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The genome assembly of the farmed European whitefish Coregonus lavaretus L. from the Finnish selective breeding programme.

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Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species.

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Related Experiment Video

Updated: May 7, 2025

High-throughput DNA Extraction and Genotyping of 3dpf Zebrafish Larvae by Fin Clipping
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Sex identification in rainbow trout using genomic information and machine learning.

Andrei A Kudinov1, Antti Kause2

  • 1Natural Resources Institute Finland, 31600, Jokioinen, Finland. andrei.kudinov@hotmail.com.

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Summary

Accurate sex identification in farmed fish is crucial for breeding programs. This study introduces a machine learning method to predict fish sex from genomic data, even when marker suitability is unknown, achieving high accuracy in rainbow trout.

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Area of Science:

  • Aquaculture
  • Genomics
  • Machine Learning

Background:

  • Accurate sex identification in farmed fish is vital for effective stock management and breeding programs, especially in juvenile stages where visual determination is challenging.
  • The increasing availability of genomic data in aquaculture, coupled with diverse sex determination systems in ray-finned fishes, necessitates advanced identification methods.
  • Existing genomic sex identification methods often rely on pre-identified markers and probabilistic approaches, which may not be universally applicable across populations.

Purpose of the Study:

  • To develop and evaluate a novel machine learning approach for predicting sex in farmed fish using genomic data.
  • To assess the performance of the Extreme Gradient Boosting (XGBoost) method on both simulated and real datasets, particularly when the suitability of genetic markers is unknown a priori.
  • To determine the accuracy and robustness of the proposed method across varying genotyping error rates.

Main Methods:

  • Utilized the Extreme Gradient Boosting (XGBoost) algorithm, a supervised machine learning technique, for sex prediction from unimputed genomic data.
  • Assessed the method's accuracy using four simulated datasets with controlled genotyping error rates (5% and 50%) and one real dataset from the Finnish Rainbow Trout Breeding Program.
  • Compared the prediction accuracy of the XGBoost approach against established methods where marker suitability is known beforehand.

Main Results:

  • The XGBoost method demonstrated high prediction quality on both simulated and real datasets.
  • Achieved perfect accuracy (1.0) on simulated data with a 5% genotyping error rate and 0.60 accuracy with a 50% error rate.
  • In the real rainbow trout dataset, the method attained a prediction accuracy of 98%, indicating its suitability for practical, routine application in aquaculture.

Conclusions:

  • Supervised machine learning, specifically Extreme Gradient Boosting, offers a powerful and accurate tool for sex identification in farmed fish using genomic data, even without prior knowledge of sex-linked markers.
  • The developed method is robust and performs well across different genotyping error levels, making it a valuable asset for aquaculture breeding programs.
  • The high accuracy achieved in real-world data suggests this approach can be readily implemented for efficient and reliable sex determination in routine aquaculture practices.