This article [attached] was described earlier — in these GEITP pages (on July 3rd) in layman’s terms [pasted again at the bottom of this email]. The Population Architecture using Genomics and Epidemiology (PAGE) study was developed by the National Human Genome Research Institute and the National Institute on Minority Health and Health Disparities — with the specific goal to conduct genetic epidemiological research in ancestrally diverse populations within the United States. The study is drawn from three existing major population-based cohorts: [a] Hispanic Community Health Study/Study of Latinos (HCHS/SOL); [b] Women’s Health Initiative (WHI) and Multiethnic Cohort (MEC); plus [c] the Icahn School of Medicine at Mount Sinai BioMe biobank in New York City (BioMe).
Genotyped individuals self-identified as Hispanic/Latino (n = 22,216), African-American (n = 17,299), Asian (n = 4,680), Native Hawaiian (n = 3,940), Native American (n = 652), or Other (n = 1,052). These 49,839 individuals were genotyped on the Multi-Ethnic Genotyping Array (MEGA), which had been developed to capture global genetic variation. Given that PAGE participants reside on a continuum of genetic ancestry (rather than discrete population groups), a joint analysis was optimally powered to allow for heterogeneous variance across populations. Authors then performed genome-wide association studies (GWAS) on 26 traits — harmonized across all the groups, adjusted for the top 10 principal components (PCs), self-identified race/ethnicity, and trait-specific covariates.
Authors used extensions of previously developed analytical tools — which explicitly model population structure, relatedness between individuals, and population-specific genetic heterogeneity. For comparison against standard multi-ethnic approaches and to assess heterogeneity by ancestry, authors also carried out analyses stratified by self-identified race/ethnicity and combined these analyses in one huge meta-analysis. Using all these strategies, authors identified 27 novel genetic loci, plus 38 secondary signals at known loci. Not surprising, these findings show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications.
In the United States — where minority populations have a disproportionately higher burden of chronic conditions — lack of representation of these diverse populations in genetic research — would result in inequitable access to precision medicine for those with the highest burden of disease. Therefore, authors strongly advocate for continued, large genome-wide efforts in diverse populations, to maximize genetic discovery and reduce health disparities. This concept is not exactly anything new: In fact, I recall being an invited speaker on this topic in 2003: “Symposium on Race and Ethnicity in Medical Scientific Research, Annual Meeting of the American Association for the Advancement of Science (AAAS), Denver, Colorado.” 😊
Nature 27 June 2o19; 570: 514-518