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

doi:10.1016/j.jhazmat.2022.129929

	GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods
	Yan, Huili ; Guo, Hanyao 1; Xu, Wenxiu ; Dai, Changhua 2; Kimani, Wilson 2; Xie, Jianyin 3; Zhang, Hezifan 2; Li, Ting 2; Wang, Feng 4; Yu, Yijun 5; Ma, Mi ; Hao, Zhuanfang 6; He, Zhenyan2,7
	2023
发表期刊	JOURNAL OF HAZARDOUS MATERIALS
ISSN	0304-3894
卷号	441
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.
关键词	Genomic prediction Maize Cd accumulation Machine learning Linear statistical methods
学科领域	Engineering, Environmental ; Environmental Sciences
DOI	10.1016/j.jhazmat.2022.129929
收录类别	SCI
语种	英语
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)
WOS记录号	WOS:000860495700001
出版者	ELSEVIER
文献子类	Article
出版地	AMSTERDAM
EISSN	1873-3336
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]
作者邮箱	haozhuanfang@163.com ; hezhenyan@ibcas.ac.cn
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	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.Chinese Acad Sci, Inst Bot, Key Lab Plant Resources, Beijing 100093, Peoples R China 2.Hebei Normal Univ, Shijiazhuang 050024, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.China Agr Univ, Key Lab Crop Heterosis & Utilizat, Beijing Key Lab Crop Genet Improvement, Minist Educ, Beijing 100193, Peoples R China 5.Beijing Union Univ, Coll Biochem Engn, Beijing 100023, Peoples R China 6.Zhejiang Stn Management Arable Land Qual & Fertili, Hangzhou 310020, Peoples R China 7.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China 8.Chinese Acad Sci, Inst Bot, Beijing 100093, 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).

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