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Analysis reveals that failure to understand the shifting genetic makeup of Mexican Americans inflates dangerous disparities in the medical field.
One way scientists seek to better their understanding of human biology is through studying the genome. This understanding can then be applied to make medical advancements for the personalized care of patients. This population-based medical genomics relies on genome-wide association studies (GWAS), which investigate links between genetic markers and phenotypes to provide information such as common genetic disease markers (Popejoy 2019). However, 80% of this research in the United States uses a demographic composed mostly of individuals of European descent (Popejoy 2019). Hispanic and Latin American participation of GWAS, for example, has been kept around 1% despite taking up almost 30% of the population, preventing Amerindigenous (AI) people from receiving benefits of precision medicine (Spear et al. 2020). This widens health care disparities already present due to financial burdens, language barriers, and mistrust between these communities and the American healthcare system (Canedo et al. 2020).
To investigate the genetic architecture of Mexican Americans, Spear et al. analyzed the genetic Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Differences in global ancestry estimates were calculated for each HCHS/SOL population, finding that AI ancestry and birth year have a positive correlation in all cases (Spear et al. 2020). The Mexican American population specifically had statistically significant results along with a large sample size, and thus, was focused on for the rest of the study.
Further analysis revealed that this statistically significant, positive correlation between birth year and AI ancestry held true across different categorizations of Mexican American data such as US-born or not US-born, recruitment region, educational attainment, and gender (Spear et al. 2020). Across these datasets, global AI ancestry increased by about 20% on average for those born during the 50 year period of 1940 to 1990. However, no one specific loci within the HCHS/SOL genome was found to be significantly associated with birth year.
The authors next investigated why AI ancestry was increasing over time in Mexican American populations, concluding there to be several environmental and cultural factors at play. By looking at runs of homozygosity (ROH) length, results showed background relatedness, and not parental relatedness, to have increased throughout time in these populations (Spear et al. 2020). This suggests that Mexican American individuals are tending to mate with one another more often than expected of random mating. While alone, this would not cause a significant change in AI ancestry, this ancestry-based assortative mating can magnify genetic differences caused by increased birth rates within a specific group. The authors concluded through simulating this possibility that the combination of both of these factors result in changes in ancestry proportions over time similar to what they had observed in Mexican American populations. A model was also constructed to consider the added effect migration patterns may have and it was concluded that this was also consistent with the observed data.
In consideration of why these trends matter, this study next considered the biomedical traits that this AI ancestry is associated with. Among Mexican Americans, height had the strongest association with global AI ancestry (Spear et al. 2020). A regression model also demonstrated a strong positive relationship with birth year. This is important because height is a major complex trait that is primarily studied through GWAS, and the results of such studies are important for creating genetic predictions through polygenic risk scores (PRS). Thus, these PRS are most effective for those of European descent due to GWAS studies being mostly composed of those populations. The authors of this specific study confirmed that PRS correlated with height among Mexican Americans only when they had the lowest levels of AI ancestry.
While the changing genetic architecture of an ethnic group in America is not surprising to me, the reasonings behind it are fascinating. In a country that self proclaims as a “melting pot,” ancestry-based assortative mating magnified by increased intergroup birth rates is not what I would have expected. This makes me wonder why these dynamics are occurring and if these trends are found in other minority populations of America. A major weakness of this study is that it focuses on very specific areas of America, and further research would be needed to confirm that these conclusions are consistent throughout the country. However, in general, this study concisely conveys the importance of research on the genetic makeup of non-white individuals in America and makes me appropriately fear the harm these racial and ethnic groups will face at the hands of misguided doctors relying on uneducated information. The most direct way to combat this specific issue is through the immediately increased diversity in GWAS studies, but overall, more research for minorities must be a priority for the scientific community of the United States in order to ensure equitable access to care in the largely biased medical institutions of this country.
Mimi Ughetta is a Genomics Major at Davidson College in Davidson, NC. Contact her at miughetta@davidson.edu.
References:
Canedo J. R., C. H. Wilkins, N. Senft, A. Romero, K. Bonnet, et al., 2020 Barriers and facilitators to dissemination and adoption of precision medicine among Hispanics/Latinos. BMC Public Health 20: 603. https://pubmed.ncbi.nlm.nih.gov/32357943/
Popejoy A. B., 2019 Diversity In Precision Medicine And Pharmacogenetics: Methodological And Conceptual Considerations For Broadening Participation. Pharmgenomics Pers Med 12: 257–271. https://pubmed.ncbi.nlm.nih.gov/31686892/
Spear M. L., A. Diaz-Papkovich, E. Ziv, J. M. Yracheta, S. Gravel, et al., 2020 Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits. eLife 9: e56029. https://pubmed.ncbi.nlm.nih.gov/33372659/
TheDigitalArtist, (2018). https://pixabay.com/illustrations/dna-matrix-genetics-control-3888228/
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