Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
Hebei Normal University, Shijiazhuang 050024, China.
Sci Total Environ. 2023 Feb 10;859(Pt 1):160135. doi: 10.1016/j.scitotenv.2022.160135. Epub 2022 Nov 12.
Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.
快速的全球工业化导致农业土壤和农产品中广泛受到镉污染。相当一部分的稻米消费者接触到的镉含量超过了暂定安全摄入量的限制，这引起了人们对风险管理的广泛环境关注。因此，迫切需要一种通用的方法来正确评估和预警稻米产品中的镉污染物。本研究结合大数据和计算机科学，开发了一个名为“SMART Cd 早期预警”的系统，该系统集成了包括基因型到表型（G2P）建模、高通量测序、G2P 预测和稻米镉污染风险评估在内的 4 个模块，用于稻米镉积累的早期预警。该系统可以通过在播种阶段对叶片进行基因分型，快速评估稻米镉积累的风险。评估了统计方法、种群大小、训练群体-测试群体比例、SNP 密度等参数，以确保 G2P 模型在预测精度（高达 0.76±0.003）和计算效率（2 小时内）方面表现出卓越的性能。在浙江省温岭市和富阳市镉污染农田的田间试验中，“SMART Cd 早期预警”表现出了识别风险稻米品种的卓越能力，这表明“SMART Cd 早期预警系统”在智能时代的 OsGCd 风险评估和预警方面具有潜力。
