Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.
Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.
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DOI：
10.1111/1755-0998.13413
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发表时间：
2021-11
影响因子：
7.7
通讯作者：
Estoup A
中科院分区：
文献类型：
--
作者：
Collin FD;Durif G;Raynal L;Lombaert E;Gautier M;Vitalis R;Marin JM;Estoup A
基于模拟的方法，如近似贝叶斯计算（ABC），非常适合分析复杂的种群和物种遗传历史。在这种情况下，监督机器学习（SML）方法提供了有吸引力的统计解决方案，以进行有关场景选择和参数估计的有效推断。随机森林方法（RF）是用于分类或回归问题的SML算法的强大集成。随机森林允许以较低的计算成本进行推断，而无需初步选择ABC汇总统计量的相关组成部分，并绕过ABC公差水平的推导。我们已经实现了一组RF算法来处理推理使用模拟数据集从扩展版本的人口遗传模拟器中实现DIYABC v2.1.0。由此产生的计算机软件包名为DIYABC Random Forest v1.0，将两个功能集成到用户友好的界面中：在不同类型分子数据（微卫星，DNA序列或SNP）的自定义进化场景下进行模拟，以及包括统计工具在内的RF处理，以评估推断的能力和准确性。我们通过分析对应于池测序和个体测序SNP数据集的伪观测数据集和真实的数据集，说明DIYABC随机森林v1.0在场景选择和参数估计方面的功能。由于所实现的RF方法的固有属性和SNP数据的大特征向量（包括各种汇总统计量及其线性组合），DIYABC Random Forest v1.0可以有效地分析大型SNP数据集，从而推断复杂的群体遗传历史。
Simulation‐based methods such as approximate Bayesian computation (ABC) are well‐adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user‐friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo‐observed and real data sets corresponding to pool‐sequencing and individual‐sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.
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