Computational Prediction of Single-Gene Phenotype Expression
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Solution Overview
Problem
Current carrier testing methods are limited in predicting the expression of single gene phenotypes in progeny due to their reliance on simple Mendelian models, which fail to account for the complexity of gene interactions and result in inaccurate diagnoses, particularly for recessive diseases like cystic fibrosis, as they do not distinguish between mild and severe forms and are based on incomplete data.
Innovation Solution
A system and method that generates a haplopath from the genome profiles of potential parents, assigns variance scores to alleles, and computes gene-specific penetrance scores to determine the probability of phenotype expression in virtual progeny, incorporating a randomization process to simulate the natural correlation between genotype and phenotype, allowing for a scale of severity and accuracy in predicting phenotypic expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple Mendelian models are used for carrier testing, then the testing process is simple and easy to implement, but the diagnostic accuracy is poor and cannot distinguish between mild and severe forms of disease
Solution Approach 1:
The patent transforms the binary pathogenic/non-pathogenic classification into a continuous penetrance score spectrum (0-1 scale). This parameter change allows for nuanced prediction of phenotype expression probability, enabling distinction between mild and severe disease forms while maintaining computational feasibility through standardized scoring algorithms.
Solution Approach 2:
The patent segments the genome into multiple loci and evaluates each locus independently with its own penetrance score. This segmentation allows comprehensive analysis of complex genetic architectures involving multiple genes and variants, improving diagnostic accuracy by capturing the cumulative effect of multiple genetic factors rather than relying on single-locus Mendelian models.
2Measurement precision
If comprehensive genetic data and multiple loci are analyzed to improve prediction accuracy, then the diagnostic precision improves, but the computational complexity and system requirements increase
Solution Approach 1:
The patent replaces complex wet-lab genetic analysis procedures with computational algorithms that process genomic data. By substituting physical/biological processes with mathematical models and computer simulations, the system achieves high prediction accuracy for complex multi-locus interactions without proportionally increasing laboratory complexity or cost.
Solution Approach 2:
The patent creates virtual progeny genomes through computational simulation rather than physical breeding experiments. This copying approach allows extensive analysis of phenotype expression probabilities across multiple hypothetical offspring without the ethical, temporal, and resource constraints of actual genetic experimentation, thereby improving prediction accuracy while controlling system complexity.
3Productivity
If traditional carrier testing is performed, then the testing can be conducted with current methods, but false positives and false negatives occur due to incomplete data and oversimplified models
Solution Approach 1:
The patent incorporates feedback mechanisms where penetrance scores are continuously refined based on observed phenotype data from actual progeny. This iterative learning process allows the system to improve diagnostic reliability over time by adjusting probability estimates based on real-world outcomes, reducing false positives and negatives while maintaining high testing throughput through automated computational analysis.
Data Source
AI summary
In accordance with an embodiment of the invention, a system and method is provided for determining a probability of a progeny having one or more phenotypes Phj each associated with a single gene Qj. A score sip may be assigned to each allele hip at a plurality of genetic loci (i) in a haploid genome profile Hp of a parent (p). A plurality (Nj) of the alleles hkp (k=1, . . . , Nj) associated with the gene Qj may be identified. The scores sip may be mapped or indexed to gene-specific scores ŝj,kp associated with gene Qj for the plurality of (Nj) alleles hkp. A probability may be computed for altering the gene product from gene Qj in a progeny of the parent (p) to be a function of the gene-specific scores ŝj,kp.