Relaxed significance criteria for linkage analysis
Relaxed significance criteria for linkage analysis
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DOI：
10.1534/genetics.105.052506
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发表时间：
2006-08-01
期刊：
影响因子：
3.3
通讯作者：
Storey, John D.
中科院分区：
文献类型：
--
作者：
Chen, Lin;Storey, John D.
连锁分析涉及在位于整个基因组中的许多基因座上进行显著性检验。宣布连锁具有统计学意义的传统标准已经制定，其目标是控制任何单一假阳性发生的速率，称为基因组错误率（GWER）。由于复杂性状已成为连锁分析的焦点，人们越来越普遍地期望许多基因座与性状真正连锁。这在数量性状基因座（QTL）定位中尤其如此，其中有时可能存在数十个QTL。因此，最近已经探索了防止任何单个假阳性的严格目标的替代方案，例如错误发现率（FDR）标准。在这里，我们描述了一些挑战时出现的定义宽松的显着性标准，允许至少一个假阳性的联系发生。特别是，我们发现，FDR遭受几个问题时，应用于单一性状的连锁分析。因此，我们的结论是，在一个单一的性状的分析中，FDR宣布显着的联系的普遍适用性是值得怀疑的。相反，我们提出了一个显着的标准，比传统的GWER更宽松，但似乎并没有受到FDR的问题。GWER（k）的广义版本，提出了，它允许一个提供一个更自由的真阳性和假阳性之间的平衡，在计算或假设没有额外的成本。
Linkage analysis involves performing significance tests at many loci located throughout the genome. Traditional criteria for declaring a linkage statistically significant have been formulated with the goal of controlling the rate at which any single false positive occurs, called the genomewise error rate (GWER). As complex traits have become the focus of linkage analysis, it is increasingly common to expect that a number of loci are truly linked to the trait. This is especially true in mapping quantitative trait loci (QTL), where sometimes dozens of QTL may exist. Therefore, alternatives to the strict goal of preventing any single false positive have recently been explored, such as the false discovery rate (FDR) criterion. Here, we characterize some of the challenges that arise when defining relaxed significance criteria that allow for at least one false positive linkage to occur. In particular, we show that the FDR suffers from several problems when applied to linkage analysis of a single trait. We therefore conclude that the general applicability of FDR for declaring significant linkages in the analysis of a single trait is dubious. Instead, we propose a significance criterion that is more relaxed than the traditional GWER, but does not appear to suffer from the problems of the FDR. A generalized version of the GWER is proposed, called GWER(k), that allows one to provide a more liberal balance between true positives and false positives at no additional cost in computation or assumptions.