Mutation-Phenotype Association Scoring for Small Pedigrees
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Solution Overview
Problem
Existing methods for identifying causative mutations associated with phenotypes in forward genetics are limited by errors due to cosegregation of multiple mutations, paucity of homozygotes, and reliance on p-values, leading to incorrect declarations of cause and effect, particularly in small pedigrees.
Innovation Solution
A candidate explorer (CE) system using machine learning integrates genetic mapping data into a single numeric score to predict the probability of mutation-phenotype associations, incorporating features like damage scores, essentiality scores, and linkage data to improve the identification of causative mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If p-value based methods are used to identify mutation-phenotype associations, then statistical significance can be determined, but false positives increase due to cosegregation of multiple mutations and paucity of homozygotes
Solution Approach 1:
The patent changes the parameter used for evaluation from simple p-values to a comprehensive CE score that integrates multiple parameters including damage scores, essentiality scores, and linkage data. This parameter transformation allows for more accurate discrimination between true causative mutations and false positives arising from cosegregation
Solution Approach 2:
The CE score functions as a composite indicator integrating multiple independent scores (damage score, essentiality score, linkage data) into a single predictive metric. This composite approach similar to how composite materials combine properties, enables more reliable identification of causative mutations by weighing multiple evidence types simultaneously
2Productivity
If traditional forward genetics methods are used, then mutation-phenotype associations can be identified, but accuracy decreases in small pedigrees due to cosegregation and paucity of homozygotes
Solution Approach 1:
The system performs preliminary filtering and scoring of mutations using damage scores and essentiality scores before final association determination. This preliminary action allows prioritization of high-probability candidates and reduces the impact of limited pedigree data by pre-evaluating mutation quality independent of segregation patterns
Solution Approach 2:
The CE score acts as an intermediary metric that bridges the gap between limited pedigree data and reliable causation determination. By introducing this intermediate predictive score that integrates multiple data types, the system can accurately identify causative mutations even when traditional segregation analysis is compromised by small pedigree size
Data Source
AI summary
Aspects of the present inventive concept generally relate to systems and methods for mutation processing, and more specifically, for identifying associations between phenotypes and mutations. One example method generally includes receiving one or more input features including phenotype data and mutation data, generating, via a machine learning model, a candidate explorer (CE) score indicating a probability of association between a phenotype and a mutation based on the one or more input features, and outputting an indication of the association between the phenotype and the mutation based on the CE score.