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均存在基因分型错误（genotyping error），此类错误会对研究的生物学结论造成干扰，针对基于个体识别与亲权分析的研究而言，这一影响尤为显著。目前已有多种统计方法可纳入基因分型错误的影响，但通常需要准确的错误率估计值。本研究采用一套专为褐岩鱼（Sebastes auriculatus）开发的新型微卫星数据集，通过三种方法估算基因分型错误率：（1）对5%的样本开展重复基因分型；（2）比对意外重捕的个体；（3）对已知亲子对进行孟德尔遗传错误校验。在各数据集内，我们针对等位基因缺失（allele drop-out）与假等位基因（false alleles）两类情形，量化了每个等位基因的基因分型错误率。经直接计数法统计，重复基因型组、已知亲子对组与意外重捕个体组的各位点平均总基因分型错误率分别为0.3%、1.5%与1.7%（对应等位基因错误率分别为0.002、0.007与0.008）。经直接计数法估算，重捕组与已知亲子对组的错误率较重复基因型组高出四倍。三类数据集均未显示错误率与位点变异度存在相关性；重复基因型组的错误在各位点间呈随机分布，但重捕组与亲子对比较组并非如此。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。本研究结果提示，重复基因分型法可能低估真实错误率，且无法准确估算位点特异性错误率。因此，我们建议根据研究的整体目标选择适配的错误估算方法（例如亲权分析中采用已知亲子对比较法）。