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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
refutedsupported
the weight of evidence
5 sources for · 0 against

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

Evidence for · 5
2018 · cited by 170
RAiSD uses SNP vectors and multiple signatures to detect selective sweeps with high accuracy.
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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.

More for · 4
2017 · cited by 112
Different methods and summary statistics (such as SFS-based and LD-based statistics) are categorized by the specific selective sweep signatures they detect.
2019 · cited by 45
partialS/HIC uses machine learning trained on summary statistics to distinguish between various types of sweeps and regions directly affected by selection.
2022 · cited by 13
Population genomics methods use summary statistics and machine learning to detect selective sweeps and selection signatures.
2020 · cited by 12
Population genetic summary statistics are evaluated on their ability to capture the distinct footprints left by selective sweeps.
The paper trail · every fact has a biography
first checked04 Aug 2026
judged → SUPPORTED · 8904 Aug 2026
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