Genetic architecture of soybean tolerance to off-target dicamba

Frontiers in Plant Science

Plant Science

Diego Jarquin¹  Jing Zhou²  Caio Canella Vieira³  Dean E. Riechers⁴  Brian Diers⁴  Henry T. Nguyen⁵  Grover Shannon⁵  Jianfeng Zhou⁵ 

[1] Agronomy Department, University of Florida, Gainesville, FL, United States;Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States;Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States;Department of Crop Sciences, University of Illinois, Urbana, IL, United States;Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States;

关键词: soybean;    genome-wide association studies;    machine learning;    plant breeding;    dicamba;    abiotic stress;   

DOI  :  10.3389/fpls.2023.1230068

 received in 2023-05-27, accepted in 2023-09-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Canella Vieira, Zhou, Jarquin, Zhou, Diers, Riechers, Nguyen and Shannon