Contingency Table DNA Loci Interaction Detection
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Solution Overview
Problem
Genome Wide Association studies (GWAS) face challenges in identifying DNA features statistically significantly associated with diseases or traits due to the 'curse of dimensionality' from vast genomic data, leading to modest results from single locus analysis, necessitating the development of higher order interaction detection techniques.
Innovation Solution
A computer-implemented method for detecting interacting DNA loci involves constructing contingency tables from genotype calls, determining measures of association, and using binary classification rules to classify genotype calls into statistically significant groups, reducing computational complexity and improving efficiency, especially with graphical processing unit (GPU) implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher order interaction detection techniques are used to improve detection accuracy, then the ability to identify interacting DNA loci is improved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The patent segments the complex interaction detection problem into multiple manageable stages: (1) generating genotype calls for individual SNPs, (2) combining genotype calls into higher-order interactions, (3) testing for statistical significance using contingency tables. This segmentation allows the system to handle complex interactions systematically without overwhelming computational resources at any single step.
Solution Approach 2:
The patent transforms the problem from a high-dimensional interaction space into a series of lower-dimensional contingency table analyses. By organizing data into contingency tables with dimensions representing trait categories and genotype combinations, the system reduces computational complexity while maintaining detection capability for higher-order interactions.
2Quantity of substance
If the number of genetic markers measured is increased to improve coverage, then the comprehensiveness of genetic analysis is improved, but the curse of dimensionality increases making analysis more difficult
Solution Approach 1:
The patent segments the analysis of numerous genetic markers into individual SNP genotype calling first, then combines results systematically. This segmentation allows the system to process large numbers of markers without analyzing all possible combinations simultaneously, reducing the curse of dimensionality.
Solution Approach 2:
The patent introduces contingency tables as an intermediary structure that mediates between the large number of genetic markers and the limited computational resources. The contingency tables organize and summarize marker data in a way that enables statistical testing without requiring exhaustive analysis of all marker combinations.
3Ease of operation
If single locus analysis approach is used to simplify analysis, then ease of operation is improved, but detection capability for interacting loci deteriorates
Solution Approach 1:
The patent segments the analysis process into clear sequential steps: genotype calling for individual loci, combination into higher-order interactions, and statistical testing. This segmentation maintains operational simplicity through systematic procedures while enabling detection of interacting loci that single-locus analysis would miss.
Solution Approach 2:
The patent extends analysis from single-locus to higher-order interactions by adding dimensional layers. Instead of analyzing only individual SNPs, the system incorporates interactions between multiple loci as additional dimensions, enabling detection of epistatic effects while maintaining a structured, manageable analysis framework.
Data Source
AI summary
A computer method of detecting interacting DNA loci by constructing a contingency table from samples of a first trait and samples of a second trait. The samples of the first and second trait are associated with one of a plurality of genotype calls, each relating to an interaction between multiple DNA loci. The contingency table includes frequencies of each genotype call in the samples. Based on the contingency table, measuring the association between the plurality of genotype calls and the first and second traits. Classifying the genotype calls into a first group that is statistically associated with the first trait and a second group that is statistically associated with the second trait.