LightGBM: accelerated genomically designed crop breeding through ensemble learning

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 stabil...

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Bibliographic Details
Published in:Genome Biology 2021-09, Vol.22 (1), p.271-271, Article 271
Main Authors: Yan, Jun, Xu, Yuetong, Cheng, Qian, Jiang, Shuqin, Wang, Qian, Xiao, Yingjie, Ma, Chuang, Yan, Jianbing, Wang, Xiangfeng
Format: Article
Language:English
Subjects:
Algorithms
Corn
Crop breeding
Crops
Crops, Agricultural - genetics
Decision Trees
Design
Ensemble learning
Feature selection
Genome-Wide Association Study
Genomes
Genomic prediction
Genomic selection
Genomics
Genomics - methods
Inbreeding
Machine Learning
Maize
Method
Neural networks
Plant breeding
Plant Breeding - methods
Polymorphism, Single Nucleotide
Population
Predictions
Siblings
Software
Zea mays - genetics
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Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-021-02492-y