Improving multi-trait genomic prediction by incorporating local genetic correlations

Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. With the development of local genetic correlation (LGC) estimation methods, it is now possible to analyze LGCs confined to specific genomic regions and it is expected that incorporating LGCs into multi-trait prediction model would enhance the prediction ability. Here, we proposed three models to address this issue and evaluated their performances using simulated data and three real datasets from human, cow, and pig populations. Our results demonstrate that LGCs are heterogeneous across the genome and incorporating LGCs in multi-trait prediction would increase the prediction accuracy by an average of 12.76% ± 2.07% compared to conventional multi-trait genomic prediction method (MTGBLUP) in the real datasets. Our findings highlight the importance of considering LGCs in improving multi-trait genomic prediction.