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Recommendations for improving statistical inference in population genomics.

Parul Johri1, Charles F Aquadro2, Mark Beaumont3

  • 1School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America.

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|May 31, 2022
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Summary
This summary is machine-generated.

Population genomics is generating vast data faster than analysis. Best practices emphasize exploring evolutionary processes and defining baseline models for accurate interpretation of genomic data.

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Area of Science:

  • Population genomics
  • Evolutionary biology
  • Bioinformatics

Background:

  • Affordable, large-scale sequencing technologies have accelerated population genomics.
  • Genomic data generation now outpaces meaningful analysis and interpretation.
  • A tendency exists to fit specific models, neglecting broader evolutionary processes.

Purpose of the Study:

  • To highlight the risks of focusing on specific models in population genomics.
  • To present consensus views on best practices for population genomic data analysis.
  • To identify areas needing further attention in statistical inference and theory.

Main Methods:

  • Review of current trends in population genomic data analysis.
  • Consensus-building on best practices.
  • Identification of critical areas for future research in statistical inference.

Main Results:

  • Over-reliance on specific models can obscure understanding of evolutionary processes.
  • Characterizing nonadaptive processes is crucial for accurate inference.
  • There's a need for biologically relevant baseline models and critical interpretation of results.

Conclusions:

  • Emphasize biologically relevant baseline models tailored to each analysis.
  • Advocate for skepticism and scrutiny when interpreting model-fitting results.
  • Stress the importance of clearly defining addressable hypotheses and uncertainties in population genomics.