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Using genotyping-by-sequencing to predict gender in animals.

T P Bilton1, A J Chappell1, S M Clarke1

  • 1Invermay Agricultural Centre, AgResearch, Private Bag 50034, Mosgiel, 9053, New Zealand.

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PubMed
Summary
This summary is machine-generated.

This study introduces a new method using genotyping-by-sequencing (GBS) data to accurately predict animal gender. The developed model achieves high concordance, improving data quality for genetic analyses.

Keywords:
XX/XY systemallosomal SNPsdeerlow depthsex assignment

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Area of Science:

  • Genomics
  • Animal Genetics
  • Bioinformatics

Background:

  • Gender assignment errors in animal studies can compromise downstream analyses.
  • Existing methods for gender misassignment detection are limited for genotyping-by-sequencing (GBS) data.

Purpose of the Study:

  • To develop and validate a method for predicting gender using GBS data.
  • To improve the accuracy of gender assignment in animal populations.

Main Methods:

  • Utilized X and Y chromosomal single nucleotide polymorphisms (SNPs) from GBS data.
  • Built a prediction model based on genotype proportions on sex chromosomes.
  • Validated the model on independent datasets of Red deer and Wapiti deer.

Main Results:

  • Achieved 98.6% concordance in the training dataset and 99.3% in the test dataset.
  • Identified five individuals with incorrect recorded gender and five with unassignable gender.
  • Demonstrated high accuracy in gender prediction using GBS data.

Conclusions:

  • The developed GBS-based method accurately predicts gender in deer species.
  • This approach can serve as a quality control for gender assignment or for assigning unrecorded gender.
  • Availability of a reference genome is crucial for implementing this method.