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。解析作物对干旱的动态响应并揭示其遗传架构，将有助于耐旱作物的育种工作。然而，目前对于植物应对干旱的内外动态响应，以及这些响应的遗传调控机制，仍知之甚少。结果：本研究构建了一套高通量多光学表型分型系统，可在98天的周期内对368份施加或未施加干旱胁迫的玉米基因型进行无创表型鉴定，并采集了约14 TB的多光学成像数据，包括彩色（红、绿、蓝，RGB）相机扫描成像、高光谱成像（hyperspectral imaging, HSI）以及X射线计算机断层扫描（X-ray computed tomography, CT）图像。本研究开发了高通量分析流程，共提取得到26910项基于图像的性状（image-based traits, i-traits）。其中10080项为玉米外部（RGB成像i-traits）与内部（HSI和CT成像i-traits）干旱响应的有效可遗传指标，被筛选用于后续遗传研究。通过基于i-traits的全基因组关联分析（genome wide association study, 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 loci, QTLs）以及2318个候选基因。在这些QTL中，有1092个（占比71.4%）与此前报道的玉米干旱响应QTL共定位。表达QTL（expression QTL, eQTL）分析揭示了众多调控候选基因表达的局部与远端调控变异。本研究还鉴定得到34个与多项i-traits相关的热点基因。我们进一步通过基因突变分析，验证了两个全新的调控i-traits与耐旱性的基因ZmcPGM2和ZmFAB1A。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项i-traits作为玉米耐旱育种的潜在标记。结论：本研究表明，将高通量多光学表型分型技术与GWAS相结合，是解析复杂性状遗传架构、克隆耐旱相关基因的一种全新且高效的研究策略。