Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in β -cell function, glucose homeostasis, and lipids traits.

中文翻译：

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使用公开可用的全基因组关联研究摘要数据来检测疾病相关基因的强大统计方法。

迄今为止，全基因组关联研究（GWAS）取得了实质性的成功。在过去的十年中，已经通过GWAS确定了许多复杂疾病的常见变异。在大多数现有的GWAS中，可通过基于单个标记的测试（即一次测试一个单核苷酸多态性（SNP））获得已识别的常见变体。通常，遗传的基本功能单位是基因，而不是SNP。因此，基因水平关联测试的结果可以更容易地与下游功能和病原学研究相结合。在本文中，我们提出了一种通用的基于基因的p值自适应组合方法（GPA），该方法可以仅使用GWAS摘要统计信息（p值或其他检验统计数据）整合多个遗传变异的关联证据。所提出的方法不仅可以通过一项研究，而且可以通过多项研究用于测试连续性和二元性状的遗传关联，这将有助于克服仅适用于特定类型数据的现有方法的局限性。我们进行了全面的模拟研究，以验证所提出的方法能够很好地控制I型错误，并且与单标记分析和其他现有方法相比，具有出色的表现。通过对GWAS荟萃分析结果的分析，我们分别从国际MAGIC财团和Global Lipids财团中禁食了葡萄糖和脂质，从而证明了我们提出的方法的实用性。拟议的方法确定了一些新的性状相关基因，可以增进我们对β细胞功能，葡萄糖稳态，