Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations—SURF, TURF, and TLand—to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.Author summary: Polygenic risk score models leverage effect size estimates from ancestry-targeted GWAS to generate well-powered disease stratification models. When ancestry-targeted GWAS is unavailable for understudied populations, trans-ancestral PRS models may be implemented. However, transferring PRS models across ancestries results in limited predictive accuracy due to linkage differences between ancestries. Here we show that isolating GWAS variants with strong functional evidence identified from RegulomeDB-derived annotations in tissues enriched for trait heritability can improve portability of PRS models across distant ancestries. The motivation is mutations with evidence of regulatory impact are more likely to be shared between ancestries than genome-wide significant signals from ancestry-targeted GWAS. Further, we developed the novel TITR algorithm to aggregate functional GWAS mutations across multiple trait-implicated tissues to iteratively construct PRSs. These models provide a more comprehensive view of functional GWAS mutations that influence variation in complex disease expression and can help improve portability of PRS models in under-represented populations.