精神病理和共病的跨诊断多基因风险模型:我们所有人研究计划中的跨祖先分析。

Phil H Lee, Jae-Yoon Jung, Brandon T Sanzo, Rui Duan, Tian Ge, Irwin Waldman, Jordan W Smoller, Ted Schwaba, Elliot M Tucker-Drob, Andrew D Grotzinger
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引用次数: 0

摘要

精神疾病表现出大量的遗传重叠,提出了关于跨诊断遗传风险模型的实用性的问题。使用来自我们所有人研究计划(N=102,091)的数据,我们将基于常见精神遗传(CPG)因素的多基因风险评分(PRSs)与标准疾病特异性PRSs进行了比较。CPG PRS在预测个体疾病风险方面一直优于疾病特异性评分,在11种精神疾病中解释了1.07至24.6倍的表型差异。同时,许多疾病特异性PRSs保留了独立但较小的贡献,突出了共享和疾病特异性遗传风险的互补性。虽然其他多因素模型改进了模型拟合,但CPG PRS在大多数疾病中提供了相当或更好的预测性能,包括总体合并症负担。然而,跨祖先分析显示,由于祖先遗传结构的差异,以欧洲为中心的GWAS数据集对其他人群有显著的局限性。这些发现强调了跨诊断PRSs对精神病学遗传学的潜在价值,同时强调了对更公平的遗传风险模型的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the All of Us Research Program.

Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the All of Us Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.07 to 24.6 times more phenotypic variance across 11 psychiatric conditions. Meanwhile, many disorder-specific PRSs retained independent but smaller contributions, highlighting the complementary nature of shared and disorder-specific genetic risk. While alternative multi-factor models improved model fit, the CPG PRS provided comparable or superior predictive performance across most disorders, including overall comorbidity burden. Cross-ancestry analyses however revealed notable limitations of European-centric GWAS datasets for other populations due to ancestral differences in genetic architecture. These findings underscore the potential value of transdiagnostic PRSs for psychiatric genetics while highlighting the need for more equitable genetic risk models.

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