Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

Abstract

Background Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

RatXcan: A framework for cross-species integration of genome-wide association and gene expression data

“…PTRS are premised on the regulatory nature of most GWAS loci [ Maurano et al, 2012 ] and use genetically regulated gene expression (transcript abundance), in-stead of SNPs as features for prediction. We recently showed that PTRS are useful fortranslating polygenic signals between different human ancestry groups [ Liang et al, 2022 ], supporting the view that the effects of genes on a phenotype are conserved across ancestry groups. In the current project we hypothesized that the relationships between genes and phenotypes are conserved not only between human ancestry groups, but also across species.…”

“…To test the portability of PTRS across species, we started by calculating the human PTRS weights, as described in Liang et al [ 2022 ]. Using 356,476 UK Biobank unrelated European descent individuals, we fitted an elastic net regression with height as the outcome variable and the imputed gene expression as the predictor (height = ∑ g γ g · T g + ϵ with ϵ , an error term and T g the imputed gene expression in humans).…”

“…4e). To compare our prediction performance to that in the human population, we used the partial R 2 (, the proportion of variance explained by the predictor after accounting for other co-variates) reported by Liang et al [ 2022 ]. The partial for body length in rats was 0.64% ( P = 8.57 × 10 -6 ), while in the UK Biobank European test set, the partial for height in humans was 9.40%.…”

“…These results demonstrate that PTRS is a viable strategy for translating polygenic results between humans and rats. Even though, the proportion of body length variance explained by our PTRS was only 0.64% compared to the 9.40% in the European target set, that proportion dropped substantially as low as 1.46% when testing in non European target sets (See supplementary Table 6 in [Liang et al, 2022]). Closer examination of these results revealed that prediction of height improved until about 100 genes were included in the model, whereas prediction of BMI continued to improve until about 250 genes were included in the model.…”

“…The magnitude and significance of prediction using human PTRS for BMI to predict rat BMI was smaller than it was for human height to rat body length, which was expected given the lower heritability of human BMI. For reference, heritability estimates were more than three-fold lower for human BMI as compared to human height: 15% for BMI vs. 55% for height [ Liang et al, 2022 ]. The ability to transfer polygenic signals to other species creates novel opportunities to explore the mechanisms underlying those traits.…”