May 29, 2019

PyBSASeq: a novel, simple, and effective algorithm for BSA-Seq data analysis

BioRxiv : the Preprint Server for Biology
Jianbo Zhang, Dilip Panthee

Abstract

Bulked segregant analysis (BSA), coupled with next generation sequencing (NGS), 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 major single nucleotide polymorphism (SNP)-trait associations, which leads to high sequencing cost. Here 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 likely trait-associated SNPs (ltaSNPs) were identified via Fisher's exact test and then the ratio of the ltaSNPs to total SNPs in a chromosomal interval was used to identify the genomic regions that condition the trait of interest. The results obtained this way are similar to those generated by the current methods, but with more than five times higher sensitivity, which can reduce the sequencing cost by ~80% and makes BSA-Seq more applicable for the species with a large genome.

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Mentioned in this Paper

Quantitative Trait Loci
Genome
Nucleic Acid Sequencing
Massively-Parallel Sequencing
Chromosomes
Species
Analysis
Single Nucleotide Polymorphism
Serum Albumin, Bovine
Body Surface: Area: Point in Time: ^Patient: Quantitative

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