SNP Selection for Nucleic Acid Probe Array Design
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Solution Overview
Problem
Current methods for genome-wide association studies face challenges in selecting and designing nucleic acid probe arrays that efficiently identify and genotype single nucleotide polymorphisms (SNPs) across the human genome, due to the complexity and vastness of genomic data, which hinders the diagnosis and treatment of diseases like cancer and mental illness.
Innovation Solution
The development of computer-implemented methods for selecting relevant SNPs with high information content, using specific restriction enzymes like Sty I and Nsp I, and designing nucleic acid probe arrays with optimized probe sets to efficiently genotype SNPs, ensuring accurate and reproducible results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genome-wide association studies use comprehensive SNP selection methods, then the accuracy of disease diagnosis and treatment is improved, but the complexity of genomic data analysis increases
Solution Approach 1:
The patent segments the genomic data analysis process into multiple stages: initial SNP selection based on information content, conversion to probe sets, screening for performance criteria, and final selection for array design. This segmentation allows systematic management of complexity while maintaining comprehensive analysis capability for improved diagnostic accuracy.
Solution Approach 2:
The patent extracts and filters SNPs based on specific criteria (information content, minor allele frequency, Hardy-Weinberg equilibrium) to identify the most relevant markers for disease association studies. This extraction process reduces the complexity of analyzing all genomic data while preserving the most informative SNPs for accurate disease diagnosis.
2Loss of information
If nucleic acid probe arrays are designed with comprehensive probe sets, then the coverage of genomic information is improved, but the manufacturing complexity increases
Solution Approach 1:
The patent applies partial action by selecting only the most informative SNPs and converting them to a reduced set of probe quartets rather than creating comprehensive probe sets for all possible SNPs. This approach maintains adequate genomic coverage while significantly reducing manufacturing complexity and array design burden.
Solution Approach 2:
The patent changes parameters such as reducing the number of probes per SNP from comprehensive coverage to optimized probe quartets (typically 4-8 probes), and adjusting the selection criteria for SNPs based on information content and statistical criteria. These parameter changes enable manageable manufacturing while preserving essential genomic information coverage.
3Reliability
If SNP selection criteria are made more stringent, then the quality of genotyping results is improved, but the number of selectable SNPs decreases
Solution Approach 1:
The patent implements feedback mechanisms through iterative screening processes where probe sets are evaluated against performance criteria (call rate, accuracy, heterozygosity) and SNPs are selectively retained or discarded. This feedback loop ensures high-quality genotyping results while maintaining an adequate number of selectable SNPs through data-driven optimization.
Solution Approach 2:
The patent creates composite selection criteria that combine multiple factors (information content, minor allele frequency thresholds, Hardy-Weinberg equilibrium p-values, call rate requirements) to evaluate SNPs holistically. This composite approach allows stringent quality control while preserving sufficient SNPs by considering the combined impact of multiple criteria rather than applying single-criterion filters.
Data Source
AI summary
The invention relates to the selection of a collection of relevant single nucleotide polymorphisms across a genome to design a nucleic acid probe array. As such, the invention relates to diverse fields impacted by the nature of genetics, including biology, medicine, and medical diagnostics.