May 15, 2009
Understanding Maize Genomics to Achieve Agricultural Sustainability Lead Researcher: Dr. Steven Rothstein
Provincial Funding: $2,792,542
Number of Researchers Affected: 10
Increasing demand for food in developing nations like China and India, diminishing supplies and rising costs of fossil fuel energy, and global climate change are putting unprecedented stress on agricultural productivity. Corn is a major crop in Ontario, worth over $1 billion to the economy annually. The University of Guelph’s research, with its industrial partner Syngenta, will focus on maize plants’ ability to use nitrogen-based fertilizers more effectively and use water under stress conditions. Since nitrogen-based fertilizers are a major pollution source, reducing their use will lessen environmental impacts. The research will identify genes that control plant growth and alter their activity to specifically target traits to improve the plant. Genomic information gained from studying maize can be transferred to other important grain crops, such as barley, rice and wheat.
Key Private sector Partners:
Syngenta
Environmental Barcoding through Massively Parallelized Sequencing Lead Researcher: Paul Hebert
Provincial Funding: $400,000
Number of Researchers Affected: 5
The assignment of a DNA barcode involves the analysis of variations in the sequence of a select region of a particular gene, mitochondrial cytochrome c oxidase I (COI) in each species. Using these DNA barcodes, it will be possible to identify any organism, be it juvenile or adult, male or female, large or small, from only a tiny piece of tissue. All DNA barcodes are stored in the Barcode of Life Data Systems (BOLD), which is used by all international barcode campaigns in the Consortium for the Barcode of Life (CBOL). The Canadian group, led by Dr. Paul Hebert and funded through a Genome Canada Competition III project, are leaders in this field and have contributed many of the records in BOLD. This technology development project, the first application of the information contained in BOLD, will expand the current protocol from single sample analysis and develop new informatics tools to enable the analysis of mixed biotic samples, allowing ‘environmental barcoding’ and biodiversity monitoring of any environmental sample.
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