HybridQC: A SNP-Based quality control application for rapid hybridity verification in diploid plants

Ongom, P.O., Ajibade, Y.A., Mohammed, S.B., Dieng, I., Fatokun, C. & Boukar, O. (2024). HybridQC: A SNP-Based quality control application for rapid hybridity verification in diploid plants. Genes, 15(10): 1252, 1-13.

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.