Maize-RGB&CT&HSI-data

Background : Drought threatens the food supply of the world population. Dissecting the dynamic responses to drought and revealing their genetic architectures will be beneficial for breeding drought-tolerant crops. However, the dynamic responses of plant to drought, both external and internal, and the genetic controls of these responses remain largely unknown. Results : Here we developed a high-throughput multiple optical phenotyping system to non-invasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected ~14 terabytes of multiple optical images, including color (red, green, blue) camera scanning (RGB), hyperspectral imaging (HSI) and x-ray computed tomography (CT) images. High-throughput analysis pipelines were developed to extract 26,910 the image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external (RGB i-traits) and internal (HSI and CT i-traits) drought responses and selected for further genetic study. A total of 4,322 significant locus-trait associations were identified via i-trait-based genome wide association study (GWAS), which represent 1,529 quantitative trait loci (QTLs) and 2,318 candidate genes. Of these QTLs, 1,092 (71.4%) co-localized with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovered many local and distant regulatory variants that control the exp ression of the candidate genes. Thirty-four hotspot genes associated with multiple i-traits were identified. We further used genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A that regulated i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers was revealed by genome selection analysis, and 15 i-traits were identified as potential markers for maize drought tolerance breeding. Conclusion : Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

**背景**：干旱威胁着全球人口的粮食供给。解析植物对干旱的动态响应并揭示其遗传调控结构，将有助于耐旱作物的育种工作。然而，植物应对干旱的内外动态响应过程，以及这些响应的遗传调控机制，目前仍未得到充分解析。
**结果**：本研究构建了一套高通量多光学表型鉴定系统，可在98天的周期内对368个施加或不施加干旱胁迫的玉米基因型进行非侵入式表型鉴定，并采集了约14太字节（TB）的多模态光学图像数据，包括彩色（红、绿、蓝，RGB）相机扫描图像、高光谱成像（Hyperspectral Imaging，缩写为HSI）以及X射线计算机断层扫描（X-ray Computed Tomography，缩写为CT）图像。研究开发了高通量分析流程，从图像中提取了26910个基于图像的性状（image-based traits，缩写为i-traits）。其中10080个i-traits可有效且可遗传地反映玉米应对干旱的外部（RGB i-traits）与内部（HSI和CT i-traits）响应，被选作后续遗传研究的指标。通过基于i-traits的全基因组关联分析（Genome Wide Association Study，缩写为GWAS），本研究共鉴定得到4322个显著的位点-性状关联，对应1529个数量性状位点（Quantitative Trait Locus，缩写为QTL）与2318个候选基因。在这些QTL中，1092个（占比71.4%）与已报道的玉米干旱响应QTL共定位。表达数量性状位点（expression Quantitative Trait Locus，缩写为eQTL）分析揭示了大量调控候选基因表达的局部及远端调控变异。研究共鉴定得到34个与多个i-traits相关的热点基因。进一步通过遗传突变分析验证了两个全新基因ZmcPGM2和ZmFAB1A，它们可调控i-traits与玉米耐旱性。此外，通过基因组选择分析证实了候选基因作为耐旱遗传标记的应用价值，并鉴定得到15个i-traits可作为玉米耐旱育种的潜在标记。
**结论**：本研究证实，将高通量多光学表型鉴定技术与全基因组关联分析（GWAS）相结合，是解析复杂性状遗传结构、克隆耐旱相关基因的一种高效新颖的研究策略。