序性状遗传关联分析的有力检验。

IF 0.9 4区 数学 Q3 Mathematics
Yuan Xue, Jinjuan Wang, Juan Ding, Sanguo Zhang, Qizhai Li
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引用次数: 2

摘要

与前瞻性设计相比,反应选择性抽样设计可以大大减少时间成本,提高识别人类复杂疾病易感性的有害遗传变异的能力,是遗传流行病学研究中常用的方法。比例赔率模型(POM)可用于拟合该设计获得的数据。与logistic回归模型不同,采用前瞻性纳入数据的POM估计遗传效应是不一致的。因此,得到的Wald检验的有效性不能令人满意。改进的POM适合拟合这类数据,但当遗传效应较小时,Wald检验不是最优的。在这里,我们提出一个新的关联测试来解决这个问题。仿真研究表明,该方法能较好地控制I型错误率,比现有的两种方法更有效。最后,我们对来自遗传研讨会16的抗环瓜氨酸蛋白抗体数据进行了三种测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A powerful test for ordinal trait genetic association analysis.

Response selective sampling design is commonly adopted in genetic epidemiologic study because it can substantially reduce time cost and increase power of identifying deleterious genetic variants predispose to human complex disease comparing with prospective design. The proportional odds model (POM) can be used to fit data obtained by this design. Unlike the logistic regression model, the estimated genetic effect based on POM by taking data as being enrolled prospectively is inconsistent. So the power of resulted Wald test is not satisfactory. The modified POM is suitable to fit this type of data, however, the corresponding Wald test is not optimal when the genetic effect is small. Here, we propose a new association test to handle this issue. Simulation studies show that the proposed test can control the type I error rate correctly and is more powerful than two existing methods. Finally, we applied three tests to Anticyclic Citrullinated Protein Antibody data from Genetic Workshop 16.

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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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