iPG2P: Relating Genotypes to Phenotypes in Complex Environments

iPG2P GC Team Members Voting on Priorities, Chicago, July 2009 (photo: T. Lee) Elucidating the relationship between plant genotypes and the resultant phenotypes in complex (e.g., non-constant) environments is one of the foremost challenges in plant biology (NRC, 2008). Plant phenotypes are determined by often intricate interactions between genetic controls and environmental contingencies. In a world where the environment is undergoing rapid, anthropogenic change, predicting altered plant responses is central to studies of plant adaptation, ecological genomics, crop improvement activities (ranging from international agriculture to biofuels), physiology (photosynthesis, stress, etc.), plant development, and many many more. 

A concerted attack on the G2P problem will require the combined and integrated efforts of specialists in functional-, quantitative-, and computational genetics/genomics, bioinformatics, modelers, physiologists, computer scientists (for topics from high performance computing to visualization), etc. Cyberinfrastructural innovations are required to facilitate collaborations this diverse. Planning efforts leading up to the iPG2P project have identified five, high priority areas where progress is needed:

1. Pipelining of NextGen sequence data into virtual genotype and molecular phenotype databases. Virtual data bases are comprised of multiple, individual databases located at multiple sites, that are effectively integrated by middle ware that provides common access.

2. Data integration - the infrastructure necessary to combine/overlay data from such virtual databases to permit deeper insights, generation of hypotheses, evaluation of models, practical applications, etc.

3. Statistically-based tools for use in inferring relationships ranging from marker associations (primarily) to, where practical, links in network structures. Many such tools exist, but the value-added aspect in the current context is to make them smoothly interoperable with the other features of the cyberinfrastructure.
4. Visual analysis tools. It is necessary to present integrated data to users in ways that are both concise and revealing. Such presentations must include capabilities for both static and (increasingly) dynamic/kinetic displays of plant biology information (e.g. .omics., ecophysiological data), be it multidimensional [2d (e.g. geographical, comparative genomics), 3d (e.g. PCA), 4d, or higher], and/or in the form of networks or pathways.
5. Modeling framework tools to support the construction, parameter estimation, sensitivity analysis, and utilization of models. Again, the value added is in interoperablility. In the short term, operation within an integrated data environment will facilitate all forms of modeling (including statistical). Over the near-to-intermediate term, components of ecophysiological models will increasingly employ the results of gene-based network studies, thus enhancing their application in breeding and other contexts.

Education, Outreach and Training

The iPlant Collaborative offers opportunities for novel approaches to education, outreach, and training at multiple levels, from K12 to the citizen naturalist to the scientifically literate layperson to the fledgling scientist in training. We envision creative ways to use cyberinfrastructure (CI) to teach about plant biology and new opportunities to train teachers and students in the use of CI. We propose cross-training in biology and computer science for students of all ages and teacher workshops for training in the use and implementation of CI for teaching plant biology. Our basic, general goals for K12 education and public outreach are:

1. To develop CI for application to K12 education and provide training to teachers to integrate the resulting tools into curricula. 
2. Facilitate access to effective educational materials for a broad public audience (e.g., through websites, YouTube, and new CI developed through this project).
3. Facilitate access to journals, data, and other information for students and post-docs.

We propose to meet these goals through collaboration with personnel from iPlant and from other Grand Challenge projects.Read more...

Project Steering Committee

Stephen M Welch, Department of Agronomy, Kansas State University
Systems simulation with emphasis on applications, genomics and crop modeling. Electronic information delivery and decision support systems, especially in agriculture.

Tom Brutnell, Boyce Thompson Institute, Cornell University
Plant molecular biology/genetics.

Doreen Ware, Agricultural Research Service, USDA
Computational biology; comparative genomics; genome evolution; diversity; gene regulation; plant biology.

Dan Kliebenstein, Department of Plant Sciences, University of California at Davis
Roles and mechanisms associated with plant secondary metabolites in multiple model species.

Ruth Grene, Department of Pathology, Physiology, & Weed Science, Virginia Tech
Functional genomics of abiotic stress responses in crop plants. Diversity of stress resistances within and across crop species. Teaches Biological Paradigms for Bioinformatics.

Chris Myers, Computational Biology Service Unit, Life Sciences Core Laboratories Center, Cornell University
Computational physics & systems biology / Development of modeling framework for photosynthesis and linking to infrastructure for data analysis of high throughput data sets

Steve Goff, BIO5 Unit, University of Arizona
iPlant Project Director

Dan Stanzione, University of Texas, Austin
Dep. Dir., Texas Advanced Computing Center

Matt Vaughn, Cold Spring Harbor Laboratory
Computational genomics;epigenetics;bioinformatics, iPlant G2P Scientific Lead

iPG2P Engagement Team

National Research Council, Achievements of the National Plant Genome
Initiative and New Horizons in Plant Biology. 2008, Washington, D.C.:
The National Academies Press

Working groups have been formulated in each of these areas (NextGen Sequence Pipeline, Data Integration, Statistical Inference, Visual Analytics, and Modeling Tools) with co-leaders to serve as points of contact.