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Joint Selection of SNPs for Improving Prediction in Genome-wide Association Studies

机译：联合选择SNPs以改善基因组 - 宽协会研究预测

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摘要

It is of great interest to select single-nucleotide polymorphism (SNP) associated with diseases in genome-wide association studies (GWAS). Since genetic variants affect diseases in multiple ways, the joint analysis of SNPs is needed to understand the full effects of genetic variants. However, since the number of SNPs is large and there exists linkage disequilibrium (LD) among SNPs, it is not easy to identify the joint effects of SNPs on complex traits. Thus, the multi-step approach is commonly used for handling these problems. First, SNPs marginally associated with diseases are selected via single SNP analysis. Next, joint identification of putative SNPs via penalized regularization method is carried out for the pre-selected SNP set. Finally, SNPs from the joint identification step are ordered by a measure which is yielded from the joint analysis. Some current approaches have proposed scoring measures to select causal SNPs such as selection stabilities and effect sizes. In this paper, we discuss some pros and cons of these measures and propose new joint SNP selection measures based on re-sampling methods such as permutation and bootstrap. We illustrate the joint SNP selection based on our measure by using bipolar disorder data from Welcome Trust Case Control Consortium (WTCCC). We demonstrate that the proposed method substantially improves the prediction of disease status compared to other scoring measures.
机译：选择与基因组关联研究（GWAs）中的疾病相关的单核苷酸多态性（SNP）非常感兴趣。由于遗传变异以多种方式影响疾病，因此需要对SNP的联合分析来理解遗传变异的全部效果。然而，由于SNP的数量大并且在SNP中存在连接不平衡（LD），因此不容易识别SNP对复杂性状的关节效果。因此，多步方法通常用于处理这些问题。首先，通过单一SNP分析选择与疾病略微相关的SNP。接下来，对预选的SNP集进行惩罚正则化方法的推定SNP的联合识别。最后，来自联合识别步骤的SNP通过从联合分析产生的措施进行排序。一些目前的方法已经提出了选择措施，以选择因果SNP，例如选择稳定性和效果大小。在本文中，我们讨论了这些措施的一些优缺点，并提出了基于重新采样方法的新联合SNP选择措施，如排列和引导。我们通过使用来自欢迎信托案例控制联盟（WTCCC）的双极性障碍数据来说明基于我们的措施的联合SNP选择。我们证明，与其他评分措施相比，该方法显着提高了疾病状况的预测。

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