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

ABSTRACT: The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trai

SUBMITTER: Wang P 

PROVIDER: S-EPMC11316822 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature