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Epistasis and Quantitative Resistance to Pyricularia oryzae Revealed by GWAS in Advanced Rice Breeding Populations

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  • Juan E. Rosas

    (Programa Producción de Arroz, Estación Experimental INIA Treinta y Tres, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 8 km 281, Treinta y Tres 33000, Uruguay
    Departamento de Biometría y Estadística, Facultad de Agronomía, Universidad de la República, Garzón 780, Montevideo 12900, Uruguay
    These authors contributed equally to this work.)

  • Maia Escobar

    (Unidad de Biotecnología, Estación Experimental Wilson Ferreira Aldunate, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 48 km 10, Canelones 90200, Uruguay
    These authors contributed equally to this work.)

  • Sebastián Martínez

    (Programa Producción de Arroz, Estación Experimental INIA Treinta y Tres, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 8 km 281, Treinta y Tres 33000, Uruguay)

  • Pedro Blanco

    (Programa Producción de Arroz, Estación Experimental INIA Treinta y Tres, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 8 km 281, Treinta y Tres 33000, Uruguay)

  • Fernando Pérez

    (Programa Producción de Arroz, Estación Experimental INIA Treinta y Tres, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 8 km 281, Treinta y Tres 33000, Uruguay)

  • Gastón Quero

    (Unidad de Biotecnología, Estación Experimental Wilson Ferreira Aldunate, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 48 km 10, Canelones 90200, Uruguay
    Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Garzón 780, Montevideo 12900, Uruguay)

  • Lucía Gutiérrez

    (Departamento de Biometría y Estadística, Facultad de Agronomía, Universidad de la República, Garzón 780, Montevideo 12900, Uruguay
    Department of Agronomy, College of Agricultural and Life Sciences, University of Wisconsin-Madison, 1575 Linden Dr., Madison, WI 53706, USA)

  • Victoria Bonnecarrère

    (Unidad de Biotecnología, Estación Experimental Wilson Ferreira Aldunate, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 48 km 10, Canelones 90200, Uruguay)

Abstract

Rice blast caused by Pyricularia oryzae is a major rice disease worldwide. Despite the detailed knowledge on major resistance genes available to date, little is known about how these genes interact with quantitative blast resistance loci and with the genetic background. Knowledge on these interactions is crucial for assessing the usefulness of introgressed resistance loci in breeding germplasm. Our goal was to identify quantitative trait loci (QTL) for blast resistance in rice breeding populations and to describe how they interact among each other and with the genetic background. To that end, resistance to blast was mapped by genome-wide association study (GWAS) in two advanced rice breeding subpopulations, one made of 305 indica type inbred lines, and the other of 245 tropical japonica inbred lines. The interactions and main effects of blast resistance loci were assessed in a multilocus model. Well known, major effect blast resistance gene clusters were detected in both tropical japonica ( Pii/Pi3/Pi5 ) and indica ( Piz/Pi2/Pi9 ) subpopulations with the GWAS scan 1. When these major effect loci were included as fixed cofactors in subsequent GWAS scans 2 and 3, additional QTL and more complex genetic architectures were revealed. The multilocus model for the tropical japonica subpopulation showed that Pii/Pi3/Pi5 had significant interaction with two QTL in chromosome 1 and one QTL in chromosome 8, together explaining 64% of the phenotypic variance. In the indica subpopulation a significant interaction among the QTL in chromosomes 6 and 4 and the genetic background, together with Piz/Pi2/Pi9 and QTL in chromosomes 1, 4 and 7, explained 35% of the phenotypic variance. Our results suggest that epistatic interactions can play a major role modulating the response mediated by major effect blast resistance loci such as Pii/Pi3/Pi5 . Furthermore, the additive and epistatic effects of multiple QTL bring additional layers of quantitative resistance with a magnitude comparable to that of major effect loci. These findings highlight the need of genetic background-specific validation of markers for molecular assisted blast resistance breeding and provide insights for developing quantitative resistance to blast disease in rice.

Suggested Citation

  • Juan E. Rosas & Maia Escobar & Sebastián Martínez & Pedro Blanco & Fernando Pérez & Gastón Quero & Lucía Gutiérrez & Victoria Bonnecarrère, 2020. "Epistasis and Quantitative Resistance to Pyricularia oryzae Revealed by GWAS in Advanced Rice Breeding Populations," Agriculture, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:12:p:622-:d:460489
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    References listed on IDEAS

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    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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