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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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Spatial detection of outlier loci with Moran eigenvector maps.

Helene H Wagner1, Mariana Chávez-Pesqueira1,2, Brenna R Forester3

  • 1Department of Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada.

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Summary

This study introduces a novel spatial outlier detection method using Moran eigenvector maps (MEM) to identify loci under selection. The approach effectively leverages spatial genetic data for improved detection of evolutionary processes.

Keywords:
Moran spectral randomizationdemographic historygenotype-environment associationloci under selectionsampling designspatial signature

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Area of Science:

  • Evolutionary Biology
  • Spatial Genetics
  • Population Genetics

Background:

  • Microevolutionary processes shape genetic variation, influencing the detection of loci under selection.
  • Spatial sampling information has been underutilized in quantifying these processes.
  • Existing methods may not fully capture spatially structured genetic variation.

Purpose of the Study:

  • To develop and validate a novel two-step spatial outlier detection method.
  • To quantify the role of spatial signatures in identifying loci under selection.
  • To integrate spatial information into the analysis of genetic variation.

Main Methods:

  • Developed a two-step method using Moran eigenvector maps (MEM) power spectrum for spatial outlier detection.
  • Implemented Moran spectral outlier detection (MSOD) to identify outlier loci.
  • Utilized Moran spectral randomization (MSR) to test associations with environmental predictors while accounting for spatial autocorrelation.

Main Results:

  • The MSOD method successfully identified outlier loci at individual and deme levels in scenarios with spatial structure.
  • MSOD alone was sufficient, often negating the need for environmental predictors.
  • MSR generally reduced false-positive rates with minimal impact on detection power.
  • The method demonstrated robustness across various landscape configurations, selection strengths, dispersal capacities, and sampling designs.

Conclusions:

  • The new method effectively leverages neglected spatial information for detecting loci under selection.
  • It offers a powerful tool for both individual-based and population-based sampling.
  • This approach enhances our ability to understand microevolutionary processes and identify adaptive genetic variation.