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Fast and accurate local ancestry inference with Recomb-Mix
Yuan Wei1, Degui Zhi2, Shaojie Zhang1
1Department of Computer Science, University of Central Florida, Orlando, FL, USA.
Biorxiv : the Preprint Server for Biology
|November 28, 2023
Summary
Recomb-Mix enhances local ancestry inference (LAI) for admixed populations, improving genetic structure analysis in large biobanks. This new method offers greater accuracy and efficiency compared to existing approaches.
Area of Science:
- Genetics
- Bioinformatics
- Population Genetics
Background:
- Large genotyped cohorts offer opportunities for high-resolution genetic structure analysis in admixed populations.
- Local ancestry inference (LAI) is crucial but faces challenges with similar reference populations, numerous references, or deep admixture events.
Purpose of the Study:
- To introduce Recomb-Mix, a novel method for local ancestry inference (LAI).
- To improve the accuracy and efficiency of LAI in complex admixed populations.
Main Methods:
- Recomb-Mix integrates the Li and Stephens model with a novel graph collapsing technique.
- The method simplifies counting paths with identical ancestry label readouts.
Main Results:
- Recomb-Mix demonstrates superior accuracy over existing LAI methods across diverse simulated scenarios.
- The method shows competitive resource efficiency.
Conclusions:
- Recomb-Mix is a valuable tool for advancing the genetic studies of admixed populations.
- The method is expected to be particularly useful for large biobanks with complex admixture histories.
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