Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction
Our result demonstrates that the WMKRR model, which integrates genomic and genetically predicted transcriptomic data, achieves better prediction performance compared to traditional genomic prediction models. This study showed the potential of enhanced genomic breeding application using omics data with no further omics sequencing cost.
Authors
- Lingzhao Fang (ORCID: https://orcid.org/0000-0003-1103-3679)
- Yi Zhang (ORCID: https://orcid.org/0000-0003-1642-5890)
- Yachun Wang (ORCID: https://orcid.org/0000-0002-9264-7607)
- Zhe Zhang (ORCID: https://orcid.org/0000-0001-7338-7718)
- Jingfang Si (ORCID: https://orcid.org/0009-0001-9232-3995)
- Xue Wang (ORCID: https://orcid.org/0009-0006-9991-0419)
Institutions
- South China Agricultural University (CN)
- Aarhus University (DK)
- Ministry of Agriculture (ID)
- Center for Innovation (US)
- Ministry of Agriculture and Rural Affairs (CN)
Publication Details
- Journal
- Genetics Selection Evolution
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1186/s12711-025-00997-9
- Citations
- 1
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- University of New England
- China Agricultural University
- National Key Research and Development Program of China