Sarah Yim, Seoul International School, Seongnam-si, South Korea
When the Human Genome Project was completed in 2003, it was referred to as “the first draft of the human book life.” However, this project, along with many other concurrent studies, did not represent the entire “human book,” for it primarily applied to a minute portion of the global population — the Europeans (Mapes, 2019). Despite a marginal improvement in diverse representation, population diversity in genomic research remains moderately low today. Public willingness to donate genomic data also remains low, as people are often discouraged by a lack of public trust in data use and familiarity with the genomic field (“Willingness to Donate DNA,” 2019). With continuous disproportionate populations in genomic research, it is imperative for researchers to include more diverse populations in genomic studies to enhance clinical intervention and continuously promote a new understanding of human biology.
Discovering how each human body responds to different challenges remarkably augments the accuracy of clinical diagnoses. Approximately 88% of sample participants in genome-wide association studies (GWAS) represent white European ancestry (Mills & Rahal, 2019). Hence, Europeans have had the most accurate and reliable diagnoses for many years. However, non-Europeans frequently receive false positives and ambiguous clinical diagnoses because data used to diagnose conditions misrepresent their population. Each population most likely carries a different combination of genetic variants, so clinical conclusions and applications to non-Europeans are often limited and erroneous. In 2018, researchers found nearly 300 million base pairs in African ancestry not present in the default, standardized genome (Manrai et al., 2016). Moreover, when black patients were diagnosed with hypertrophic cardiomyopathy, an inherited disease involving an abnormal heart muscle thickening, their benign variants were misclassified as pathogenic variants (Shaw, 2019). Further simulations displayed that incorporating data from even a small population of African Americans into genomic data cohorts could have led to more accurate classifications of variants for black patients (Manrai et al., 2016). Increasing diversity in genomic studies would reduce misclassifications to ameliorate the accuracy of clinical diagnoses and, in a broader sense, contribute to precision medicine to create suitable treatments specified to a group sharing similar genetics (Burke, 2019).
Increasing diversity in genomic research also engenders a novel understanding of human biology from nascent research. Today, the lack of diversity in data collection significantly reduces opportunities to detect distinctive features in different populations, curtailing genomic progress to trace human history. By gathering a group of 50,000 multi-ethnic participants (Hispanics, Latinos, African Americans, Asians, etc.) and running a GWA study, one team identified 27 new trait-variant associations of complex diseases that were ancestry-specific and had not been previously discovered (Mapes, 2019). These conspicuous findings further enhanced the existing understanding of the genetic architecture of traits, demonstrating the importance of inspecting underrepresented populations in research. So what about new understanding? For future genomic research, this means ample data for each varying population, opening many more opportunities to analyze evolutionary patterns, medical advancements, and even modern migrations specific to a genetic ancestry (Bentley et al., 2017). Because evolution and migration continue to alter genetic variants of different populations, researchers must constantly create an accurate picture of global diversity and maximize the usage of all data open to humans.
Failure to reflect population diversity in genomic research has already had and will continue to have huge ramifications. One prime reason for such a wide diversity gap in genomic data stems from public unfamiliarity with the value of genomic research. Studies show that individuals who are more knowledgeable about genetics are more likely to donate their data (“Willingness to Donate DNA,” 2019). Geographical bias also limits diversity in genomic research. Most genomic studies are conducted in the United States and Europe. They tend to utilize data from the most prevalent and convenient racial group in their area, leaving out genetic variants from other ancestries (Burke, 2019).
To increase genetic diversity in research, researchers should first ensure that patients understand the value and purpose of their participation in donating their DNA. Full comprehension of the aim and requirements of the genomic study in a patient-centered approach will increase the willingness of participants to contribute their data, allowing researchers to obtain data from a greater variety of ancestries. Funding agencies or organizational programs could also support genomic research in a wider range of areas not limited to the U.S. or Europe to access data from a more inclusive sample of patients (Burke, 2019).
On the bright side, efforts to enlarge diversity in genomic data have increased in recent years. In particular, the Genome Aggregation Database (gnomAD), a database developed by global investigators to aggregate genomic data from various sequencing projects, has assembled and released 15,708 whole-genome sequences from genetically unrelated individuals for the benefit of the biomedical community (Gnomad, n.d.). In addition, projects such as the Estonian Biobank and the UK Biobank have also allowed access to clinical data from more than 200,000 participants and 500,000 participants, respectively, to improve clinical diagnosis and treatment of fatal illnesses such as cancer, arthritis, and forms of dementia (Leitsalu, 2015; Sudlow, 2015). Although such efforts to increase genomic diversity are progressively reinforced in the biomedical community today, both a greater diversity of participants and a larger number of projects and databases are imperative to accelerate the progress to better tailor novel treatments that benefit all human populations. Enlarging diversity will undoubtedly supplement clinical diagnoses and deepen understanding from novel findings, advancing future genetic research to improve human health.
References
Bentley, A. R., Callier, S., & Rotimi, C. N. (2017). Diversity and inclusion in genomic research: why the uneven progress?. Journal of community genetics, 8(4), 255–266. https://doi.org/10.1007/s12687-017-0316-6
Burke, C. W. (2019, June 18). The Importance of Increasing Diversity in Genetic Research. Retrieved from https://www.biospace.com/article/the-importance-of-increasing-diversity-in-genetic-research/
Gnomad. gnomAD. (n.d.). Retrieved January 28, 2022, from https://gnomad.broadinstitute.org/
Leitsalu, L., Haller, T., Esko, T., Tammesoo, M. L., Alavere, H., Snieder, H., Perola, M., Ng, P. C., Mägi, R., Milani, L., Fischer, K., & Metspalu, A. (2015). Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. International journal of epidemiology, 44(4), 1137–1147. https://doi.org/10.1093/ije/dyt268
Mapes, Diane. (2019, June 19). Lack of diversity in genetic research a problem. Retrieved from https://www.fredhutch.org/en/news/center-news/2019/06/lack-diversity-genetic-research-problem.html
Manrai, A. K., Funke, B. H., Rehm, H. L., Olesen, M. S., Maron, B. A., Szolovits, P., Margulies, D. M., Loscalzo, J., & Kohane, I. S. (2016). Genetic Misdiagnoses and the Potential for Health Disparities. The New England journal of medicine, 375(7), 655–665. https://doi.org/10.1056/NEJMsa1507092
Mills, M. C., & Rahal, C. (2019). A scientometric review of genome-wide association studies. Communications biology, 2, 9. https://doi.org/10.1038/s42003-018-0261-x
Shaw, M. L. (2020, July 30). Healthcare, Genetic Testing Disparities Persist in Black Patients With Hypertrophic Cardiomyopathy. Retrieved from https://www.ajmc.com/view/healthcare-genetic-disparities-persist-in-black-patients-with-hypertrophic-cardiomyopathy
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine, 12(3), e1001779. https://doi.org/10.1371/journal.pmed.1001779
Willingness to Donate DNA to Research Is Low, Global Survey Finds. (2020, September 18). Retrieved from https://www.genomeweb.com/genetic-research/willingness-donate-dna-research-low-global-survey-finds
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