Association Variable Identification Using Haplotype Blocks
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Solution Overview
Problem
In biological research, identifying associations between genetic variables and traits is challenging due to underdetermined problems, where the number of variables exceeds the number of data points, leading to complexity, time, and expense issues, especially in polygenetic diseases and environmental factor involvement.
Innovation Solution
An apparatus and method that use statistical relationships and mathematical interactions to identify association variables by determining compound variables based on patterns of occurrence and calculating statistical confidence values, employing non-parametric analysis techniques like chi-square analysis and supervised learning methods to reduce the complexity of underdetermined problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of SNPs to be analyzed is increased to improve the comprehensiveness of genetic analysis, then the completeness of trait association identification is improved, but the complexity and computational burden increase exponentially
Solution Approach 1:
The patent segments the large set of SNPs into smaller subsets based on linkage disequilibrium relationships and haplotype structures. By dividing the genome into manageable blocks (haplotype blocks) and analyzing SNPs within these blocks separately, the method reduces the overall computational complexity while maintaining comprehensive coverage of genetic variations associated with traits.
Solution Approach 2:
The patent introduces haplotype blocks as intermediary structures between individual SNPs and trait associations. Instead of directly analyzing all SNPs against traits, the method uses haplotype blocks as intermediate units that capture correlated SNP patterns, thereby reducing the dimensionality of the analysis and computational burden while preserving association information.
2Reliability
If the population size is increased to improve the statistical power of association studies, then the reliability of trait association identification is improved, but the time and expense required for obtaining biological samples increase
Solution Approach 1:
The patent performs preliminary analysis of SNP patterns and linkage disequilibrium structures before conducting the main association study. By pre-processing the genetic data to identify haplotype blocks and correlated SNP patterns in advance, the method reduces the computational and sampling requirements for the actual association analysis, thereby reducing time and expense while maintaining statistical power.
3Measurement precision
If multiple genes and environmental factors are included to improve the accuracy of polygenetic disease analysis, then the completeness of trait association identification is improved, but the size of the fitting space increases causing the problem to become vastly underdetermined
Solution Approach 1:
The patent merges multiple genetic factors (SNPs, haplotypes) and environmental factors into a unified analysis framework using interaction terms and composite variables. By combining these factors systematically rather than analyzing them separately, the method captures their joint effects on traits while managing the complexity through structured modeling approaches that reduce the effective fitting space.
Data Source
AI summary
An apparatus determines patterns of occurrence of compound variables based on a set of mathematical interactions and patterns of occurrence of a set of biological variables. Then, the apparatus calculates statistical relationships corresponding to a pattern of occurrence of a trait in a group of life forms and the patterns of occurrence of the compound variables. Moreover, the apparatus determines numbers of occurrences of biological variables that were used to determine compound variables in at least a statistically significant subset of the compound variables, and determines numbers of different mathematical interactions that were used to determine the compound variables in the subset of the compound variables for the biological variables that are associated with the corresponding numbers of occurrences. Next, the apparatus identifies one or more of the biological variables as one or more association variables based on the numbers of occurrences and the numbers of different mathematical interactions.