Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa.

ABSTRACT: Fall dormancy (FD) is an essential trait to overcome winter damage and for alfalfa (Medicago sativa) cultivar selection. The plant regrowth height after autumn clipping is an indirect way to evaluate FD. Transcriptomics, proteomics, and quantitative trait locus mapping have revealed crucial genes correlated with FD; however, these genes cannot predict alfalfa FD very well. Here, we conducted genomic prediction of FD using whole-genome SNP markers based on machine learning-related methods, including support vector machine (SVM) regression, and regularization-related methods, such as Lasso and ridge regression. The results showed that using SVM regression with linear kernel and the top 3000 genome-wide association study (GWAS)-associated markers achieved the highest prediction accurac

SUBMITTER: Zhang F 

PROVIDER: S-EPMC9832841 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature