A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system
A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system
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改进野生动物物种多位点基因分型的新颖工作流程：使用已知模型系统的实验装置
DOI：
10.1101/638288
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
P. S. C.
中科院分区：
文献类型：
--
作者：
Gillingham;M. A. F;Montero;Wilhelm;Grudzus;Sommer;Santos;P. S. C.
对新型复杂多基因系统进行基因分型在非模式生物中尤其具有挑战性。靶引物经常同时扩增多个基因座，导致高PCR和测序假象，如嵌合体和等位基因扩增偏倚。大多数下一代测序基因分型管道已经在非模型系统中得到验证，其中真实的基因型是未知的，并且产生的伪影可能是高度可重复的。进一步阻碍了准确的基因分型，PCR中的伪影和拷贝数变异（CNV）之间的关系仍然描述不清。在这里，我们调查后者通过实验结合多种已知的主要组织相容性复合体（MHC）单倍型（鸡，原鸡，43个人工基因型，每个扩增子2-13个等位基因）。除了定义明确的“最佳”引物，我们模拟了一个非模型物种的情况下，通过设计“幼稚”引物，与密切相关的鸡形物种的序列数据。我们将一种新的开源基因分型管道（ACACIA）应用于数据，并将其性能与另一种先前发表的管道进行了比较。最后，我们将ACACIA应用于具有高CNV（MHC I类外显子2，多达11个位点）的非模型系统（灰棕色鼠狐猴，灰红狐猴）。ACACIA产生非常高的等位基因识别准确性（>98%）。非嵌合伪影随着CNV的增加而线性增加，但当扩增超过4-6个等位基因时，嵌合伪影水平化。正如预期的那样，我们发现当共扩增多个基因座时等位基因变体的异质扩增效率。使用我们经过验证的ACACIA管道和本研究的示例数据，我们详细讨论了研究人员应该避免的陷阱，以便可靠地对复杂的多基因系统进行基因分型。ACACIA可在https://gitlab.com/psc_santos/ACACIA上公开获取。
Genotyping novel complex multigene systems is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most next-generation sequencing genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and artefacts generated may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and copy number variation (CNV) within a PCR remains poorly described. Here we investigate the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes (chicken,Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined “optimal” primers, we simulated a non-model species situation by designing “naive” primers, with sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA) to the data, and compared its performance with another, previously published, pipeline. Finally, we applied ACACIA on a non-model system (grey-brown mouse lemurs,Microcebus griseorufus) with high CNV (MHC Class I exon 2 with up to 11 loci). ACACIA yielded very high allele calling accuracy (>98%). Non-chimeric artefacts increased linearly with increasing CNV but chimeric artefacts leveled when amplifying more than 4-6 alleles. As expected, we found heterogeneous amplification efficiency of allelic variants when co-amplifying multiple loci. Using our validated ACACIA pipeline and the example data of this study, we discuss in detail the pitfalls researchers should avoid in order to reliably genotype complex multigene systems. ACACIA is publicly available at https://gitlab.com/psc_santos/ACACIA.
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