Integrated model for genomic prediction under additive and non-additive genetic architecture - Budhlakoti, Neeraj; Mishra, Dwijesh Chandra; Majumdar, Sayanti Guha; Kumar, Anuj; Srivastava, Sudhir; Rai, S. N.; Rai, Anil (Frontiers in Plant Science, 2022)
Using data from genome-wide molecular markers, genomic selection procedures have proved beneficial for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a no. of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance & epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of current study GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance. Additionally, it has been observed that our proposed model is robust to diverse genetic architectures.