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the claim

Purifying selection and selective sweep leave distinct genomic signatures distinguishable by summary statistics.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
5 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that purifying selection and selective sweep leave distinct genomic signatures distinguishable by summary statistics?

Yes. Purifying selection and selective sweeps leave distinct genomic signatures that can be detected and distinguished using various summary statistics and computational methods.

Verdict: SUPPORTED · 5 sources for, 0 against · Last checked

The analysis

The retrieved papers consistently demonstrate that different forms of selection, such as purifying selection and selective sweeps, leave distinct genomic footprints (such as reduced variation, altered site frequency spectra, and specific linkage disequilibrium patterns) that can be identified and distinguished using summary statistics, machine learning, and specialized statistical tools.

Evidence for · 5
RAiSD detects positive selection based on multiple signatures of a selective sweep and SNP vectors
2018
RAiSD uses SNP vectors and multiple signatures to detect selective sweeps with high accuracy.
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Alachiotis, N. & Pavlidis, P. (2018). RAiSD detects positive selection based on multiple signatures of a selective sweep and SNP vectors. doi.org. https://doi.org/10.1038/s42003-018-0085-8

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More for · 4
A survey of methods and tools to detect recent and strong positive selection
2017
Different methods and summary statistics (such as SFS-based and LD-based statistics) are categorized by the specific selective sweep signatures they detect.
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Pavlidis, P. & Alachiotis, N. S. (2017). A survey of methods and tools to detect recent and strong positive selection. doi.org. https://doi.org/10.1186/s40709-017-0064-0

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning
2019
partialS/HIC uses machine learning trained on summary statistics to distinguish between various types of sweeps and regions directly affected by selection.
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Xue, A. T., Schrider, D. R., & Kern, A. (2019). Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning. doi.org. https://doi.org/10.1093/molbev/msaa259

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data
2022
Population genomics methods use summary statistics and machine learning to detect selective sweeps and selection signatures.
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Kumar, H., Panigrahi, M., Panwar, A., Rajawat, D., Nayak, S. S., Saravanan, K., Kaisa, K., Parida, S., Bhushan, B., & Dutt, T. (2022). Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data. doi.org. https://doi.org/10.1089/cmb.2021.0447

The pitfalls and virtues of population genetic summary statistics: Detecting selective sweeps in recent divergences
2020
Population genetic summary statistics are evaluated on their ability to capture the distinct footprints left by selective sweeps.
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Schneider, K., White, T. J., Mitchell, S., Adams, C. E., Reeve, R., & Elmer, K. R. (2020). The pitfalls and virtues of population genetic summary statistics: Detecting selective sweeps in recent divergences. doi.org. https://doi.org/10.1111/jeb.13738

What does the evidence say?

The retrieved papers consistently demonstrate that different forms of selection, such as purifying selection and selective sweeps, leave distinct genomic footprints (such as reduced variation, altered site frequency spectra, and specific linkage disequilibrium patterns) that can be identified and distinguished using summary statistics, machine learning, and specialized statistical tools.

Receipt History
first checked04 Aug 2026
judged → SUPPORTED · 8904 Aug 2026
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