A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.
A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.
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DOI：
10.1002/gepi.20351
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发表时间：
2009-01
影响因子：
2.1
通讯作者：
Elston, Robert C.
中科院分区：
文献类型：
--
作者：
Wang, Tao;Ho, Gloria;Ye, Kenny;Strickler, Howard;Elston, Robert C.
遗传关联研究通过基因区域密集snp的基因分型，在绘制疾病基因图谱方面达到了前所未有的水平。同时，这些研究需要新的强大的统计工具来优化处理基因型数据提供的大量信息。由此产生的一个问题是如何模拟两个基因之间的相互作用。简单地对两个基因区域内snp之间的所有可能的相互作用进行建模是不可取的，因为检验统计量中可能涉及的自由度大大增加。我们介绍了一种在相互作用建模中降低基因型维数的方法。该方法的基因型压缩是建立在性状信息和两个相互作用基因间snp的交叉位点配子不平衡信息的基础上的，这样可以在降维过程中简洁地模拟相互作用，而不会损失有用的信息。因此，它提高了检测基因-基因相互作用存在的关联的能力。这种方法同样可以应用于基因-环境相互作用的建模。我们将此方法与其他方法进行了比较：相应的测试没有建模任何相互作用，基于饱和相互作用模型，基于主成分分析，以及基于Tukey的1-df模型。我们的模拟表明，这种新方法比其他方法具有更优越的性能。在一项来自妇女健康倡议（WHI）的子宫内膜癌病例对照数据的应用中，考虑到AKT1和AKT2与BMI的相互作用，该方法检测到AKT1和AKT2与子宫内膜癌易感性显著相关。
Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI.
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影响因子：
4.5
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
通讯作者：
Cardon, Lon R.
影响因子：
2.1
作者：
Gauderman, W. James;Murcray, Cassandra;Conti, David V.
通讯作者：
Conti, David V.
影响因子：
9.8
作者：
Zaykin, DV;Meng, ZL;Ehm, MG
通讯作者：
Ehm, MG
影响因子：
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作者：
Kraft, Peter;Yen, Yu-Chun;Gauderman, W. James
通讯作者：
Gauderman, W. James
影响因子：
9.8
作者：
Zhao, Jinying;Jin, Li;Xiong, Momiao
通讯作者：
Xiong, Momiao