NUR 610 Module 5 Health Equity and Genomics Discussion Example

Discussion Board: Health Equity and Genomics

A Score That Sees Some People Better Than Others: Polygenic Risk, Ancestry and Health Equity

Initial Post

Health inequality describes differences in health between groups. Health inequity refers to differences that are unfair and avoidable, rooted in social conditions such as income, education and discrimination (Braveman et al., 2011). The distinction matters for genomics, because a new tool can create an inequity even when no one intends it.

Polygenic risk scores are a clear example. A polygenic score adds up the small effects of many genetic variants to estimate a person's risk of conditions such as coronary artery disease or type 2 diabetes. These scores are being marketed and tested for use in clinics. But a 2019 analysis showed that today's scores predict risk several times better for people of European descent than for anyone else (Martin et al., 2019). The cause is not biological: it is that the genome-wide association studies used to build the scores have overwhelmingly included people of European descent. As the authors note, clinical use of current scores would systematically give greater benefit to European-descent populations.

Imagine a clinic in Phoenix that begins using a polygenic score to decide who should start cholesterol-lowering medication early. A white patient might receive a fairly accurate estimate, while a Latino, Black or Native American patient receives a less reliable one, leading to under-treatment or over-treatment. The tool appears neutral, yet it delivers better care to one group. That is an inequity created by research decisions made years earlier.

West et al. (2017) argue that genomics research has missed opportunities to reduce disparities by focusing on discovery rather than on the social and structural factors that drive them. Polygenic scores show both sides of that problem: the research base is not diverse, and even a perfect score would not address the housing, food and care access that shape heart disease risk far more than genes.

I think fair use of polygenic scores would require at least three conditions. First, scores should not be used in clinical decisions for groups in which they have not been validated. Second, research funders should prioritize diverse cohorts, which Martin et al. (2019) found can improve accuracy even with smaller sample sizes. Third, clinics should continue to rely on proven, ancestry-neutral measures, such as cholesterol levels and blood pressure, which already identify most people at risk.

For public health nurses, the lesson is to ask of every new genomic tool: for whom does it work, and who might it leave behind?

Reply to Hannah

Hannah, your post described the history of genetic research with the Havasupai Tribe, where blood samples given for diabetes research were used for other studies without consent. That history is essential context for this week's question. It helps explain why some Native American communities are wary of genomic research, which in turn contributes to the lack of diversity that makes tools like polygenic scores less accurate for them. The answer cannot be simply to recruit more participants. As you noted, tribal nations increasingly set their own rules for research, including community control over data and samples. I think genomic equity depends on research partnerships that respect those rules, even when they slow research down. Do you think large national programs can work within tribal research codes, or will tribes need their own programs to benefit from genomics on their own terms? Some tribal nations have built their own research review boards and data agreements, which could become models for how large programs work with sovereign communities. That approach takes time, but it may be the only one that rebuilds trust.

Reply to Kevin

Kevin, you argued that polygenic scores should be used now because some information is better than none. I understand the appeal, especially for people at high risk. But the evidence (Martin et al., 2019) suggests the problem is not only less information for some groups; it is systematically uneven benefit that could widen existing gaps in heart disease and diabetes outcomes. A tool that improves care for one group while doing little for another increases inequity, even if no one is harmed directly. I also wonder whether scores add much to what clinicians can already measure cheaply, such as cholesterol and family history. Your idea of using scores only in research until they are validated across ancestries seems like a reasonable compromise. Would you support requiring ancestry-specific validation before any clinical use, similar to how drugs must show safety in the populations that will use them? A standard like that might slow adoption, but it would protect patients from tools that look precise but are not, and it would create a strong incentive for developers to include diverse participants from the start rather than adding them later.

References

Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: Coming of age. Annual Review of Public Health, 32, 381-398. https://doi.org/10.1146/annurev-publhealth-031210-101218

Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584-591. https://doi.org/10.1038/s41588-019-0379-x

West, K. M., Blacksher, E., & Burke, W. (2017). Genomics, health disparities, and missed opportunities for the nation's research agenda. JAMA, 317(18), 1831-1832. https://doi.org/10.1001/jama.2017.3096