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.
