LightGBM: accelerated genomically designed crop breeding through ensemble learning.

Journal: Genome biology
Published Date: Sep 20, 2021

Abstract

LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.

Authors

  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
  • Yuetong Xu
    National Maize Improvement Center, Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
  • Qian Cheng
    Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Shuqin Jiang
    National Maize Improvement Center, Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
  • Qian Wang
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Yingjie Xiao
  • Chuang Ma
    State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, 712100, Shaanxi, China. cma@nwafu.edu.cn.
  • Jianbing Yan
  • Xiangfeng Wang
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China.

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