Haplotype Reconstruction via Observable Crossover Counting
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Solution Overview
Problem
The challenge in computational biology is accurately reconstructing haplotypes from genotype data, particularly in F2 populations, due to the combinatorial complexity and the need for efficient algorithms that can handle the uncertainty of crossovers during meiosis, which existing methods struggle with, especially in models like parsimony and maximum likelihood.
Innovation Solution
A method is developed to reconstruct haplotypes by determining a mathematical lower bound on the number of crossovers, focusing on an 'agglomerate' of equally-likely feasible solutions, allowing for precise haplotype reconstruction in linear time, and visualizing solutions as mosaics of ancestor haplotypes, using a reconstruction module that processes genotype data to identify parent haplotypes and compute crossover parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing haplotype reconstruction methods are used, then haplotype sequences can be obtained, but the computational complexity is high and accuracy is insufficient due to combinatorial challenges
Solution Approach 1:
The invention changes the parameter of counting from total possible haplotype sequences to the minimum number of observable crossovers. This parameter transformation simplifies the computational problem by focusing on a specific, countable metric (observable crossovers) rather than enumerating all possible haplotype configurations, thereby reducing computational complexity while maintaining reconstruction accuracy
Solution Approach 2:
The invention extracts and focuses on the key feature of observable crossovers from the complex haplotype reconstruction problem. By identifying and counting only the observable crossovers that can be directly inferred from genotype data, the method separates the essential information needed for accurate reconstruction from the computationally intensive task of enumerating all possible haplotype sequences
2Reliability
If parsimony or maximum likelihood models are used, then haplotype reconstruction can be performed, but the methods struggle with uncertainty of crossovers during meiosis
Solution Approach 1:
The invention uses the genotype data itself to directly identify and count observable crossovers without requiring external probabilistic models. The method extracts crossover information that is already embedded in the genotype data, allowing the data to serve its own analysis needs without requiring complex parsimony or maximum likelihood frameworks, thereby improving both reliability in handling uncertainty and computational efficiency
3Measurement precision
If complete enumeration of haplotype sequences is performed, then all possible solutions are obtained, but the computational time increases exponentially
Solution Approach 1:
The invention extracts only the essential information needed for haplotype reconstruction by counting observable crossovers directly from genotype data. This extraction approach obtains the necessary solution information without performing complete enumeration of all possible haplotype sequences, achieving computational efficiency while maintaining the precision needed for accurate reconstruction
Data Source
AI summary
Various embodiments reconstruct haplotypes from genotype data. In one embodiment, a set of progeny genotype data comprising n progenies encoded with m genetic markers is accessed. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. A total minimum number of observable crossovers in the n progenies is determined. An agglomerate data structure comprising a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to the total minimum number of observable crossovers in the n progenies.