@article{Li2019Integrating,
 abstract = {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 nine traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms (SNPs) 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 concept of {\textquotedbl}omics-augmented broad sense heritability{\textquotedbl} for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception 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 transcriptome data can be included on a larger scale.},
 author = {Li, Zhengcao and Gao, Ning and Martini, Johannes W. R. and Simianer, Henner},
 year = {2019},
 title = {Integrating Gene Expression Data Into Genomic Prediction},
 keywords = {gen;phd},
 pages = {126},
 volume = {10},
 issn = {1664-8021},
 journal = {Frontiers in Genetics},
 doi = {10.3389/fgene.2019.00126},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/30858865},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397893},
 howpublished = {refereed}
}

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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 concept of \\textquotedblomics-augmented broad sense heritability\\textquotedbl for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception 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 transcriptome data can be included on a larger scale.","author":[{"propositions":[],"lastnames":["Li"],"firstnames":["Zhengcao"],"suffixes":[]},{"propositions":[],"lastnames":["Gao"],"firstnames":["Ning"],"suffixes":[]},{"propositions":[],"lastnames":["Martini"],"firstnames":["Johannes","W.","R."],"suffixes":[]},{"propositions":[],"lastnames":["Simianer"],"firstnames":["Henner"],"suffixes":[]}],"year":"2019","title":"Integrating Gene Expression Data Into Genomic Prediction","keywords":"gen;phd","pages":"126","volume":"10","issn":"1664-8021","journal":"Frontiers in Genetics","doi":"10.3389/fgene.2019.00126","file":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397893","howpublished":"refereed","bibtex":"@article{Li2019Integrating,\r\n abstract = {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 nine traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms (SNPs) 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 concept of {\\textquotedbl}omics-augmented broad sense heritability{\\textquotedbl} for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception 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 transcriptome data can be included on a larger scale.},\r\n author = {Li, Zhengcao and Gao, Ning and Martini, Johannes W. R. and Simianer, Henner},\r\n year = {2019},\r\n title = {Integrating Gene Expression Data Into Genomic Prediction},\r\n keywords = {gen;phd},\r\n pages = {126},\r\n volume = {10},\r\n issn = {1664-8021},\r\n journal = {Frontiers in Genetics},\r\n doi = {10.3389/fgene.2019.00126},\r\n file = {http://www.ncbi.nlm.nih.gov/pubmed/30858865},\r\n file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397893},\r\n howpublished = {refereed}\r\n}\r\n\r\n\r\n","author_short":["Li, Z.","Gao, N.","Martini, J. W. 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