Suppr超能文献

在大规模病例对照遗传关联研究中,比较 Cochran-Armitage 趋势检验与等位基因和基因型检验的功效。

Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies.

机构信息

Agrocampus Ouest, UMR 6625, IRMAR, Rennes, France.

出版信息

Stat Methods Med Res. 2018 Sep;27(9):2657-2673. doi: 10.1177/0962280216683979. Epub 2016 Dec 23.

Abstract

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.

摘要

Cochran-Armitage 趋势检验(CA)已成为大规模全基因组关联研究(GWAS)中关联检验的标准程序。然而,当疾病模型未知时,CA、等位基因和基因型检验之间哪种检验最有效还没有共识。在本文中,我们探讨了 CA 是否最适合 GWAS 中单基因扫描的问题,并提出了 CA 与等位基因和基因型检验的功效比较。我们的方法依赖于非中心参数泰勒分解的评估,从而允许对检验的功效函数进行分析比较。与基于模拟的比较相比,我们的方法具有同时考虑功效函数所涉及的特征集的多维性的优势。尽管 CA 的功效取决于样本量、病例对照比和次要等位基因频率(MAF),但我们的结果首先表明,它在很大程度上受到遗传模式和偏离 Hardy-Weinberg 平衡(HWE)的影响。此外,与其他检验相比,在乘法疾病模型下或当单核苷酸多态性严重偏离 HWE 时,CA 是最有效的检验。在所有其他情况下,CA 的功效不足,差异可能很大,尤其是在隐性遗传模式下。最后,我们通过比较两个基因组扫描中统计数据的性能来说明我们的结果。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验