Identifying Signatures of Selection in Genetic Time Series
Identifying Signatures of Selection in Genetic Time Series
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DOI：
10.1534/genetics.113.158220
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发表时间：
2014-02-01
期刊：
影响因子：
3.3
通讯作者：
Plotkin, Joshua B.
中科院分区：
文献类型：
--
作者：
Feder, Alison F.;Kryazhimskiy, Sergey;Plotkin, Joshua B.
遗传漂变和自然选择都会导致种群中等位基因的频率随时间而变化。区分这两种进化的力量，从一个人口的样本的时间序列的基础上，仍然是一个突出的问题，越来越相关的现代数据集。即使在理想化的情况下，当采样的基因座独立于所有其他基因座时，这个问题也很难解决，特别是当从中抽取样本的群体的大小未知时。先前提出了基于标准（2）的似然比检验来解决这个问题。在这里，我们表明，（2）-测试的选择大大低估了第一类错误的概率，导致更多的假阳性比其P值，特别是在严格的P值。我们介绍两种方法来纠正这种偏见。经验似然比检验（ELRT）拒绝中性时，似然比统计量福尔斯落在最可能的中性人口规模下获得的经验分布的尾部。如果标准化等位基因频率增量的分布呈现显著偏离零的平均值，则频率增量检验（FIT）拒绝中性。我们表征这两个测试的选择的统计能力，我们将它们应用到三个实验数据集。我们表明，ELRT和FIT有能力检测选择在实际的参数制度，如微生物进化实验中遇到的。我们的分析适用于一个单一的双等位基因位点，假设独立于所有其他位点，这是最相关的有性生物体的全基因组选择扫描，并在无性生物的进化实验，只要克隆干扰是弱的。需要不同的技术来检测共分离连锁位点时间序列中的选择。
Both genetic drift and natural selection cause the frequencies of alleles in a population to vary over time. Discriminating between these two evolutionary forces, based on a time series of samples from a population, remains an outstanding problem with increasing relevance to modern data sets. Even in the idealized situation when the sampled locus is independent of all other loci, this problem is difficult to solve, especially when the size of the population from which the samples are drawn is unknown. A standard (2)-based likelihood-ratio test was previously proposed to address this problem. Here we show that the (2)-test of selection substantially underestimates the probability of type I error, leading to more false positives than indicated by its P-value, especially at stringent P-values. We introduce two methods to correct this bias. The empirical likelihood-ratio test (ELRT) rejects neutrality when the likelihood-ratio statistic falls in the tail of the empirical distribution obtained under the most likely neutral population size. The frequency increment test (FIT) rejects neutrality if the distribution of normalized allele-frequency increments exhibits a mean that deviates significantly from zero. We characterize the statistical power of these two tests for selection, and we apply them to three experimental data sets. We demonstrate that both ELRT and FIT have power to detect selection in practical parameter regimes, such as those encountered in microbial evolution experiments. Our analysis applies to a single diallelic locus, assumed independent of all other loci, which is most relevant to full-genome selection scans in sexual organisms, and also to evolution experiments in asexual organisms as long as clonal interference is weak. Different techniques will be required to detect selection in time series of cosegregating linked loci.