Dynamic Polygenic Model Evaluation Using Partial Genetic Loci
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Solution Overview
Problem
Current polygenic models face challenges in accurately predicting phenotypic traits due to the complexity of the human genome, with large datasets and variations in genetic data, leading to issues in identifying specific genetic variants contributing to traits, especially in subpopulations and with incomplete genetic information.
Innovation Solution
A system and method that dynamically evaluate the performance of polygenic models based on the specific combination of genetic variants provided, using predetermined genetic loci and weights to generate personalized feedback in the form of confidence scores and Proportion of Variance Explained (PVE), allowing for predictions even with incomplete genetic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polygenic models use complete genetic data from all predetermined loci, then prediction accuracy is improved, but data completeness and reliability cannot be guaranteed when individuals provide incomplete genetic information
Solution Approach 1:
The system performs dynamic evaluation using only the subset of genetic loci for which data is actually provided, rather than requiring complete data from all predetermined loci. This allows the model to operate with partial information while still generating meaningful predictions and confidence scores
Solution Approach 2:
The system changes the parameter of evaluation from fixed (assuming complete data) to dynamic (adapting to actual data provided). By dynamically determining performance based on the number and combination of genetic loci provided, the system adjusts its assessment to match the actual data completeness level
2Adaptability or versatility
If polygenic models are designed to handle incomplete genetic data, then adaptability to individual users is improved, but prediction accuracy may deteriorate due to missing genetic information
Solution Approach 1:
The system transitions from static evaluation (fixed accuracy assumptions) to dynamic evaluation (performance determined based on actual input data). The dynamic determination of model performance adapts to each individual's provided genetic data, allowing the system to function effectively with varying levels of data completeness
Solution Approach 2:
The system provides feedback in the form of confidence scores that indicate the expected accuracy of predictions based on the quality and quantity of input data. This feedback mechanism allows users to understand the reliability of predictions made with incomplete data
3Adaptability or versatility
If dynamic evaluation based on provided genetic loci is implemented, then system flexibility and personalization are improved, but computational complexity increases
Solution Approach 1:
The system performs self-evaluation by automatically determining its own performance based on the input data it receives. The dynamic evaluation process is self-contained, using the provided genetic loci information to assess model performance without requiring external validation for each case
Data Source
AI summary
Systems and methods are provided for evaluating polygenic models. One embodiment is a system that includes a memory storing a polygenic model that uses genetic variants which occupy predetermined genetic loci as inputs, and makes predictions for individuals based on the inputs. The system also includes an interface that receives an indication of known genetic variants exhibited by an individual, and a controller. The controller operates the model to make a prediction for the individual, determines that the indication does not provide known genetic variants for a subset of the predetermined genetic loci, and evaluates a performance of the prediction of the model based on the subset of the predetermined genetic loci that have not been provided known genetic variants.