KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.

Journal: Genome biology
PMID: 32552725

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.

Authors

  • Lilin Yin
    Northeastern University, Department of Chemistry, CHINA.
  • Haohao Zhang
    School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, China.
  • Xiang Zhou
    Department of Sociology, Harvard University, Cambridge, Massachusetts, USA.
  • Xiaohui Yuan
  • Shuhong Zhao
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, People's Republic of China.
  • Xinyun Li
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, People's Republic of China. xyli@mail.hzau.edu.cn.
  • Xiaolei Liu
    Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China.

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