Prediction of plant complex traits via integration of multi-omics data

Abstract

The mechanistic bases of complex traits are consequences of activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. We built prediction models using genomic, transcriptomic, and methylomic data for six Arabidopsis traits. Single data-based models performed similarly but identified different benchmark genes. In addition, distinct genes contributed to trait prediction in different genetic backgrounds. Models integrating multi-omics data performed best and revealed gene interactions, extending knowledge about regulatory networks. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.