Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany.
BHZP GmbH, An der Wassermühle 8, 21368, Dahlenburg-Ellringen, Germany.
Genet Sel Evol. 2022 Feb 19;54(1):16. doi: 10.1186/s12711-022-00706-w.
Next-generation sequencing is a promising approach for the detection of causal variants within previously identified quantitative trait loci. Because of the costs of re-sequencing experiments, this application is currently mainly restricted to subsets of animals from already genotyped populations. Imputation from a lower to a higher marker density could represent a useful complementary approach. An analysis of the literature shows that several strategies are available to select animals for re-sequencing. This study demonstrates an animal selection workflow under practical conditions. Our approach considers different data sources and limited resources such as budget and availability of sampling material. The workflow combines previously described approaches and makes use of genotype and pedigree information from a Landrace and Large White population. Genotypes were phased and haplotypes were accurately estimated with AlphaPhase. Then, AlphaSeqOpt was used to optimize selection of animals for re-sequencing, reflecting the existing diversity of haplotypes. AlphaSeqOpt and ENDOG were used to select individuals based on pedigree information and by taking into account key animals that represent the genetic diversity of the populations. After the best selection criteria were determined, a subset of 57 animals was selected for subsequent re-sequencing. In order to evaluate and assess the advantage of this procedure, imputation accuracy was assessed by setting a set of single nucleotide polymorphism (SNP) chip genotypes to missing. Accuracy values were compared to those of alternative selection scenarios and the results showed the clear benefits of a targeted selection within this practical-driven approach. Especially imputation of low-frequency markers benefits from the combined approach described here. Accuracy was increased by up to 12% compared to a randomized or exclusively haplotype-based selection of sequencing candidates.
下一代测序是一种很有前途的方法，可以检测先前确定的数量性状基因座内的因果变异。由于重新测序实验的成本，这种应用目前主要局限于已经进行基因分型的群体中的动物子集。从低标记密度向高标记密度的推断可能是一种有用的补充方法。对文献的分析表明，有几种策略可用于选择进行重测序的动物。本研究在实际条件下展示了一种动物选择工作流程。我们的方法考虑了不同的数据源和有限的资源，如预算和采样材料的可用性。该工作流程结合了先前描述的方法，并利用了长白猪和大白猪群体的基因型和系谱信息。使用 AlphaPhase 对基因型进行相位处理，并准确估计单倍型。然后，使用 AlphaSeqOpt 来优化选择用于重测序的动物，反映现有单倍型的多样性。使用 AlphaSeqOpt 和 ENDOG 根据系谱信息和考虑代表群体遗传多样性的关键动物来选择个体。确定最佳选择标准后，选择了 57 个动物子集进行后续重测序。为了评估和评估该程序的优势，通过将一组单核苷酸多态性 (SNP) 芯片基因型设置为缺失，评估了推断的准确性。将准确性值与替代选择方案进行比较，结果表明，在这种以实践为导向的方法中，有针对性的选择具有明显优势。特别是，低频率标记的推断受益于这里描述的联合方法。与随机选择或仅基于单倍型选择测序候选者相比，准确性提高了高达 12%。
