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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
26/09/2017 |
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Data da última atualização: |
26/09/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BASSI, D.; BRIÑEZ, B.; ROSA, J. S.; OBLESSUC, P. R.; ALMEIDA, C. P. de; NUCCI, S. M.; SILVA, L. C. D. da; CHIORATO, A. F.; VIANELLO, R. P.; CAMARGO, L. E. A.; BLAIR, M. W.; BENCHIMOL-REIS, L. L. |
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Afiliação: |
DENIS BASSI, IAC; BORIS BRIÑEZ, IAC; JULIANA SANTA ROSA, IAC; PAULA RODRIGUES OBLESSUC, IAC; CALÉO PANHOCA DE ALMEIDA, IAC; STELLA MARIS NUCCI, IAC; LARISSA CHARIEL DOMINGOS DA SILVA, IAC; ALISSON FERNANDO CHIORATO, IAC; ROSANA PEREIRA VIANELLO, CNPAF; LUIS EDUARDO ARANHA CAMARGO, ESALQ; MATTHEW WOHLGEMUTH BLAIR, TENNESSEE STATE UNIVERSITY; LUCIANA LASRY BENCHIMOL-REIS, IAC. |
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Título: |
Linkage and mapping of quantitative trait loci associated with angular leaf spot and powdery mildew resistance in common beans. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Biology, Ribeirão Preto, v. 40, n. 1, p. 109-122, jan./mar. 2017. |
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ISSN: |
1678-4685 |
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DOI: |
10.1590/1678-4685-GMB-2015-0314 |
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Idioma: |
Inglês |
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Conteúdo: |
Angular leaf spot (ALS) and powdery mildew (PWM) are two important fungi diseases causing significant yield losses in common beans. In this study, a new genetic linkage map was constructed using single sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), in a segregating population derived from the AND 277 x SEA 5 cross, with 105 recombinant inbred lines. Phenotypic evaluations were performed in the greenhouse to identify quantitative trait loci (QTLs) associated with resistance by means of the composite interval mapping analysis. Four QTLs were identified for ALS resistance. The QTL ALS11AS, linked on the SNP BAR 5054, mapped on chromosome Pv11, showed the greatest effect (R2 = 26.5%) on ALS phenotypic variance. ForPWMresistance, two QTLs were detected, PWM2AS and PWM11AS, on Pv2 and Pv11, explaining 7% and 66% of the phenotypic variation, respectively. Both QTLs on Pv11 were mapped on the same genomic region, suggesting that it is a pleiotropic region. The present study resulted in the identification of new markers closely linked to ALS and PWM QTLs, which can be used for marker-assisted selection, fine mapping and positional cloning. |
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Palavras-Chave: |
Pseudocercospora griseola; Quantitative inheritance. |
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Thesagro: |
Erysiphe Polygoni; Feijão; Mancha foliar; Phaseolus vulgaris. |
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Thesaurus Nal: |
Leaf spot; Microsatellite repeats; Single nucleotide polymorphism. |
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Categoria do assunto: |
S Ciências Biológicas |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/164328/1/CNPAF-2017-gmb.pdf
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Marc: |
LEADER 02376naa a2200385 a 4500
001 2076287
005 2017-09-26
008 2017 bl uuuu u00u1 u #d
022 $a1678-4685
024 7 $a10.1590/1678-4685-GMB-2015-0314$2DOI
100 1 $aBASSI, D.
245 $aLinkage and mapping of quantitative trait loci associated with angular leaf spot and powdery mildew resistance in common beans.$h[electronic resource]
260 $c2017
520 $aAngular leaf spot (ALS) and powdery mildew (PWM) are two important fungi diseases causing significant yield losses in common beans. In this study, a new genetic linkage map was constructed using single sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), in a segregating population derived from the AND 277 x SEA 5 cross, with 105 recombinant inbred lines. Phenotypic evaluations were performed in the greenhouse to identify quantitative trait loci (QTLs) associated with resistance by means of the composite interval mapping analysis. Four QTLs were identified for ALS resistance. The QTL ALS11AS, linked on the SNP BAR 5054, mapped on chromosome Pv11, showed the greatest effect (R2 = 26.5%) on ALS phenotypic variance. ForPWMresistance, two QTLs were detected, PWM2AS and PWM11AS, on Pv2 and Pv11, explaining 7% and 66% of the phenotypic variation, respectively. Both QTLs on Pv11 were mapped on the same genomic region, suggesting that it is a pleiotropic region. The present study resulted in the identification of new markers closely linked to ALS and PWM QTLs, which can be used for marker-assisted selection, fine mapping and positional cloning.
650 $aLeaf spot
650 $aMicrosatellite repeats
650 $aSingle nucleotide polymorphism
650 $aErysiphe Polygoni
650 $aFeijão
650 $aMancha foliar
650 $aPhaseolus vulgaris
653 $aPseudocercospora griseola
653 $aQuantitative inheritance
700 1 $aBRIÑEZ, B.
700 1 $aROSA, J. S.
700 1 $aOBLESSUC, P. R.
700 1 $aALMEIDA, C. P. de
700 1 $aNUCCI, S. M.
700 1 $aSILVA, L. C. D. da
700 1 $aCHIORATO, A. F.
700 1 $aVIANELLO, R. P.
700 1 $aCAMARGO, L. E. A.
700 1 $aBLAIR, M. W.
700 1 $aBENCHIMOL-REIS, L. L.
773 $tGenetics and Molecular Biology, Ribeirão Preto$gv. 40, n. 1, p. 109-122, jan./mar. 2017.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
06/03/2020 |
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Data da última atualização: |
20/04/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
RAMIREZ-VILLEGAS, J.; MOLERO MILAN, A.; ALEXANDROV, N.; ASSENG, S.; CHALLINOR, A. J.; CROSSA, J.; VAN EEUWIJK, F.; GHANEM, M. E.; GRENIER, C.; HEINEMANN, A. B.; WANG, J.; JULIANA, P.; KEHEL, Z.; KHOLOVA, J; KOO, J.; PEQUENO, D.; QUIROZ, R.; REBOLLEDO, M. C.; SUKUMARAN, S.; VADEZ, V.; WHITE, J. W.; REYNOLDS, M. |
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Afiliação: |
JULIAN RAMIREZ-VILLEGAS, CIAT; ANABEL MOLERO MILAN, CIMMYT; NICKOLAI ALEXANDROV, IRRI; SENTHOLD ASSENG, UNIVERSITY OF FLORIDA, Gainesville-FL; ANDREW J. CHALLINOR, UNIVERSITY OF LEEDS, Leeds-UK; JOSE CROSSA, CIMMYT; FREED VAN EEUWIJK, WAGENINGEN UNIVERSITY, The Netherlands; MICHEL EDMOND GHANEM, ICARDA; CECILE GRENIER, CIAT; ALEXANDRE BRYAN HEINEMANN, CNPAF; JIANKANG WANG, INSTITUTE OF CROP SCIENCES, Beijing; PHILOMIN JULIANA, CIMMYT; ZAKARIA KEHEL, ICARDA; JANA KHOLOVA, ICRISAT; JAWOO KOO, IFPRI; DIEGO PEQUENO, CIMMYT; ROBERTO QUIROZ, CIP; MARIA C. REBOLLEDO, CIAT; SIVAKUMAR SUKUMARAN, CIMMYT; VINCENT VADEZ, ICRISAT; JEFFREY W. WHITE, USDA-ARS; MATTHEW REYNOLDS, CIMMYT. |
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Título: |
CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Crop Science, 2020. |
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ISSN: |
0011-183X |
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DOI: |
10.1002/csc2.20048 |
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Idioma: |
Inglês |
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Notas: |
Online Version of Record before inclusion in an issue. |
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Conteúdo: |
Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better-targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts. MenosCrop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follo... Mostrar Tudo |
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Palavras-Chave: |
Crop improvement; Crop modeling. |
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Thesagro: |
Clima. |
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Thesaurus NAL: |
Breeding; Climate change; Crops; Food security; Plant adaptation; Simulation models. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/211586/1/CNPAF-2020-cs.pdf
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Marc: |
LEADER 03114naa a2200517 a 4500
001 2121007
005 2020-04-20
008 2020 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $a10.1002/csc2.20048$2DOI
100 1 $aRAMIREZ-VILLEGAS, J.
245 $aCGIAR modeling approaches for resource-constrained scenarios$bI. Accelerating crop breeding for a changing climate.$h[electronic resource]
260 $c2020
500 $aOnline Version of Record before inclusion in an issue.
520 $aCrop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better-targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts.
650 $aBreeding
650 $aClimate change
650 $aCrops
650 $aFood security
650 $aPlant adaptation
650 $aSimulation models
650 $aClima
653 $aCrop improvement
653 $aCrop modeling
700 1 $aMOLERO MILAN, A.
700 1 $aALEXANDROV, N.
700 1 $aASSENG, S.
700 1 $aCHALLINOR, A. J.
700 1 $aCROSSA, J.
700 1 $aVAN EEUWIJK, F.
700 1 $aGHANEM, M. E.
700 1 $aGRENIER, C.
700 1 $aHEINEMANN, A. B.
700 1 $aWANG, J.
700 1 $aJULIANA, P.
700 1 $aKEHEL, Z.
700 1 $aKHOLOVA, J
700 1 $aKOO, J.
700 1 $aPEQUENO, D.
700 1 $aQUIROZ, R.
700 1 $aREBOLLEDO, M. C.
700 1 $aSUKUMARAN, S.
700 1 $aVADEZ, V.
700 1 $aWHITE, J. W.
700 1 $aREYNOLDS, M.
773 $tCrop Science, 2020.
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