Abstract
BackgroundBulked 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.ResultsWe 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.ConclusionsThe 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.
Highlights
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 single nucleotide polymorphism (SNP) with a high G-statistic value would be more likely related to the trait. Both methods identify SNP-trait associations via quantifying the reference base (REF)/alternative base (ALT) enrichment of a single SNP, and some of the major QTLs can be detected only with high sequencing coverage [3, 5, 22], which leads to high sequencing cost and limits the application of BSA-Seq to the species with a large genome
Identification of significant SNPs In BSA-Seq studies, if a SNP is not associated with the trait, its REF/ALT reads would be randomly segregated in both bulks, and the ALT read proportions in two bulks should be similar; if a SNP is associated with the trait, its REF/ALT reads would be enriched in either bulk due to phenotypic selection via bulking, and the ALT read proportions should be significantly different between the bulks
Summary
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 application of nextgeneration sequencing technology to BSA has eliminated the time-consuming and labor-intensive marker development and genetic mapping steps and has dramatically sped up the detection of gene-trait associations [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] This technique was termed either QTL-seq or BSA-Seq in different publications [5, 6, 21]; we adapted the latter here because it can be applied to study both qualitative and quantitative traits
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