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Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Theoretical and Applied Genetics volume 128, pages 501–516 (2015)Cite this article

Abstract

Key message

Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL.

Abstract

Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

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Abbreviations

AMMI:

Additive main effect and multiplicative interaction model

BOPA:

Barley oligonucleotide pool assays

CAN_LAN:

Lacombe Research Center (Alberta, Canada)

ECU_PIC:

“Santa Catalina” Experimental Research Station in Pichincha of the National Agricultural Research Center (INIAP, Ecuador)

ECU_TOL:

“Granja Tolilla” Experimental Research Station of the National Agricultural Research Center (INIAP, Ecuador)

GxE:

Genotype-by-environment Interaction

GS:

Genomic selection

GWAS:

Genome-wide association mapping

ME:

Mega-environment

MEMQ:

Multi-environment multi-QTL model

MEX_TOL:

“Toluca” experimental research station of the International Center for Maize and Wheat Improvement (CIMMYT-Toluca, Mexico)

PER_AND:

“Andenes” experimental research station of the National Center for Innovation in Agriculture (INIA-Andenes, Cusco, Peru)

PER_COM:

“Combapata” experimental research station of the National Center for Innovation in Agriculture (INIA-Combapata, Cusco, Peru)

QEI:

QTL-by-environment interaction

QTL:

Quantitative trait loci

URU_LE:

“La Estanzuela” experimental research station of the National Agricultural Research Institute (INIA-EELE, Colonia, Uruguay)

URU_MC:

“Dr. Mario A. Cassinoni” experimental station of the Universidad de la República (UDELAR-EEMAC, Paysandu, Uruguay)

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Acknowledgments

Funding for this project was provided by FONTAGRO (Project FTG 0617-06) and Competitive Grants program for scientific visits of the Comisión Sectorial de Investigación Científica (CSIC), Universidad de la República (UDELAR), Uruguay. The authors wish to express their appreciation for the effort of the technical personnel of all the involved institutions.

Conflict of interest

All authors have no conflict of interest. All the experiments conducted under this study comply with the current laws of all the countries in which they were conducted.

Author information

Authors and Affiliations

  1. Departmento de Biometria, Estadistica y Computo, Facultad de Agronomía, Universidad de la República, Garzón 780, Montevideo, Uruguay

    Lucia Gutiérrez & Natalia M. Berberian

  2. National Agricultural Research Institute (INIA), Colonia, Uruguay

    Silvia Germán & Silvia Pereyra

  3. Department of Crop and Soil Science, Oregon State University, Corvallis, OR, 97331, USA

    Patrick M. Hayes

  4. Departmento de Protección Vegetal, Facultad de Agronomía, Universidad de la República, Paysandú, 60000, Montevideo, Uruguay

    Carlos A. Pérez & Dario Fros

  5. FCDC, Alberta Agriculture and Rural Development, Lacombe, AB, T4L 1W1, Canada

    Flavio Capettini & Kelly Turkington

  6. Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de la República, Paysandú, 60000, Montevideo, Uruguay

    Andres Locatelli & Ariel J. Castro

  7. Instituto Nacional de Investigaciones Agropecuarias (INIAP), Quito, Ecuador

    Esteban E. Falconi

  8. Estación Experimental Andenes, Instituto Nacional de Innovacion Agraria (INIA), Cuzco, Peru

    Rigoberto Estrada, Victor Gonza & Hernan Altamirano

  9. CIMMYT, Texcoco, Mexico

    Julio Huerta-Espino & Ravi Singh

  10. Unidad de Genómica, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru

    Edgar Neyra & Gisella Orjeda

  11. COLPOS, Chapingo, Mexico

    Sergio Sandoval-Islas

Authors
  1. Lucia Gutiérrez
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  2. Silvia Germán
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  3. Silvia Pereyra
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  4. Patrick M. Hayes
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  5. Carlos A. Pérez
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  6. Flavio Capettini
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  7. Andres Locatelli
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  8. Natalia M. Berberian
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  9. Esteban E. Falconi
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  10. Rigoberto Estrada
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  11. Dario Fros
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  12. Victor Gonza
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  13. Hernan Altamirano
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  14. Julio Huerta-Espino
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  15. Edgar Neyra
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  16. Gisella Orjeda
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  17. Sergio Sandoval-Islas
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  18. Ravi Singh
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  19. Kelly Turkington
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  20. Ariel J. Castro
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Corresponding author

Correspondence to Lucia Gutiérrez.

Additional information

Communicated by Xiaoquan Qi.

Electronic supplementary material

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Gutiérrez, L., Germán, S., Pereyra, S. et al. Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America. Theor Appl Genet 128, 501–516 (2015). https://doi.org/10.1007/s00122-014-2448-y

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  • DOI: https://doi.org/10.1007/s00122-014-2448-y

Keywords

  • Quantitative Trait Locus
  • Leaf Rust
  • Stripe Rust
  • Adult Plant Resistance
  • Resistance Quantitative Trait Locus