Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.

ABSTRACT: We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence suggests that linear models may be impeded by their inability to make use of non-additive effects to explain phenotypic variation at the population level. The results presented here use a nested cross-validation approach to confirm that some machine learning methods have the ability to statistically outperform linear prediction models, with the optimal model dependent on availability of training data and genetic architecture of the trait in question. Linear models were competitive in their perfor

SUBMITTER: Kelly CM 

PROVIDER: S-EPMC11355539 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature