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首页> 外文期刊>Statistical methods in medical research >Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies
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Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies

机译:大型案例控制遗传关联研究中的Cochran-armitage趋势试验Cochran-armitage趋势试验

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The Cochran-Armitage trend test (CA) has become a standard procedure for association testing in large-scale genome-wide association studies (GWAS). However, when the disease model is unknown, there is no consensus on the most powerful test to be used between CA, allelic, and genotypic tests. In this article, we tackle the question of whether CA is best suited to single-locus scanning in GWAS and propose a power comparison of CA against allelic and genotypic tests. Our approach relies on the evaluation of the Taylor decompositions of non-centrality parameters, thus allowing an analytical comparison of the power functions of the tests. Compared to simulation-based comparison, our approach offers the advantage of simultaneously accounting for the multidimensionality of the set of features involved in power functions. Although power for CA depends on the sample size, the case-to-control ratio and the minor allelic frequency (MAF), our results first show that it is largely influenced by the mode of inheritance and a deviation from Hardy–Weinberg Equilibrium (HWE). Furthermore, when compared to other tests, CA is shown to be the most powerful test under a multiplicative disease model or when the single-nucleotide polymorphism largely deviates from HWE. In all other situations, CA lacks in power and differences can be substantial, especially for the recessive mode of inheritance. Finally, our results are illustrated by the comparison of the performances of the statistics in two genome scans.
机译:Cochran-Armitage趋势试验(CA)已成为大规模基因组协会研究(GWAS)中的关联测试的标准程序。然而,当疾病模型未知时,在CA,等位基因和基因型测试之间使用的最强大的测试没有共识。在本文中,我们解决了CA在GWAS中最适合单轨扫描的问题,并提出CA对等位基因和基因型测试的功率比较。我们的方法依赖于评估非中心参数的泰勒分解,从而允许测试测试功率功能的分析比较。与基于仿真的比较相比,我们的方法提供了同时占功率功能涉及的集合功能的多征的优势。虽然CA的动力取决于样品大小,但是对照比和次要的等位基因频率(MAF),我们的结果首先表明它主要受到遗传模式的影响和与HARY-WEINBERG均衡的偏差(HWE )。此外,与其他试验相比,Ca被证明是在繁殖疾病模型下或单核苷酸多态性在很大程度上偏离HWE时最强大的测试。在所有其他情况下,CA缺乏权力和差异可能是大量的,特别是对于遗传的隐性模式。最后,我们的结果是通过比较两个基因组扫描中统计的性能的比较来说明。

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