Department of Horticultural Science, North Carolina State University, Mountain Horticultural Crops Research and Extension Center, 455 Research Drive, Mills River, NC, 28759, USA.
BMC Bioinformatics. 2020 Mar 6;21(1):99. doi: 10.1186/s12859-020-3435-8.
Bulked segregant analysis (BSA), coupled with next-generation sequencing, allows the rapid identification of both qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here. The current SNP index method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverage to detect significant single nucleotide polymorphism (SNP)-trait associations, which leads to high sequencing cost.
We developed a simple and effective algorithm for BSA-Seq data analysis and implemented it in Python; the program was named PyBSASeq. Using PyBSASeq, the significant SNPs (sSNPs), SNPs likely associated with the trait, were identified via Fisher's exact test, and then the ratio of the sSNPs to total SNPs in a chromosomal interval was used to detect the genomic regions that condition the trait of interest. The results obtained this way are similar to those generated via the current methods, but with more than five times higher sensitivity. This approach was termed the significant SNP method here.
The significant SNP method allows the detection of SNP-trait associations at much lower sequencing coverage than the current methods, leading to ~ 80% lower sequencing cost and making BSA-Seq more accessible to the research community and more applicable to the species with a large genome.
分离群体分析（BSA）与新一代测序相结合，可以快速鉴定定性和定量性状位点（QTL），在这里我们将这种技术称为 BSA-Seq。BSA-Seq 数据分析的当前 SNP 指数方法和 G 统计量方法需要相对较高的测序覆盖度来检测显著的单核苷酸多态性（SNP）-性状关联，这导致测序成本较高。
我们开发了一种用于 BSA-Seq 数据分析的简单有效的算法，并在 Python 中实现了该算法；该程序命名为 PyBSASeq。使用 PyBSASeq，通过 Fisher 精确检验鉴定显著 SNP（sSNP）和可能与性状相关的 SNP，然后用染色体区间内 sSNP 与总 SNP 的比值来检测与感兴趣性状相关的基因组区域。这种方法得到的结果与当前方法相似，但灵敏度提高了五倍以上。我们将这种方法称为显著 SNP 方法。
显著 SNP 方法允许在比当前方法低得多的测序覆盖度下检测 SNP-性状关联，从而降低约 80%的测序成本，使 BSA-Seq 更容易被研究界接受，并更适用于基因组较大的物种。
