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

Peipei Wang (), Melissa D. Lehti-Shiu, Serena Lotreck, Kenia Segura Abá, Patrick J. Krysan and Shin-Han Shiu ()
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Peipei Wang: Michigan State University
Melissa D. Lehti-Shiu: Michigan State University
Serena Lotreck: Michigan State University
Kenia Segura Abá: Michigan State University
Patrick J. Krysan: University of Wisconsin-Madison
Shin-Han Shiu: Michigan State University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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 trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.

Date: 2024
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DOI: 10.1038/s41467-024-50701-6

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