HybridQC: A SNP-Based Quality Control Application for Rapid Hybridity Verification in Diploid Plants

Background/Objectives: Hybridity authentication is an important component of quality assurance and control (QA/QC) in breeding programs. Here, we introduce HybridQC v1.0, a QA/QC software program specially designed for parental purity and hybridity determination. HybridQC rapidly detects molecular marker polymorphism between parents of a cross and utilizes only the informative markers for hybridity authentication. Methods: HybridQC is written in Python and designed with a graphical user interface (GUI) compatible with Windows operating systems. We demonstrated the QA/QC analysis workflow and functionality of HybridQC using Kompetitive allele-specific PCR (KASP) SNP genotype data for cowpea (Vigna unguiculata). Its performance was validated in other crop data, including sorghum (Sorghum bicolor) and maize (Zea mays). Results: The application efficiently analyzed low-density SNP data from multiple cowpea bi-parental crosses embedded in a single Microsoft Excel file. HybridQC is optimized for the auto-generation of key summary statistics and visualization patterns for marker polymorphism, parental heterozygosity, non-parental alleles, missing data, and F₁ hybridity. An added graphical interface correctly depicted marker efficiency and the proportions of true F₁ versus self-fertilized progenies in the data sets used. The output of HybridQC was consistent with the results of manual hybridity discernment in sorghum and maize data sets. Conclusions: This application uses QA/QC SNP markers to rapidly verify true F₁ progeny. It eliminates the extensive time often required to manually curate and process QA/QC data. This tool will enhance the optimization efforts in breeding programs, contributing to increased genetic gain.