Educational Framework of Genomic Data Science for Regional Populations; Hakim et al - Background: The Navajo Nation (NN) Human Research Code states that “research information and data generated by and about residents, local populations, and cultural heritage represent inalienable intellectual properties of the community and over which the NN will provide oversight” (NN Code, 1994). On the contrary, publicly available data may effectively bypass the NN’s oversight. Regardless of the rapid expansion of genomic science, less than 0.05% of inhabitants comprise participation in genetic research worldwide. Health disparities in these populations are evident when reviewing metrics such as rates of diabetes, pre-diabetes, life expectancy, and access to primary care, which underscores the need for improved healthcare infrastructure and a technically trained local workforce to handle institutional oversight. The ultimate goal of this proposed educational framework is to enhance research and education capabilities in scientific disciplines, particularly genomics and genomic data science. The plan includes expanding undergraduate and graduate-level course offerings, utilizing available resources like the CGDS Hub or AnVIL to participate in advanced research, and increasing the number of graduates in STEM fields. We, at Diné College, by undertaking this project, aim to prepare a skilled local workforce in genomic data sciences to facilitate collaboration between Diné College, other organizations, and higher education institutions to strengthen research in regional settings. The proposed educational framework grant 1) will improve training and research capacity at Diné College by building on existing competencies to provide new capabilities in data and genomic data science with a forward-looking approach. 2) will establish a pipeline framework to function as a central technical hub for creating and managing research data. 3) will increase understanding of local needs in epidemiological studies, genetic disorder identification, and risk factor analysis for autoimmune disorders, leading to improved healthcare management strategies, and 4) will strengthen local workforce development in several critical areas with implications beyond the recent pandemic. Expected Outcome: we will assemble a versatile team from STEM disciplines and collaborating mentors to coordinate training, research, and academic activities in genomic data science, address health-related issues in the Navajo Nation, and provide a technical support system (TSS) to address these challenges. Our model emphasizes building institutional capacity and preparing skilled personnel within affected regions as the key approach to long-term success.
