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Genomic prediction powered by multi-omics data
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DOI：10.3389/fgene.2025.1636438.png)
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Abstract
En 中文
Genomic selection (GS) has transformed plant breeding by enabling early and accurate prediction of complex traits. However; its predictive performance is often constrained by the limited information captured through genomic markers alone; especially for traits influenced by intricate biological pathways. To address this; the integration of complementary omics layers—such as transcriptomics and metabolomics—has emerged as a promising strategy to enhance prediction accuracy by providing a more comprehensive view of the molecular mechanisms underlying phenotypic variation. We used three datasets; each collected under a single-environment condition; which allowed us to isolate the effects of omics integration without the confounding influence of genotype-by-environment interaction. We assessed 24 integration strategies combining three omics layers: genomics; transcriptomics; and metabolomics. These strategies encompassed both early data fusion (concatenation) and model-based integration techniques capable of capturing non-additive; nonlinear; and hierarchical interactions across omics layers. The evaluation was conducted using three real-world datasets from maize and rice; which varied in population size; trait complexity; and omics dimensionality. Our results indicate that specific integration methods—particularly those leveraging model-based fusion—consistently improve predictive accuracy over genomic-only models; especially for complex traits. Conversely; several commonly used concatenation approaches did not yield consistent benefits and; in some cases; underperformed. These findings underscore the importance of selecting appropriate integration strategies and suggest that more sophisticated modeling frameworks are necessary to fully exploit the potential of multi-omics data. Overall; this work highlights both the value and limitations of multi-omics integration for genomic prediction and offers practical insights into the design of omics-informed selection strategies for accelerating genetic gain in plant breeding programs.
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