Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10− 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.

中文翻译：

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美国农业部迷你核心水稻品种的基因组和农艺性状全基因组关联研究及不同作图方法的比较分析

大米是重要的人类主食，易受重金属污染，引起人们的严重关注。由于缺乏遗传知识和标记，高产量和低重金属污染是全球水稻育种者普遍但极富挑战性的目标。为鉴定候选QTL并开发水稻产量和重金属含量的分子标记，美国农业部水稻小核心种质中的191份保藏物具有超过320万个SNP，用于研究QTL。利用两种单变量（GLM和MLM）和两种多元（MLMM和FarmCPU）GWAS方法分析了16种ionomic和13种农艺性状。对于p值<1.53×10−8的条件，分别针对淹没的ionomics，未淹没的ionomics和农艺性状鉴定了106、47和97个QTL。通过Bonferroni校正将p值确定为0.05。虽然250个QTL中有49个（约20％）与先前报道的QTL /基因相吻合，但约有201个（约80％）是新的。此外，通过分析DNA序列，基因表达和QTL区域的同源物，鉴定了一些参与基因组和农艺性状控制的新候选基因。我们的结果进一步表明，四种GWAS方法中的每一种都可以识别独特的QTL和通用QTL，这表明使用多个GWAS方法可以在QTL识别中相互补充，尤其是通过组合单变量和多变量方法。虽然重新发现了49个先前报道的QTL /基因，但在水稻基因组中发现了200多个新的有关基因组和农艺性状的QTL。此外，确定了多个农艺和碘特性的新候选基因。这项研究为水稻的基因组和农艺学变异的遗传基础提供了新颖的见解，为育种中标记物的开发奠定了基础，并为减少重金属污染和提高农作物产量进行了进一步研究。最后，对GWAS方法的比较分析表明，每种方法都具有独特的功能，不同的方法可以相互补充。