Polygenic scoring accuracy varies across the genetic ancestry continuum

Abstract

Polygenic scores (PGSs) have limited portability across different groupings of individuals (for example, by genetic ancestries and/or social determinants of health), preventing their equitable use1–3. PGS portability has typically been assessed using a single aggregate population-level statistic (for example, R2)4, ignoring inter-individual variation within the population. Here, using a large and diverse Los Angeles biobank5 (ATLAS, n = 36,778) along with the UK Biobank6 (UKBB, n = 487,409), we show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries7 in all considered populations, even within traditionally labelled ‘homogeneous’ genetic ancestries. The decreasing trend is well captured by a continuous measure of genetic distance (GD) from the PGS training data: Pearson correlation of −0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained on individuals labelled as white British in the UKBB to individuals with European ancestries in ATLAS, individuals in the furthest GD decile have 14% lower accuracy relative to the closest decile; notably, the closest GD decile of individuals with Hispanic Latino American ancestries show similar PGS performance to the furthest GD decile of individuals with European ancestries. GD is significantly correlated with PGS estimates themselves for 82 of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestries in PGS interpretation. Our results highlight the need to move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs.

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

“…Admix-kit holds significant potentials in the development of Polygenic Risk Scores (PRS). The efficacy of PRS is known to hinge on the similarity of the target population to the training population (Ding et al , 2023). With the PRIMED consortium working on methods to improve the performance of PRS in diverse populations, simulations will be pivotal for method evaluation (Kachuri et al , 2023).…”

Ensembled best subset selection using summary statistics for polygenic risk prediction

“…The current model is designed for single ancestry analysis. While a large portion of GWAS data currently comes from individuals of European ancestry, it is well-known that polygenic risk scores do not transfer well between individuals of different ancestries, which can impact their utility for patients of non-European ancestry [64][65][66][67][68][69] . Many methods that utilize summary data from multiple populations have already been proposed and demonstrate improved prediction in under-represented populations 25,29,30,[70][71][72][73][74] .…”

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort

“…In the African and Hispanic or Latin American ancestries, we used a different approach. PGS accuracy decays continuously as target samples differ in ancestry from the discovery GWAS, even within relatively homogenous genetic clusters, and we lacked ancestry-matched GWAS for EXT to use methods that boost power of PGS in underpowered samples, such as PRS-CSx . Therefore, in the African and Hispanic or Latin American ancestries, we created EXT PGS using the 579 loci reported in the externalizing GWAS, as using genome-wide significant variants is more robust to population stratification .…”