By Taine Archbold
Precision medicine is a revolutionary, rapidly developing field of healthcare with the goal of providing tailored treatments for all patients. This innovative approach has been utilized by global research efforts towards informing disease research and clinical care, including the development of genetic-level therapies such as RNA vaccines.
The ongoing analysis of large-scale genomic data serves as a crucial component in
understanding the burden of disease across varying populations and thus providing effective treatment…
…so why is the very resource used to advance these personalised approaches not
entirely reflective of the world’s actual genomic diversity?
deposited in the 2022 GISAID. From the 2% contribution of African sequences, a continental distribution
map visualises the approximate sequence data submissions of each country within Africa – notably,
South Africa, Kenya, and Nigeria contribute close to 50% of all data generated in the continent.
Despite Africa forming 17% of the world’s population, being home to the origin of modern humans and possessing a diverse range in population genetics, African
genomic data only forms a minor percentage of the genomic data used in worldwide biomedical research. This discrepancy can be seen by the GISAID composition visualised in Figure 1, wherein just three countries – South Africa, Kenya, and Nigeria – form 51% of Africa’s minimal contribution towards the database.
A severe underrepresentation of total genome diversity means precision medicine falls victim to the very same shortcoming that prevents the “one size fits all” approach of generalized healthcare from being effective to all peoples. Recognizing the limitations of this lopsided data representation, a 2022 study sought to review the impacts of this genomic data gap and investigate measures to ensure the genomic data contributing to
global research efforts is truly representative of real-world population diversity.
It is herein reported that many global health pitfalls arise through this African genomic data gap. Efficacy is one such concern, as cures may only demonstrate effectiveness in a limited population. Another is the possibility of variants from unreported or uncharacterized genetic mutations that hamper disease prevention and management, indicating a significant inclusion of African genomic data should provide greater insights towards disease research, benefiting the global population.
It should also be noted how the lack of appropriate genetic diversity representation isexacerbated by factors including lack of demand, high cost, logistics challenges, and the threat of exploitation/misuse of African genomic data. Thus, while organizations such as H3Africa, ACEGID, 54Gene and others alike have already risen to the challenge of closing this gap and building a larger genomic data repository, bridging the genomic
data gap entails a multidisciplinary approach to provide the supportive infrastructure and resources required for local researchers to suitably reap the benefits of equitable data representation.
By prioritizing measures to improve health equity, this shift towards inclusion will benefit not just African populations, but global precision medicine as a whole.
Reference:
Omotoso, O.E., Teibo, J.O., Atiba, F.A. et al. Bridging the genomic data gap in Africa:
implications for global disease burdens. Global Health 18, 103 (2022).
https://doi.org/10.1186/s12992-022-00898-2
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