Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data.
Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data.
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DOI：
10.1016/j.envpol.2023.121501
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发表时间：
2023-03
影响因子：
8.9
通讯作者：
Zhong Tang;T. You;Ya-fang Li;Zhipeng Tang;Miao-Qing Bao;Ge Dong;Zhongliang Xu;Peng Wang;Fangni Zhao
中科院分区：
文献类型：
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作者：
Zhong Tang;T. You;Ya-fang Li;Zhipeng Tang;Miao-Qing Bao;Ge Dong;Zhongliang Xu;Peng Wang;Fangni Zhao
稻米中镉（Cd）的过量积累威胁着食品安全和人体健康。培育低镉积累型水稻品种是生产低镉水稻的有效途径。然而，低镉水稻品种的田间筛选既费时费力，又受环境与基因型相互作用的影响。在本研究中，我们研究了基于机器学习的方法结合基因型和土壤Cd浓度是否可以识别高和低Cd积累的水稻品种。167个地方适应的高产水稻品种在3块不同土壤Cd水平的田地中种植，并利用4种与籽粒Cd积累相关的分子标记进行基因分型。我们鉴定出16个品种是稳定的低镉积累体，在所有3个稻田中，籽粒镉浓度均低于0.2 mg kg - 1的食品安全限值。此外，我们开发了8个基于机器学习的模型，以基因型和土壤Cd水平作为输入数据来预测低镉和高镉积累水稻品种。优化后的模型对低Cd和高Cd品种（即籽粒Cd浓度低于或高于0.2 mg kg−1）进行分类，总体准确率为76%。这些结果表明，基于分子标记和土壤Cd水平构建的机器学习分类模型可以快速准确地识别高、低Cd积累水稻品种。
Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human health. Growing low Cd accumulating rice cultivars is an effective approach to produce low-Cd rice. However, field screening of low-Cd rice cultivars is laborious, time-consuming, and subjected to the influence of environment × genotype interactions. In the present study, we investigated whether machine learning-based methods incorporating genotype and soil Cd concentration can identify high and low-Cd accumulating rice cultivars. One hundred and sixty-seven locally adapted high-yielding rice cultivars were grown in three fields with different soil Cd levels and genotyped using four molecular markers related to grain Cd accumulation. We identified sixteen cultivars as stable low-Cd accumulators with grain Cd concentrations below the 0.2 mg kg−1food safety limit in all three paddy fields. In addition, we developed eight machine learning-based models to predict low- and high-Cd accumulating rice cultivars with genotypes and soil Cd levels as input data. The optimized model classifies low- or high-Cd cultivars (i.e., the grain Cd concentration below or above 0.2 mg kg−1) with an overall accuracy of 76%. These results indicate that machine learning-based classification models constructed with molecular markers and soil Cd levels can quickly and accurately identify the high- and low-Cd accumulating rice cultivars.