Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum‐likelihood approaches based on allele frequencies and linkage‐disequilibrium have been extensively used to infer admixture processes from genome‐wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two‐way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios. MetHis allows users to draw model‐parameter values from prior distributions set by the user, and, for each simulation, MetHis can calculate numerous summary statistics describing genetic diversity patterns and moments of the distribution of individual admixture fractions. We coupled MetHis with existing machine‐learning ABC algorithms and investigated the admixture history of admixed populations. Results showed that random forest ABC scenario‐choice could accurately distinguish among most complex admixture scenarios, and errors were mainly found in regions of the parameter space where scenarios were highly nested, and, thus, biologically similar. We focused on African American and Barbadian populations as two study‐cases. We found that neural network ABC posterior parameter estimation was accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we found that monotonically decreasing contributions over time, from Europe and Africa, explained the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum‐likelihood methods are intractable.

中文翻译：

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用近似贝叶斯计算重建的复杂遗传混合历史

混合是一个基本的进化过程，它影响了许多物种的遗传模式。基于等位基因频率和连锁不平衡的最大似然方法已被广泛用于从全基因组数据集中推断混合过程，主要是在人群中。然而，复杂的混合物历史，超过一两个脉冲的混合物，在方法上仍然具有重建的挑战性。我们开发了一个近似贝叶斯计算 (ABC) 框架来从独立的遗传标记重建高度复杂的混合历史。我们构建了软件包MetHis模拟双向混合群体中的独立 SNP 或微卫星，适用于具有多个混合脉冲、单调减少或增加重复混合的场景，或这些场景的组合。MetHis允许用户从用户设置的先验分布中绘制模型参数值，并且对于每次模拟，MetHis可以计算大量汇总统计数据，描述遗传多样性模式和单个混合分数分布的矩。我们结合了MetHi使用现有的机器学习 ABC 算法并研究混合种群的混合历史。结果表明，随机森林 ABC 情景选择可以准确地区分最复杂的混合情景，错误主要出现在参数空间中场景高度嵌套的区域，因此具有生物学相似性。我们将非裔美国人和巴巴多斯人口作为两个研究案例。我们发现神经网络 ABC 后验参数估计在复杂的混合场景下是准确且合理的。对于这两个混合种群，我们发现来自欧洲和非洲的随时间单调递减的贡献比多个混合脉冲更准确地解释了观察到的数据。