Collaborative Research: ABI Innovation: Plant Genotype-Phenotype (G2P) Association Discovery via Integrative Genome-scale Biological Network & Genome-wide Association Analysis

了解基因型和表型（G2P）关联的机制一直是现代生物学中一项重要而具有挑战性的任务。挑战在于高维基因变量和基因调控和相互作用的复杂性，它们共同定义了特定的表型（也称为性状）。该项目将开发创新的方法、工具和生物信息学系统，通过整合基因组规模的生物网络和全基因组关联分析来破译植物G2P关联。这项工作的突破将导致对植物中生物过程，途径和复杂性状如何分层调节的系统级理解。通过提供农业重要性状的潜在调控机制和关键调控因子，推进这些基础知识将极大地有利于现代基因组辅助植物育种。这反过来又有很大的潜力转化为改善植物质量和农业生产的新手段，从而使整个社会受益。这三个研究所将开发研究植物G2P关联的尖端技术，为培养计算生物学、生物信息学、植物基因组学和统计遗传学等跨学科领域的本科生、研究生和博士后提供良好的机会。在该项目的招聘中，代表性不足的少数民族和妇女将成为特别的对象。这项研究将构成以生物信息学和统计遗传学为中心的拟议教育讲习班的基础。透过这三个学院，我们会安排富有创意的实践外展活动。与当地K-12学校合作，鼓励年轻人成为生物信息学科学家。通过一个完全集成的生物信息学平台，将开发创新的方法来分析基因组尺度的生物网络和全基因组关联，从而发现植物中的G2P关联。该项目的具体目标包括：1)开发新的自上而下和自下而上的图形高斯模型（GGM）算法，以重建控制生物过程和途径的分层基因网络；2)开发模型和算法，利用新的统计遗传学方法进行高精度的大规模标记-性状关联分析；3)开发一个基于图形搜索的综合生物信息学平台，以促进G2P关联的整合、破译和发现。为了验证我们的方法和工具，来自全基因组植物组学研究和全基因组关联研究（GWAS）的公开数据将被整合和分析，将性状与SNP标记相关联，并微调与表型相关的分层和/或多效性调节因子和功能网络的预测。从这个项目中产生的新知识和分析方法和工具将通过演讲、出版物和网络应用程序向广大公众传播。所有工具和数据资源将在http://plantgrn.org/上免费提供给植物研究界，加速植物生物信息学和植物科学研究、教育和应用。

Understanding the mechanisms of genotype and phenotype (G2P) associations has been an important and challenging task in modern biology. The challenge lies in the high-dimensional gene variables and the complexity of gene regulation and interactions that collectively define particular phenotypes (also called traits). The project will develop innovative methods, tools and bioinformatics systems to decipher the plant G2P associations through integrative genome-scale biological network and genome-wide association analysis. A breakthrough in this work will lead to a systems-level understanding of how biological processes, pathways and complex traits in plants are hierarchically regulated. Advancing such fundamental knowledge will greatly benefit modern genome-assisted plant breeding by providing the underlying regulatory mechanisms and key regulators of agriculturally important traits. This in turn will have great potential to be translated into new means of improving plant quality and production for agriculture, thus benefiting society as a whole. Cutting-edge technologies will be developed to study G2P associations in plants, providing excellent opportunities for training undergraduates, graduates and postdocs in interdisciplinary fields such as computational biology, bioinformatics, plant genomics, and statistical genetics, at the three institutes. Underrepresented minorities and women will be especially targeted in the recruitment of the project. The research will form the basis of the proposed educational workshops centering on bioinformatics and statistical genetics. Creative and innovative hands-on outreach activities will be arranged through the three institutes? outreach programs with local K-12 schools to inspire young minds to become bioinformatics scientists. Innovative methods will be developed to analyze genome-scale biological networks and genome-wide associations through a fully integrated bioinformatics platform, enabling the discovery of G2P associations in plants. Specific aims of the project include 1) to develop novel top-down and bottom-up graphical Gaussian model (GGM) algorithms to reconstruct the hierarchical gene networks that control biological processes and pathways; 2) to develop models and algorithms that enable large-scale marker-trait association analysis with high precision using novel statistical genetics approaches; and 3) to develop a Graph-search-empowered integrative bioinformatics platform to facilitate the integration, deciphering and discovery of G2P associations. To validate our approaches and tools, public data from genome-wide plant 'omics' studies and genome-wide association studies (GWAS) will be integrated and analyzed, associating traits with SNP markers and fine-tuning the prediction of phenotype-associated hierarchical and/or pleiotropic regulators and functional networks. The novel knowledge and analytic methods and tools yielded from this project will be disseminated into the public at large through presentations, publications and web applications. All the tools and data resources will be made freely available at http://plantgrn.org/ to the plant research communities, accelerating plant bioinformatics and plant science research, education and applications.