A computationally efficient algorithm to leverage average information REML for (co)variance component estimation in the genomic era
The augmented AI-REML can considerably reduce the computing time within each REML iteration, particularly when using an iterative solver. Our results demonstrate the potential of the augmented AI-REML as an appealing approach for large-scale VC estimation in the genomic era.
Authors
- Ismo Strandén (ORCID: https://orcid.org/0000-0003-0161-2618)
- R. Thompson (ORCID: https://orcid.org/0000-0003-3319-6938)
- Martin Lidauer (ORCID: https://orcid.org/0000-0003-0508-9991)
- Hongding Gao (ORCID: https://orcid.org/0000-0002-6018-0766)
- Esa Mäntysaari (ORCID: https://orcid.org/0000-0003-0044-8473)
Institutions
- Rothamsted Research (GB)
- Natural Resources Institute Finland (FI)
Publication Details
- Journal
- Genetics Selection Evolution
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1186/s12711-024-00939-x
- Citations
- 5
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 2.75