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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
24/04/2023 |
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Actualizado : |
24/04/2023 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
RAMOS, R.F.; SOBUCKI, L.; PAWLOWSKI, E.; SARZI, J.S.; RABUSKE, J.E.; SAVIAN, L.G.; KASPARY, T. E.; BELLÉ, C. |
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Afiliación : |
RODRIGO F. RAMOS, Instituto Phytus - Staphyt Brasil, Brazil; LISIANE SOBUCKI, Federal University of Santa Maria, Brazil; ESTÉFANY PAWLOWSKI, Federal University of Santa Maria, Brazil; JANAINA S. SARZI, Federal University of Santa Maria, Brazil; JESSICA E. RABUSKE, Regional Integrated University of Upper Uruguai and Missions, Brazil; LUCAS G. SAVIAN, Federal University of Santa Maria, Brazil; TIAGO EDU KASPARY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTIANO BELLÉ, Instituto Phytus - Staphyt Brasil, Brazil. |
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Título : |
Perspective chapter: Microorganisms and their relationship with tree health. [book chapter] |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
IntechOpen, 2023. doi: http://doi.org/10.5772/intechopen.110461 |
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DOI : |
10.5772/intechopen.110461 |
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Idioma : |
Inglés |
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Notas : |
Chapter history: Submitted 17th June 2022; Reviewed 9th February 2023; Published 2nd March 2023. -- |
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Contenido : |
The health of plants depends on numerous environmental factors. All plants, including trees, live in close relationship with microorganisms. Plants harbor microbial communities in above-and below-ground tissues, where plant-associated microbial communities are influenced by environmental conditions and host genotype.
The microbiome of trees is composed of mutualistic, commensal, and pathogenic microorganisms. Mutualistic microorganisms can help trees obtain nutrients (e.g., phosphorus and nitrogen) and defend against plant pathogens. Ecological interactions between different microbial groups directly influence host health, and endophytic microorganisms can inhibit pathogen growth or induce the expression of genes related to tree defense against these adverse organisms. Hence, understanding host-microbiome-environment interactions are crucial for modulating tree health. |
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Palabras claves : |
Arbuscular mycorrhizae; Bacteria; Ectomycorrhizas; Microbial ecology; Plant holobiont. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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URL : |
https://www.intechopen.com/online-first/86354
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Marc : |
LEADER 01808naa a2200289 a 4500
001 1064052
005 2023-04-24
008 2023 bl uuuu u00u1 u #d
024 7 $a10.5772/intechopen.110461$2DOI
100 1 $aRAMOS, R.F.
245 $aPerspective chapter$bMicroorganisms and their relationship with tree health. [book chapter]$h[electronic resource]
260 $c2023
500 $aChapter history: Submitted 17th June 2022; Reviewed 9th February 2023; Published 2nd March 2023. --
520 $aThe health of plants depends on numerous environmental factors. All plants, including trees, live in close relationship with microorganisms. Plants harbor microbial communities in above-and below-ground tissues, where plant-associated microbial communities are influenced by environmental conditions and host genotype. The microbiome of trees is composed of mutualistic, commensal, and pathogenic microorganisms. Mutualistic microorganisms can help trees obtain nutrients (e.g., phosphorus and nitrogen) and defend against plant pathogens. Ecological interactions between different microbial groups directly influence host health, and endophytic microorganisms can inhibit pathogen growth or induce the expression of genes related to tree defense against these adverse organisms. Hence, understanding host-microbiome-environment interactions are crucial for modulating tree health.
653 $aArbuscular mycorrhizae
653 $aBacteria
653 $aEctomycorrhizas
653 $aMicrobial ecology
653 $aPlant holobiont
700 1 $aSOBUCKI, L.
700 1 $aPAWLOWSKI, E.
700 1 $aSARZI, J.S.
700 1 $aRABUSKE, J.E.
700 1 $aSAVIAN, L.G.
700 1 $aKASPARY, T. E.
700 1 $aBELLÉ, C.
773 $tIntechOpen, 2023. doi: http://doi.org/10.5772/intechopen.110461
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/03/2021 |
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Actualizado : |
31/03/2021 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
MISZTAL, I.; WANG, H.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.; FRAGOMENI, B. O.; ZHANG, X.; MUIR, W. M.; CHENG, H. H.; OKIMOTO, R.; WING, T.; HAWKEN, R. R.; ZUMBACH, B.; FERNANDO, R. |
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Afiliación : |
IGNACY MISZTAL, University of Georgia, Athens, GA, USA; H. WANG, Genus PIC, Hendersonville, TN; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA, Toulouse, France; S. TSURUTA, University of Georgia, Athens, GA, USA; D.A.L. LOURENCO, University of Georgia, Athens, GA, USA; B. O. FRAGOMENI, University of Georgia, Athens, GA, USA; X. ZHANG, University of Georgia, Athens, GA, USA; W. M. MUIR, Purdue University, West Lafayette, IN, USA; H. H. CHENG, Cobb-Vantress Inc., Siloam Springs, AR, USA; R. OKIMOTO, Cobb-Vantress Inc., Siloam Springs, AR, USA; T. WING, Cobb-Vantress Inc., Siloam Springs, AR, USA; R. R. HAWKEN, Cobb-Vantress Inc., Siloam Springs, AR, USA; B. ZUMBACH, Cobb-Vantress Inc., Siloam Springs, AR, USA; R. FERNANDO, Department of Animal Science, Iowa State University, Ames, IA, USA. |
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Título : |
GWAS using ssGBLUP. |
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Complemento del título : |
Volume Species Breeding: Poultry, 325. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325. |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
This study aimed to compare results of genome-wide associations obtained from various methodologies for GWAS when applied to two lines of broiler chicken. Each line contained >250k birds with 3 traits and 5k SNP60k genotypes. Methods included single-step GWAS, single marker model and BayesB. Mannhattan plots were based on variances of 20-SNP segments, as shorter segments produced noisy plots. Only a few segments explained >1 % of the additive variance. One segment explained >20% variance in BayesB but 3% with ssGWAS and <1% with a single marker model. In two lines, no major segment overlapped for any trait. When analyses used slices of generations (1-3,2-4,3-5,1-5), variances for the same segment varied greatly. The plots were more distinct with a new data set that included >16k genotypes, but no segment explained >1% of the variance. Strength of associations strongly depends on methodologies and details of implementations. |
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Palabras claves : |
GWAS; SNP variance; SsGBLUP. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15454/1/Misztal-et-al.-2014.WCGALP.pdf
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Marc : |
LEADER 01803nam a2200313 a 4500
001 1061930
005 2021-03-31
008 2014 bl uuuu u01u1 u #d
100 1 $aMISZTAL, I.
245 $aGWAS using ssGBLUP.$h[electronic resource]
260 $aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325.$c2014
520 $aABSTRACT. This study aimed to compare results of genome-wide associations obtained from various methodologies for GWAS when applied to two lines of broiler chicken. Each line contained >250k birds with 3 traits and 5k SNP60k genotypes. Methods included single-step GWAS, single marker model and BayesB. Mannhattan plots were based on variances of 20-SNP segments, as shorter segments produced noisy plots. Only a few segments explained >1 % of the additive variance. One segment explained >20% variance in BayesB but 3% with ssGWAS and <1% with a single marker model. In two lines, no major segment overlapped for any trait. When analyses used slices of generations (1-3,2-4,3-5,1-5), variances for the same segment varied greatly. The plots were more distinct with a new data set that included >16k genotypes, but no segment explained >1% of the variance. Strength of associations strongly depends on methodologies and details of implementations.
653 $aGWAS
653 $aSNP variance
653 $aSsGBLUP
700 1 $aWANG, H.
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aLOURENCO, D.
700 1 $aFRAGOMENI, B. O.
700 1 $aZHANG, X.
700 1 $aMUIR, W. M.
700 1 $aCHENG, H. H.
700 1 $aOKIMOTO, R.
700 1 $aWING, T.
700 1 $aHAWKEN, R. R.
700 1 $aZUMBACH, B.
700 1 $aFERNANDO, R.
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