KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters
Lilin Yin, Haohao Zhang, Xiang Zhou, Xiaohui Yuan, Shuhong Zhao, Xinyun Li, Xiaolei Liu
Abstract
Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at https://github.com/YinLiLin/KAML.