The accuracy of polygenic scores (PGSs) varies from individual to individual, highlighting the need to incorporate the genetic ancestry continuum when assessing the performance and bias of these disease and trait prediction tools, according to a study appearing in Nature this week. PGSs have garnered a lot of attention across many fields in recent years, but their portability across different genetic ancestries is limited. Assessing this portability is generally done using a single aggregate population-level statistic, which ignores inter-individual variation within the population. To determine the impact of such variation, a team led by University of California, Los Angeles researchers applied simulations and real-world analyses to a Los Angeles biobank of roughly 36,000 individuals, as well as nearly 500,000 individuals in the UK Biobank, to investigate the interplay between genetic ancestries and PGS for 84 complex traits and diseases. They show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries in all populations analyzed, even within traditionally labelled homogeneous genetic ancestries. The finding underscores the need to "move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs," the study's authors write. "Improving the portability of traditional clinical risk factor models in diverse populations is an essential component of health equity and requires thorough investigation."
Polygenic Score Accuracy Impacted by Inter-Individual Variation, Study Shows
May 19, 2023