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Asymptotic distribution for epistatic tests in case–control studies

机译:病例对照研究中上位性测试的渐近分布

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We propose a statistical model for dissecting a multilocus genotypic value into its main (additive and dominant) effects and epistatic effects between different loci in a case–control association study. The model can discern four different kinds of epistasis, additive?×?additive, additive?×?dominant, dominant?×?additive, and dominant?×?dominant interactions. To test each kind of epistasis, a χ2 test statistic was computed for a two by two contingency table derived from combined genotypes in both case and control groups. We derived an analytical approach for estimating the asymptotic distribution of the χ2 test statistic for epistatic tests under the null hypothesis, with the result being consistent with that from Monte Carlo simulations. The new model was used to analyze a case–control data set for candidate gene studies of stroke, leading to the identification of several significant interactions between causal SNPs on this disease.
机译:我们提出了一种统计模型,用于在病例对照研究中将多基因座基因型值分解为主要位点(加性和显性)和不同位点之间的上位性作用。该模型可以辨别四种不同的上位性:加性××加性,加性××显性,显性××性加性和显性××显性相互作用。为了检验每种上位性,对病例组和对照组的组合基因型得出的两乘两列的权变表计算了χ2检验统计量。我们推导了一种分析方法,用于估计零假设下上位测试的χ2检验统计量的渐近分布,其结果与蒙特卡洛模拟的结果一致。该新模型用于分析中风候选基因研究的病例对照数据集,从而确定了该疾病的因果SNP之间的几种重要相互作用。

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