Efficient Bayesian approach for multilocus association mapping including gene-gene interactions.
Efficient Bayesian approach for multilocus association mapping including gene-gene interactions.
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DOI：
10.1186/1471-2105-11-443
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发表时间：
2010-09-02
影响因子：
3
通讯作者：
Corander J
中科院分区：
文献类型：
--
作者：
Marttinen P;Corander J
自从引入大规模基因分型方法，可用于全基因组关联（GWA）研究破译复杂的疾病，统计遗传学已提出了一个巨大的挑战，如何最适当地分析这些数据。在动物和植物育种中，用于性状遗传作图的大量先进的基于模型的方法已经存在了10多年。然而，大多数这样的方法在全基因组研究的背景下在计算上是棘手的。因此，这并不奇怪，GWA分析在实践中一直占主导地位的简单的统计测试与一个单一的标记位点的时间，而更先进的方法只出现在最近的生物医学和统计文献。我们介绍了一种新的贝叶斯建模框架关联映射，使检测多个位点和它们的相互作用，影响一个二分法表型的利益。该方法被证明在模拟研究中表现良好，相比广泛使用的标准替代品，其计算复杂性通常是相当小的最大似然为基础的方法。我们还详细讨论了灵敏度的贝叶斯推理相对于先验分布的选择在GWA的上下文中。我们的研究结果表明，明确考虑基因-基因相互作用的贝叶斯模型平均方法可以在两个方面提高疾病相关遗传标记的检测：第一，通过提供更好的估计的位置的因果基因座;第二，通过减少假阳性的数量。当相互作用的基因没有表现出主效应时，这种益处最为明显。然而，我们的研究结果也表明，这种方法是有点敏感的先验分布分配的模型结构。
Since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature. We introduce a novel Bayesian modeling framework for association mapping which enables the detection of multiple loci and their interactions that influence a dichotomous phenotype of interest. The method is shown to perform well in a simulation study when compared to widely used standard alternatives and its computational complexity is typically considerably smaller than that of a maximum likelihood based approach. We also discuss in detail the sensitivity of the Bayesian inferences with respect to the choice of prior distributions in the GWA context. Our results show that the Bayesian model averaging approach which explicitly considers gene-gene interactions may improve the detection of disease associated genetic markers in two respects: first, by providing better estimates of the locations of the causal loci; second, by reducing the number of false positives. The benefits are most apparent when the interacting genes exhibit no main effects. However, our findings also illustrate that such an approach is somewhat sensitive to the prior distribution assigned on the model structure.
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影响因子：
30.8
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