Principles and Policy Recommendations for Comprehensive Genetic Data Governance.

Area of Science:

• Genomics
• Bioethics
• Data Privacy

Background:

• Ubiquitous genetic data collection for health, ancestry, and traits raises privacy and discrimination concerns.
• Advancing artificial intelligence (AI), especially multi-modal systems, intensifies these risks by integrating diverse datasets.
• Current scientific understanding and regulatory frameworks lag behind the rapid evolution of genetic data usage.

Purpose of the Study:

• To organize the uses of genetic data into four distinct pillars.
• To develop a risk assessment framework for genetic data governance, preserving key values.
• To identify regulatory gaps and threats to privacy and liberties, particularly genetic discrimination.

Main Methods:

• Organizing genetic data uses into four pillars.
• Developing a risk assessment framework based on privacy scholarship (contextual integrity, data relationality, Belmont principle).
• Applying the framework to four real-world case studies.

Main Results:

• Identification of critical gaps in existing regulatory frameworks for genetic data.
• Specific threats to privacy and personal liberties, especially through genetic discrimination, were identified.
• The framework highlighted the need for robust governance to address risks from multi-modal data integration.

Conclusions:

• Existing regulations are insufficient to manage the risks associated with large-scale genetic data collection and AI integration.
• Policy recommendations are crucial for safeguarding individual rights in the current under-regulated ecosystem.
• Enhanced genetic data governance is necessary to mitigate privacy violations and prevent genetic discrimination.