Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies.
Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies.
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DOI：
10.1002/gepi.20610
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发表时间：
2011-11
影响因子：
2.1
通讯作者：
Lin, Xihong
中科院分区：
文献类型：
--
作者：
Lin, Xinyi;Cai, Tianxi;Wu, Michael C.;Zhou, Qian;Liu, Geoffrey;Christiani, David C.;Lin, Xihong
关键词：
在本文中，我们开发了一个强大的测试来识别SNP集，这些SNP集可以预测全基因组关联研究（GWAS）的生存数据。我们首先基于基因组特征将分型的SNP分组为SNP集，然后通过内核机器考克斯回归框架应用得分测试来评估每个SNP集对生存结果的总体影响。这种方法同时使用来自SNP集中所有SNP的遗传信息，并考虑连锁不平衡（LD），从而当分型的SNP彼此处于LD时，导致具有降低的自由度的强大测试。这种类型的测试还具有捕获SNP的潜在非线性效应、SNP-SNP相互作用（上位性）和多个因果变体的联合效应的优点。通过模拟单核苷酸多态性数据的LD结构的真实的基因的HapMap项目的基础上，我们证明，我们提出的测试是更强大的比标准的单核苷酸多态性最小p值为基础的测试与删失生存结果的关联研究。我们用一个真实的数据应用说明了所提出的测试。
In this paper, we develop a powerful test for identifying SNP-sets that are predictive of survival with data from genome-wide association studies (GWAS). We first group typed SNPs into SNP-sets based on genomic features and then apply a score test to assess the overall effect of each SNP-set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP-set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially non-linear effects of the SNPs, SNP-SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum p-value based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application.
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