Data from: Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Phylogeographic inferences about gene flow are strengthened through comparison of co-distributed taxa, but also depend on adequate genomic sampling. Amplified Fragment Length Polymorphisms (AFLP) provide a rapid and inexpensive source of multilocus allele frequency data for making genomically robust inferences. Every AFLP study initially generates markers with a range of locus-specific genotyping error rates and applies criteria to select a subset for analysis. However, there has been very little empirical evaluation of the best tradeoff between culling all but the lowest-error loci to minimize overall genotyping error versus the potential for increasing population genetic signal by retaining more loci. Here, we used AFLPs to compare population structure in co-distributed broadcast spawning (Crassostrea virginica) and brooding (Ostrea equestris) oyster species. Using existing methods for almost entirely automated marker selection and scoring, genotyping error tradeoffs were evaluated by comparing results across a nested series of datasets with mean mismatch errors of 0, 1, 2, 3, 4 and >4%. Artifactual population structure was diagnosed in high-error datasets and we assessed the low-error point at which expected population substructure signal was lost. In both species we identified substructure patterns deemed to be inaccurate at error rates {less than or equal to}2% and >4%. In the species comparison, the optimum datasets showed higher gene flow for the brooding oyster with more oceanic salinity tolerances. AFLP tradeoffs may differ among studies, but our results suggest that important signal may be lost in the pursuit of 'acceptable' error levels and our procedures provide a general method for empirically exploring these tradeoffs.

关于基因流的系统地理学推断，可通过比对同分布类群得到强化，但同时也依赖于充足的基因组采样。扩增片段长度多态性（Amplified Fragment Length Polymorphisms，AFLP）能够快速且低成本地获取多位点等位基因频率数据，从而开展稳健的基因组水平推断。每项AFLP研究在初始阶段都会生成一系列具有位点特异性基因分型错误率差异的标记，并通过设定筛选标准选取部分标记用于后续分析。然而，目前极少有实证研究对二者间的最佳权衡进行评估：是仅保留最低错误率的位点以最小化整体基因分型误差，还是保留更多位点以增强群体遗传信号。
本研究利用AFLP技术，比对了同分布的排放型产卵美洲牡蛎（*Crassostrea virginica*）与育幼型攀牡蛎（*Ostrea equestris*）的群体结构。我们采用近乎全自动化的标记筛选与分型方法，通过比对平均错配错误率分别为0、1、2、3、4%及>4%的嵌套系列数据集，评估了基因分型错误的权衡效应。研究发现，高错误率数据集会出现人为假象的群体结构，并确定了预期群体亚结构信号丢失的低错误率临界点。在两个物种中，我们均发现：当错误率≤2%及>4%时，所识别的亚结构模式被判定为不准确。
在物种比对分析中，最优数据集显示：海洋盐度耐受性更强的育幼型牡蛎，其基因流水平更高。尽管不同研究中AFLP的权衡策略可能存在差异，但本研究结果表明，一味追求‘可接受’的错误水平可能会丢失重要的遗传信号，而我们提出的分析流程可为实证探索这类权衡关系提供通用方法。