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Haplotype matching in large cohorts using the Li and Stephens model
1University of Oxford, Wellcome Centre for Human Genetics, Oxford, UK.
Bioinformatics (Oxford, England)
|August 31, 2018
Summary
A new algorithm, fastLS, implements the Li and Stephens model for population genetics. It achieves faster runtimes independent of cohort size using the Burrows-Wheeler transform.
Area of Science:
- Population genetics
- Computational biology
- Bioinformatics
Background:
- The Li and Stephens model is crucial for understanding genetic variation patterns in populations.
- Current implementations face scalability challenges with large reference cohorts.
- Heuristics are often used, limiting exactness and efficiency.
Purpose of the Study:
- To introduce a novel, exact algorithm for the Li and Stephens model.
- To overcome computational limitations of existing methods for large datasets.
Main Methods:
- Developed an exact algorithm named 'fastLS'.
- Utilized the Burrows-Wheeler transform for efficient haplotype matching.
- Proposed a data structure generalizing Durbin's positional Burrows-Wheeler transform.
Main Results:
- Achieved runtimes independent of reference cohort size.
- Enabled efficient identification of partial haplotype matches.
- Demonstrated the algorithm's exactness and efficiency.
Conclusions:
- The fastLS algorithm provides a scalable and exact solution for the Li and Stephens model.
- This advancement facilitates the analysis of genetic variation in large populations.
- The method offers a significant improvement over existing heuristic approaches.
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