一个与多个遗传变异和协变量的强大关联检验。

IF 0.9 4区 数学 Q3 Mathematics
Jen-Yu Lee, Pao-Sheng Shen, Kuang-Fu Cheng
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引用次数: 0

摘要

由于基因组测序技术的进步,外显子组测序取得了长足的进步,使得疾病与遗传变异的关联研究成为可能。已经提出了一些强大而知名的关联测试来测试一组基因与感兴趣的疾病之间的关联。然而,仍然存在一些挑战,特别是许多因素会影响测试能力的表现,例如样本量,因果变量和非因果变量的数量,以及因果变量的影响方向。最近,一个强大的测试,称为TREM,是基于随机效应模型推导出来的。TREM的优点是对包含非因果罕见变异或低影响常见变异或缺失基因型的存在不太敏感。然而,当部分因果变量具有相反方向的影响时,TREM的检验能力可能较低。为了改善TREM的缺点,我们提出了一种新的测试,称为TROB,它保留了TREM的优点,并且在具有相反作用方向的变体的情况下具有足够的功率,比TREM更稳健。仿真结果表明,当风险变量的比例减小到一定程度时,TROB具有稳定的I类错误率,优于TREM,并且其优于TREM的优势随着比例的减小而增大。此外,在大多数情况下,TROB优于其他几个竞争测试。所提出的方法用上海乳腺癌研究来说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A robust association test with multiple genetic variants and covariates.

Due to the advancement of genome sequencing techniques, a great stride has been made in exome sequencing such that the association study between disease and genetic variants has become feasible. Some powerful and well-known association tests have been proposed to test the association between a group of genes and the disease of interest. However, some challenges still remain, in particular, many factors can affect the performance of testing power, e.g., the sample size, the number of causal and non-causal variants, and direction of the effect of causal variants. Recently, a powerful test, called TREM , is derived based on a random effects model. TREM has the advantages of being less sensitive to the inclusion of non-causal rare variants or low effect common variants or the presence of missing genotypes. However, the testing power of TREM can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of TREM , we propose a novel test, called TROB , which keeps the advantages of TREM and is more robust than TREM in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that TROB has a stable type I error rate and outperforms TREM when the proportion of risk variants decreases to a certain level and its advantage over TREM increases as the proportion decreases. Furthermore, TROB outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.

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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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