KASP-IEva: an intelligent typing evaluation model for KASP primers

Area of Science:

• Genetics and Molecular Biology
• Bioinformatics
• Agricultural Science

Background:

• KASP marker technology is crucial for efficient molecular marker-assisted breeding.
• Developing large-scale KASP marker applications requires an intelligent evaluation model for primer typing results.
• Current methods may lack the efficiency needed for high-throughput genotype screening.

Purpose of the Study:

• To develop an intelligent evaluation model for KASP marker primer typing results.
• To enhance the accuracy and efficiency of KASP marker development and application.
• To provide technical support for large-scale screening of excellent genotypes.

Main Methods:

• Proposed a gene population delineation method using NTC identification and data distribution judgment modules.
• Improved K-Means clustering accuracy for grouping and categorizing amplification product signals.
• Constructed the KASP-IEva model using a decision tree for eigenvalue discrimination and score prediction.

Main Results:

• The model visualizes amplification product signals for NTC, pure, heterozygous, and untyped genotypes.
• Achieved an average accuracy rate of 87% across four evaluation grades.
• Reduced evaluation time from 15 hours (expert) to approximately 8.5 minutes (model) for 2519 samples.

Conclusions:

• The intelligent KASP-IEva model enables rapid and accurate KASP marker typing evaluation.
• The model significantly outperforms expert evaluation in terms of time efficiency.
• This advancement supports the broader application of KASP markers in molecular breeding and genotype identification.