A fast and efficient approach for gene-based association studies of ordinal phenotypes.

IF 0.9 4区 数学 Q3 Mathematics
Nanxing Li, Lili Chen, Yajing Zhou, Qianran Wei
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引用次数: 0

Abstract

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 P 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.

序型基因关联研究的一种快速有效的方法。
许多人类疾病状况需要通过有序表型来测量,因此分析有序表型在全基因组关联研究(GWAS)中是有价值的。然而,现有的二分类或定量表型关联方法不适用于顺序表型。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来检测遗传变异与有序表型之间的关联。为了丰富稀有变异的关联信号,我们首先采用负担法对稀有变异进行聚合。然后分别对汇总的稀有变异和其他常见变异的显著性进行检验。最后,将变换后的变水平P值组合作为检验统计量,在零假设下近似服从柯西分布。对GAW19的大量仿真研究和分析表明,作为一种基于基因的方法,我们提出的方法功能强大,计算速度快。特别是,在一个基因中存在极低比例的因果变异时,我们的方法具有更好的性能。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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