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 m...

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Veröffentlicht in:Genes Jg. 15; H. 10; S. 1252
Hauptverfasser: Ongom, Patrick Obia, Ajibade, Yakub Adebare, Mohammed, Saba Baba, Dieng, Ibnou, Fatokun, Christian, Boukar, Ousmane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.10.2024
MDPI
Schlagworte:
Alleles
Breeding
Diploids
Diploidy
Gene polymorphism
Genetic crosses
Genotype
Genotype & phenotype
Germplasm
Heterozygosity
Hybridization, Genetic
Missing data
Plant Breeding - methods
Polymorphism
Polymorphism, Single Nucleotide - genetics
Quality assurance
Quality Control
Single-nucleotide polymorphism
Software
Sorghum
Sorghum - genetics
Sorghum bicolor
Statistical analysis
User interface
Vigna - genetics
Vigna unguiculata
Zea mays
Zea mays - genetics
hybridity determination
marker efficiency
parental purity
software program
quality control
single nucleotide polymorphism
quality assurance
F1 verification
KASP assay
ISSN:2073-4425, 2073-4425
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2073-4425
2073-4425
DOI:10.3390/genes15101252