent QTLs can be deployed for marker-assisted breeding, and the new QTLs should be further characterized and validated. While the QTLs detected for various agronomic traits advances our understanding of phenotypic variability, those associated with drought treatments are significant for improving rice performance in drought-prone areas of the upland ecology.

对干旱胁迫下性状的表型变异进行遗传分析，以确定相关位点，将有助于边缘化干旱易发地区的作物改良和表现。为此，我们在连锁分析中使用基于多样性阵列技术 (DArT) 的标记确定了干旱胁迫和控制环境下 WAB638-1/BRS-PRIMAVERA 水稻重组自交系 (RIL) 的基因组区域/位点潜在变异。94 个 RIL 和 581 个基于 DArT 的标记（295 个 SNP 和 286 个 SilicoDArT）的连锁图涵盖了所有 12 条水稻染色体。连锁图的总长度为3057.85 cM，每条染色体平均为254.82 cM。两个相邻标记之间的平均间隔为 5.82 cM，范围从 0.56 cM 到 39.8 cM，其中 429 个（73.8%）标记与其最近的邻居的距离小于 5.0 cM。与两种环境中的 7 个农艺性状相关的总共 28 个加性效应 QTL 位于 1、2、3、8、10 和 11 号染色体上。 QTL 分别解释了 0.96% 至 41.6% 的表型方差 (PVE)，有 11 个 (40%) 主要效应 QTL，每个 QTL 都解释了超过 10% 的 PVE。四个主要效应 QTL 被映射到与其他研究一致的区域，而七个是新报告的。一致的 QTL 可用于标记辅助育种，新的 QTL 应进一步表征和验证。虽然针对各种农艺性状检测到的 QTL 促进了我们对表型变异的理解，但与干旱处理相关的 QTL 对于提高旱地生态干旱易发地区的水稻性能具有重要意义。