Background

Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism.

Objective

This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes.

Methods

We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as “Multi-ACAT”). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis.

Results

Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19).

Conclusion

The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.

中文翻译：

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一种快速而强大的聚合柯西关联检验，用于联合分析多种表型

背景

多效性是复杂人类疾病中的普遍现象。联合分析多个表型可以提高检测遗传变异的能力，并揭示潜在的遗传机制。

客观的

本研究旨在检测基因组区域中的遗传变异与多种表型之间的关联。

方法

我们开发了聚合柯西关联测试来检测基因组区域中的罕见变异与多种表型（缩写为“Multi-ACAT”）之间的关联。Multi-ACAT 首先基于反向回归检测每个罕见变异与多个表型之间的关联，并获得变异级别的p 值，然后将转换后的p 值的线性组合作为检验统计量，在零假设下近似遵循柯西分布。

结果

广泛的模拟研究表明，当基因组区域中因果变异的比例极小时，Multi-ACAT 比其他几种方法更强大，并且对因果变异的双向影响具有鲁棒性。最后，我们通过分析来自 Genetic Analysis Workshop 19 (GAW19) 的两种表型 [收缩压 (SBP) 和舒张压 (DBP)] 来说明我们提出的方法。

结论

Multi-ACAT 计算速度极快，不考虑多个相关表型的复杂分布，可以应用于具有噪声表型的情况。