medRxivGenetic & Genomic MedicinePreprint
Evaluating Polygenic Score Transferability for Lipid Traits in Underrepresented Populations: Evidence from Samoan Cohorts
Toni-Ann J. YappiD, Mohanraj KrishnaniD, S H LiuiD, Samantha L. MannaiD, Hong ChengiD, Take K. NaseriiD, Muagututi‘a Sefuiva ReupenaiD, Satupa’itea Viali, John Tuitele, Ranjan DekaiD, Nicola L. HawleyiD, Stephen T. McGarveyiD, Daniel E. WeeksiD, Ryan L. MinsteriD, Jenna C. CarlsoniD
Abstract
Dyslipidemia is a significant risk factor for cardiovascular disease (CVD), the leading cause of death in Samoa.1 Polygenic scores (PGS) for lipid traits offer promise for improved CVD risk prediction,4,5 yet their performance in Pacific Islander populations -- comprising only 0.002% of GWAS participants as of 20243 -- remains unknown. We evaluated the transferability of multi-ancestry PGS for LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides (TG), and total cholesterol (TC) in 4,342 Samoan adults across five cohorts spanning 1990-2010. PGS from Graham et al.8 and Kanoni et al.9 multi-ancestry meta-analyses were harmonized with genome-wide imputed genotypes using a Samoan-specific reference panel21 and performance was assessed via incremental R{superscript 2} from linear mixed models with bootstrapped confidence intervals.25 HDL-C showed the highest performance (incremental R{superscript 2} 5.0-15.0%), followed by TC (5.0-10.7%), LDL-C (5.7-8.6%), and TG (3.5-7.0%). Critically, meaningful LDL-C performance was achieved only with the genome-wide PRS-CS score (99.6-99.7% variant matching), while a curated pruning-and-thresholding score achieved [~]9% matching and near-zero performance. These findings establish the first systematic lipid PGS benchmarks in Samoans, demonstrating meaningful transferability when genome-wide variant coverage is ensured, and highlight variant harmonization as a critical precondition for PGS deployment in underrepresented populations.
- organism: human
- assay: GWAS summary statistics
- research focus: benchmarking
- approach: statistical model
- Citations
- 0
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