Ferrandiz-Rovira M, Bigot T, Allainé D, Callait-Cardinal M-P, Cohas A
1] Laboratoire Biométrie et Biologie Evolutive, Université de Lyon, CNRS, UMR5558, Université Lyon 1, F-69622, Villeurbanne, F-69000 Lyon, France [2] Université Lyon, VetAgro Sup Campus Vet, Marcy-L'Étoile, France.
Laboratoire Biométrie et Biologie Evolutive, Université de Lyon, CNRS, UMR5558, Université Lyon 1, F-69622, Villeurbanne, F-69000 Lyon, France.
Heredity (Edinb). 2015 May;114(5):485-93. doi: 10.1038/hdy.2015.13. Epub 2015 Mar 11.
Studying the different roles of adaptive genes is still a challenge in evolutionary ecology and requires reliable genotyping of large numbers of individuals. Next-generation sequencing (NGS) techniques enable such large-scale sequencing, but stringent data processing is required. Here, we develop an easy to use methodology to process amplicon-based NGS data and we apply this methodology to reliably genotype four major histocompatibility complex (MHC) loci belonging to MHC class I and II of Alpine marmots (Marmota marmota). Our post-processing methodology allowed us to increase the number of retained reads. The quality of genotype assignment was further assessed using three independent validation procedures. A total of 3069 high-quality MHC genotypes were obtained at four MHC loci for 863 Alpine marmots with a genotype assignment error rate estimated as 0.21%. The proposed methodology could be applied to any genetic system and any organism, except when extensive copy-number variation occurs (that is, genes with a variable number of copies in the genotype of an individual). Our results highlight the potential of amplicon-based NGS techniques combined with adequate post-processing to obtain the large-scale highly reliable genotypes needed to understand the evolution of highly polymorphic functional genes.
在进化生态学中,研究适应性基因的不同作用仍是一项挑战,需要对大量个体进行可靠的基因分型。新一代测序(NGS)技术能够实现这种大规模测序,但需要严格的数据处理。在此,我们开发了一种易于使用的方法来处理基于扩增子的NGS数据,并将该方法应用于对阿尔卑斯旱獭(Marmota marmota)MHC I类和II类的四个主要组织相容性复合体(MHC)位点进行可靠的基因分型。我们的后处理方法使我们能够增加保留读数的数量。使用三种独立的验证程序进一步评估了基因型分配的质量。在四个MHC位点上,共为863只阿尔卑斯旱獭获得了3069个高质量的MHC基因型,基因型分配错误率估计为0.21%。所提出的方法可应用于任何遗传系统和任何生物体,但广泛的拷贝数变异情况除外(即个体基因型中具有可变拷贝数的基因)。我们的结果突出了基于扩增子的NGS技术与适当的后处理相结合的潜力,以获得理解高度多态性功能基因进化所需的大规模高度可靠的基因型。