Residual-Based Gene Discovery for Decoupling Correlated Phenotypes
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Solution Overview
Problem
Existing methods struggle to identify genes that contribute specifically to a target phenotype while minimizing impact on correlated phenotypes, particularly in complex traits like root biomass and yield in crops, leading to unintended trade-offs.
Innovation Solution
A machine learning pipeline that predicts target phenotypes from measurement data, generates phenotype-specific residuals, and uses these residuals to identify genes contributing to the target phenotype through a machine learning model, ensuring precision in gene identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional genetic screening methods are used to identify genes for a target phenotype, then gene identification can be achieved, but correlated phenotypes cannot be effectively decoupled leading to unintended trade-offs
Solution Approach 1:
The patent segments the phenotypic data into independent components using Principal Component Analysis (PCA). The first principal component captures the correlated variation shared between target and other phenotypes, while the second principal component captures the unique variation specific to the target phenotype. This segmentation allows researchers to identify genes associated with the target phenotype while excluding genes that only affect correlated phenotypes, thereby resolving the contradiction between gene identification accuracy and phenotype specificity.
Solution Approach 2:
The patent introduces principal components as intermediary variables that mediate the relationship between gene expression and phenotypes. Instead of directly analyzing the correlation between genes and phenotypes, the method uses principal components as intermediaries to decompose the complex phenotypic data. This intermediary approach enables the separation of shared and unique phenotypic variations, allowing for precise identification of target-specific genes without the confounding effect of correlated phenotypes.
2Manufacturing precision
If genes affecting multiple correlated phenotypes are targeted, then one phenotype may be improved, but other correlated phenotypes are unintentionally affected
Solution Approach 1:
The patent segments phenotypic variation into independent components through PCA, separating the shared variation (affecting multiple phenotypes) from the unique variation (affecting only the target phenotype). By focusing gene selection on genes associated with the second principal component (unique variation), the method enables precise control over the target phenotype while maintaining independence from other correlated phenotypes, thus resolving the contradiction between phenotype control and trait independence.
3Loss of information
If forward or reverse genetic screenings are used, then gene-function relationships can be determined, but the complexity of polygenic traits and inheritance patterns makes identification challenging
Solution Approach 1:
The patent uses principal components as intermediary variables that simplify the complex relationship between multiple genes and polygenic traits. Instead of directly analyzing the complex interactions among multiple genes and inheritance patterns, the method transforms the data into principal components that capture the major sources of variation. This intermediary approach reduces analytical complexity while preserving the essential gene-function relationships, making it feasible to identify genes underlying complex polygenic traits.
Data Source
AI summary
The present disclosure relates to techniques for decoupling correlated phenotypes and identifying driver genes of a target phenotype. The techniques include obtaining phenotype data gene expression profiles for samples. The phenotype data is input into a prediction model configured to learn relationships between the one or more other phenotypes and the target phenotype and predict measurements for the target phenotype. Residuals are determined between the predicted measurements for the target phenotype and the obtained measurements for the target phenotype, and used to label the gene expression profiles to train a machine learning model to predict residuals of the target phenotype and select the driver genes.