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Detecting Recent Positive Selection with a Single Locus Test Bipartitioning the Coalescent Tree
Zongfeng Yang1,2, Junrui Li1,3, Thomas Wiehe4
1Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Gesellschaft (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Genetics
|December 9, 2017
Summary
This study introduces a new method using coalescent tree topology to detect positive selection in populations. It effectively distinguishes selection signals from demographic effects, even with limited data.
Area of Science:
- Population genetics
- Evolutionary biology
- Genomics
Background:
- Detecting positive selection is crucial for understanding evolution.
- Demographic factors like population size changes can obscure signals of positive selection.
- Existing methods struggle to differentiate selection from demographic effects.
Purpose of the Study:
- To develop a robust method for detecting recent positive selection.
- To minimize the confounding impact of demographic history on selection detection.
- To utilize coalescent tree topology for enhanced accuracy.
Main Methods:
- Inferred coalescent trees from sequence samples.
- Partitioned trees into two subtrees based on root branching patterns.
- Compared statistics calculated for the two subtrees to detect selection.
- Utilized analytical computation for summary statistics.
Main Results:
- The proposed test shows high power in detecting recent positive selection.
- The method is robust to demographic variations and works with single-locus data.
- Misinference of ancestral and derived alleles has minimal impact on the test's performance.
Conclusions:
- Coalescent tree topology provides a powerful tool for detecting positive selection.
- The method effectively disentangles selection signals from demographic noise.
- This approach offers a reliable way to identify recent positive selection events.
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