Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background: Circulating lipoprotein(a) [Lp(a)] levels are highly heritable and linked to atherosclerotic cardiovascular disease, yet clinical measurement rates remain low (<1%) in the United States. The high heritability of Lp(a) across populations makes genetic prediction an attractive approach for closing this testing gap, but existing polygenic scores transfer poorly across populations. Haplotype-based prediction models, which use standard genome-wide genotype data to capture common-, rare-, and structural-variation at the LPA locus, could bridge this gap, enabling opportunistic identification of individuals with elevated Lp(a) levels across diverse populations within existing large, genotyped cohorts. Objectives: This study sought to develop and validate a haplotype-based prediction model using genome-wide genotype data to identify individuals with elevated Lp(a) levels across diverse populations. Methods: ) and identifying elevated Lp(a) levels (>125 nmol/L) through positive predictive value (PPV) and number needed to test (NNT). Results: of the haplotype model was 0.46 (95% Credible Interval [CrI] 0.32 to 0.6), with similar performance across genetically inferred ancestries and cohorts. For identifying elevated Lp(a) levels >125 nmol/L the overall PPV was 0.81 (95% CrI 0.6 to 0.89), corresponding to a NNT of 1.2 (95% CrI 1.1 to 1.7) individuals predicted to have elevated levels needing to undergo clinical testing to identify one true elevation. In the full PMBB cohort (n = 49310), the haplotype model identified elevated Lp(a) at a rate of 128 per 1000 (95% CrI 125 to 130), corresponding to an estimated 14.4-fold improvement (95% CrI 13.1 to 15.9; P(improvement) = 1) in identification rate compared with the existing rate of clinical assessment. Conclusions: A haplotype-based genetic model effectively identified individuals with elevated Lp(a) levels across diverse populations, with potential utility for opportunistic screening among cohorts where genotype data is available, but Lp(a) testing rates are low.
Source: medRxiv
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, U.S. Department of Veterans Affairs, University of Pennsylvania, Regeneron Pharmaceuticals, Georgia Clinical and Translational Science Alliance, University of Pennsylvania Health System, National Institutes of Health, Perelman School of Medicine, University of Pennsylvania, National Center for Advancing Translational Sciences
Awards: Healthy Americas: All of Us Research Program, G1969433312, G2187930703, G3639549833, G471519714, University of Arizona-Banner Health All of Us Research Program , G6104092673, G6945916705, SouthEast Enrollment Center (SEEC) , G8355735039, G853874183, G9000004253, G9000006085, G9000011423, G9000011771, G9000013175, G9000021463, G9000021538, G9000032397, G9000035207, G9000041091, G9000041420, G9000042288, G9000043450, G9000046817, G9000051044, G9000055272, G9000058670, G9000063695, G9000063797, G9000071344, G9000072038, G9000077829, G9000085573, G9000092963, G9000128449, G9000145785, G9000147789, G9000169788, G9000200762, G9000206094, G9000219966, G9000279044, G9000290334, G9000303384
OpenAlex ID: W7130957223