Department of Horticultural Science, North Carolina State University, Mountain Horticultural Crops Research and Extension Center, Mills River, NC 28759, USA.
G3 (Bethesda). 2022 Feb 4;12(2). doi: 10.1093/g3journal/jkab400.
Genomic regions that control traits of interest can be rapidly identified using BSA-Seq, a technology in which next-generation sequencing is applied to bulked segregant analysis (BSA). We recently developed the significant structural variant method for BSA-Seq data analysis that exhibits higher detection power than standard BSA-Seq analysis methods. Our original algorithm was developed to analyze BSA-Seq data in which genome sequences of one parent served as the reference sequences in genotype calling and, thus, required the availability of high-quality assembled parental genome sequences. Here, we modified the original script to effectively detect the genomic region-trait associations using only bulk genome sequences. We analyzed two public BSA-Seq datasets using our modified method and the standard allele frequency and G-statistic methods with and without the aid of the parental genome sequences. Our results demonstrate that the genomic region(s) associated with the trait of interest could be reliably identified via the significant structural variant method without using the parental genome sequences.
利用 BSA-Seq 技术可以快速鉴定控制目标性状的基因组区域，BSA-Seq 是一种将下一代测序应用于混池分离分析（BSA）的技术。我们最近开发了用于 BSA-Seq 数据分析的显著结构变异方法，该方法比标准 BSA-Seq 分析方法具有更高的检测能力。我们最初的算法是为分析 BSA-Seq 数据而开发的，其中一个亲本的基因组序列用作基因型调用的参考序列，因此需要高质量组装的亲本基因组序列。在这里，我们修改了原始脚本，仅使用批量基因组序列有效地检测基因组区域-性状关联。我们使用修改后的方法和标准等位基因频率和 G 统计方法，以及有无亲本基因组序列的帮助，分析了两个公共的 BSA-Seq 数据集。我们的结果表明，通过显著结构变异方法，即使不使用亲本基因组序列，也可以可靠地鉴定与目标性状相关的基因组区域。
