Genetic Variant Assessment via Iterative Computational Feedback
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Solution Overview
Problem
Current methods for assessing genetic variants in organisms are limited in efficiency and accuracy, hindering progress in genetic improvement of complex traits in agricultural species and human genetics, particularly in predicting and prioritizing variants for genetic modification or selection.
Innovation Solution
A method involving providing genetic variants, predicting their effects using a statistical model, altering them, identifying impacts on endophenotypes, updating the model, and modifying variants with negative effects to improve organism performance, while prioritizing variants based on predicted effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phenotype-based breeding or genomic selection is used, then significant achievement has been made in improving economically valuable traits, but further progress requires better understanding of underlying genetic variants which current methods cannot provide efficiently and accurately
Solution Approach 1:
The patent implements an iterative feedback loop where computational predictions of variant effects are tested in model organisms, and the results feed back to refine the computational models. This feedback mechanism enables continuous improvement of both accuracy and efficiency in assessing genetic variants, resolving the contradiction between measurement precision and productivity.
Solution Approach 2:
The patent introduces model organisms as intermediaries between computational predictions and actual phenotypic outcomes. These model organisms serve as a mediating system that allows systematic testing of variant effects under controlled conditions, enabling efficient and accurate assessment without requiring direct testing in complex agricultural species.
2Extent of automation
If computational techniques and machine learning methods are used to predict phenotypic effects, then prediction capability is enhanced, but accuracy and efficiency remain limited without systematic experimental validation
Solution Approach 1:
The patent establishes a feedback mechanism where experimental results from model organisms are systematically integrated back into computational models to refine predictions. This iterative process maintains high automation while improving accuracy by continuously calibrating computational algorithms with empirical data.
Solution Approach 2:
The patent performs preliminary computational screening and prediction before experimental validation. By pre-ranking and prioritizing variants based on computational models, the system automates the assessment process while reserving experimental resources for the most promising candidates, thus maintaining both automation extent and measurement precision.
3Ease of manufacture
If genome editing is used to test phenotypic effects of genetic variants, then direct testing capability is enabled, but efficiency and accuracy of variant assessment remain limited
Solution Approach 1:
The patent segments the variant assessment process into distinct phases: computational prediction, model organism testing, and integration/refinement. By dividing the complex assessment into manageable segments, the system enables systematic and efficient testing of multiple variants through genome editing in model organisms, improving overall productivity while maintaining ease of manufacture.
Solution Approach 2:
The patent uses model organisms as copies or surrogates for complex agricultural species. By creating and manipulating simplified models that replicate key biological functions, the system enables efficient and accurate variant assessment without requiring direct work with complex target species, thus improving productivity while maintaining ease of manufacture.
Data Source
AI summary
Provided herein are methods for assessing genetic variants for use in genetically improving organisms and in human genetics and medicine. Also provided herein are systems for implementing such methods, as well as computer-readable storage media storing instructions for performing such methods.