GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods

doi:10.1016/j.jhazmat.2022.129929

CORC > 植物研究所 > 中国科学院植物研究所 > 中科院北方资源植物重点实验室

	GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods
	Yan, Huili ; Guo, Hanyao 7; Xu, Wenxiu ; Dai, Changhua 8; Kimani, Wilson 8; Xie, Jianyin 1; Zhang, Hezifan 8; Li, Ting 8; Wang, Feng 2; Yu, Yijun 3
刊名	JOURNAL OF HAZARDOUS MATERIALS
	2023
卷号	441
关键词	Genomic prediction Maize Cd accumulation Machine learning Linear statistical methods
ISSN号	0304-3894
DOI	10.1016/j.jhazmat.2022.129929
文献子类	Article
英文摘要	The production and use of many heavy meal contained materials almost inevitably release cadmium (Cd) into environment, generating Cd pollutants with adverse impacts on food and human health. Developing an effective method for Cd concentration evaluation in food crops could be an effective approach for toxicity prediction and pollution control. Here, we exploited the genotype-to-phenotype relationship of maize kernel Cd accumulation at whole-genome level, and developed genome-wide association study (GWAS) assisted genomic-enabled prediction (GP) models using machine learning and linear statistical methods. In benchmark tests, marker density and training populations were key parameters in determining GP baseline precision. With optimized parameters, three statistical methods, including Bayes A, ridge regression-best linear unbiased prediction (rrBLUP) and random forest (RF), showed the highest prediction accuracy (Bayes A, 0.83; rrBLUP, 0.89; RF, 0.75) with 100 iterations of cross-validation. In field trial, GP models with rrBLUP performed better than Bayes A and RF, with a higher GP accuracy (r(MG)) and lower mean absolute error value. Integrating GP with GWAS can be implemented as an effective strategy for accurate evaluation of Cd concentration, which could provide useful guidelines for accelerating the selection and breeding cycle of low-Cd food crops and addressing the environmental Cd contamination problem.
学科主题	Engineering, Environmental ; Environmental Sciences
电子版国际标准刊号	1873-3336
出版地	AMSTERDAM
WOS关键词	IRON TRANSPORTER ; WIDE ASSOCIATION ; TRACE-ELEMENTS ; RICE ; TRANSLOCATION ; SELECTION ; EXPOSURE ; RISK ; MANGANESE ; OSNRAMP1
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000860495700001
资助机构	Chinese Academy of Sciences [XDA24010404, XDA26030201] ; Ministry of Science and Technology of China [2015FY11130] ; National Key Research and Development Program of China [2017YFD0800900]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/29143]
专题	中科院北方资源植物重点实验室
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.China Agr Univ, Key Lab Crop Heterosis & Utilizat, Beijing Key Lab Crop Genet Improvement, Minist Educ, Beijing 100193, Peoples R China 3.Beijing Union Univ, Coll Biochem Engn, Beijing 100023, Peoples R China 4.Chinese Acad Sci, Inst Bot, Beijing 100093, Peoples R China 5.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China 6.Zhejiang Stn Management Arable Land Qual & Fertili, Hangzhou 310020, Peoples R China 7.Chinese Acad Sci, Inst Bot, Key Lab Plant Resources, Beijing 100093, Peoples R China 8.Hebei Normal Univ, Shijiazhuang 050024, Peoples R China
推荐引用方式 GB/T 7714	Yan, Huili,Guo, Hanyao,Xu, Wenxiu,et al. GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods[J]. JOURNAL OF HAZARDOUS MATERIALS,2023,441.
APA	Yan, Huili.,Guo, Hanyao.,Xu, Wenxiu.,Dai, Changhua.,Kimani, Wilson.,...&He, Zhenyan.(2023).GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods.JOURNAL OF HAZARDOUS MATERIALS,441.
MLA	Yan, Huili,et al."GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods".JOURNAL OF HAZARDOUS MATERIALS 441(2023).