Zhu Huanhuan, Zhang Shuanglin, Sha Qiuying
Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America.
PLoS One. 2018 Jan 12;13(1):e0190788. doi: 10.1371/journal.pone.0190788. eCollection 2018.
Many complex diseases like diabetes, hypertension, metabolic syndrome, et cetera, are measured by multiple correlated phenotypes. However, most genome-wide association studies (GWAS) focus on one phenotype of interest or study multiple phenotypes separately for identifying genetic variants associated with complex diseases. Analyzing one phenotype or the related phenotypes separately may lose power due to ignoring the information obtained by combining phenotypes, such as the correlation between phenotypes. In order to increase statistical power to detect genetic variants associated with complex diseases, we develop a novel method to test a weighted combination of multiple phenotypes (WCmulP). We perform extensive simulation studies as well as real data (COPDGene) analysis to evaluate the performance of the proposed method. Our simulation results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the methods we compared. WCmulP also has an outstanding performance for identifying single-nucleotide polymorphisms (SNPs) associated with COPD-related phenotypes.
许多复杂疾病，如糖尿病、高血压、代谢综合征等，是通过多个相关表型来衡量的。然而，大多数全基因组关联研究（GWAS）关注的是一种感兴趣的表型，或者分别研究多种表型以识别与复杂疾病相关的基因变异。单独分析一种表型或相关表型可能会因为忽略了通过合并表型获得的信息（如表型之间的相关性）而失去效力。为了提高检测与复杂疾病相关基因变异的统计效力，我们开发了一种新方法来检验多种表型的加权组合（WCmulP）。我们进行了广泛的模拟研究以及真实数据（COPDGene）分析，以评估所提出方法的性能。我们的模拟结果表明，WCmulP具有正确的I型错误率，并且在我们比较的方法中要么是最具效力的检验方法，要么与最具效力的检验方法相当。WCmulP在识别与慢性阻塞性肺疾病（COPD）相关表型的单核苷酸多态性（SNP）方面也具有出色的表现。
