Haplotype Segmentation for Ancestry Prediction Accuracy
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Solution Overview
Problem
Existing methods for identifying ancestral relatedness in large datasets are not scalable and struggle to differentiate between recent genealogical history and distant common ancestry, leading to inefficiencies and inaccuracies in ancestral relationship prediction.
Innovation Solution
The TIMBER system divides haplotype data into segment windows, calculates weights based on per-window match counts, and uses a weighted sum of centimorgan widths to estimate ancestral relatedness, prioritizing segments likely from recent genealogical history while down-weighting noisy segments, thus improving computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing IBD analysis methods (e.g., GERMLINE, Refined IBD) are used to identify ancestral relatedness, then matching accuracy is improved, but computational scalability deteriorates for large datasets
Solution Approach 1:
The patent segments the genome into fixed-size windows (e.g., 1000 SNPs per window) and processes each window independently. This segmentation allows the system to handle large datasets efficiently by breaking down the computational problem into smaller, manageable units that can be processed in parallel, thereby improving scalability while maintaining matching accuracy through systematic evaluation of each segment.
Solution Approach 2:
The patent introduces a scoring system that changes parameters such as window match counts, segment lengths, and score thresholds to differentiate between recent and distant relatedness. By dynamically adjusting these parameters based on population genetics models, the system achieves both computational efficiency and accurate distinction between different types of ancestral relationships.
2Speed
If simple filtering methods (e.g., GERMLINE ibs filter) are used to reduce computational load, then processing speed is improved, but ability to distinguish recent from distant ancestry deteriorates
Solution Approach 1:
The patent applies different evaluation criteria and scoring weights to different genomic regions and match patterns. By assigning local quality metrics to specific windows and segments based on their match characteristics, the system can quickly identify promising regions while applying more rigorous evaluation only where needed, thereby maintaining both speed and accuracy in distinguishing recent from distant ancestry.
Solution Approach 2:
The patent replaces simple mechanical filtering (yes/no decisions) with a probabilistic scoring system that substitutes binary filtering with continuous evaluation. This substitution allows the system to process data faster by using statistical thresholds while maintaining the ability to differentiate between recent and distant relatedness through score-based ranking rather than rigid filtering criteria.
3Reliability
If comprehensive IBD segment analysis is performed across the entire genome, then relatedness detection accuracy is improved, but computational resources required deteriorate for large populations
Solution Approach 1:
The patent divides the genome into fixed-size windows and processes each window independently, requiring minimal memory per window. This segmentation strategy enables the system to analyze entire genomes comprehensively while using manageable computational resources by processing windows in parallel and discarding intermediate results, thus maintaining high detection accuracy without requiring excessive memory or computational power.
Solution Approach 2:
The patent performs comprehensive analysis by evaluating all windows but uses scoring thresholds to focus computational effort on significant matches. By calculating scores for all segments but only reporting those exceeding predetermined thresholds, the system achieves thorough genome-wide coverage while conserving computational resources by avoiding detailed analysis of clearly non-significant segments.
Data Source
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AI summary
System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-windows cM widths are used to estimate a degree of ancestral relatedness between two individuals.