Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.
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全球生物库分析为在各种队列中发展多基因风险评分提供了教训。

DOI:
10.1016/j.xgen.2022.100241
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发表时间:
2023-01-11
期刊:
CELL GENOMICS
影响因子:
--
通讯作者:
Hirbo, Jibril
Hirbo, Jibril
中科院分区:
其他
文献类型:
--
作者:
Wang, Ying;Namba, Shinichi;Lopera, Esteban;Kerminen, Sini;Tsuo, Kristin;Lall, Kristi;Kanai, Masahiro;Zhou, Wei;Wu, Kuan-Han;Fave, Marie-Julie;Bhatta, Laxmi;Awadalla, Philip;Brumpton, Ben;Deelen, Patrick;Hveem, Kristian;Lo Faro, Valeria;Magi, Reedik;Murakami, Yoshinori;Sanna, Serena;Smoller, Jordan W.;Uzunovic, Jasmina;Wolford, Brooke N.;Willer, Cristen;Gamazon, Eric R.;Cox, Nancy J.;Surakka, Ida;Okada, Yukinori;Martin, Alicia R.;Hirbo, Jibril

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多基因风险评分(PRSs)在精准医学中得到了广泛的研究。然而,很少有研究彻底调查了他们在全球不同疾病人群中的最佳实践。我们在此利用全球生物库荟萃分析倡议(GBMI)的数据,探讨9个不同生物库中14个疾病终点的方法学考虑和PRS性能。具体来说,我们构建PRS使用修剪和阈值(P + T)和PRS连续收缩(CS)。对于这两种方法,使用基于欧洲的连锁不平衡(LD)参考面板相比,其他几个非欧洲为基础的面板导致相当或更高的预测准确性。PRS-CS总体优于经典P + T方法,尤其是对于具有较高SNP遗传力的终点。值得注意的是,预测准确性在终点、生物库和祖先中是异质的,特别是对于哮喘,已知哮喘在人群中的疾病患病率存在差异。总的来说,我们提供的PRS建设,评估和解释使用GBMI资源的经验教训,并强调在生物库规模的基因组学时代的PRS的最佳实践的重要性。PRS准确性在疾病终点、祖先和生物库中是异质的。更大的样本量和更大的GBMI多样性提高了PRS准确性。Wang et al.使用来自全球生物库荟萃分析倡议的独特资源,开发和评估具有不同遗传结构和患病率的14种疾病终点的PRS。他们制定了关于多祖先和异质GWAS,性状特异性遗传结构和PRS方法对不同人群预测性能的影响的指南。
Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era. PRS accuracy is heterogeneous across disease endpoints, ancestries, and biobanks Larger sample sizes and greater diversity of GBMI improves PRS accuracy Lessons and guidelines for developing PRS with multi-ancestry GWASs are provided Wang et al. used the unique resource from Global Biobank Meta-analysis Initiative to develop and evaluate PRSs for 14 disease endpoints with varying genetic architectures and prevalences. They developed guidelines regarding the effects of multi-ancestry and heterogeneous GWASs, trait-specific genetic architecture, and PRS methods on prediction performance across diverse populations.
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