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

This study investigates the improvement in prediction accuracy for six *Arabidopsis* traits by integrating genomic, transcriptomic, and methylomic data. Transcriptome- and methylome-based models showed comparable performance to genome-based models. However, different omics data identified distinct benchmark genes for flowering time. Nine additional genes, identified as important for flowering time, were experimentally validated. Gene contributions were accession-dependent, and multi-omics data integration yielded the best predictive models, revealing known and novel gene interactions.

Peipei Wang, Melissa D. Lehti-Shiu, Serena Lotreck, Kenia Segura Abá, Patrick J. Krysan, Shin-Han Shiu