Covariate adaptive familywise error rate control for genome-wide association studies
Covariate adaptive familywise error rate control for genome-wide association studies
复制标题
用于全基因组关联研究的协变量自适应家族错误率控制
DOI：
10.1093/biomet/asaa098
复制
发表时间：
2020
期刊：
影响因子：
2.7
通讯作者：
Chen, Jun
中科院分区：
文献类型：
--
作者：
Zhou, Huijuan;Zhang, Xianyang;Chen, Jun
家族错误率已广泛应用于全基因组关联研究。随着功能基因组学数据的可用性不断增加，可以通过利用这些基因组功能注释来提高检测能力。先前在多重测试中适应协变量的努力主要集中在错误发现率控制上，而控制族错误率的协变量自适应程序仍然不发达。在这里，我们提出了一种新颖的协变量自适应程序来控制族错误率，该程序包含外部协变量，这些协变量可能提供统计功效或先验零概率的信息。开发了一种有效的算法来实现所提出的方法。我们证明了它的渐进有效性并通过扰动型论证获得了收敛率。我们的数值研究表明，新程序比竞争方法更强大，并且在不同设置下保持稳健性。我们将所提出的方法应用于英国生物银行数据，并分析了 27 个性状和 900 万个单核苷酸多态性，并进行了关联测试。七十五个基因组注释用作协变量。我们的方法在 27 个性状中的 21 个性状中检测到了比其他方法更多的全基因组显着位点。
The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.