Genomic Prediction of Quantitative Traits using Sparse and Locally Epistatic Models
This work addresses genomic prediction for breeders by leveraging epistatic effects, though it appears incremental as it builds on existing mixed models with added sparsity and local considerations.
The paper tackled the problem of genomic prediction in plant and animal breeding by developing models that estimate local epistatic effects in low-recombination regions, resulting in good predictive performance and explanatory information.
In plant and animal breeding studies a distinction is made between the genetic value (additive + epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that some of the epistatic genetic effects will be lost due to recombination. In this paper, we argue that the breeder can take advantage of some of the epistatic marker effects in regions of low recombination. The models introduced here aim to estimate local epistatic line heritability by using the genetic map information and combine the local additive and epistatic effects. To this end, we have used semi-parametric mixed models with multiple local genomic relationship matrices with hierarchical designs and lasso post-processing for sparsity in the final model. Our models produce good predictive performance along with good explanatory information.