Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population

Our results show that incorporating QTL information in GBLUP and SVR can improve prediction accuracy, but the extent of improvement varies across models. RF had a much lower prediction accuracy than the other models and did not show improvements when QTL information was added. Two possible reasons for this result are that the data structure in our data does not allow RF to fully realize its potential and that RF is not designed well for this particular prediction problem. Our study highlighted the importance of selecting appropriate models for genomic prediction and underscored the potential limitations of machine learning models when applied to genomic prediction in livestock.