Effective Genetic-Risk Prediction Using Mixed Models
Effective Genetic-Risk Prediction Using Mixed Models
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DOI：
10.1016/j.ajhg.2014.09.007
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发表时间：
2014-10-02
影响因子：
9.8
通讯作者：
Rosset, Saharon
中科院分区：
文献类型：
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作者：
Golan, David;Rosset, Saharon
为了预测遗传风险，我们提出了一种统计方法，专门适用于处理疾病表型和病例对照抽样带来的挑战。我们的方法（称为遗传风险评分推断[GeRSI]）在广泛使用的责任阈值模型框架内结合了固定效应模型（估计和汇总单个SNP的影响）和随机效应模型（主要依赖于个体之间的全基因组相似性）的能力。我们在广泛的模拟表明，GeRSI产生的预测是一贯优于当前最先进的方法上级。当将GeRSI应用于来自Wellcome Trust Case Control Consortium（WTCCC）研究的七种表型时，我们证实了随机效应的使用对于已知高度多基因的疾病是最有益的：高血压（HT）和双相情感障碍（BD）。对于HT，在WTCCC数据中没有显著的关联。固定效应模型产生的ROC曲线下面积（AUC）为54%，而GeRSI将其提高到59%。对于BD，使用GeRSI将AUC从55%提高到62%。对于在BD风险预测中排名前10%的个体，使用GeRSI将BD相对风险从1.4增加到2.5。
For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.