KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

ABSTRACT: Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects. The predictive performance and computational efficiency of KPRR were evaluated using six datasets from various species, encompassing a total of 18 traits. All the traits were known to be influenced by additive, dominance, or epistatic genetic effects. We compared the performance of KPRR against six other genomic prediction methods: SPVR, BayesB, GBLUP, GEBLUP, GDBLUP, and Deep

SUBMITTER: Li M 

PROVIDER: S-EPMC11695904 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature