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PyBSASeq: a simple and effective algorithm for bulked segregant analysis with whole-genome sequencing data
Jianbo Zhang1, Dilip R Panthee2
1Department of Horticultural Science, North Carolina State University, Mountain Horticultural Crops Research and Extension Center, 455 Research Drive, Mills River, NC, 28759, USA. zhang.jianbo@gmail.com.
BMC Bioinformatics
|March 8, 2020
Summary
A new significant SNP method for Bulked Segregant Analysis (BSA-Seq) significantly lowers sequencing costs. This method increases sensitivity and makes BSA-Seq more accessible for genetic research across various species.
Area of Science:
- Genetics
- Bioinformatics
- Genomics
Background:
- Bulked Segregant Analysis (BSA) with next-generation sequencing (BSA-Seq) identifies trait-linked loci.
- Current BSA-Seq analysis methods (SNP index, G-statistic) require high sequencing coverage, increasing costs.
- High sequencing costs limit BSA-Seq accessibility and application, especially for large-genome species.
Purpose of the Study:
- To develop a cost-effective and sensitive algorithm for BSA-Seq data analysis.
- To improve the accessibility and applicability of BSA-Seq technology.
Main Methods:
- Developed a Python-based algorithm named PyBSASeq for BSA-Seq data analysis.
- Implemented the significant SNP (sSNP) method using Fisher's exact test to identify trait-associated SNPs.
- Calculated the ratio of sSNPs to total SNPs within chromosomal intervals to pinpoint trait-conditioning genomic regions.
Main Results:
- The PyBSASeq algorithm successfully identified significant SNPs and genomic regions associated with traits.
- The significant SNP method demonstrated over five times higher sensitivity compared to existing methods.
- Results were comparable to current methods but achieved with significantly lower sequencing coverage.
Conclusions:
- The significant SNP method enables SNP-trait association detection at substantially lower sequencing coverage.
- This approach reduces sequencing costs by approximately 80%, enhancing BSA-Seq's accessibility.
- The method is particularly beneficial for species with large genomes, broadening BSA-Seq's research scope.
