Pathway analysis for family data using nested random-effects models.
Pathway analysis for family data using nested random-effects models.
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DOI：
10.1186/1753-6561-5-s9-s22
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发表时间：
2011-11-29
期刊：
影响因子：
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通讯作者：
Tsonaka, Roula
中科院分区：
文献类型：
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作者：
Houwing-Duistermaat, Jeanine J;Uh, Hae-Won;Tsonaka, Roula
最近，我们提出了一种新的两步法来测试疾病进展中的通路效应。这种方法的目的是研究属于某些基因的多个单核苷酸多态性的联合作用。通过使用随机效应，我们的方法在测试通路效应时承认了基因内部和基因之间的相关性。基因-基因和基因-环境相互作用可以包括在模型中。该方法可以用标准软件实现，并且可以通过使用标准卡方分布来近似零假设下的检验统计量的分布。因此，不需要大量的排列来计算p值。在本文中，我们适应和家庭数据的方法，我们研究其性能的序列数据从遗传分析研讨会17。对于一组不相关的受试者来说，新测试的表现令人失望。我们发现二元结果的功效为6%，数量性状Q1的功效为18%。对于家庭数据，新的方法似乎表现良好，特别是对于定量结果。我们发现二元结果的功效为39%，数量性状Q1的功效为89%。
Recently we proposed a novel two-step approach to test for pathway effects in disease progression. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to certain genes. By using random effects, our approach acknowledges the correlations within and between genes when testing for pathway effects. Gene-gene and gene-environment interactions can be included in the model. The method can be implemented with standard software, and the distribution of the test statistics under the null hypothesis can be approximated by using standard chi-square distributions. Hence no extensive permutations are needed for computations of the p-value. In this paper we adapt and apply the method to family data, and we study its performance for sequence data from Genetic Analysis Workshop 17. For the set of unrelated subjects, the performance of the new test was disappointing. We found a power of 6% for the binary outcome and of 18% for the quantitative trait Q1. For family data the new approach appears to perform well, especially for the quantitative outcome. We found a power of 39% for the binary outcome and a power of 89% for the quantitative trait Q1.