Genome Edit Prediction System for Aggregate Trait Effects
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Solution Overview
Problem
Conventional breeding techniques for introducing desired traits into organisms are time-consuming and resource-intensive, as they rely on random genetic combinations, whereas genome editing technologies like CRISPR can be inefficient when dealing with multiple traits and require individual testing of genome edits.
Innovation Solution
A system and method that identifies and rates potential genome edits based on their predicted aggregate effect on specific traits, allowing for the selection of multiple candidate edits to be validated collectively, reducing the need for individual testing and accelerating the introduction of desired traits into organisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional breeding techniques are used to introduce multiple traits, then the desired traits can be integrated into the genome, but the process becomes extremely time-consuming and resource-intensive requiring hundreds or thousands of crosses
Solution Approach 1:
The system performs preliminary identification and rating of multiple candidate genome edits before actual breeding operations. By pre-selecting edits with predicted aggregate effects above a threshold, the system prepares the genetic modification plan in advance, avoiding the need for numerous sequential crosses and reducing breeding time while maintaining trait integration reliability
2Productivity
If CRISPR genome editing is used to introduce multiple traits, then the number of cross-matings can be reduced, but individual testing of each genome edit becomes necessary which reduces efficiency
Solution Approach 1:
The system merges multiple individual genome edit evaluations into a single aggregate effect assessment. By evaluating the combined effect of multiple candidate edits together rather than testing each edit separately, the system reduces testing complexity while maintaining the ability to identify beneficial trait combinations, thereby improving productivity without proportionally increasing testing burden
3Measurement precision
If multiple candidate genome edits are selected for validation, then the aggregate effect on traits can be assessed, but the number of individual tests required increases
Solution Approach 1:
The system applies local quality by setting a threshold criterion for aggregate effect measurement. Only candidate edit combinations with predicted aggregate effects above the threshold are selected for validation, concentrating testing resources on the most promising candidates. This approach maintains measurement precision for aggregate effects while reducing the quantity of tests required by filtering out less promising combinations
Data Source
AI summary
Exemplary systems and methods for selecting from population of candidate edits and predicting an aggregate effect of the candidate edits are disclosed. One exemplary method includes identifying a population of candidate edits to a genomic sequence of said organism and ranking each of the candidate edits based on a predicted ability of each candidate edit to affect a trait of interest in said organism. The exemplary method further includes selecting one or more of the candidate edits based on the ranking and predicting, by the computing device, an aggregate effect of the selected one or more of the candidate edits for the trait of interest when expressed by a specimen of the organism having a genomic sequence and edited according to the selected one or more of the candidate edits, as compared to an unedited specimen of the organism.