Canella Vieira Caio, Zhou Jing, Jarquin Diego, Zhou Jianfeng, Diers Brian, Riechers Dean E, Nguyen Henry T, Shannon Grover
Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States.
Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States.
Front Plant Sci. 2023 Oct 9;14:1230068. doi: 10.3389/fpls.2023.1230068. eCollection 2023.
The adoption of dicamba-tolerant (DT) soybean in the United States resulted in extensive off-target dicamba damage to non-DT vegetation across soybean-producing states. Although soybeans are highly sensitive to dicamba, the intensity of observed symptoms and yield losses are affected by the genetic background of genotypes. Thus, the objective of this study was to detect novel marker-trait associations and expand on previously identified genomic regions related to soybean response to off-target dicamba. A total of 551 non-DT advanced breeding lines derived from 232 unique bi-parental populations were phenotyped for off-target dicamba across nine environments for three years. Breeding lines were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip. Filtered SNPs were included as predictors in Random Forest (RF) and Support Vector Machine (SVM) models in a forward stepwise selection loop to identify the combination of SNPs yielding the highest classification accuracy. Both RF and SVM models yielded high classification accuracies (0.76 and 0.79, respectively) with minor extreme misclassifications (observed tolerant predicted as susceptible, and vice-versa). Eight genomic regions associated with off-target dicamba tolerance were identified on chromosomes 6 [Linkage Group (LG) C2], 8 (LG A2), 9 (LG K), 10 (LG O), and 19 (LG L). Although the genetic architecture of tolerance is complex, high classification accuracies were obtained when including the major effect SNP identified on chromosome 6 as the sole predictor. In addition, candidate genes with annotated functions associated with phases II (conjugation of hydroxylated herbicides to endogenous sugar molecules) and III (transportation of herbicide conjugates into the vacuole) of herbicide detoxification in plants were co-localized with significant markers within each genomic region. Genomic prediction models, as reported in this study, can greatly facilitate the identification of genotypes with superior tolerance to off-target dicamba.
美国耐麦草畏(DT)大豆的采用导致麦草畏对大豆种植州的非DT植被造成广泛的非靶标损害。尽管大豆对麦草畏高度敏感,但观察到的症状强度和产量损失受基因型遗传背景的影响。因此,本研究的目的是检测新的标记-性状关联,并扩展先前确定的与大豆对非靶标麦草畏反应相关的基因组区域。对来自232个独特双亲群体的551个非DT高级育种系在9个环境中进行了为期三年的非靶标麦草畏表型分析。使用Illumina Infinium BARCSoySNP6K芯片对育种系进行基因分型。在向前逐步选择循环中,将经过筛选的单核苷酸多态性(SNP)作为预测因子纳入随机森林(RF)和支持向量机(SVM)模型,以确定产生最高分类准确率的SNP组合。RF和SVM模型均产生了较高的分类准确率(分别为0.76和0.79),且极少出现极端错误分类(观察到的耐受型被预测为敏感型,反之亦然)。在第6号染色体[连锁群(LG)C2]、第8号染色体(LG A2)、第9号染色体(LG K)、第10号染色体(LG O)和第19号染色体(LG L)上鉴定出8个与非靶标麦草畏耐受性相关的基因组区域。尽管耐受性的遗传结构复杂,但当将在第6号染色体上鉴定出的主效SNP作为唯一预测因子时,仍获得了较高的分类准确率。此外,在每个基因组区域内,具有与植物除草剂解毒的II期(羟基化除草剂与内源性糖分子结合)和III期(除草剂共轭物转运到液泡中)相关注释功能的候选基因与显著标记共定位。本研究中报道的基因组预测模型可极大地促进对非靶标麦草畏具有优异耐受性的基因型的鉴定。