Krystal Tsosie

Dr. Krystal Tsosie (Diné/Navajo Nation), PhD, MPH, MA, is an Indigenous geneticist-bioethicist and genomic data systems architect building the infrastructure that will shape the future of precision medicine and genomics. Trained in genetic epidemiology and applied ethics, her work spans laboratory science, population-level analysis, and the design of large-scale data systems that guide how genomic research is conducted, shared, and applied.

Her research integrates human genomics, paleogenomics, and biodiversity genomics with bioethics and data science. She develops scalable, community-governed data infrastructure, including Indigenous-led platforms such as biobanks and tribal data repositories. These systems improve representation in genomic datasets and support more accurate and inclusive applications in precision medicine, ancestry, and biodiversity research.

As Principal Investigator on major federally funded initiatives, including NIH Common Fund programs, Dr. Tsosie advances new models for data governance, interoperability, and responsible data sharing that are shaping national and emerging global genomic data ecosystems. Her work has helped define—and continues to shape—the field of Indigenous genomics and data sovereignty, with contributions published in leading journals such as Nature Genetics, Cell, and Nature.

She is a co-founder of the Native BioData Consortium and serves in leadership roles shaping global standards for genomic data through organizations such as the Global Alliance for Genomics and Health. She is an invited speaker across North America, Europe, and Oceania and contributes to national and international policy efforts through the National Academies of Sciences, Engineering, and Medicine, among others.

Dr. Tsosie also studies how emerging technologies—including artificial intelligence and large-scale genomic data systems—can be designed to reduce bias, prevent harm, and support community-driven innovation. Through her research, leadership, and mentorship, she is helping define how genomic data systems will be designed and used in the future.