Truncated tests for combining evidence of summary statistics
Truncated tests for combining evidence of summary statistics
复制标题
合并汇总统计证据的截断检验
DOI：
10.1002/gepi.22330
复制
发表时间：
2020-06-24
影响因子：
2.1
通讯作者：
Li, Qizhai
中科院分区：
文献类型：
--
作者：
Bu, Deliang;Yang, Qinglong;Li, Qizhai
迄今为止，全基因组关联研究（GWAS）已鉴定出数千种与多种人类特征和疾病相关的遗传变异。 GWAS 专注于测试单一性状和遗传变异之间的关联。然而，对多个性状和单核苷酸多态性（SNP）的分析可能反映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 仅报告汇总统计数据，而不是个体水平的表型和基因型数据，以避免后勤和隐私问题。现有的结合多表型GWAS汇总统计的方法主要集中在低维表型，而在高维情况下失去功效。为了克服这个缺陷，我们提出了两种截断检验来结合多个表型汇总统计。大量的模拟表明，当表型维度较高并且只有部分表型与 SNP 相关时，所提出的方法是稳健且强大的。我们将所提出的方法应用于从芬兰人群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所遗漏的其他遗传标记。
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.