Polygenic scores (PGS) have limited portability across different groupings of individuals (e.g., by genetic ancestries and/or social determinants of health), preventing their equitable use. PGS portability has typically been assessed using a single aggregate population-level statistic (e.g., R2), ignoring inter-individual variation within the population. Here we evaluate PGS accuracy at individual-level resolution, independent of its annotated genetic ancestries. We show that PGS accuracy varies between individuals across the genetic ancestry continuum in all ancestries, even within traditionally "homogeneous" genetic ancestry clusters. Using a large and diverse Los Angeles biobank (ATLAS, N= 36,778) along with the UK Biobank (UKBB, N= 487,409), we show that PGS accuracy decreases along a continuum of genetic ancestries in all considered populations and the 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 in UKBB "white British" individuals to European-ancestry individuals of ATLAS, individuals in the highest GD decile have 14% lower accuracy relative to the lowest decile; notably the lowest GD decile of Hispanic/Latino American ancestry individuals showed similar PGS performance as the highest GD decile of European ancestry ATLAS individuals. GD is significantly correlated with PGS estimates themselves for 82 out of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestry in PGS interpretation. Our results highlight the need for moving away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGS and their applications.

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.

No abstract

Individuals of admixed ancestries (e.g., African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. Their genomic diversity offers the unique opportunity of investigating genetic effects on disease across multiple ancestries within the same population. Quantifying the similarity in causal effects across local ancestries is paramount to studying genetic basis of diseases in admixed individuals. Such similarity can be defined as the genetic correlation of causal effects (radmix) across African and European local ancestry backgrounds. Existing studies investigating causal effects variability across ancestries focused on cross-continental comparisons; however, such differences could be due to heterogeneities in the definition of environment/phenotype across continental ancestries. Studying genetic effects within admixed individuals avoids these confounding factors, because the genetic effects are compared across local ancestries within the same individuals. Here, we introduce a new method that models polygenic architecture of complex traits to quantify radmix across local ancestries. We model genome-wide causal effects that are allowed to vary by ancestry and estimate radmix by inferring variance components of local ancestry-aware genetic relationship matrices. Our method is accurate and robust across a range of simulations. We analyze 38 complex traits in individuals of African and European admixed ancestries (N = 53K) from: Population Architecture using Genomics and Epidemiology (PAGE), UK Biobank (UKBB) and All of Us (AoU). We observe a high similarity in causal effects by ancestry in meta-analyses across traits, with estimated radmix=0.95 (95% credible interval [0.93, 0.97]), much higher than correlation in causal effects across continental ancestries. High estimated radmix is also observed consistently for each individual trait. We replicate the high correlation in causal effects using regression-based methods from marginal GWAS summary statistics. We also report realistic scenarios where regression-based methods yield inflated estimates of heterogeneity-by-ancestry due to local ancestry-specific tagging of causal variants, and/or polygenicity. Among regression-based methods, only Deming regression is robust enough for estimation of correlation in causal effects by ancestry. In summary, causal effects on complex traits are highly similar across local ancestries and motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry.

Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields from agriculture to personalized medicine. We analyze data from two large biobanks in the US (All of Us) and the UK (UK Biobank) to find widespread variability in PGS performance across contexts. Many contexts, including age, sex, and income, impact PGS accuracies with similar magnitudes as genetic ancestry. PGSs trained in single versus multi-ancestry cohorts show similar context-specificity in their accuracies. We introduce trait prediction intervals that are allowed to vary across contexts as a principled approach to account for context-specific PGS accuracy in genomic prediction. We model the impact of all contexts in a joint framework to enable PGS-based trait predictions that are well-calibrated (contain the trait value with 90% probability in all contexts), whereas methods that ignore context are mis-calibrated. We show that prediction intervals need to be adjusted for all considered traits ranging from 10% for diastolic blood pressure to 80% for waist circumference. Adjustment of prediction intervals depends on the dataset; for example, prediction intervals for education years need to be adjusted by 90% in All of Us versus 8% in UK Biobank. Our results provide a path forward towards utilization of PGS as a prediction tool across all individuals regardless of their contexts while highlighting the importance of comprehensive profile of context information in study design and data collection.

Individuals of admixed ancestries (e.g., African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. Their genomic diversity offers the unique opportunity of investigating genetic effects on disease across multiple ancestries within the same population. Quantifying the similarity in causal effects across local ancestries is paramount to studying genetic basis of diseases in admixed individuals. Such similarity can be defined as the genetic correlation of causal effects (radmix) across African and European local ancestry backgrounds. Existing studies investigating causal effects variability across ancestries focused on cross-continental comparisons; however, such differences could be due to heterogeneities in the definition of environment/phenotype across continental ancestries. Studying genetic effects within admixed individuals avoids these confounding factors, because the genetic effects are compared across local ancestries within the same individuals. Here, we introduce a new method that models polygenic architecture of complex traits to quantify radmix across local ancestries. We model genome-wide causal effects that are allowed to vary by ancestry and estimate radmix by inferring variance components of local ancestry-aware genetic relationship matrices. Our method is accurate and robust across a range of simulations. We analyze 38 complex traits in individuals of African and European admixed ancestries (N = 53K) from: Population Architecture using Genomics and Epidemiology (PAGE), UK Biobank (UKBB) and All of Us (AoU). We observe a high similarity in causal effects by ancestry in meta-analyses across traits, with estimated radmix=0.95 (95% credible interval [0.93, 0.97]), much higher than correlation in causal effects across continental ancestries. High estimated radmix is also observed consistently for each individual trait. We replicate the high correlation in causal effects using regression-based methods from marginal GWAS summary statistics. We also report realistic scenarios where regression-based methods yield inflated estimates of heterogeneity-by-ancestry due to local ancestry-specific tagging of causal variants, and/or polygenicity. Among regression-based methods, only Deming regression is robust enough for estimation of correlation in causal effects by ancestry. In summary, causal effects on complex traits are highly similar across local ancestries and motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry.