Identity-by-Function BLUP for Complex Trait Prediction
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Solution Overview
Problem
Current methods for predicting phenotypes and selecting genetic variants in agricultural and human genetics are limited in efficiency and accuracy, particularly for complex traits, leading to incomplete and inaccurate predictions due to allelic heterogeneity and inadequate capture of genetic relationships.
Innovation Solution
A method involving the computation of a functional unit dosage matrix and an identity by function relationship matrix, combined with statistical models like linear regression and neural networks, to predict unobserved phenotypes and select organisms with improved performance, accounting for allelic heterogeneity by aggregating functionally equivalent variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phenotype-based breeding or genomic selection techniques are used, then significant achievement in improving economically valuable traits is obtained, but further progress in genetic improvement of complex traits requires better prediction and understanding of underlying genetic variants
Solution Approach 1:
The method segments the genome into functional units (genes, pathways, or biological processes) and aggregates variants within each functional unit. This segmentation allows for better prediction of complex traits by capturing the combined effect of multiple variants while maintaining interpretability of underlying genetic mechanisms.
Solution Approach 2:
The invention introduces functional units as intermediaries between individual genetic variants and phenotypic traits. By aggregating variants at the functional unit level, the method serves as a mediator that bridges the gap between raw genetic data and complex trait prediction, improving both accuracy and biological interpretability.
2Productivity
If computational techniques and machine learning methods are used to predict phenotypic consequences, then prediction capability is enhanced, but current methods are limited in efficiency and accuracy of predicting unobserved phenotypes and selecting genetic variants
Solution Approach 1:
The method merges multiple genetic variants into functional units based on their biological relationships (genes, pathways, or processes). This combining approach improves prediction accuracy for complex traits by capturing synergistic effects of variants while maintaining computational efficiency through reduced dimensionality.
Solution Approach 2:
The invention changes the organizational parameter of genetic data from individual variant level to functional unit level. By aggregating variants based on functional annotations, the method transforms the data structure to improve both prediction accuracy and computational efficiency simultaneously.
3Ease of operation
If genomic prediction approaches are used to associate phenotypes to genotypes, then selection without phenotypic evaluation is facilitated, but accuracy of selections can be improved by accounting for allelic heterogeneity
Solution Approach 1:
The method segments genetic variants into functional units and aggregates them, which improves selection accuracy by capturing the combined effect of multiple variants. This segmentation approach maintains the ease of genomic selection while improving precision through better biological modeling.
Solution Approach 2:
Functional units serve as intermediaries between genotype and phenotype in the prediction model. This intermediary layer improves selection accuracy by accounting for allelic heterogeneity and biological context, while still enabling efficient selection without phenotypic evaluation.
Data Source
AI summary
Provided herein are methods for predicting unobserved phenotypes and selecting genetic variant organisms for effective use in genetically improving non-human animal species. Also provided herein are systems for implementing such methods, as well as computer-readable storage media storing instructions for performing such methods.