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Usovsky M, Gamage VA, Meinhardt CG, Dietz N, Triller M, Basnet P, Gillman JD, Bilyeu KD, Song Q, Dhital B, Nguyen A, Mitchum MG, Scaboo AM. Loss-of-function of an α-SNAP gene confers resistance to soybean cyst nematode. Nat Commun 2023; 14:7629. [PMID: 37993454 PMCID: PMC10665432 DOI: 10.1038/s41467-023-43295-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/06/2023] [Indexed: 11/24/2023] Open
Abstract
Plant-parasitic nematodes are one of the most economically impactful pests in agriculture resulting in billions of dollars in realized annual losses worldwide. Soybean cyst nematode (SCN) is the number one biotic constraint on soybean production making it a priority for the discovery, validation and functional characterization of native plant resistance genes and genetic modes of action that can be deployed to improve soybean yield across the globe. Here, we present the discovery and functional characterization of a soybean resistance gene, GmSNAP02. We use unique bi-parental populations to fine-map the precise genomic location, and a combination of whole genome resequencing and gene fragment PCR amplifications to identify and confirm causal haplotypes. Lastly, we validate our candidate gene using CRISPR-Cas9 genome editing and observe a gain of resistance in edited plants. This demonstrates that the GmSNAP02 gene confers a unique mode of resistance to SCN through loss-of-function mutations that implicate GmSNAP02 as a nematode virulence target. We highlight the immediate impact of utilizing GmSNAP02 as a genome-editing-amenable target to diversify nematode resistance in commercially available cultivars.
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Affiliation(s)
- Mariola Usovsky
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Vinavi A Gamage
- Department of Plant Pathology and Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, 30602, USA
| | - Clinton G Meinhardt
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Nicholas Dietz
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Marissa Triller
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Pawan Basnet
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Jason D Gillman
- Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Kristin D Bilyeu
- Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
| | - Bishnu Dhital
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Alice Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Melissa G Mitchum
- Department of Plant Pathology and Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, 30602, USA.
| | - Andrew M Scaboo
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA.
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Basnet P, Meinhardt CG, Usovsky M, Gillman JD, Joshi T, Song Q, Diers B, Mitchum MG, Scaboo AM. Epistatic interaction between Rhg1-a and Rhg2 in PI 90763 confers resistance to virulent soybean cyst nematode populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2025-2039. [PMID: 35381870 PMCID: PMC9205835 DOI: 10.1007/s00122-022-04091-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/25/2022] [Indexed: 05/19/2023]
Abstract
KEY MESSAGE An epistatic interaction between SCN resistance loci rhg1-a and rhg2 in PI 90763 imparts resistance against virulent SCN populations which can be employed to diversify SCN resistance in soybean cultivars. With more than 95% of the $46.1B soybean market dominated by a single type of genetic resistance, breeding for soybean cyst nematode (SCN)-resistant soybean that can effectively combat the widespread increase in virulent SCN populations presents a significant challenge. Rhg genes (for Resistance to Heterodera glycines) play a key role in resistance to SCN; however, their deployment beyond the use of the rhg1-b allele has been limited. In this study, quantitative trait loci (QTL) were mapped using PI 90763 through two biparental F3:4 recombinant inbred line (RIL) populations segregating for rhg1-a and rhg1-b alleles against a SCN HG type 1.2.5.7 (Race 2) population. QTL located on chromosome 18 (rhg1-a) and chromosome 11 (rhg2) were determined to confer SCN resistance in PI 90763. The rhg2 gene was fine-mapped to a 169-Kbp region pinpointing GmSNAP11 as the strongest candidate gene. We demonstrated a unique epistatic interaction between rhg1-a and rhg2 loci that not only confers resistance to multiple virulent SCN populations. Further, we showed that pyramiding rhg2 with the conventional mode of resistance, rhg1-b, is ineffective against these virulent SCN populations. This highlights the importance of pyramiding rhg1-a and rhg2 to maximize the impact of gene pyramiding strategies toward management of SCN populations virulent on rhg1-b sources of resistance. Our results lay the foundation for the next generation of soybean resistance breeding to combat the number one pathogen of soybean.
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Affiliation(s)
- Pawan Basnet
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Clinton G Meinhardt
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | - Mariola Usovsky
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA
| | | | - Trupti Joshi
- Department of Health Management and Informatics, MUIDSI, and Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, 65211, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, USDA-ARS, Beltsville, MD, USA
| | - Brian Diers
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, IL, USA
| | - Melissa G Mitchum
- Department of Plant Pathology and Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, USA
| | - Andrew M Scaboo
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA.
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Beche E, Gillman JD, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo AM. Genomic prediction using training population design in interspecific soybean populations. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:15. [PMID: 37309481 PMCID: PMC10236090 DOI: 10.1007/s11032-021-01203-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/11/2021] [Indexed: 06/14/2023]
Abstract
Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01203-6.
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Affiliation(s)
- Eduardo Beche
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | | | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD USA
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois, and USDA-Agricultural Research Service (retired), 1101 W. Peabody Dr., Urbana, IL 61801 USA
| | - Tim Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, Georg-August-Universität, Göttingen, Germany
| | - Jared Decker
- Division of Animal Science, University of Missouri, Columbia, MO USA
| | - Grover Shannon
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | - Andrew M. Scaboo
- Division of Plant Science, University of Missouri, Columbia, MO USA
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Beche E, Gillman JD, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo AM. Nested association mapping of important agronomic traits in three interspecific soybean populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1039-1054. [PMID: 31974666 DOI: 10.1007/s00122-019-03529-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/30/2019] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE Glycine soja germplasm can be used to successfully introduce new alleles with the potential to add valuable new genetic diversity to the current elite soybean gene pool. Given the demonstrated narrow genetic base of the US soybean production, it is essential to identify beneficial alleles from exotic germplasm, such as wild soybean, to enhance genetic gain for favorable traits. Nested association mapping (NAM) is an approach to population development that permits the comparison of allelic effects of the same QTL in multiple parents. Seed yield, plant maturity, plant height and plant lodging were evaluated in a NAM panel consisting of 392 recombinant inbred lines derived from three biparental interspecific soybean populations in eight environments during 2016 and 2017. Nested association mapping, combined with linkage mapping, identified three major QTL for plant maturity in chromosomes 6, 11 and 12 associated with alleles from wild soybean resulting in significant increases in days to maturity. A significant QTL for plant height was identified on chromosome 13 with the allele increasing plant height derived from wild soybean. A significant grain yield QTL was detected on chromosome 17, and the allele from Glycine soja had a positive effect of 166 kg ha-1; RIL's with the wild soybean allele yielded on average 6% more than the lines carrying the Glycine max allele. These findings demonstrate the usefulness and potential of alleles from wild soybean germplasm to enhance important agronomic traits in a soybean breeding program.
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Affiliation(s)
- Eduardo Beche
- Division of Plant Science, University of Missouri, Columbia, MO, USA
| | | | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois, 1101 W. Peabody Dr, Urbana, IL, 61801, USA
- USDA-Agricultural Research Service, 1101 W. Peabody Dr, Urbana, IL, 61801, USA
| | - Tim Beissinger
- Center for Integrated Breeding Research, Georg-August-Universität, Göttingen, Germany
| | - Jared Decker
- Division of Animal Science, University of Missouri, Columbia, MO, USA
| | - Grover Shannon
- Division of Plant Science, University of Missouri, Columbia, MO, USA
| | - Andrew M Scaboo
- Division of Plant Science, University of Missouri, Columbia, MO, USA.
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5
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Melo ATO, Hale I. 'apparent': a simple and flexible R package for accurate SNP-based parentage analysis in the absence of guiding information. BMC Bioinformatics 2019; 20:108. [PMID: 30819089 PMCID: PMC6396488 DOI: 10.1186/s12859-019-2662-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 01/29/2019] [Indexed: 11/30/2022] Open
Abstract
Background The accurate determination of parent-progeny relationships within both in situ natural populations and ex situ genetic resource collections can greatly enhance plant breeding/domestication efforts and support plant genetic resource conservation strategies. Although a range of parentage analysis tools are available, none are designed to infer such relationships using genome-wide single nucleotide polymorphism (SNP) data in the complete absence of guiding information, such as generational groups, partial pedigrees, or genders. The R package (‘apparent’) developed and presented here addresses this gap. Results ‘apparent’ adopts a novel strategy of parentage analysis based on a test of genetic identity between a theoretically expected progeny (EPij), whose genotypic state can be inferred at all homozygous loci for a pair of putative parents (i and j), and all potential offspring (POk), represented by the k individuals of a given germplasm collection. Using the Gower Dissimilarity metric (GD), genetic identity between EPij and POk is taken as evidence that individuals i and j are the true parents of offspring k. Significance of a given triad (parental pairij + offspringk) is evaluated relative to the distribution of all GDij|k values for the population. With no guiding information provided, ‘apparent’ correctly identified the parental pairs of 15 lines of known pedigree within a test population of 77 accessions of Actinidia arguta, a performance unmatched by five other commonly used parentage analysis tools. In the case of an inconclusive triad analysis due to the absence of one parent from the test population, ‘apparent’ can perform a subsequent dyad analysis to identify a likely single parent for a given offspring. Average dyad analysis accuracy was 73.3% in the complete absence of pedigree information but increased to 100% when minimal generational information (adults vs. progeny) was provided. Conclusions The ‘apparent’ R package is a fast and accurate parentage analysis tool that uses genome-wide SNP data to identify parent-progeny relationships within populations for which no a priori knowledge of family structure exists. Electronic supplementary material The online version of this article (10.1186/s12859-019-2662-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arthur T O Melo
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA
| | - Iago Hale
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.
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Hwang S, King CA, Ray JD, Cregan PB, Chen P, Carter TE, Li Z, Abdel-Haleem H, Matson KW, Schapaugh W, Purcell LC. Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2015; 128:2047-65. [PMID: 26163767 DOI: 10.1007/s00122-015-2566-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 06/16/2015] [Indexed: 06/04/2023]
Abstract
KEY MESSAGE QTLs for delayed canopy wilting from five soybean populations were projected onto the consensus map to identify eight QTL clusters that had QTLs from at least two independent populations. Quantitative trait loci (QTLs) for canopy wilting were identified in five recombinant inbred line (RIL) populations, 93705 KS4895 × Jackson, 08705 KS4895 × Jackson, KS4895 × PI 424140, A5959 × PI 416937, and Benning × PI 416937 in a total of 15 site-years. For most environments, heritability of canopy wilting ranged from 0.65 to 0.85 but was somewhat lower when averaged over environments. Putative QTLs were identified with composite interval mapping and/or multiple interval mapping methods in each population and positioned on the consensus map along with their 95% confidence intervals (CIs). We initially found nine QTL clusters with overlapping CIs on Gm02, Gm05, Gm11, Gm14, Gm17, and Gm19 identified from at least two different populations, but a simulation study indicated that the QTLs on Gm14 could be false positives. A QTL on Gm08 in the 93705 KS4895 × Jackson population co-segregated with a QTL for wilting published previously in a Kefeng1 × Nannong 1138-2 population, indicating that this may be an additional QTL cluster. Excluding the QTL cluster on Gm14, results of the simulation study indicated that the eight remaining QTL clusters and the QTL on Gm08 appeared to be authentic QTLs. QTL × year interactions indicated that QTLs were stable over years except for major QTLs on Gm11 and Gm19. The stability of QTLs located on seven clusters indicates that they are possible candidates for use in marker-assisted selection.
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Affiliation(s)
- Sadal Hwang
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 Altheimer Drive, Fayetteville, AR, 72704, USA
| | - C Andy King
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 Altheimer Drive, Fayetteville, AR, 72704, USA
| | - Jeffery D Ray
- Crop Genetics and Production Research Unit, USDA-ARS, Stoneville, MS, 38776, USA
| | - Perry B Cregan
- Soybean Genomics and Improvement Laboratory, USDA-ARR, BARC-West, Beltsville, MD, 20705-2350, USA
| | - Pengyin Chen
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 Altheimer Drive, Fayetteville, AR, 72704, USA
| | - Thomas E Carter
- Department of Crop Science, North Carolina State University, USDA-ARS, Raleigh, NC, 27695, USA
| | - Zenglu Li
- Department of Crop and Soil Sciences and Center for Applied Genetic Technologies, The University of Georgia, 111 Riverbend Rd., Athens, GA, 30602-6810, USA
| | - Hussein Abdel-Haleem
- US Arid-Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ, 85138, USA
| | - Kevin W Matson
- Global Soybean Breeding, Monsanto Company, St. Louis, MO, 63167, USA
| | - William Schapaugh
- Department of Agronomy, Kansas State University, 2004C Throckmorton Hall, Manhattan, KS, 6506-5501, USA
| | - Larry C Purcell
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 Altheimer Drive, Fayetteville, AR, 72704, USA.
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