Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs

Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs

This study investigates the efficacy of various genomic prediction models—Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—in predicting genomic breeding values (gEBVs). The phenotypic...

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Bibliographic Details
Main Authors: Joseph A. Thorsrud, Katy M. Evans, Kyle C. Quigley, Krishnamoorthy Srikanth, Heather J. Huson
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Animals
Subjects:
genomic prediction
breeding values
machine learning models
dog breeding
genetic selection
genomic best linear unbiased prediction
Online Access:https://www.mdpi.com/2076-2615/15/3/408
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