Deep Learning Models for Differential Selective Constraint Identification
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Solution Overview
Problem
Current methods for interpreting the clinical significance of human genetic variants, particularly rare variants, are limited by data scarcity and the inability to accurately differentiate selective constraints between humans and non-human primates, which hampers the prediction of variant pathogenicity and understanding of genetic diseases.
Innovation Solution
The development of a system that uses deep learning models, such as PrimateAI, to analyze protein sequences and estimate selective constraints across species, allowing for the identification of differential selective constraint between humans and non-human primates by comparing selection coefficients and missense-to-synonymous ratios, thereby improving the accuracy of variant pathogenicity prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to analyze protein sequences and estimate selective constraints, then the accuracy of variant pathogenicity prediction is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces deep learning models as intermediary systems that process protein sequences and evolutionary data to generate pathogenicity predictions. These models act as mediators between raw genomic data and clinical interpretation, automatically learning complex patterns without requiring explicit programming of evolutionary principles.
Solution Approach 2:
The system transforms multiple input parameters including protein sequence data, missense-to-synonymous ratios, and selection coefficients into a unified pathogenicity score. By changing and integrating multiple parameters through the deep learning framework, the system achieves higher prediction accuracy while managing complexity through automated parameter optimization.
2Loss of information
If comparative analysis between humans and non-human primates is conducted to identify differential selective constraint, then the understanding of genetic diseases is improved, but the quantity of data required increases
Solution Approach 1:
The patent merges data from multiple primate species and human genomes into a unified comparative analysis framework. By combining evolutionary data across species and integrating it with genomic variant information, the system extracts meaningful patterns about differential selective constraint without requiring exhaustive data from every possible source.
Solution Approach 2:
The system performs preliminary evolutionary analysis by pre-computing selection coefficients and missense-to-synonymous ratios across species before conducting the main pathogenicity assessment. This preliminary action prepares the data in advance, reducing the computational burden during actual variant interpretation and enabling more efficient use of the available genomic data.
3Measurement precision
If selection coefficients and missense-to-synonymous ratios are compared across species, then the identification of genes under differential selective constraint is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces manual or rule-based comparison methods with deep learning models that automatically compute and compare selection coefficients and missense-to-synonymous ratios. The mechanical process of evolutionary analysis is substituted with an intelligent system that learns optimal comparison strategies from training data, reducing the difficulty of detecting subtle differences in selective constraint across species.
Data Source
AI summary
The technology disclosed relates to identifying differential selective constraint on a gene-by-gene basis between a target species and one or more non-target species. The disclosed systems and methods can use a population genetics model wherein an average selection coefficient per gene per species is estimated and further applied to estimate selective constraint. The disclosed systems and methods can use a generalized linear mixed model wherein depletion of missense variants per gene per species is estimated and further applied to estimate selective constraint. In some cases, the disclosed systems and methods can use various combinations of the components from the population genetics model or the generalized linear mixed model to identify the intersection of genes classified as having differential selective constraint by numerous approaches for validation purposes.