BSArray: An automated bulked segregant analysis pipeline for QTL discovery using Illumina Infinium SNP genotyping array

Abstract

Efficient identification of quantitative trait loci (QTL) is essential for accelerating crop genetic improvement through marker-assisted selection (MAS). Bulked segregant analysis (BSA) using Illumina Infinium SNP genotyping arrays provides a cost-effective approach for detecting QTL in biparental populations; however, unlike sequencing-based BSA, computational workflows for bulked segregant analysis using Illumina Infinium SNP arrays often require manual comparison of parental and bulk genotypes, together with custom filtering and scripting, reducing scalability and reproducibility. Here, we present BSArray, an R Shiny-based application that provides an automated, user-friendly pipeline for BSA using Illumina Infinium SNP array data. The pipeline estimates allele frequencies from normalized intensity metrics and computes a quality-weighted allele frequency difference (W statistic) that integrates signal magnitude and genotype confidence. A sliding window framework is then applied to identify putative QTL regions based on empirical genome-wide thresholds, followed by prioritization of candidate markers and annotation of nearby gene models. Using the SoySNP50K array in soybean as a case study, we demonstrate three implementation scenarios: (i) detection of a previously reported major-effect locus, (ii) discovery of a previously unmapped locus, and (iii) detection of a novel minor-effect locus within a known QTL region. BSArray provides a rapid, reproducible, and user-friendly framework for BSA with SNP array data. By reducing manual intervention and computational requirements, it offers a practical and scalable alternative to sequencing-based approaches for QTL discovery and marker development.