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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
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Localization of adaptive variants in human genomes using averaged one-dependence estimation
Lauren Alpert Sugden1,2, Elizabeth G Atkinson3, Annie P Fischer4
1Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA. lauren_alpert@brown.edu.
Nature Communications
|February 21, 2018
Summary
We developed SWIF(r), a new method to detect adaptive mutations using population genetic data. This approach enhances the identification of beneficial mutations, particularly those linked to metabolism and obesity in human populations.
Area of Science:
- Population Genetics
- Evolutionary Biology
- Genomics
Background:
- Identifying adaptive mutations is challenging due to issues with genomic outlier significance, integrating selection measures, and distinguishing adaptive from neutral variants.
- Existing statistical methods struggle to effectively address these complexities in population genetic data.
Purpose of the Study:
- To introduce SWIF(r), a novel probabilistic method for detecting selective sweeps and localizing adaptive mutations.
- To improve the power and accuracy of identifying beneficial mutations by modeling joint distributions of selection statistics.
Main Methods:
- SWIF(r) learns distributions of multiple selection statistics under various evolutionary scenarios.
- It calculates the posterior probability of a sweep at each genomic site.
- The method is trained using simulations based on user-specified demographic models.
Main Results:
- SWIF(r) demonstrated increased power in identifying regions with sweeps and pinpointing adaptive mutations.
- Analysis of Khomani San hunter-gatherer data revealed an enrichment of adaptive signals in metabolism and obesity-related genes.
- The method effectively models joint distributions of selection statistics for enhanced detection.
Conclusions:
- SWIF(r) offers a transparent and probabilistic framework for localizing beneficial mutations.
- The method is extensible to diverse evolutionary scenarios and improves upon existing approaches.
- Findings highlight adaptive genetic signals related to metabolism in the studied human population.
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