Introduction: Dyslipidemia is a significant risk factor for cardiovascular disease and a major contributor to the global burden of disease. Cardiovascular disease is the leading cause of death in Samoa, accounting for 34% of deaths. Exploring the genetic determinants of blood lipid traits in Polynesian individuals could help address research inequities and has the potential to provide additional insight about the biological foundations of such traits in other populations. A 2021 study by Graham et al. conducted a multi-ancestry genome-wide genetic discovery meta-analysis of lipid levels and generated polygenic scores (PGS) from ~1.65 million individuals from East Asian, admixed African American, Hispanic, and South Asian populations. In the current study, we applied these PGS for four traits—LDL-C, HDL-C, triglycerides (TG), and total cholesterol (TC)—in a sample of 3,119 participants across three time-separated cohorts recruited from Samoa and American Samoa in 1990-1991, 2002-2003, and 2010.
Methods: We calculated PGS in the Samoan cohorts using the variants and weights derived from the 2021 study. First, the PGS variants were lifted over to hg38 and then harmonized with genome-wide imputed variants in the Samoan cohorts. We calculated individual-level PGS by summing the products of genotype dosage and variant weights for each PRS variant. We assessed performance of the PGSs in each cohort with partial r 2 and bootstrapped confidence intervals from linear regression models for each trait adjusting for age and sex.
Results: The PGS for LDL-C had r 2 = ~8% across the 3 Samoan cohorts, which was marginally lower in performance to East Asian and South Asian populations (r 2 = ~8-10%) and much lower than African American and Hispanic populations (r 2 = ~10-16%) in the 2021 study. The PGS for HDL-C had r 2 = ~10% for the discovery and 2002 cohort but had r 2 = ~5% in the 1990 cohort. TC had a PGS r 2 = ~10% across the three cohorts. The PGS for TG had r 2 = ~5-7% across the three cohorts.
Discussion: Our findings suggest that PGS derived from multi-ethnic ancestry populations have reduced predictive power when applied to the Samoan population. Additionally, the differences in r 2 values between traits could provide evidence that certain traits are more influenced by the environment than others. This highlights the need to build PGS in a variety of environmental contexts and ancestries to improve the accuracy and transferability of genetic risk prediction across diverse populations. Further research is needed to refine and validate PGS in the Samoan population and to assess their potential clinical utility in risk stratification and targeted interventions for blood lipid traits.