Li Nanxing, Chen Lili, Zhou Yajing, Wei Qianran
School of Mathematical Sciences, Heilongjiang University, Harbin 150080, P. R. China.
Stat Appl Genet Mol Biol. 2023 Feb 1;22(1). doi: 10.1515/sagmb-2021-0068. eCollection 2023 Jan 1.
Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.
许多人类疾病状况需要通过有序表型来衡量，因此在全基因组关联研究（GWAS）中，对有序表型的分析很有价值。然而，现有的针对二分或定量表型的关联方法并不适用于有序表型。因此，基于聚合柯西关联检验，我们提出了一种快速有效的关联方法，以检验基因变异与有序表型之间的关联。为了富集稀有变异的关联信号，我们首先使用负担法对稀有变异进行聚合。然后分别检验聚合后的稀有变异和其他常见变异的显著性。最后，将转换后的变异水平值的组合作为检验统计量，在零假设下其近似服从柯西分布。大量的模拟研究和GAW19分析表明，我们提出的方法作为一种基于基因的方法，具有强大的功效且计算速度快。特别是，当一个基因中因果变异的比例极低时，我们的方法具有更好的性能。
