Genomic Variation Imaging for Causal Phenotype Pathway Prediction
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Solution Overview
Problem
Genome-wide association studies (GWAS) are data-intensive and require high-performance computing clusters, limiting their availability and often fail to identify causal variants and genes, making it difficult to determine genetic drivers of phenotype.
Innovation Solution
A compute device converts genomic variation data into images using k-mer spectral representations and applies machine learning models, particularly neural networks, to identify relationships between genotypes and phenotypes, determining impactful genomic regions and biological pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GWAS is performed using traditional methods, then comprehensive genetic screening can be achieved, but computational resources and time requirements become prohibitively large
Solution Approach 1:
The patent transforms genomic variation data into image format, changing the data representation parameter from raw genetic sequences to visual spectra. This enables the use of convolutional neural networks that are computationally more efficient than traditional GWAS methods while maintaining comprehensive screening capability
Solution Approach 2:
The patent replaces traditional statistical GWAS algorithms with machine learning models (convolutional neural networks) that process genomic data as images. This substitution of computational methodology reduces the computational burden while achieving similar or superior genetic variant identification
2Reliability
If GWAS is performed with traditional statistical methods, then association signals can be detected, but causal variants and genes remain difficult to identify
Solution Approach 1:
The patent introduces image processing techniques as an intermediary between raw genomic data and causal variant identification. By converting genomic variations into spectral images and using convolutional neural networks, the system can distinguish causal variants from spurious associations through visual pattern recognition and attribution mapping
Solution Approach 2:
The patent segments the genomic data into positional k-mer representations and processes them through multiple convolutional layers that progressively refine the identification of causal variants. This segmentation allows the model to focus on specific genomic regions and their contextual relationships
3Measurement precision
If GWAS is performed on large datasets, then statistical power increases, but accessibility is limited to those with HPC access
Solution Approach 1:
The patent creates a computational model (convolutional neural network) that can be trained once on large datasets and then deployed on standard workstations. The trained model serves as a copy that replicates HPC-level analysis capabilities without requiring actual HPC infrastructure, making the tool accessible to researchers with standard computing resources
Data Source
AI summary
Technologies for predicting phenotype and associated biological pathways from genomic variation data are disclosed. According to one aspect of the disclosure, a method may include converting, by a compute device, data indicative of genomic variation into images. The method may also include applying, by the compute device, a machine learning model to the images to identify relationships between the genomic variation and phenotypic variation. Further, the method may include determining, by the compute device, one or more impactful genomic regions that underlie an identified relationship between a genotype and a phenotype.