Performance of qpAdm-based screens for genetic admixture on graph-shaped histories and stepping stone landscapes
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View abstract on PubMed
Summary
This summary is machine-generated.High-throughput qpAdm protocols show high false discovery rates, exceeding 50% in many scenarios. This is due to low prestudy odds, complex models violating assumptions, and issues with admixture fraction estimates.
Area Of Science
- Population genetics
- Computational biology
- Statistical genomics
Background
- qpAdm is a widely used statistical tool for testing admixture models.
- Its performance on complex scenarios like 2D stepping stone landscapes and low prestudy odds is largely untested.
Purpose Of The Study
- To evaluate the accuracy and reliability of high-throughput qpAdm protocols.
- To identify factors contributing to false discoveries in qpAdm analyses.
- To propose improvements for qpAdm protocols.
Main Methods
- Simulated admixture graph-shaped and stepping stone histories.
- Applied high-throughput qpAdm protocols with varying model complexity and source combinations.
- Analyzed P-values, model optimality, and admixture fraction estimates.
Main Results
- False discovery rates exceeded 50% under various parameter combinations.
- Low prestudy odds and increasing model complexity rapidly decreased accuracy.
- Complex migration networks and asymmetric source-target configurations inflated false positives.
Conclusions
- Current high-throughput qpAdm protocols have significant limitations, especially in complex scenarios.
- Improvements include temporal stratification, focused model exploration, and stringent parameter conditions.
- Optimized protocols are needed for reliable admixture analysis in population genetics.
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