16 ERA-CAPS Barley yield associated networks

我们的目标是确定和表征新的大麦基因，调节产量，特别是那些影响种子，穗（花序）和分蘖（种子轴承茎）性状。我们已经从192个两行地方品种的地理参考集合中先前生成的外显子组捕获序列数据，揭示了超过160万个SNP等位基因。在这里，我们计划表型的种质资源收集产量相关的参数，特别注重种子，穗，和生产分蘖性状。我们将在每个合作伙伴所在地以相同的方式评估相同的特征。我们的方法的独特之处在于我们建议增加转录组序列数据层（每个基因型六个性状相关组织），并分析基因表达与性状发育的关系。在植物中，该方法的力量最近已在芸苔属植物中以“关联转录组学”的名义证明，以确定控制种子组成性状的基因。然而，在酵母和哺乳动物模式生物中，现在正在确定将SNP与转录丰度变化、生理转化和最终疾病风险联系起来的“因果链”。我们建议，通过使用多种组织类型，我们将采取这种形式的分析超出了国家的最先进的，允许有害的SNP在表达序列，转录丰度的模式，和表达网络，以解开复杂的产量相关的遗传性状的相互作用。为了能够准确定量转录物读取深度，我们建议通过使用来自CV的六个组织的深度配对末端Illumina RNA-seq和PacBio ISO-seq数据来开发参考转录物数据集。莫雷克斯我们将使用这一点来量化从人群中采样的相同六种组织中收集的RNA-seq数据的转录本丰度。RNA-seq衍生的SNP等位基因将补充外显子组捕获SNP，并且这些数据和转录物读取深度变化都将用于分析产量相关性状。基因共表达网络将使用来自Morex的参考转录数据集和来自长白猪收集的六个组织的RNA-seq数据来构建。这两个数据集将被整合，以确定候选基因的关键调控产量相关性状。候选基因的功能表征将通过从三个合作实验室的TILLING群体中鉴定有害等位基因来启动。该项目将提供全球社区资源，包括：参考转录数据集，来自长白种收集的其他SNP，以及用于探索关键调控基因及其与产量相关性状关系的基因共表达网络。我们将了解关键性状的GxE相互作用。我们的方法是唯一可行的，现在由于即将到来的大麦基因组的释放和独特的组装和特征种质可供财团。

Our goal is to identify and characterize novel barley genes that regulate yield, specifically those affecting seed, spike (the inflorescence) and tiller (seed bearing stems) traits. We have previouslygenerated exome capture sequence data from a geo-referenced collection of 192 two-row landraces, revealing over 1.6 million SNP alleles. Here we plan to phenotype this germplasm collection for yield-related parameters with particular focus on seed, spike, and productive tiller traits. We will assess the same traits, in the same way at each partners location. The uniqueness of our approach lies in our proposal to add layers of transcriptome sequence data (six trait related tissues per genotype) and to analyse how gene expression relates to trait development. In plants, the power of the approach has recently been demonstrated in brassicas under the banner of 'Associative Transcriptomics' to identify genes controlling seed compositional traits. However in yeast and mammalian model organisms, 'chains of causality' are now being identified that link SNPs to transcript abundance variation, to physiological transformation and ultimately risk of disease. We propose that by using multiple tissue types we will take this form of analysis beyond the state-of-the-art, allowing deleterious SNPs in expressed sequences, patterns of transcript abundance, and expression networks to unravel complex yield-related genetic trait interactions. To enable accurate quantification of transcript read depth, we propose to develop a reference transcript dataset by using deep paired-end Illumina RNA-seq and PacBio ISO-seq data from the six tissuesfrom cv. Morex. We will use this to quantify transcript abundance from RNA-seq data collected from the same six tissues sampled across the population. RNA-seq derived SNP alleles will supplement the exome capture SNPs and both these data and transcript read depth variation will be used for analysis of the yield-related traits. Gene co-expression networks will be constructed using the reference transcript dataset from Morex and the RNA-seq data from the six tissues sampled from the landrace collection. Both datasets will be integrated to identify candidate genes for key regulators of yield-related traits. Functional characterization of candidate genes will be initiated by identifying deleterious alleles from TILLING populations available in each of the three partner labs. The project will provide global community resources including: a reference transcript dataset, additional SNPs derived from the landrace collection, and gene co-expression networks for exploring key regulatory genes and their relationship to yield-related traits. We will gain an understanding of GxE interactions for key traits. Our approach is only feasible now due to the imminent barley genome release and the unique assembled and characterised germplasm available to the consortium.