Assessing genomewide statistical significance in linkage studies
Assessing genomewide statistical significance in linkage studies
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DOI：
10.1002/gepi.20017
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发表时间：
2004-11-01
影响因子：
2.1
通讯作者：
Zou, F
中科院分区：
文献类型：
--
作者：
Lin, DY;Zou, F
多点连锁分析中全基因组统计显著性的评估是一个棘手的问题。现有的分析解决方案依赖于强假设（即无限密集或等间隔的遗传标记，具有充分的信息和完全的观察，以及单一类型的相对对），这在真实的人体研究中很少得到满足，而基于模拟的方法计算量大，可能不适用于复杂的数据结构和复杂的遗传模型。在这里，我们提出了一个概念上简单和数字上有效的蒙特卡罗程序，用于确定适用于所有连锁研究的全基因组显著性水平。谱系结构是完全通用的；标记数据在数量、间距、信息量和缺失性方面完全是任意的；特征可以是定性的、定量的或多元的；备选假设可以是双面的或单面的；统计量可以是参数的也可以是非参数的。通过广泛的模拟研究和第十届遗传分析研讨会对核心家庭数据的应用，证明了所提出方法的有用性。(C) 2004 Wiley-Liss, Inc。
Assessment of genomewide statistical significance in multipoint linkage analysis is a thorny problem. The existing analytical solutions rely on strong assumptions (i.e., infinitely dense or equally spaced genetic markers that are fully informative and completely observed, and a single type of relative pair) which are rarely satisfied in real human studies, while simulation-based methods are computationally intensive and may not be applicable to complex data structures and sophisticated genetic models. Here, we propose a conceptually simple and numerically efficient Monte Carlo procedure for determining genomewide significance levels that is applicable to all linkage studies. The pedigree structure is completely general; the marker data are totally arbitrary in respect to number, spacing, informativeness, and missingness; the trait can be qualitative, quantitative, or multivariate; the alternative hypothesis can be two-sided or one-sided; and the statistic can be parametric or nonparametric. The usefulness of the proposed approach is demonstrated through extensive simulation studies and an application to the nuclear family data from the Tenth Genetic Analysis Workshop. (C) 2004 Wiley-Liss, Inc.