Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits
Transcriptomic data account for a high portion of phenotypic expression for all phenotypes and incorporating them enables more accurate predictions of phenotypes for efficiency and performance traits. Models that integrate both genetic and transcriptomic information are the most effective, offering valuable insights for improving phenotype prediction accuracy and insights in biological mechanisms underlying phenotypic variation of traits.
Authors
- Valentin P. Haas (ORCID: https://orcid.org/0000-0002-9651-1205)
- Michael Oster (ORCID: https://orcid.org/0000-0002-6835-7562)
- Siriluck Ponsuksili (ORCID: https://orcid.org/0000-0003-4714-1057)
- M.P.L. Calus (ORCID: https://orcid.org/0000-0002-3213-704X)
- Pascal Duenk (ORCID: https://orcid.org/0000-0001-8403-968X)
- Jörn Bennewitz (ORCID: https://orcid.org/0000-0001-6450-1160)
- Robin Wellmann (ORCID: https://orcid.org/0000-0003-4748-8042)
Institutions
- University of Hohenheim (DE)
- Centre for BioSystems Genomics (NL)
- Research Institute for Farm Animal Biology (FBN) (DE)
- Wageningen University & Research (NL)
Publication Details
- Journal
- Genetics Selection Evolution
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1186/s12711-025-01008-7
- Citations
- 2
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 3.63
Funders
- Universität Hohenheim
- Deutsche Forschungsgemeinschaft