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Medium-density SNP arrays offer a cost-effective balance for wildlife genetic studies. Using filtered SNPs with specific software provides accurate relatedness analysis in white-tailed deer populations.

Keywords:
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Area of Science:

  • Genomics
  • Wildlife Biology
  • Population Genetics

Background:

  • Single nucleotide polymorphism (SNP) arrays are increasingly utilized in genetic research due to their cost-effectiveness, commercial availability, and reliability.
  • The development of SNP arrays across diverse species necessitates evaluating their performance for specific applications, such as relatedness analysis in wildlife.

Purpose of the Study:

  • To assess the efficacy of medium- and high-density SNP arrays for identifying related individuals in white-tailed deer (Odocoileus virginianus).
  • To investigate the influence of SNP filtering thresholds on relatedness analyses.
  • To compare the performance of four common relatedness software packages (KING, COLONY, Sequoia, COANCESTRY).

Main Methods:

  • Evaluation of medium-density (72,732 SNPs) and high-density (702,183 SNPs) SNP arrays.
  • Analysis of genetic relatedness in a wild white-tailed deer population using known related pairs.
  • Assessment of SNP filtering impacts and comparison of relatedness software performance.

Main Results:

  • The medium-density SNP array demonstrated higher tolerance to filtering and reduced sensitivity to bioinformatic pipelines.
  • Optimal results were achieved using a subset of 600 loci with no missing data, coupled with the Sequoia relatedness estimator.
  • Sequoia, incorporating life history data, provided computationally efficient and accurate relatedness estimations.

Conclusions:

  • Medium-density SNP arrays present a practical option for wildlife population studies, balancing cost, computational demands, and statistical power.
  • Careful SNP filtering and selection of appropriate relatedness analysis tools are crucial for accurate genetic insights in wildlife.
  • The findings provide guidance on selecting SNP array density, filtering strategies, and software for effective wildlife genetic relatedness studies.