To date, thousands of genetic variants to be associated with numerous human traits and diseases have been identified by genome‐wide association studies (GWASs). The GWASs focus on testing the association between single trait and genetic variants. However, the analysis of multiple traits and single nucleotide polymorphisms (SNPs) might reflect physiological process of complex diseases and the corresponding study is called pleiotropy association analysis. Modern day GWASs report only summary statistics instead of individual‐level phenotype and genotype data to avoid logistical and privacy issues. Existing methods for combining multiple phenotypes GWAS summary statistics mainly focus on low‐dimensional phenotypes while lose power in high‐dimensional cases. To overcome this defect, we propose two kinds of truncated tests to combine multiple phenotypes summary statistics. Extensive simulations show that the proposed methods are robust and powerful when the dimension of the phenotypes is high and only part of the phenotypes are associated with the SNPs. We apply the proposed methods to blood cytokines data collected from Finnish population. Results show that the proposed tests can identify additional genetic markers that are missed by single trait analysis.

中文翻译：

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截断测试，用于组合摘要统计的证据。

迄今为止，全基因组关联研究（GWAS）已鉴定出与众多人类特征和疾病相关的数千种遗传变异。GWAS专注于测试单性状和遗传变异之间的关联。然而，对多性状和单核苷酸多态性（SNPs）的分析可能反映了复杂疾病的生理过程，相应的研究称为多效性关联分析。现代GWAS仅报告摘要统计信息，而不报告单个级别的表型和基因型数据，以避免物流和隐私问题。组合多种表型的现有方法GWAS摘要统计主要关注低维表型，而在高维情况下会失去功能。为了克服这个缺陷，我们提出两种截断检验，以结合多种表型的汇总统计。大量的仿真表明，当表型的维数较高且仅部分表型与SNP相关时，所提出的方法既强大又强大。我们将建议的方法应用于从芬兰人群中收集的血液细胞因子数据。结果表明，所提出的测试可以识别单性状分析遗漏的其他遗传标记。