Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR‐related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image‐based digital traits (i‐traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i‐traits, as well as some traditional i‐traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome‐wide association study (GWAS) using 56 morphological and 63 texture i‐traits. DR‐related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome‐based breeding for improved DR in cotton.

中文翻译：

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基于经济学的GWAS分析揭示了棉花抗旱性的遗传结构。

抗旱性（DR）是一个复杂的性状，受多种基因调控。如果没有对DR相关性状进行全面分析，那么棉花DR的遗传结构知识仍然有限。因此，需要弥合基因组学和表型学之间的差距。在这项研究中，系统地应用了自动表型平台（APP），在200个有代表性的陆地棉天然种群的苗期干旱胁迫期间，检查了119个基于图像的数字性状（i-traits）。一些新颖的性状以及一些传统的性状被用于评估棉花的DR。形态学数据使我们能够利用56个形态特征和63个纹理特征通过全基因组关联研究（GWAS）识别390个遗传位点。与DR相关的基因，包括GhRD2，GhNAC4，GhHAT22和GhDREB2，被确定为一些数字的性状候选基因。对候选基因的进一步分析表明，Gh_A04G0377和Gh_A04G0378充当棉花干旱反应的负调控因子。基于组合的数字表型，GWAS分析和转录组数据，我们得出结论：表型组学数据集以前所未有的分辨率为表征关键遗传位点提供了极好的资源，可为将来基于基因组的育种提供信息，从而改善棉花的DR。