LightGBM: accelerated genomically designed crop breeding through ensemble learning

被引：0
作者：
Jun Yan
Yuetong Xu
Qian Cheng
Shuqin Jiang
Qian Wang
Yingjie Xiao
Chuang Ma
Jianbing Yan
Xiangfeng Wang
机构：
[1] China Agricultural University,National Maize Improvement Center, Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology
[2] Northwest A&F University,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture
[3] Huazhong Agricultural University,National Key Laboratory of Crop Genetic Improvement
来源：
Genome Biology | / 22卷
关键词：
Genomic prediction; Genomic selection; Machine learning; Ensemble learning; Maize; Crop breeding; LightGBM; rrBLUP;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
相关论文
共 216 条
[1]  
Hickey JM(2017)Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery Nature Genetics 49 1297-1303
[2]  
Chiurugwi T(2019)State-of-the-art and novel developments of in vivo haploid technologies Theor Appl Genet 132 593-605
[3]  
Mackay I(2019)One-step genome editing of elite crop germplasm during haploid induction Nat Biotechnol 37 287-292
[4]  
Powell W(2018)Speed breeding is a powerful tool to accelerate crop research and breeding Nature Plants 4 23-29
[5]  
Cgi IGS(2014)Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits Livestock Science 166 217-231
[6]  
Kalinowska K(2017)Genomic selection in plant breeding: methods, models, and perspectives Trends Plant Sci 22 961-975
[7]  
Chamas S(2014)Genomic selection: genome-wide prediction in plant improvement Trends Plant Sci 19 592-601
[8]  
Unkel K(2011)Ridge regression and other kernels for genomic selection with R Package rrBLUP Plant Genome 4 250-255
[9]  
Demidov D(2012)Will genomic selection be a practical method for plant breeding? Annals of Botany 110 1303-1316
[10]  
Lermontova I(2020)Genome optimization for improvement of maize breeding Theor Appl Genet 133 1491-1502
12345678910