A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.
A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.
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基于全基因组关联研究的摘要统计数据并纳入外部信息的汇总风险模型，用于构建多基因风险模型的惩罚回归框架。
DOI：
10.1080/01621459.2020.1764849
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发表时间：
2021
影响因子：
3.7
通讯作者：
Shi J
中科院分区：
文献类型：
--
作者：
Chen TH;Chatterjee N;Landi MT;Shi J
大规模全基因组关联（GWAS）研究为开发遗传风险预测模型提供了机会，这些模型有可能改善疾病预防、干预或治疗。关键步骤是开发对特定疾病具有高预测性能的多基因风险评分（PRS）模型，这通常需要大量的训练数据集来选择真正相关的单核苷酸多态性（snp）并准确估计效应大小。在这里，我们开发了一个综合惩罚回归，用于将l1正则化回归模型拟合到GWAS汇总统计。我们建议通过适当的惩罚函数和相关的调优参数，将多效性和注释信息纳入到PRS （PANPRS）的开发中。大量的仿真表明，在不考虑功能注释或多效性的情况下，PANPRS的性能与现有的PRS方法一样好，甚至更好。当功能标注数据和多效性具有信息量时，PANPRS在模拟中显著优于现有的PRS方法。最后，我们应用我们的方法构建了2型糖尿病和黑色素瘤的PRS，发现结合相关的功能注释和遗传相关性状的GWAS改善了这两种复杂疾病的预测。
Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training data set for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a comprehensive penalized regression for fitting l1 regularized regression models to GWAS summary statistics. We propose incorporating Pleiotropy and ANnotation information into PRS (PANPRS) development through suitable formulation of penalty functions and associated tuning parameters. Extensive simulations show that PANPRS performs equally well or better than existing PRS methods when no functional annotation or pleiotropy is incorporated. When functional annotation data and pleiotropy are informative, PANPRS substantially outperforms existing PRS methods in simulations. Finally, we applied our methods to build PRS for type 2 diabetes and melanoma and found that incorporating relevant functional annotations and GWAS of genetically related traits improved prediction of these two complex diseases.
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影响因子：
9.8
作者：
Ning Z;Lee Y;Joshi PK;Wilson JF;Pawitan Y;Shen X
通讯作者：
Shen X
影响因子：
9.8
作者：
Golan, David;Rosset, Saharon
通讯作者：
Rosset, Saharon
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
DOI：
10.1214/10-aoas388
发表时间：
2011-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Breheny P;Huang J
通讯作者：
Huang J