Universität zu Köln

Wu, Po-Ya ORCID: 0000-0002-7342-2867, Stich, Benjamin, Weisweiler, Marius ORCID: 0000-0001-6000-065X, Shrestha, Asis ORCID: 0000-0003-4878-6965, Erban, Alexander, Westhoff, Philipp and Van Inghelandt, Delphine (2022). Improvement of prediction ability by integrating multi-omic datasets in barley. BMC Genomics, 23 (1). LONDON: BMC. ISSN 1471-2164

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Abstract

Background 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. Results The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone. Conclusions The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3'end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Wu, Po-Ya UNSPECIFIED orcid.org/0000-0002-7342-2867 UNSPECIFIED Stich, Benjamin UNSPECIFIED UNSPECIFIED UNSPECIFIED Weisweiler, Marius UNSPECIFIED orcid.org/0000-0001-6000-065X UNSPECIFIED Shrestha, Asis UNSPECIFIED orcid.org/0000-0003-4878-6965 UNSPECIFIED Erban, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Westhoff, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Van Inghelandt, Delphine UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-671115
DOI:	10.1186/s12864-022-08337-7
Journal or Publication Title:	BMC Genomics
Volume:	23
Number:	1
Date:	2022
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1471-2164
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language GENOMIC PREDICTION; GAS-CHROMATOGRAPHY; SELECTION; TRAITS; FEED; GENE Multiple languages Biotechnology & Applied Microbiology; Genetics & Heredity Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67111