Lepais Olivier, Chancerel Emilie, Boury Christophe, Salin Franck, Manicki Aurélie, Taillebois Laura, Dutech Cyril, Aissi Abdeldjalil, Bacles Cecile F E, Daverat Françoise, Launey Sophie, Guichoux Erwan
INRAE, Univ. Bordeaux, BIOGECO, Cestas, France.
INRAE, Université de Pau et Pays de l'Adour, ECOBIOP, Saint-Peé-sur-Nivelle, France.
PeerJ. 2020 May 4;8:e9085. doi: 10.7717/peerj.9085. eCollection 2020.
Application of high-throughput sequencing technologies to microsatellite genotyping (SSRseq) has been shown to remove many of the limitations of electrophoresis-based methods and to refine inference of population genetic diversity and structure. We present here a streamlined SSRseq development workflow that includes microsatellite development, multiplexed marker amplification and sequencing, and automated bioinformatics data analysis. We illustrate its application to five groups of species across phyla (fungi, plant, insect and fish) with different levels of genomic resource availability. We found that relying on previously developed microsatellite assay is not optimal and leads to a resulting low number of reliable locus being genotyped. In contrast, de novo ad hoc primer designs gives highly multiplexed microsatellite assays that can be sequenced to produce high quality genotypes for 20-40 loci. We highlight critical upfront development factors to consider for effective SSRseq setup in a wide range of situations. Sequence analysis accounting for all linked polymorphisms along the sequence quickly generates a powerful multi-allelic haplotype-based genotypic dataset, calling to new theoretical and analytical frameworks to extract more information from multi-nucleotide polymorphism marker systems.
高通量测序技术在微卫星基因分型(SSRseq)中的应用已被证明能够消除基于电泳方法的许多局限性,并完善对群体遗传多样性和结构的推断。我们在此展示一种简化的SSRseq开发流程,该流程包括微卫星开发、多重标记扩增与测序以及自动化生物信息学数据分析。我们阐述了其在跨门类的五组物种(真菌、植物、昆虫和鱼类)中的应用,这些物种具有不同水平的基因组资源可用性。我们发现,依赖先前开发的微卫星检测方法并非最佳选择,会导致能够进行基因分型的可靠位点数量较少。相比之下,从头进行的特别引物设计能够提供高度多重的微卫星检测方法,可对其进行测序以生成20至40个位点的高质量基因型。我们强调了在广泛情形下进行有效SSRseq设置时需要考虑的关键前期开发因素。对序列上所有连锁多态性进行分析的序列分析能够快速生成一个强大的基于多等位基因单倍型的基因型数据集,这需要新的理论和分析框架来从多核苷酸多态性标记系统中提取更多信息。