The consequences of not accounting for background selection in demographic inference
The consequences of not accounting for background selection in demographic inference
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DOI：
10.1111/mec.13390
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发表时间：
2016-01-01
影响因子：
4.9
通讯作者：
Jensen, Jeffrey D.
中科院分区：
文献类型：
--
作者：
Ewing, Gregory B.;Jensen, Jeffrey D.
最近，人们越来越认识到背景选择(BGS)在数据分析和建模进步中的作用。然而，BGS仍然很难考虑，因为模拟的可操作性问题和非平衡人口模型的困难。通常，使用简单的重新调整有效人口规模的方法。然而，既没有恰当地描述BGS在没有适当考虑的情况下如何偏向或转移推理，也没有对重新调整比例是否是充分的解决方案进行彻底分析。在这里，我们对BGS进行了广泛的模拟，以确定人口统计推断的偏差和行为，使用近似贝叶斯方法。我们发现，结果可能是具有显著偏差的正向误导，并描述了BGS模型复制观察到的中性非平衡预期的参数空间。
Recently, there has been increased awareness of the role of background selection (BGS) in both data analysis and modelling advances. However, BGS is still difficult to take into account because of tractability issues with simulations and difficulty with nonequilibrium demographic models. Often, simple rescaling adjustments of effective population size are used. However, there has been neither a proper characterization of how BGS could bias or shift inference when not properly taken into account, nor a thorough analysis of whether rescaling is a sufficient solution. Here, we carry out extensive simulations with BGS to determine biases and behaviour of demographic inference using an approximate Bayesian approach. We find that results can be positively misleading with significant bias, and describe the parameter space in which BGS models replicate observed neutral nonequilibrium expectations.