Centre for Biodiversity and Biosecurity, School of Biological Sciences, University of Auckland, Auckland, New Zealand.
Institute of Zoology, Zoological Society of London, London, UK.
Mol Ecol Resour. 2022 Jul;22(5):1855-1867. doi: 10.1111/1755-0998.13575. Epub 2022 Jan 9.
Sample mix-ups occur when samples have accidentally been duplicated, mislabelled or swapped. When samples are subsequently genotyped or sequenced, this can lead to individual IDs being incorrectly linked to genetic data, resulting in incorrect or biased research results, or reduced power to detect true biological patterns. We surveyed the community and found that almost 80% of responding researchers have encountered sample mix-ups. However, many recent studies in the field of molecular ecology do not appear to systematically report individual assignment checks as part of their publications. Although checks may be done, lack of consistent reporting means that it is difficult to assess whether sample mix-ups have occurred or been detected. Here, we present an easy-to-follow sample verification framework that can utilise existing metadata, including species, population structure, sex and pedigree information. We demonstrate its application to a data set representing individuals of a threatened Aotearoa New Zealand bird species, the hihi, genotyped on a 50K SNP array. We detected numerous incorrect genotype-ID associations when comparing observed and genetic sex or comparing to relationships in a verified microsatellite pedigree. The framework proposed here helped to confirm 488 individuals (39%), correct another 20 bird-genotype links, and detect hundreds of incorrect sample IDs, emphasizing the value of routinely checking genetic and genomic data sets for their accuracy. We therefore promote the implementation and reporting of this simple yet effective sample verification framework as a standardized quality control step for studies in the field of molecular ecology.
当样品意外复制、标记错误或调换时，就会发生样品混淆。当随后对样品进行基因分型或测序时，这可能导致个体 ID 与遗传数据不正确地关联，从而导致不正确或有偏差的研究结果，或降低检测真实生物学模式的能力。我们调查了该领域的研究人员，发现近 80%的受访者都遇到过样品混淆的问题。然而，分子生态学领域的许多近期研究似乎并没有系统地报告个体分配检查作为其出版物的一部分。尽管可能进行了检查，但缺乏一致的报告意味着很难评估是否发生了样品混淆或是否已被发现。在这里，我们提出了一个易于遵循的样品验证框架，可以利用现有的元数据，包括物种、种群结构、性别和系谱信息。我们将其应用于一个代表濒危新西兰鸟类物种 hihi 的个体数据集，该数据集在 50K SNP 芯片上进行了基因分型。我们在比较观察到的和遗传性别时，或在与经过验证的微卫星系谱中的关系进行比较时，发现了许多不正确的基因型-ID 关联。这里提出的框架有助于确认 488 个个体（39%），纠正另外 20 个鸟-基因型链接，并检测数百个不正确的样品 ID，强调了定期检查遗传和基因组数据集准确性的重要性。因此，我们提倡实施和报告这个简单而有效的样品验证框架，作为分子生态学领域研究的标准化质量控制步骤。
