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This study benchmarks software for analyzing adaptive evolution using the evolve and resequence (E&R) method. CLEAR, LRT-1, and CMH tests are recommended for identifying selected SNPs and estimating selection coefficients accurately.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Population genetics

Background:

  • Evolve and resequence (E&R) combines experimental evolution with pooled whole-genome resequencing.
  • E&R is a powerful method for studying selection and adaptive variation.
  • Numerous software tools exist to identify selected single nucleotide polymorphisms (SNPs) and estimate selection coefficients from E&R data.

Purpose of the Study:

  • To benchmark and compare the performance of 15 test statistics from 10 software tools for E&R data analysis.
  • To evaluate the effectiveness of different methods across various evolutionary scenarios.
  • To guide the selection of appropriate analytical tools for E&R studies.

Main Methods:

  • Comparison of 15 test statistics implemented in 10 different software packages.
  • Evaluation using three distinct simulated evolutionary scenarios.
  • Assessment of statistical power and accuracy of selection coefficient estimation.

Main Results:

  • Performance varied across scenarios, with some methods consistently outperforming others.
  • LRT-1, CLEAR, and the CMH test demonstrated superior performance.
  • CLEAR provided the most accurate selection coefficient estimates; LRT-1 and CMH did not require time-series data.

Conclusions:

  • This benchmark facilitates the analysis of existing E&R data.
  • Findings will inform the design of future E&R experiments and data collection strategies.
  • Recommends specific tools for robust identification of selected SNPs and accurate selection coefficient estimation.