Virtual Progeny Genetic Risk Prediction System
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Solution Overview
Problem
Current methods cannot accurately predict the likelihood of genetic diseases in offspring before conception, especially for complex traits influenced by multiple genes, as existing computational tools are not applicable to pre-conception prediction.
Innovation Solution
The method involves generating Virtual Progeny genomes by simulating the combination of VirtualGametes from personal genome profiles using Mendel's Law of independent assortment and random number generators, incorporating phasing information and genotype imputation, to predict the likelihood of genetic traits and diseases in hypothetical children.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If carrier testing is used to identify potential parents with the same mutation, then the likelihood of transmitting Mendelian diseases can be determined, but this method cannot predict complex genetic traits influenced by multiple genes
Solution Approach 1:
The patent segments the complex genetic prediction problem into multiple independent SNP locus analyses. Each SNP locus is evaluated separately for its contribution to the trait, and results are aggregated to provide comprehensive predictions for both Mendelian and complex traits. This segmentation allows the system to handle the complexity of multiple gene interactions while maintaining prediction accuracy.
Solution Approach 2:
The patent creates a universal prediction system that can simultaneously assess both simple Mendelian diseases and complex polygenic traits using the same SNP array technology and analytical framework. The system is designed to be multi-functional, handling various types of genetic inheritance patterns through a unified approach that evaluates multiple SNP loci across the genome.
2Loss of information
If complete personal genome sequencing is performed, then comprehensive genetic information is obtained, but the cost remains prohibitive for average consumers
Solution Approach 1:
The patent extracts only the most informative SNP loci from the complete genome sequence using high-density SNP arrays. Instead of analyzing all 3 billion base pairs, the system selectively genotypes approximately 500,000 to 2 million strategically selected SNP positions that capture the majority of genetic variation and provide sufficient predictive power for both Mendelian and complex traits at a fraction of the cost of whole genome sequencing.
Solution Approach 2:
The patent employs cost-effective SNP array technology as a disposable or temporary solution that provides sufficient genetic information for prediction purposes without requiring the expensive and permanent infrastructure of complete genome sequencing. The SNP arrays offer an economical alternative that delivers adequate predictive accuracy for most applications.
3Measurement precision
If computational tools are developed to predict disease likelihood from individual genome data, then post-conception disease prediction is possible, but these tools cannot be applied to pre-conception prediction
Solution Approach 1:
The patent performs preliminary genetic assessment by genotyping potential parents before conception occurs. By analyzing SNP data from both prospective parents in advance, the system predicts the genetic composition of potential offspring and identifies disease risks before pregnancy is established. This preliminary action enables informed reproductive decisions without requiring post-conception analysis.
Data Source
AI summary
Methods and systems for assessing the probabilities of the expression of one or more traits in progeny are described.