Ethical, Legal and Social Issues in Diversifying Genomic Data: Literature Review and Synthesis

Advances in technology have resulted in the ability to sequence entire human genomes as a routine, relatively inexpensive, investigation in healthcare. This offers many promises of personalising, stratifying, and targeting healthcare with an understanding of genetic susceptibility to particular diseases or conditions. However, research collections (databases, biobanks etc) that underpin these developments are significantly skewed towards populations of European ancestry meaning that our understanding of genetic susceptibility (or indeed of genetic protection to disease) is less good for many other populations in the world. Just as a dermatology text book skewed towards skin problems on white skin may be less useful to black populations, so genomic knowledge derived from one particular ancestry means it may be less useful to people with different ancestries. The need to diversify genomic data, to improve the evidence base for genomic medicine for all ancestries, is well recognised, but is more complex than simply increasing the collection of data from people from a range of ancestries. We reviewed the literature to understand the challenges of diversifying genomic data to identify key ethical, legal and social issues. Our findings were: 1.Many research practices are exclusionary and need to change. Examples include approaches to recruitment or data collection that do not consider the cultural setting in which potential participants are situated. Research also often lacks reflexivity about diversity on the part of researchers and research institutions. 2.Co-design is key to identifying and avoiding potential problems around data diversification. This requires an understanding of the concerns of underserved individuals and communities regarding exploitation and stigmatisation, as well as issues of data ownership and sovereignty. Without attention to group as well as individual concerns, participant engagement may become tokenistic which in turn risks exacerbating existing, as well as creating new, inequalities. 3.There are wider structural issues that influence researchers’ and participants’ attempts to generate diverse data. For example, (a) some researchers view data as neutral, but this ignores the social construction of data and technologies, and their tendencies to reflect societal inequalities. (b). Efforts to diversify data should be contextualised within the historical trajectory of structural racism and legacies of colonialism. (c) Classification and categorisation of populations have political consequences and need to be closely interrogated. These findings show that deliberation between researchers and participants, during all stages of research from planning and recruitment through to analysis, interpretation and dissemination is key to successful diversification.