PyBSASeq: a Simple and Effective Algorithm for Bulked Segregant Analysis with Whole-genome Sequencing Data

Background: 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.

Results: 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.

Conclusions: 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.