Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare
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Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare. / Hansen, Pernille Bjarup; Ruud, Anja Karine; de Los Campos, Gustavo; Malinowska, Marta; Nagy, Istvan; Svane, Simon Fiil; Thorup-Kristensen, Kristian; Jensen, Jens Due; Krusell, Lene; Asp, Torben.
In: Plants, Vol. 11, No. 17, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare
AU - Hansen, Pernille Bjarup
AU - Ruud, Anja Karine
AU - de Los Campos, Gustavo
AU - Malinowska, Marta
AU - Nagy, Istvan
AU - Svane, Simon Fiil
AU - Thorup-Kristensen, Kristian
AU - Jensen, Jens Due
AU - Krusell, Lene
AU - Asp, Torben
PY - 2022
Y1 - 2022
N2 - Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
AB - Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
U2 - 10.3390/plants11172190
DO - 10.3390/plants11172190
M3 - Journal article
C2 - 36079572
VL - 11
JO - Plants
JF - Plants
SN - 2223-7747
IS - 17
ER -
ID: 319786962