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母体-后代基因-基因相互作用建模:扩展 MFG 检验。

Modeling maternal-offspring gene-gene interactions: the extended-MFG test.

机构信息

Department of Biostatistics, University of California, Los Angeles, California, USA.

出版信息

Genet Epidemiol. 2010 Jul;34(5):512-21. doi: 10.1002/gepi.20508.

Abstract

Maternal-fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended-MFG (EMFG) Test, a model-based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear-family based "mating type" approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type-I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes.

摘要

母体-胎儿基因型(MFG)不兼容是指母体和后代在特定基因座上的基因相互作用,对发育中的胎儿产生不利影响,从而增加患病的易感性。已经开发出用于核家庭的检查 MFG 不兼容性作为疾病风险因素的统计方法。由于作为研究一部分收集的家庭可能很大且复杂,包含多个世代和婚姻循环,因此我们创建了扩展 MFG(EMFG)测试,这是一种基于模型的似然方法,允许任意的家庭结构。我们通过用 pedigree likelihood 的 Ott 表示替换基于核家庭的“交配类型”方法,以及沿着孟德尔传递概率计算 MFG 不兼容性,从而修改 MFG 测试。为了允许扩展到任意的家庭结构,我们对感兴趣的基因座做出了关于随机交配的稍微更严格的假设。模拟表明,当随机交配成立时,EMFG 测试具有适当的 I 型错误率、功效和精确的参数估计。我们的模拟和真实数据示例表明,EMFG 测试相对于早期的核家庭版本的 MFG 测试的主要优势在于,在存在缺失基因型的情况下,参数估计的准确性和功效得到了提高。

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本文引用的文献

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