Segmented Phased Genotype Selection for Large-Scale Progeny Prediction
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Solution Overview
Problem
Existing genomic mating methods struggle with computational feasibility and inefficiency in large-scale animal and plant breeding scenarios, particularly in predicting progeny genetics and optimizing mating allocations, as they fail to effectively handle higher-order moments like skewness and kurtosis, and require computationally burdensome simulations for thousands of sire-dam combinations.
Innovation Solution
A method involving phased genotypes and marker effects to segment and calculate direct genomic values (DGVs) for chromosomal segments, estimating skewness and kurtosis, and selecting parents based on these distributions to produce progeny, using a sliding window method and visual differentiation of DGVs for optimal mating pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling methods (Monte Carlo simulations) are used to predict progeny distributions, then accuracy of distribution prediction is improved, but computational burden increases significantly for large-scale breeding scenarios
Solution Approach 1:
The patent segments the chromosomal genome into multiple independent segments and calculates DGV for each segment separately. This segmentation allows the use of analytical methods for each segment rather than requiring full-genome simulations, dramatically reducing computational burden while maintaining prediction accuracy for higher-order moments.
Solution Approach 2:
The patent replaces the mechanical simulation approach (Monte Carlo sampling) with an analytical mathematical approach. By using analytical formulas to calculate higher-order moments (skewness, kurtosis) directly from phased genotypes and marker effects, the method eliminates the need for computationally intensive repeated sampling while achieving the same prediction accuracy.
2Productivity
If analytical methods are used to calculate DGV and distribution moments, then computational efficiency is improved, but ability to capture higher-order moments (skewness and kurtosis) is worsened
Solution Approach 1:
The patent performs preliminary phasing of genotypes into haplotypes before DGV calculation. This preliminary action of organizing genetic data into phased segments enables subsequent analytical calculation of higher-order moments by establishing the linkage phase information needed for accurate skewness and kurtosis computation without requiring simulations.
Solution Approach 2:
The patent changes the mathematical parameters used in DGV calculation by incorporating phased genotype information and linkage phase data into the analytical framework. This parameter change allows the analytical method to capture higher-order distribution moments that would otherwise require simulation approaches, thereby improving both efficiency and accuracy simultaneously.
3Measurement precision
If thousands of sire-dam combinations are evaluated using traditional simulation methods, then mating optimization accuracy is improved, but time consumption and computational resources increase
Solution Approach 1:
The patent segments the evaluation process into independent chromosomal segment calculations. By calculating DGV and distribution moments for each segment separately and then combining results, the method enables parallel processing of multiple sire-dam combinations, dramatically reducing the time required to evaluate thousands of mating pairs while maintaining optimization accuracy.
Data Source
AI summary
The invention encompasses methods of selecting an animal or plant, and producing progeny from the animal or plant, using segmented phased genotypes.