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Inferring past demographic changes from contemporary genetic data: A simulation‐based evaluation of the ABCABC methods implemented in diyabcdiyabc

机译：推断从当代遗传数据的过去的人口变化： abc abc方法在 diyabc< /sc -ceadiyabc.

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Abstract > Inferring the demographic history of species and their populations is crucial to understand their contemporary distribution, abundance and adaptations. The high computational overhead of likelihood‐based inference approaches severely restricts their applicability to large data sets or complex models. In response to these restrictions, approximate Bayesian computation ( <fc>ABC</fc> ) methods have been developed to infer the demographic past of populations and species. Here, we present the results of an evaluation of the <fc>ABC</fc> ‐based approach implemented in the popular software package <sc>diyabc</sc> using simulated data sets (mitochondrial <fc>DNA</fc> sequences, microsatellite genotypes and single nucleotide polymorphisms). We simulated population genetic data under five different simple, single‐population models to assess the model recovery rates as well as the bias and error of the parameter estimates. The ability of <sc>diyabc</sc> to recover the correct model was relatively low (0.49): 0.6 for the simplest models and 0.3 for the more complex models. The recovery rate improved significantly when reducing the number of candidate models from five to three (from 0.57 to 0.71). Among the parameters of interest, the effective population size was estimated at a higher accuracy compared to the timing of events. Increased amounts of genetic data did not significantly improve the accuracy of the parameter estimates. Some gains in accuracy and decreases in error were observed for scaled parameters (e.g., N <sub>e</sub> μ) compared to unscaled parameters (e.g., N <sub>e</sub> and μ). We concluded that <sc>diyabc</sc> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract xmlns =“http://www.wiley.com/namespaces/wiley”type =“main”xml：id =“men12696-abs-0001”> <标题类型=“main”>抽象</ title> < P>推断物种的人口统计历史，他们的人口至关重要，以了解他们的当代分配，丰富和适应性。基于似然的推断方法的高计算开销严重限制了它们对大数据集或复杂模型的适用性。响应于这些限制，已经开发了近似贝叶斯计算（<FC> ABC </ FC>）方法以推断人口和物种的人口统计。在这里，我们介绍了使用模拟数据集（线粒体<FC> DNA </ FC>）在流行的软件包<SC> DIYABC </ SC>中实现的<FC> ABC </ FC>基于方法的评估结果。（线粒体<FC> DNA>序列，微卫星基因型和单核苷酸多态性）。我们在五种不同简单的单一人口模型下模拟人口遗传数据，以评估模型恢复速率以及参数估计的偏差和误差。 <sc> diyabc </ sc>恢复正确模型的能力相对较低（0.49）：最简单模型的0.6，更复杂的模型为0.3。当将候选模型的数量从五到三（从0.57到0.71）减少时，回收率显着提高。在感兴趣的参数中，与事件的时序相比，以更高的准确性估计有效的人口大小。增加的遗传数据量没有显着提高参数估计的准确性。与未划分的参数相比，观察到缩放参数（例如， n </ i> <sub> e </ sub> <sub> e </ sub>μ），观察到的准确性和减少。（例如， n </ i> < Sub> E </ sub>和μ）。我们得出结论，<sc> diyabc </ sc> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-25257/'>《Molecular ecology resources》</a> <b style="margin: 0 2px;">|</b><span>2017年第6期</span><b style="margin: 0 2px;">|</b><span>共17页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Cabrera Andrea A.&option=202" target="_blank" rel="nofollow">Cabrera Andrea A.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Palsb?ll Per J.&option=202" target="_blank" rel="nofollow">Palsb?ll Per J.;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Marine Evolution and ConservationUniversity of GroningenGroningen The Netherlands;</p> <p>Marine Evolution and ConservationUniversity of GroningenGroningen The Netherlands;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/179.html" title="分子生物学">分子生物学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=approximate Bayesian computation&option=203" rel="nofollow">approximate Bayesian computation;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=demographic inference&option=203" rel="nofollow">demographic inference;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=model selection&option=203" rel="nofollow">model selection;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=population genetics&option=203" rel="nofollow">population genetics;</a> </p> <div class="translation"> 机译：近似贝叶斯计算;人口统计推断;模型选择;人口遗传学; 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