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Applications of Bayesian Skyline Plots and Approximate Bayesian Computation for Human Demography
Fernando A Villanea1, Andrew Kitchen2, Brian M Kemp3
1Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA, fervillanea@gmail.com.
Human Biology
|August 9, 2020
Summary
Bayesian methods, including Bayesian skyline plots and approximate Bayesian computation, are increasingly used in anthropological genetics. This review clarifies their application in analyzing genetic data for human history and demography.
Area of Science:
- Anthropological Genetics
- Computational Biology
- Population Genetics
Background:
- Bayesian methods offer advantages in analyzing complex genetic data for human history.
- Key methods include Bayesian skyline plots and approximate Bayesian computation.
- Understanding their assumptions and applications is crucial for anthropological research.
Observation:
- These Bayesian techniques are becoming standard in anthropological genetics for demographic and historical inference.
- The coalescent and Bayesian inference are core components of these analyses.
- Examples illustrate their application to anthropological research questions.
Findings:
- Bayesian methods facilitate simple model comparison and probability distribution summaries.
- Prior information is explicitly incorporated into analyses.
- The review demystifies the mechanics of Bayesian demographic analysis.
Implications:
- Enhanced understanding of Bayesian methods can improve the analysis of human genetic history.
- This work aims to equip anthropologists with the knowledge to properly apply these powerful tools.
- Accurate demographic inference from genetic data aids in reconstructing human evolutionary past.
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