Cargando…

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diver...

Descripción completa

Detalles Bibliográficos
Autores principales: Canella Vieira, Caio, Jarquin, Diego, do Nascimento, Emanuel Ferrari, Lee, Dongho, Zhou, Jing, Smothers, Scotty, Zhou, Jianfeng, Diers, Brian, Riechers, Dean E., Xu, Dong, Shannon, Grover, Chen, Pengyin, Nguyen, Henry T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780662/
https://www.ncbi.nlm.nih.gov/pubmed/36570921
http://dx.doi.org/10.3389/fpls.2022.1090072
_version_ 1784856886656368640
author Canella Vieira, Caio
Jarquin, Diego
do Nascimento, Emanuel Ferrari
Lee, Dongho
Zhou, Jing
Smothers, Scotty
Zhou, Jianfeng
Diers, Brian
Riechers, Dean E.
Xu, Dong
Shannon, Grover
Chen, Pengyin
Nguyen, Henry T.
author_facet Canella Vieira, Caio
Jarquin, Diego
do Nascimento, Emanuel Ferrari
Lee, Dongho
Zhou, Jing
Smothers, Scotty
Zhou, Jianfeng
Diers, Brian
Riechers, Dean E.
Xu, Dong
Shannon, Grover
Chen, Pengyin
Nguyen, Henry T.
author_sort Canella Vieira, Caio
collection PubMed
description The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.
format Online
Article
Text
id pubmed-9780662
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-97806622022-12-24 Identification of genomic regions associated with soybean responses to off-target dicamba exposure Canella Vieira, Caio Jarquin, Diego do Nascimento, Emanuel Ferrari Lee, Dongho Zhou, Jing Smothers, Scotty Zhou, Jianfeng Diers, Brian Riechers, Dean E. Xu, Dong Shannon, Grover Chen, Pengyin Nguyen, Henry T. Front Plant Sci Plant Science The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean. Frontiers Media S.A. 2022-12-09 /pmc/articles/PMC9780662/ /pubmed/36570921 http://dx.doi.org/10.3389/fpls.2022.1090072 Text en Copyright © 2022 Canella Vieira, Jarquin, do Nascimento, Lee, Zhou, Smothers, Zhou, Diers, Riechers, Xu, Shannon, Chen and Nguyen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Canella Vieira, Caio
Jarquin, Diego
do Nascimento, Emanuel Ferrari
Lee, Dongho
Zhou, Jing
Smothers, Scotty
Zhou, Jianfeng
Diers, Brian
Riechers, Dean E.
Xu, Dong
Shannon, Grover
Chen, Pengyin
Nguyen, Henry T.
Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title_full Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title_fullStr Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title_full_unstemmed Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title_short Identification of genomic regions associated with soybean responses to off-target dicamba exposure
title_sort identification of genomic regions associated with soybean responses to off-target dicamba exposure
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780662/
https://www.ncbi.nlm.nih.gov/pubmed/36570921
http://dx.doi.org/10.3389/fpls.2022.1090072
work_keys_str_mv AT canellavieiracaio identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT jarquindiego identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT donascimentoemanuelferrari identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT leedongho identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT zhoujing identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT smothersscotty identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT zhoujianfeng identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT diersbrian identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT riechersdeane identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT xudong identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT shannongrover identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT chenpengyin identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure
AT nguyenhenryt identificationofgenomicregionsassociatedwithsoybeanresponsestoofftargetdicambaexposure