Data from: Estimation of genotyping error rate from repeat genotyping, unintentional recaptures and known parent-offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop-out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct-count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus-specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus-specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).

几乎所有遗传数据中都存在基因分型错误（genotyping error），此类误差可能影响研究的生物学结论，对于基于个体识别和亲权分析的研究而言尤为显著。诸多统计方法可纳入基因分型误差，但通常需要精准的误差率估计值。本研究针对褐岩鱼（Sebastes auriculatus）构建了全新的微卫星数据集（microsatellite data set），并基于该数据集采用三种方法估算基因分型错误率：（1）对5%的样本进行重复基因分型；（2）比对意外重捕的个体；（3）对已知的亲代-子代对（parent–offspring pairs）进行孟德尔遗传误差校验。在各数据集内，我们针对等位基因脱扣（allele drop-out）与假等位基因（false allele）两类情况，量化了每一等位基因的基因分型错误率。通过直接计数法计算各位点的基因分型错误率，结果显示：基于重复基因型、已知亲代-子代对以及意外重捕个体的整体平均基因分型错误率分别为0.3%、1.5%与1.7%（对应每等位基因错误率分别为0.002、0.007与0.008）。经直接计数法的误差估计结果显示，重捕数据集与已知亲代-子代对数据集的错误率是重复基因型估计结果的四倍。三类数据集的错误率均未表现出与位点变异性的相关性；在重复基因型组中，错误似乎随机分布于各位点，但在重捕组与亲代-子代比对组中并非如此。此外，三类数据集两两之间的位点特异性错误率均无相关性。本研究数据表明，重复基因分型法可能低估真实错误率，且无法精准估定位点特异性错误率。因此，我们建议根据研究的整体目标选择适配的误差估计方法（例如在亲权分析研究中采用已知亲代-子代比对法）。