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Improvement of prediction ability by integrating multi-omic datasets in barley

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Erban,  A.
Applied Metabolome Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

Wu, P.-Y., Stich, B., Weisweiler, M., Shrestha, A., Erban, A., Westhoff, P., et al. (2022). Improvement of prediction ability by integrating multi-omic datasets in barley. BMC Genomics, 23(1): 200. doi:10.1186/s12864-022-08337-7.


Cite as: http://hdl.handle.net/21.11116/0000-000A-2F69-D
Abstract
Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.