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（G2 P）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的复杂性以及共同定义特定表型（也称为性状）的相互作用。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G2 P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水平的理解。推进这些基础知识将大大有利于现代基因组辅助植物育种提供潜在的调控机制和农业重要性状的关键调控因子。这反过来又有很大的潜力转化为提高植物质量和农业生产的新手段，从而造福于整个社会。将开发尖端技术来研究植物中的G2 P协会，为三个研究所在计算生物学，生物信息学，植物基因组学和统计遗传学等跨学科领域培养本科生，研究生和博士后提供极好的机会。在项目的征聘中，将特别针对任职人数不足的少数民族和妇女。该研究将成为拟议的以生物信息学和统计遗传学为中心的教育研讨会的基础。将通过这三个研究所安排富有创意和创新的实践外展活动？与当地K-12学校开展外联计划，激励年轻人成为生物信息学科学家。将开发创新方法，通过完全集成的生物信息学平台分析基因组规模的生物网络和全基因组关联，从而发现植物中的G2 P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，使大规模标记-性状关联分析具有高精度，使用新的统计遗传学方法; 3）开发一个基于图搜索的整合生物信息学平台，以促进G2 P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）的公共数据将被整合和分析，将性状与SNP标记相关联，并微调表型相关的分层和/或多效性调节因子和功能网络的预测。该项目产生的新知识、分析方法和工具将通过介绍、出版物和网络应用程序向广大公众传播。所有的工具和数据资源都将在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.