Haplotype interaction analysis of unlinked regions
Haplotype interaction analysis of unlinked regions
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DOI：
10.1002/gepi.20096
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发表时间：
2005-12-01
影响因子：
2.1
通讯作者：
Knapp, M
中科院分区：
文献类型：
--
作者：
Becker, T;Schumacher, J;Knapp, M
遗传复杂疾病是由环境因素和基因相互作用引起的。因此，同时考虑多个非连锁基因组区域的统计方法是可取的。然而，这种考虑可能导致大量不同的高维测试，其适当的分析造成问题。在这里，我们提出了一种方法来分析具有多个SNP数据的病例对照研究，该方法考虑了基因相互作用效应，同时适当地校正了多个测试。特别是，我们允许属于不同非连锁区域的单倍型相互作用，因为单倍型分析通常被证明比单标记分析更强大。此外，我们在每个非连锁区域考虑不同的标记组合。通过minP方法解决了多重测试问题；通过蒙特卡罗模拟对“最佳”标记/区域配置的P值进行校正。因此，我们没有明确地测试一个特定的预定义的相互作用模型，而是测试一个全局假设，即没有一个考虑的单倍型相互作用显示与疾病相关。我们对病例对照数据进行了模拟研究，以证实我们方法的有效性。在模拟双位点疾病模型时，我们的测试证明比单独分析每个连锁区域的关联方法更强大。此外，当其中一个测试区域与疾病的病因无关时，与没有相互作用的分析相比，相互作用分析只损失了少量的功率。我们成功地将我们的方法应用于一个真实的病例对照数据集，其中包含控制共同途径的两个基因的标记。虽然经典分析未能达到显著性，但即使在使用我们提出的单倍型相互作用分析进行多次测试校正后，我们也获得了显著的结果。这里描述的方法已经在FAMHAP中实现。
Genetically complex diseases are caused by interacting environmental factors and genes. As a consequence, statistical methods that consider multiple unlinked genomic regions simultaneously are desirable. Such consideration, however, may lead to a vast number of different high-dimensional tests whose appropriate analysis pose a problem. Here, we present a method to analyze case-control studies with multiple SNP data without phase information that considers gene-gene interaction effects while correcting appropriately for multiple testing. In particular, we allow for interactions of haplotypes that belong to different unlinked regions, as haplotype analysis often proves to be more powerful than single marker analysis. In addition, we consider different marker combinations at each unlinked region. The multiple testing issue is settled via the minP approach; the P value of the "best" marker/region configuration is corrected via Monte-Carlo simulations. Thus, we do not explicitly test for a specific pre-defined interaction model, but test for the global hypothesis that none of the considered haplotype interactions shows association with the disease. We carry out a simulation study for case-control data that confirms the validity of our approach. When simulating two-locus disease models, our test proves to be more powerful than association methods that analyze each linked region separately. In addition, when one of the tested regions is not involved in the etiology of the disease, only a small amount of power is lost with interaction analysis as compared to analysis without interaction. We successfully applied our method to a real case-control data set with markers from two genes controlling a common pathway. While classical analysis failed to reach significance, we obtained a significant result even after correction for multiple testing with our proposed haplotype interaction analysis. The method described here has been implemented in FAMHAP.