Genome-wide association studies (GWAS) can detect common variants associated with diseases. Next generation sequencing technology has made it possible to detect rare variants. Most of association tests, including burden tests and nonburden tests, mainly target rare variants by upweighting rare variant effects and downweighting common variant effects. But there is increasing evidence that complex diseases are caused by both common and rare variants. In this paper, we extend the ADA method (adaptive combination of P-values; Lin et al., 2014) for rare variants only and propose a RC-ADA method (common and rare variants by adaptive combination of P-values). Our proposed method combines the per-site P-values with the weights based on minor allele frequencies (MAFs). The RC-ADA is robust to directions of effects of causal variants and inclusion of a high proportion of neutral variants. The performance of the RC-ADA method is compared with several other association methods. Extensive simulation studies show that the RC-ADA method is more powerful than other association methods over a wide range of models.

中文翻译：

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通过P值的自适应组合来检测稀有和常见变体的关联。

全基因组关联研究（GWAS）可以检测与疾病相关的常见变异。下一代测序技术使检测稀有变体成为可能。大多数关联测试（包括负担测试和非负担测试）主要通过增加稀有变异效应和降低常见变异效应来针对稀有变异。但是，越来越多的证据表明，复杂的疾病是由常见的和罕见的变异引起的。在本文中，我们仅针对稀有变异扩展了ADA方法（P值的自适应组合; Lin等人，2014），并提出了RC-ADA方法（通过P值的自适应组合的常见和稀有变量）。我们提出的方法将每个站点的P值与基于次要等位基因频率（MAF）的权重相结合。RC-ADA在因果变体和高比例的中性变体的影响方向上都很鲁棒。将RC-ADA方法的性能与其他几种关联方法进行了比较。大量的仿真研究表明，在广泛的模型中，RC-ADA方法比其他关联方法更强大。