Phenotype Profile Similarity Analysis for Causal Variant Identification
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Solution Overview
Problem
Current mechanisms for phenotype analysis and comparison fail to account for the diverse impacts on phenotypes, leading to incomplete identification of genetic contributors to disease, particularly in multi-omic data analysis.
Innovation Solution
A system and method that utilize multi-omic data to identify causal variants by comparing individual phenotype profiles with stored profiles, determining the relevance of genetic pathways and genes based on similarity in disease/phenotype associations, and reporting the most relevant pathways and genes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current phenotype analysis mechanisms are used, then analysis simplicity is maintained, but identification completeness of genetic contributors deteriorates
Solution Approach 1:
The phenotype profile is segmented into multiple distinct components including phenotypic characteristics, differential gene expression information, differential protein expression information, and other molecular data. This segmentation allows each component to be analyzed and weighted independently, improving identification completeness while managing analysis complexity through structured breakdown
Solution Approach 2:
The analysis transitions from traditional single-dimensional phenotype comparison to multi-dimensional analysis by incorporating multi-omic data layers (genomic, transcriptomic, proteomic). This dimensional expansion enables comprehensive identification of genetic contributors by examining phenotypes across multiple molecular levels simultaneously
2Measurement precision
If multi-omic data integration is implemented, then causal variant identification accuracy is improved, but computational complexity increases
Solution Approach 1:
A phenotype profile similarity score serves as an intermediary metric that integrates multiple omic data types. This intermediary score simplifies the computational process by consolidating complex multi-omic interactions into a single comparable value, maintaining high identification accuracy while reducing computational burden
Solution Approach 2:
The system dynamically adjusts the weighting of different phenotype profile components based on their relevance to causal variant identification. By changing parameters such as weight assignments for gene expression vs. protein expression data, the system optimizes accuracy while managing computational resources efficiently
Data Source
AI summary
A method (100) for characterizing a relevance of one or more genes or pathways to a disease of an individual, comprising: (i) obtaining (110) a phenotype profile for the individual, comprising phenotypic characteristics, and differential gene and protein expression information; (ii) identifying (120) one or more database of stored phenotype profiles similar to the individual phenotype profile; (iii) determining (130) a relevance of a genetic pathway to the individual phenotype profile, based at least in part on a similarity between the genetic pathway's known disease/phenotype associations and a phenotype profile of the individual; (iv) determining (140) a relevance of a gene to the individual phenotype profile, based at least in part on a similarity between the gene's known disease/phenotype associations and a phenotype profile of the individual; and (v) reporting (150) one or more genetic pathways and/or one or more genes most relevant to the individual phenotype profile.