Reference number : 172-2024-2025-Q2-1018576, amendment number : , agreement type : G, recipient legal name : The Governors of the University of Calgary, recipient country : CA, recipient province : AB, recipient city : Calgary|Calgary, recipient postal code : T2N 1N4, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Pea Genomics: Using Computational Analytics for Enhanced Genomic Prediction., agreement title french : Génomique du pois : Utilisation de l’analyse computationnelle pour améliorer la prédiction génomique., agreement number : 1018576, agreement value : 401104.0, agreement start date : 2024-08-07, agreement end date : 2026-03-31, description english : The proposed Grant & Contribution project aims to perform essential
computational analyses in support of the PeaCE (Pea Climate Efficient)
project approved by Genome Canada. This entails evaluating the genetic
variation present within the Climate-Smart Pea Germplasm Resource
(CSPGR), a collection comprising 2000 lines.
The project also seeks to develop an advanced genomic prediction
framework for peas, with the potential to expedite the breeding cycle and
facilitate the integration of greater genetic diversity into breeding programs.
To achieve this, the project will employ a combination of cutting-edge
statistical models and artificial intelligence/machine learning (AI/ML)
algorithms to analyze genomic data and predict crucial traits in peas,
including disease resistance, stress tolerance, yield potential, and other
significant agronomic characteristics. By leveraging these computational
tools, the project aims to enhance the efficiency and effectiveness of pea
breeding programs., description french : Génomique du pois : Utilisation de l’analyse computationnelle pour améliorer la prédiction génomique., expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada