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| GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods | |
Yan, Huili; Guo, Hanyao1; Xu, Wenxiu; Dai, Changhua2; Kimani, Wilson2; Xie, Jianyin3; Zhang, Hezifan2; Li, Ting2; Wang, Feng4; Yu, Yijun5; Ma, Mi; Hao, Zhuanfang6; He, Zhenyan2,7
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| 2023 | |
| Source Publication | JOURNAL OF HAZARDOUS MATERIALS
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| ISSN | 0304-3894 |
| Volume | 441 |
| Abstract | 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. |
| Keyword | Genomic prediction Maize Cd accumulation Machine learning Linear statistical methods |
| Subject Area | Engineering, Environmental ; Environmental Sciences |
| DOI | 10.1016/j.jhazmat.2022.129929 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | IRON TRANSPORTER ; WIDE ASSOCIATION ; TRACE-ELEMENTS ; RICE ; TRANSLOCATION ; SELECTION ; EXPOSURE ; RISK ; MANGANESE ; OSNRAMP1 |
| WOS Research Area | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS ID | WOS:000860495700001 |
| Publisher | ELSEVIER |
| Subtype | Article |
| Publication Place | AMSTERDAM |
| EISSN | 1873-3336 |
| Funding Organization | Chinese Academy of Sciences [XDA24010404, XDA26030201] ; Ministry of Science and Technology of China [2015FY11130] ; National Key Research and Development Program of China [2017YFD0800900] |
| Corresponding Author Email | haozhuanfang@163.com ; hezhenyan@ibcas.ac.cn |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ibcas.ac.cn/handle/2S10CLM1/29143 |
| Collection | 中科院北方资源植物重点实验室 |
| Affiliation | 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 |
| Recommended Citation 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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| File Name/Size | DocType | Version | Access | License | ||
| 1705286170_5ec2673b8（6572KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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