Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Genet. 2024 Aug 7;20(8):e1011356. doi: 10.1371/journal.pgen.1011356. eCollection 2024 Aug.
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.
跨祖先多基因风险评分的可转移性通常受到祖先之间连锁不平衡和遗传结构差异的影响。最近的文献表明，优先考虑具有功能基因组证据的 GWAS SNP 而不是强关联信号，可以提高模型的可转移性。我们利用三种源自 RegulomeDB 的功能调控注释-SURF、TURF 和 TLand-构建了一组定量和二分类性状的多基因风险模型，突出了由与性状相关的组织注释标记的功能突变。TURF 和 TLand 的组织特异性优先级排序在所有性状上都显著提高了模型准确性，优于标准多基因风险评分（PRS）模型。我们开发了跨祖先迭代组织细化（TITR）算法来构建 PRS 模型，该模型在多个涉及性状的组织中优先考虑功能突变。TITR 构建的 PRS 模型在预测准确性上优于单一组织优先级排序。这表明我们的 TITR 方法捕捉到了更全面的跨涉及组织的调控系统视图，这些系统对性状表达的变异性有贡献。
