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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
15/12/2020 |
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Actualizado : |
08/02/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
ROSAS, J.E.; ESCOBAR, M.; MARTÍNEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; QUERO, G.; GUTIÉRREZ, L.; BONNECARRERE, V. |
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Afiliación : |
JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAIA ESCOBAR BONORA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SEBASTIÁN MARTÍNEZ KOPP, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Facultad de Agronomía, UDELAR; University of Wisconsin-Madison, USA.; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Epistasis and quantitative resistance to Pyricularia oryzae revealed by GWAS in advanced rice breeding populations. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Agriculture 2020, 10(12), 622. Open Access. DOI: https://doi.org/10.3390/agriculture10120622 |
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DOI : |
10.3390/agriculture10120622 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 30 October 2020 / Revised: 23 November 2020 / Accepted: 24 November 2020 / Published: 11 December 2020. |
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Contenido : |
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. MenosRice 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 s... Presentar Todo |
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Palabras claves : |
DISEASE RESISTANCE; GWAS; LEAF BLAST; MAGNAPORTHE ORYZAE; PYRICULARIA ORYZAE; QTL BY GENETIC BACKGROUND INTERACTION; QTL by QTL INTERACTION; RESISTENCIA A ENFERMEDADES. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14870/1/agriculture-10-00622.pdf
https://www.mdpi.com/2077-0472/10/12/622
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Marc : |
LEADER 03395naa a2200325 a 4500
001 1061583
005 2021-02-08
008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/agriculture10120622$2DOI
100 1 $aROSAS, J.E.
245 $aEpistasis and quantitative resistance to Pyricularia oryzae revealed by GWAS in advanced rice breeding populations.$h[electronic resource]
260 $c2020
500 $aArticle history: Received: 30 October 2020 / Revised: 23 November 2020 / Accepted: 24 November 2020 / Published: 11 December 2020.
520 $aRice 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.
653 $aDISEASE RESISTANCE
653 $aGWAS
653 $aLEAF BLAST
653 $aMAGNAPORTHE ORYZAE
653 $aPYRICULARIA ORYZAE
653 $aQTL BY GENETIC BACKGROUND INTERACTION
653 $aQTL by QTL INTERACTION
653 $aRESISTENCIA A ENFERMEDADES
700 1 $aESCOBAR, M.
700 1 $aMARTÍNEZ, S.
700 1 $aBLANCO, P.H.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aQUERO, G.
700 1 $aGUTIÉRREZ, L.
700 1 $aBONNECARRERE, V.
773 $tAgriculture 2020, 10(12), 622. Open Access. DOI: https://doi.org/10.3390/agriculture10120622
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INIA Treinta y Tres (TT) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
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Fecha actual : |
12/11/2015 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MARCAIDA, M.; ASSENG, S.; EWERT, F.; BASSU, S.; DURAND, J.L.; LI, T.; MARTRE, P.; ADAM, M.; AGGARWAL, P.K.; ANGULO, C.; BARON, C.; BASSO, B.; BERTUZZI, P.; BIERNATH, C.; BOOGAARD, H.; BOOTE, K.J.; BOUMAN, B.; BREGAGLIO, S.; BRISSON, N.; BUIS, S.; CAMMARANO, D.; CHALLINOR, A.J.; CONFALONIERI, R.; CONIJN, J.G.; CORBEELS, M.; DERYNG, D.; DE SANCTIS, G.; DOLTRA, J.; FUMOTO, T.; GAYDON, D.; GAYLER, S.; GOLDBERG, R.; GRANT, R.F.; GRASSINI, P.; HATFIELD, J.L.; HASEGAWA, T.; HENG, L.; HOEK, S.; HOOKER, J.; HUNT, L.A.; INGWERSEN, J.; IZAURRALDE, R.C.; JONGSCHAAP, R.E.E.; JONES, J.W.; KEMANIAN, R.A.; KERSEBAUM, K.C.; KIM, S.-H.; LIZASO, J.; MÜLLER, C.; NAKAGAWA, H.; NARESH KUMAR, S.; NENDEL, C.; O'LEARY, G.J.; OLESEN, J.E.; ORIOL, P.; OSBORNE, T.M.; PALOSUO, T.; PRAVIA, V.; PRIESACK, E.; RIPOCHE, D.; ROSENZWEIG, C.; RUANE, A.C.; RUGET, F.; SAU, F.; SEMENOV, M.A.; SHCHERBAK, I.; SINGH, B.; SINGH, U.; SOO, H.K.; STEDUTO, P.; STÖCKLE, C.; STRATONOVITCH, P.; STRECK, T.; SUPIT, I.; TANG, L.; TAO, F.; TEIXEIRA, E.I.; THORBURN, P.; TIMLIN, D.; TRAVASSO, M.; RÖTTER, R.P.; WAHA, K.; WALLACH, D.; WHITE, J.W.; WILKENS, P.; WILLIAMS, J.R.; WOLF, J.; YIN, X.; YOSHIDA, H.; ZHANG, Z.; ZHU, Y. |
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Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. |
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ISSN : |
0168-1923 |
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DOI : |
10.1016/j.agrformet.2015.09.013 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. |
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Contenido : |
ABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2].
© 2015 Elsevier B.V. All rights reserved. MenosABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in... Presentar Todo |
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Palabras claves : |
Climate change; CROP MODEL; Emulator; MAIZE; Meta-model; MODELIZACIÓN DE LOS CULTIVOS; RICE; Statistical model; WHEAT; Yield. |
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Thesagro : |
ARROZ; CAMBIO CLIMÁTICO; MAÍZ; MODELOS ESTADISTICOS; TRIGO. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 05363naa a2201417 a 4500
001 1053856
005 2019-10-09
008 2015 bl uuuu u00u1 u #d
022 $a0168-1923
024 7 $a10.1016/j.agrformet.2015.09.013$2DOI
100 1 $aMARCAIDA, M.
245 $aA statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration.
260 $c2015
500 $aArticle history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015.
520 $aABSTRACT. Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2]. © 2015 Elsevier B.V. All rights reserved.
650 $aARROZ
650 $aCAMBIO CLIMÁTICO
650 $aMAÍZ
650 $aMODELOS ESTADISTICOS
650 $aTRIGO
653 $aClimate change
653 $aCROP MODEL
653 $aEmulator
653 $aMAIZE
653 $aMeta-model
653 $aMODELIZACIÓN DE LOS CULTIVOS
653 $aRICE
653 $aStatistical model
653 $aWHEAT
653 $aYield
700 1 $aASSENG, S.
700 1 $aEWERT, F.
700 1 $aBASSU, S.
700 1 $aDURAND, J.L.
700 1 $aLI, T.
700 1 $aMARTRE, P.
700 1 $aADAM, M.
700 1 $aAGGARWAL, P.K.
700 1 $aANGULO, C.
700 1 $aBARON, C.
700 1 $aBASSO, B.
700 1 $aBERTUZZI, P.
700 1 $aBIERNATH, C.
700 1 $aBOOGAARD, H.
700 1 $aBOOTE, K.J.
700 1 $aBOUMAN, B.
700 1 $aBREGAGLIO, S.
700 1 $aBRISSON, N.
700 1 $aBUIS, S.
700 1 $aCAMMARANO, D.
700 1 $aCHALLINOR, A.J.
700 1 $aCONFALONIERI, R.
700 1 $aCONIJN, J.G.
700 1 $aCORBEELS, M.
700 1 $aDERYNG, D.
700 1 $aDE SANCTIS, G.
700 1 $aDOLTRA, J.
700 1 $aFUMOTO, T.
700 1 $aGAYDON, D.
700 1 $aGAYLER, S.
700 1 $aGOLDBERG, R.
700 1 $aGRANT, R.F.
700 1 $aGRASSINI, P.
700 1 $aHATFIELD, J.L.
700 1 $aHASEGAWA, T.
700 1 $aHENG, L.
700 1 $aHOEK, S.
700 1 $aHOOKER, J.
700 1 $aHUNT, L.A.
700 1 $aINGWERSEN, J.
700 1 $aIZAURRALDE, R.C.
700 1 $aJONGSCHAAP, R.E.E.
700 1 $aJONES, J.W.
700 1 $aKEMANIAN, R.A.
700 1 $aKERSEBAUM, K.C.
700 1 $aKIM, S.-H.
700 1 $aLIZASO, J.
700 1 $aMÜLLER, C.
700 1 $aNAKAGAWA, H.
700 1 $aNARESH KUMAR, S.
700 1 $aNENDEL, C.
700 1 $aO'LEARY, G.J.
700 1 $aOLESEN, J.E.
700 1 $aORIOL, P.
700 1 $aOSBORNE, T.M.
700 1 $aPALOSUO, T.
700 1 $aPRAVIA, V.
700 1 $aPRIESACK, E.
700 1 $aRIPOCHE, D.
700 1 $aROSENZWEIG, C.
700 1 $aRUANE, A.C.
700 1 $aRUGET, F.
700 1 $aSAU, F.
700 1 $aSEMENOV, M.A.
700 1 $aSHCHERBAK, I.
700 1 $aSINGH, B.
700 1 $aSINGH, U.
700 1 $aSOO, H.K.
700 1 $aSTEDUTO, P.
700 1 $aSTÖCKLE, C.
700 1 $aSTRATONOVITCH, P.
700 1 $aSTRECK, T.
700 1 $aSUPIT, I.
700 1 $aTANG, L.
700 1 $aTAO, F.
700 1 $aTEIXEIRA, E.I.
700 1 $aTHORBURN, P.
700 1 $aTIMLIN, D.
700 1 $aTRAVASSO, M.
700 1 $aRÖTTER, R.P.
700 1 $aWAHA, K.
700 1 $aWALLACH, D.
700 1 $aWHITE, J.W.
700 1 $aWILKENS, P.
700 1 $aWILLIAMS, J.R.
700 1 $aWOLF, J.
700 1 $aYIN, X.
700 1 $aYOSHIDA, H.
700 1 $aZHANG, Z.
700 1 $aZHU, Y.
773 $tAgricultural and Forest Meteorology, 2015$gv.214-215, p. 483-493.
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