Evolutionary Action Score for Gene Phenotype Linkage
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Solution Overview
Problem
Current methods for identifying genes associated with phenotypes, such as diseases, rely on frequency-based approaches that do not interpret downstream biological consequences, lacking accuracy in measuring the impact of mutations on the organism.
Innovation Solution
A computer-implemented method calculates an evolutionary action (EA) score for each mutation, comparing distributions to identify non-random patterns and link genes to phenotypes, using a formula that approximates the evolutionary gradient and perturbation, and applies statistical tests to distinguish disease-causing genes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency-based approaches are used to identify genes associated with phenotypes, then the method is simple to implement, but the accuracy in measuring mutation impact is insufficient
Solution Approach 1:
The patent transforms the measurement parameter from simple mutation frequency to evolutionary action score, which incorporates evolutionary gradient and perturbation magnitude. This parameter change enables more accurate measurement of mutation impact by considering both the evolutionary importance of affected residues and the magnitude of amino acid substitution, resolving the contradiction between measurement accuracy and method complexity.
Solution Approach 2:
The patent introduces evolutionary action score as an intermediary metric that bridges mutation data and phenotypic impact. This intermediary incorporates multiple factors (evolutionary gradient, perturbation magnitude) to translate raw mutation frequencies into biologically meaningful impact measurements, thereby improving accuracy while maintaining computational feasibility through established evolutionary metrics.
2Manufacturing precision
If evolutionary action score calculation is implemented, then the resolution in identifying genes under positive selection is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of evolutionary gradients and perturbation magnitudes using established methods (Evolutionary Trace analysis, substitution matrices) before combining them into evolutionary action scores. This preliminary action approach allows the use of pre-computed evolutionary metrics, reducing the overall computational complexity while maintaining high resolution in gene identification.
Solution Approach 2:
The patent utilizes existing evolutionary analysis frameworks and substitution matrices as templates for calculating evolutionary action scores. By copying and adapting established evolutionary metrics (such as ET ranks and BLOSUM matrices) rather than developing new computational methods from scratch, the patent achieves high resolution gene identification with manageable computational complexity.
3Reliability
If distributions of EA scores are quantitatively compared with random distributions, then the specificity in distinguishing disease-causing genes is improved, but the time required for analysis increases
Solution Approach 1:
The patent applies statistical tests (Kolmogorov-Smirnov, Wilcoxon rank-sum, Anderson-Darling) to compare EA score distributions, focusing on key statistical moments and distribution shapes rather than exhaustive analysis of all possible parameters. This partial action approach achieves high specificity in distinguishing disease-causing genes by concentrating computational effort on the most discriminative statistical features, thereby reducing overall analysis time.
Data Source
AI summary
A method and computer system for identifying genes associated with a phenotype includes obtaining data representing mutations in a cohort of subjects exhibiting a phenotype. An evolutionary action (EA) score is calculated for each mutation using the data obtained. For each gene in the cohort, respective distributions of the calculated EA scores are determined for mutations found in the gene. The determined distributions of EA scores are quantitatively compared within the cohort and with random distributions to establish comparison data. Based on the comparison data, distributions of EA scores are identified that are non-random, and linkage of each gene in the cohort to the phenotype is assessed based on the identified non-random distributions to identify genes associated with the phenotype. The phenotype can be a disease, such as cancer, and linkage of each gene in the cohort to the disease can be assessed to identify disease causing genes.