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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00126

Integrating gene expression data into genomic prediction

  • 1Department of Animal Sciences, University of Göttingen, Germany
  • 2South China Agricultural University, China
  • 3KWS SAAT SE, Germany

Gene expression profiles potentially hold valuable information for the prediction of breeding values and phenotypes. In this study, the utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of Drosophila melanogaster for 9 traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms and transcriptome data. The genotypic data was used to construct the common additive genomic relationship, which was used in genomic best linear unbiased prediction (GBLUP) or jointly in a linear mixed model with a transcriptome-based linear kernel (GTBLUP), or with a transcriptome-based Gaussian kernel (GRBLUP). We studied the predictive ability of the models and discuss a newly proposed “omics-augmented broad sense heritability” for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive ability, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perceptions to Ethyl Butyrate in females) in which the predictive ability of GRBLUP (0.23) was significantly higher than the predictive ability of GBLUP (0.21). Our results suggest that accounting for transcriptome data has the potential to improve genomic predictions if a more targeted collection of transcriptome data can be included on a larger scale.

Keywords: GRBLUP, Transcriptome, Phenotype prediction, Drosophila melanogaster, Epistasis

Received: 14 Oct 2018; Accepted: 04 Feb 2019.

Edited by:

Mogens Fenger, Capital Region of Denmark, Denmark

Reviewed by:

Alexander V. Favorov, Johns Hopkins University, United States
Hans D. Daetwyler, La Trobe University, Australia  

Copyright: © 2019 Li, Gao, Martini and Simianer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Henner Simianer, University of Göttingen, Department of Animal Sciences, Göttingen, 37073, Lower Saxony, Germany,