Summary

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.

中文翻译：

---

用于全基因组关联研究的协变量自适应家庭误差率控制

概括

家族错误率已广泛用于全基因组关联研究。随着功能基因组学数据可用性的增加，可以通过利用这些基因组功能注释来提高检测能力。以前在多重测试中适应协变量的努力集中在错误发现率控制上，而控制全族错误率的协变量自适应程序仍然不发达。在这里，我们提出了一种新的协变量自适应程序来控制家庭错误率，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。我们证明了它的渐近有效性，并通过微扰型论证获得了收敛速度。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 个性状与 900 万个单核苷酸多态性的关联测试。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 个中检测到比其他方法更多的全基因组显着位点。