Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population.
Journal:
Journal of animal science and biotechnology
Published Date:
Aug 19, 2025
Abstract
BACKGROUND: Genomic prediction has revolutionized animal breeding, with GBLUP being the most widely used prediction model. In theory, the accuracy of genomic prediction could be improved by incorporating information from QTL. This strategy could be especially beneficial for machine learning models that are able to distinguish informative from uninformative features. The objective of this study was to assess the benefit of incorporating QTL genotypes in GBLUP and machine learning models. This study simulated a selected livestock population where QTL and their effects were known. We used four genomic prediction models, GBLUP, (weighted) 2GBLUP, random forest (RF), and support vector regression (SVR) to predict breeding values of young animals, and considered different scenarios that varied in the proportion of genetic variance explained by the included QTL.
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