Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder.
Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder.
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DOI：
10.1186/s12859-022-04566-5
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发表时间：
2022-01-10
影响因子：
3
通讯作者：
Buu A
中科院分区：
文献类型：
--
作者：
Yang JJ;Luo X;Trucco EM;Buu A
多基因风险评分(PRS)显示，作为一种潜在的有效方法，可以总结复杂疾病的遗传风险，例如受多个变异组合影响的酒精使用障碍，每个变异的影响非常小。然而，传统的粗糙集方法往往过度调整发现样本中的混杂因素，因此预测目标样本中的表型的能力较低。本研究旨在解决这一重要的方法论问题。该研究提出了一种构造粗糙集的新方法，该方法(1)利用发现数据中基于特征相关性选择的几个主成分来逼近多基因模型；(2)对目标数据进行主成分投影。二次数据分析在两个大型数据库上进行：成瘾研究：遗传学和环境(SAGE；发现数据)和全国青少年与成人健康纵向研究(Add Health；目标数据)，以比较传统方法和建议方法的性能。结果表明，该方法具有较高的预测能力，能够处理不同血统背景的参与者。我们还提供了设置连锁不平衡(LD)和p值阈值的实用建议。
The polygenic risk score (PRS) shows promise as a potentially effective approach to summarize genetic risk for complex diseases such as alcohol use disorder that is influenced by a combination of multiple variants, each of which has a very small effect. Yet, conventional PRS methods tend to over-adjust confounding factors in the discovery sample and thus have low power to predict the phenotype in the target sample. This study aims to address this important methodological issue. This study proposed a new method to construct PRS by (1) approximating the polygenic model using a few principal components selected based on eigen-correlation in the discovery data; and (2) conducting principal component projection on the target data. Secondary data analysis was conducted on two large scale databases: the Study of Addiction: Genetics and Environment (SAGE; discovery data) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; target data) to compare performance of the conventional and proposed methods. The results show that the proposed method has higher prediction power and can handle participants from different ancestry backgrounds. We also provide practical recommendations for setting the linkage disequilibrium (LD) and p value thresholds.
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DOI：
10.1093/bioinformatics/btu848
发表时间：
2015-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Euesden J;Lewis CM;O'Reilly PF
通讯作者：
O'Reilly PF
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
7.7
作者：
Mostafavi, Hakhamanesh;Harpak, Arbel;Przeworski, Molly
通讯作者：
Przeworski, Molly
影响因子：
16.6
作者：
Duncan, L.;Shen, H.;Domingue, B.
通讯作者：
Domingue, B.
影响因子：
25.8
作者：
Andersen, Allan M.;Pietrzak, Robert H.;Han, Shizhong
通讯作者：
Han, Shizhong