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Detecting steps in spatial genetic data: Which diversity measures are best?

Alexander T Sentinella1, Angela T Moles1, Jason G Bragg1,2

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Detecting sudden changes in genetic diversity is crucial for conservation. Our study found that while some diversity measures are better than others for detecting these genetic steps, using a combination of approaches is recommended for robust results.

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Area of Science:

  • Population Genetics
  • Conservation Genetics
  • Bioinformatics

Background:

  • Accurate detection of genetic diversity shifts is vital for identifying gene flow barriers, important genetic loci, and defining management units.
  • Numerous metrics exist for detecting genetic diversity steps, but their relative robustness remains unclear.

Purpose of the Study:

  • To identify the most robust measures for detecting genetic diversity steps along linear gradients using single nucleotide polymorphism (SNP) data.
  • To compare the performance of various diversity metrics in differentiating linear versus step-like genetic diversity patterns.

Main Methods:

  • Simulations of genetic diversity gradients with varying step intensities, allele frequencies, sample sizes, loci, and localities.
  • Evaluation of alpha and beta diversity measures, including q-profile metrics (allelic richness, Shannon Information, GST, Jost-D), and Bray-Curtis dissimilarity.

Main Results:

  • Alpha diversity measures were ineffective for step detection.
  • Allelic richness-based beta diversity measures identified departures from fixation rather than genetic steps.
  • Shannon Information, GST, Jost-D, and Bray-Curtis dissimilarity measures showed the best performance for detecting genetic steps.

Conclusions:

  • No single measure was universally optimal; a trade-off exists between step detection sensitivity and false positive rates.
  • Measures like GST and Bray-Curtis excel in sensitivity, while a Shannon Information variant minimizes false positives.
  • Researchers should understand the nuances of different measures and employ a combination of approaches for reliable genetic step detection.