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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Detecting positive selection in the genome.

Tom R Booker1, Benjamin C Jackson1, Peter D Keightley2

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Natural selection drives molecular evolution. This review covers methods using DNA divergence, diversity, and haplotype data to detect positive selection, revealing widespread adaptive changes in many species.

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Genomics

Background:

  • Understanding molecular evolution requires quantifying natural selection's role.
  • Population genetics offers theoretical frameworks to assess selection.
  • Methods are needed to estimate selection from genomic data.

Purpose of the Study:

  • To review methods for estimating positive selection from population genomic data.
  • To discuss approaches using nucleotide divergence, diversity, and haplotype structure.
  • To highlight the detection of adaptive molecular evolution.

Main Methods:

  • Analyzing patterns of between-species nucleotide divergence.
  • Utilizing within-species genetic diversity data.
  • Employing population haplotype structure analysis.

Main Results:

  • Methods effectively estimate positive selection parameters.
  • Detection of pervasive adaptive molecular evolution across species.
  • Integration of diverse genomic data types enhances detection.

Conclusions:

  • Population genomic data provide powerful insights into natural selection.
  • Multiple methods confirm widespread adaptive molecular evolution.
  • Continued development of these methods will advance evolutionary studies.