Genotyping Analysis Optimizing Allele Cluster Representation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current high-throughput genotyping methods face challenges in maximizing allelic information quality, reducing costs and time, and ensuring reliability across different technologies and samples, with limitations in quality control and marker consistency.
Innovation Solution
A computer-implemented method using a reference panel to optimize allele-cluster representation, combined with new quality control indicators and marker optimization, to improve the reliability and consistency of allelic data across genotyping experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-throughput genotyping methods are used to simultaneously obtain genotyping information for a large number of individuals and multiple markers, then productivity is improved, but measurement precision and reliability of allelic information deteriorate
Solution Approach 1:
The method performs preliminary quality control assessments and cluster validation before final genotype calling. By pre-identifying problematic markers and individuals, and establishing confidence thresholds in advance, the system ensures high measurement precision is maintained even when processing large numbers of samples simultaneously through high-throughput genotyping methods
Solution Approach 2:
The system implements iterative feedback loops where genotype calls are initially made, then validated against quality metrics and cluster characteristics. Problematic calls are identified and corrected through repeated assessment cycles, with confidence scores updated based on feedback from quality control indicators. This feedback mechanism maintains high precision across large throughput by continuously monitoring and correcting deviations
2Reliability
If multiple quality control indicators are calculated and markers are classified into different classes, then reliability of genotyping results is improved, but device complexity and computational requirements increase
Solution Approach 1:
The software segments the genotyping analysis into distinct modular components: cluster detection module, quality indicator calculation module, marker classification module, and genotype calling module. Each module handles specific tasks independently with defined inputs and outputs. This segmentation allows complex quality control to be performed through coordinated simple modules, improving reliability while managing software complexity through modular design
Solution Approach 2:
The system uses parameter changes to manage complexity by transforming complex quality assessment into simple threshold-based decisions. Multiple quality indicators (call rate, cluster separation, signal intensity) are calculated and compared against predetermined thresholds to automatically classify markers into confidence classes. This parameter-based approach simplifies the decision logic while maintaining comprehensive quality control, improving reliability without proportionally increasing software complexity
3Reliability
If a fixed set of markers is used across different genotyping runs, then consistency and reliability are improved, but adaptability to different sample types and technologies deteriorates
Solution Approach 1:
The method employs universal quality control metrics and cluster validation procedures that function across different genotyping technologies and sample types. The same mathematical frameworks for calculating quality indicators and classifying markers are applied universally, while the actual marker sets and confidence thresholds are adaptively determined for each specific application. This universal approach ensures consistent reliability across diverse contexts without requiring technology-specific protocols
Solution Approach 2:
The system dynamically adjusts the set of reliable markers for each genotyping run based on empirical assessment of cluster quality and signal characteristics. Rather than using a static predetermined set, the method evaluates all available markers during each run, identifies those meeting quality criteria, and constructs an adaptive reliable marker set. This dynamic approach maintains consistency through rigorous quality standards while adapting to different sample types and technological variations
Data Source
Figure 1
Figure 2~3
Figure 4A~4B
AI summary
The invention relates to a computer implemented method for optimizing the settings of the a computer program assigning allelic information for such individuals by the use of raw data and of the allele information from individuals of a reference panel to determine the settings that provide the best output data. This increases the quality of data used in plant breeding applications.