Polygenic prediction via Bayesian regression and continuous shrinkage priors
Polygenic prediction via Bayesian regression and continuous shrinkage priors
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DOI：
10.1038/s41467-019-09718-5
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发表时间：
2019-04-16
影响因子：
16.6
通讯作者：
Smoller, Jordan W.
中科院分区：
文献类型：
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作者：
Ge, Tian;Chen, Chia-Yen;Smoller, Jordan W.
多基因风险评分（PRS）在预测人类复杂性状和疾病方面显示出了希望。在这里，我们提出了PRS-CS，多基因预测方法，推断后效应大小的单核苷酸多态性（SNP）使用全基因组关联汇总统计和外部连锁不平衡（LD）参考面板。PRS-CS利用高维贝叶斯回归框架，并且通过在SNP效应大小之前放置连续收缩（CS）而不同于先前的工作，这对不同的遗传结构是鲁棒的，提供了大量的计算优势，并且使得能够对局部LD模式进行多变量建模。使用英国生物银行数据的模拟研究表明，PRS-CS在广泛的遗传结构中优于现有方法，特别是当训练样本量很大时。我们应用PRS-CS预测Partners HealthCare生物库中的六种常见复杂疾病和六种数量性状，并进一步证明PRS-CS在预测准确性方面优于其他方法。
Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.