Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.

The development of omics techniques has increased the number of functional genomic annotations, making it possible to leverage this information in statistical models to improve genomic prediction performance. However, it remains challenging to effectively use the variety of functional annotations available. Here, we develop an adaptive model we call IFAM, which extends the linear mixed model with multiple random effects to accommodate a greater number of types of functional annotations to improve prediction accuracy for complex traits. The IFAM yields improvements in prediction accuracy across diverse datasets compared to the GBLUP model, achieving an average improvement of 9.46%, 6.15%, 4.59%, 4.09%, and 10.83% in the WTCCC1, UK Biobank, Duroc pig, Yorkshire pig and rice datasets, respectively. Notably, excluding trait-specific GWAS-derived annotations, functional annotations alone yielded a 1.40% predictive gain for the UK Biobank dataset but only marginal improvements in livestock and crop datasets. This suggests that the effectiveness of multi-omics information is constrained by species-specific annotation quality, tissue and trait relevance, and the limited resolution of existing functional datasets. As functional genomics resources continue to expand, their integration is expected to provide increasing benefits for genomic prediction.