School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China.
Genet Epidemiol. 2020 Oct;44(7):687-701. doi: 10.1002/gepi.22330. Epub 2020 Jun 24.
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.
迄今为止，通过全基因组关联研究（GWAS）已经发现了数千种与许多人类特征和疾病相关的遗传变异。GWAS 主要集中在测试单一特征与遗传变异之间的关联。然而，对多个特征和单核苷酸多态性（SNP）的分析可能反映了复杂疾病的生理过程，相应的研究被称为多效性关联分析。现代 GWAS 仅报告汇总统计信息，而不报告个体水平的表型和基因型数据，以避免后勤和隐私问题。现有的用于合并多个表型 GWAS 汇总统计信息的方法主要集中在低维表型上，而在高维情况下则失去了功效。为了克服这一缺陷，我们提出了两种截断检验方法来合并多个表型汇总统计信息。广泛的模拟表明，当表型的维数较高且只有部分表型与 SNP 相关时，所提出的方法具有稳健性和强大的功效。我们将所提出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，所提出的检验方法可以识别单个性状分析遗漏的额外遗传标记。
