Transcriptome-wide association studies (TWAS) that integrate transcriptomic reference data and genome-wide association studies (GWAS) have successfully enhanced the discovery of candidate genes for many complex traits. However, existing methods may suffer from substantial power loss because they fail to effectively consider that expression of many genes tends to be consistent across tissues. Here we propose a computationally efficient testing method, referred to as Integrative Test for Associations via Cauchy Transformation (InTACT), that effectively combines information across multiple tissues and thus improves the power of identifying associated genes. Through simulation studies, we show that InTACT maintains high power while properly controls for Type 1 error rates. We applied InTACT to the largest GWAS of Alzheimer's disease (AD) to date and identified 227 genome-wide significant genes, of which 130 were not identified by benchmark methods, TWAS and MultiXcan. Importantly, InTACT identified five novel loci for AD. We implemented InTACT in publicly available software, “InTACT.”

中文翻译：

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InTACT：用于联合组织转录组范围关联研究的自适应且强大的框架

整合转录组参考数据和全基因组关联研究（GWAS）的全转录组关联研究（TWAS）已经成功地促进了许多复杂性状候选基因的发现。然而，现有的方法可能会遭受严重的功率损失，因为它们未能有效地考虑到许多基因的表达在组织中往往是一致的。在这里，我们提出了一种计算高效的测试方法，称为通过柯西变换进行关联的综合测试（InTACT），它有效地结合了多个组织的信息，从而提高了识别相关基因的能力。通过仿真研究，我们表明 InTACT 保持高功率，同时正确控制 1 类错误率。我们将 InTACT 应用于迄今为止最大的阿尔茨海默病 (AD) GWAS，并鉴定了 227 个全基因组重要基因，其中 130 个未通过基准方法、TWAS 和 MultiXcan 鉴定。重要的是，InTACT 确定了 AD 的五个新位点。我们在公开软件“InTACT”中实施了 InTACT。