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The Role of Genomics in Fostering and Supporting Arctic Biodiversity: Implications for Wildlife Management, Policy and Indigenous Food Security

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Overview

Wildlife genome information is extremely valuable for environmental decision making, yet much remains unused for this purpose. This project draws together partners with expertise across disciplines, cultures and organizations, building upon team strengths in Arctic observation and monitoring, biology, conservation, cyber-cartography, data management, genomics, geography, Indigenous Knowledge, the legal and policy sciences, and resource management. Together the team will co-develop a suite of genomics knowledge-mobilization tools that will support environmental decision making. The focus is on supporting end-users with responsibilities for or interests in the areas of biodiversity monitoring, conservation, and the co-management of wildlife that are key to the social, cultural, physical and economic well-being of northern Indigenous Peoples.

The team will develop decision support tools building on an assessment of genomics data availability (can it be located, is it obtainable?) and accessibility (is it useable by non-experts and for decision making and policy development?), and we will consider the potential and the practical, economic, legal and ethical issues of mobilizing genomics for decision making – including those pertaining to Indigenous perspectives and rights, and national and international frameworks and commitments that may influence policy at different levels of government. Project activities and outcomes will support conservation, natural resource management, and the sustainability of Arctic wildlife. Outcomes will also support Canada’s efforts to protect Arctic species and ensure food security for Arctic People. The project can serve as a model for mobilizing genomics in different regions of Canada and in other nations.

Canadian Network for Learning Healthcare Systems and Cost-Effective ‘Omics Innovation (CLEO)

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Overview

Cancer is a collection of related genetic diseases. These are caused by DNA mutations that change how cells grow and develop. The term ‘genome’ refers to the complete set of DNA. Recent innovations allow us to sequence the complete set of DNA and RNA in a patient’s cancer, known as the tumour genome. The hope is that the knowledge generated about the tumour genome compared to the normal genome will help us to develop treatments that target and kill cancer cells based on specific cancer-causing mutations. It will also help us repurpose drugs that have been approved for other cancers sharing similar mutations. This mutation targeted approach is called precision oncology.

To date, Canadians have had limited access to precision oncology because of a lack of data about the clinical effectiveness (does it work?) and cost-effectiveness (can we afford it?). Answering these two questions requires coordinating large amounts of different kinds of data. Before precision oncology can benefit Canadian cancer patients, data systems, policies, and practices are needed to get the right data, to the right researchers and care providers, at the right time, in the right way.

Healthcare systems that generate data, produce evidence, and use this evidence to guide patient care are called Learning Healthcare Systems. Unfortunately, current systems are not designed to allow for learning healthcare. In response to this unmet need, major Canadian precision oncology initiatives are building platforms for data integration and sharing to enable learning healthcare systems. To guarantee the success of precision oncology initiatives, we need to understand their economic impact and make sure that their design is in line with patient and public values as well as Canadian laws and regulations.

Our Canadian Network for Learning Healthcare Systems and Cost-Effective ‘Omics Innovation (CLEO Net) will meet these needs by partnering with Canadian learning healthcare system initiatives for precision oncology. Together we will: (1) inform the design of learning healthcare systems that turns genomic knowledge into sustainable cancer care; (2) advance research; (3) build capacity to deliver this research and its benefits into the future; and (4) produce research that yields individual, social, and economic benefits for all Canadians.

Applying genomic signal processing methods to accelerate crop breeding

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Overview

Selective breeding improves plant and animal products by identifying desirable traits such as quality, yield, and ability to grow in difficult conditions, ensuring that that there is sufficient production for food, fuel and raw materials. Factors like climate change and population growth are making selective breeding more important than ever. One of the largest challenges facing the plant research community is identifying the suite of genes that make organisms well adapted to their environment and using this information in breeding programs.

Drs. Lewis Lukens, Cortland Griswold and their team are using bioinformatics tools to understand how organisms that adapt well to their environments can be selected to accelerate the development of new plant varieties.