Inferring selection in partially sequenced regions
Inferring selection in partially sequenced regions
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DOI：
10.1093/molbev/msm273
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发表时间：
2008-02-01
影响因子：
10.7
通讯作者：
Aquadro, Charles F.
中科院分区：
文献类型：
--
作者：
Jensen, Jeffrey D.;Thornton, Kevin R.;Aquadro, Charles F.
鉴定受正选择影响的位点的一种常用方法包括扫描基因组的大部分区域，以寻找与中性平衡模型不一致的区域或相对于数据某些方面的经验分布而言代表异常值的区域。一旦确定，部分序列生成跨越这个更局部的区域，以便量化站点频谱和评估数据的中立性和选择测试。这种方法被广泛使用，因为部分测序在时间和金钱方面都更便宜。在这里，我们证明了这种方法可以导致选择参数的偏差最大似然估计和降低拒绝率，一些参数组合导致明显误导性的结果。最重要的是，对于果蝇群体遗传学中常用的样本量（即n = 12），当没有对真正被选择的位点进行采样时，对选择目标的估计存在较大的均方误差，并且严重低估了选择的强度。我们提出的测序方法更有可能准确定位目标和估计选择的强度。此外，我们检查了在各种循环和单一扫描模型下常用的选择测试的性能。
A common approach for identifying loci influenced by positive selection involves scanning large portions of the genome for regions that are inconsistent with the neutral equilibrium model or represent outliers relative to the empirical distribution of some aspect of the data. Once identified, partial sequence is generated spanning this more localized region in order to quantify the site-frequency spectrum and evaluate the data with tests of neutrality and selection. This method is widely used as partial sequencing is less expensive with regard to both time and money. Here, we demonstrate that this approach can lead to biased maximum likelihood estimates of selection parameters and reduced rejection rates, with some parameter combinations resulting in clearly misleading results. Most significantly, for a commonly used sample size in Drosophila population genetics (i.e., n = 12), the estimate of the target of selection has a large mean square error and the strength of selection is severely under estimated when the true selected site has not been sampled. We propose sequencing approaches that are much more likely to accurately localize the target and estimate the strength of selection. Additionally, we examine the performance of a commonly used test of selection under a variety of recurrent and single sweep models.