Expression of quantitative traits is determined by multiple genes and environmental factors. By combining phenotype and genotype data, we can identify loci that influence a quantitative trait. Despite recent advances in whole genome sequencing (WGS), analysis of WGS data remains a complex and computationally intensive process. For our goal of simplifying the detection of quantitative trait loci (QTL) using WGS data based on the bulk segregant analysis (BSA) method, we developed a software tool called QTLspyer. We designed a user-friendly graphical interface using R Shiny. The process of QTL detection was divided into variant calling and QTL finding. In the first step, a Python script is used to call single nucleotide polymorphism (SNPs) using the Genome Analysis Toolkit (GATK). In the second step, the probabilities of potential QTL findings are estimated based on the G’ and QTL-seq approach using an R library called QTLseqr. The results are presented to the user in the form of data tables with the statistical properties of SNPs and QTLs and graphically with plots showing the QTL probabilities for all genome positions. The application with all required tools is contained inside a Docker image. To verify its accuracy, we performed the analysis using datasets from published studies and showed the suitability and reliability of QTLspyer by successfully repeating the results of these studies.
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