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Navigating the Interface Between Landscape Genetics and Landscape Genomics.

Andrew Storfer1, Austin Patton1, Alexandra K Fraik1

  • 1School of Biological Sciences, Washington State University, Pullman, WA, United States.

Frontiers in Genetics
|March 30, 2018
PubMed
Summary

Landscape genomics uses genome-wide data to find genes under selection for local adaptation, distinguishing it from landscape genetics which studies gene flow. Understanding demographic history is key for accurate selection detection.

Keywords:
landscape geneticslandscape genomicslocal adaptationselectionspatial analyses

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Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Genomics

Background:

  • Next-generation sequencing enables landscape genomics studies focusing on local adaptation via selection.
  • Distinguishing landscape genomics from landscape genetics is crucial due to differing research questions and marker scale.

Purpose of the Study:

  • To clarify the transition from landscape genetics to landscape genomics.
  • To discuss implications for study design, marker selection, and analysis methods.
  • To review genome scan methods for detecting selection in landscape genomics.

Main Methods:

  • Focus on outlier differentiation and genetic-environment association tests for selection detection.
  • Utilize neutral loci or prior knowledge to understand population demographic structure.
  • Summarize simulation studies evaluating genome scan method performance.

Main Results:

  • Genome-wide data allows separation of neutral loci for population structure and selected loci for adaptation.
  • Potential mismatches between species biology and analytical assumptions can increase false positives.
  • Understanding demographic history improves the accuracy of selection detection methods.

Conclusions:

  • Accurate landscape genomics requires careful method selection informed by population demographics.
  • Future method development should address limitations and explore promising new approaches.
  • Distinguishing between neutral and selected loci is vital for robust local adaptation inference.