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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
06/12/2019 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L. |
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Afiliación : |
Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA./ Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay. |
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Título : |
Training population optimization for genomic selection. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028 |
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DOI : |
10.3835/plantgenome2019.04.0028 |
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Idioma : |
Inglés |
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Notas : |
Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. |
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Contenido : |
ABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). MenosABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individ... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; SELECCIÓN GENÓMICA. |
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Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16707/1/The-Plant-Genome-2019-Berro-Training-Population-Optimization-for-Genomic-Selection.pdf
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.3835/plantgenome2019.04.0028
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LEADER 02385naa a2200241 a 4500
001 1060511
005 2022-09-05
008 2019 bl uuuu u00u1 u #d
024 7 $a10.3835/plantgenome2019.04.0028$2DOI
100 1 $aBERRO, I.
245 $aTraining population optimization for genomic selection.$h[electronic resource]
260 $c2019
500 $aArticle histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019.
520 $aABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009).
650 $aTRIGO
650 $aTRITICUM AESTIVUM
653 $aGENOMIC SELECTION
653 $aSELECCIÓN GENÓMICA
700 1 $aLADO, B.
700 1 $aNALIN, R.S.
700 1 $aQUINCKE, M.
700 1 $aGUTIÉRREZ, L.
773 $tPlant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028
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Registro original : |
INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha actual : |
23/09/2016 |
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Actualizado : |
23/09/2016 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
BASILE, P.; FORMOSO, D.; TISCORNIA, G.; BLUMETTO, O. |
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Afiliación : |
PATRICIA CECILIA BASILE LORENZO, Universidad de la República (UdelaR)/ Centro Universitario Regional Tacuarembó; DANIEL FORMOSO CUNHA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OSCAR RICARDO BLUMETTO VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Radiation use efficiency on campos graslands with contrasting grzing methods. [Resumen de poster]. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
ln: Encuentro de Investigadores de la Región Noreste: Cerro Largo-Rivera-Tacuarembó, 1., 12 de agosto de 2016, Campus Interinstitucional de Tacuarembó, Tacuarembó. Libro de Resúmenes. Tacuarembó: UDELAR; INIA, 2016. |
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Páginas : |
p. 64 |
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Idioma : |
Inglés |
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Contenido : |
Introduction: In Uruguay, the Basaltic region has de highest proportion of natural grasslands of the country. In this pastures, livestock management is the main reason of degradation of natural grasslands. Today, it's possible to estimate ANPP (Aboveground Net Primary Production) using remote sensing techniques. The RUE (Radiation Use Efficiency) is the effectiveness with which fPAR (fraction of Photosyntethically Active Radiation absorbed by plants) is transformed in ANPP and is known to vary according to temperature, precipitation and species composition. Objectives; The aims of this work were: a) to calibrate RUE and b) study the temporal variability
of RUE for two contrasting grazing methods. Materials & Methods: The study was conducted on five livestock farms located in the Basaltic region. In each site, two contrasting pastures with different historical grazing management (controlled vs continuous stocking rate) were selected. Data was collected between september 2013 and february 2015. RUE coefficient was estimated following Monteith equation: RUE= ANPP / APAR and APAR= fPAR x PAR. ANPP was estimated using the technique of regrowth in three exclusion cages. Biomass was cut at 1cm in boxes 20 x 50cm with shears every 45-50 days and was dried in forced air oven at 60 ° C. fPAR
was obtained as a function of ENVI images from MODIS sensor (US Geological Survey) and PAR was estimated from agro-climatic stations of INIA. RUE data were analyzed with a oneway ANOVA and the means were compared with T test for paired samples. Results: Between grazing methods, RUE average values were statistically different (p <0.05), with controlled management reporting values above 44%. When analysing seasonal variation between grazing methods, there were no statistical differences in RUE values. Seasonal variation of RUE for each grazing methods separately, was significantly different within seasons (p <0.05). Conclusions: The RUE values obtained could be used in the estimation of a more accurately ANPP in natural grasslands of this region. MenosIntroduction: In Uruguay, the Basaltic region has de highest proportion of natural grasslands of the country. In this pastures, livestock management is the main reason of degradation of natural grasslands. Today, it's possible to estimate ANPP (Aboveground Net Primary Production) using remote sensing techniques. The RUE (Radiation Use Efficiency) is the effectiveness with which fPAR (fraction of Photosyntethically Active Radiation absorbed by plants) is transformed in ANPP and is known to vary according to temperature, precipitation and species composition. Objectives; The aims of this work were: a) to calibrate RUE and b) study the temporal variability
of RUE for two contrasting grazing methods. Materials & Methods: The study was conducted on five livestock farms located in the Basaltic region. In each site, two contrasting pastures with different historical grazing management (controlled vs continuous stocking rate) were selected. Data was collected between september 2013 and february 2015. RUE coefficient was estimated following Monteith equation: RUE= ANPP / APAR and APAR= fPAR x PAR. ANPP was estimated using the technique of regrowth in three exclusion cages. Biomass was cut at 1cm in boxes 20 x 50cm with shears every 45-50 days and was dried in forced air oven at 60 ° C. fPAR
was obtained as a function of ENVI images from MODIS sensor (US Geological Survey) and PAR was estimated from agro-climatic stations of INIA. RUE data were analyzed with a oneway ANOVA and the mea... Presentar Todo |
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Palabras claves : |
GRASSLAND PRODUCTIVITY; LIVESTOCK MANAGEMENT; PPNA. |
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Thesagro : |
PASTURAS. |
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Asunto categoría : |
P30 Ciencia del suelo y manejo del suelo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/6099/1/PAGINA-64.pdf
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Marc : |
LEADER 02852naa a2200217 a 4500
001 1055722
005 2016-09-23
008 2016 bl uuuu u00u1 u #d
100 1 $aBASILE, P.
245 $aRadiation use efficiency on campos graslands with contrasting grzing methods. [Resumen de poster].$h[electronic resource]
260 $c2016
300 $ap. 64
520 $aIntroduction: In Uruguay, the Basaltic region has de highest proportion of natural grasslands of the country. In this pastures, livestock management is the main reason of degradation of natural grasslands. Today, it's possible to estimate ANPP (Aboveground Net Primary Production) using remote sensing techniques. The RUE (Radiation Use Efficiency) is the effectiveness with which fPAR (fraction of Photosyntethically Active Radiation absorbed by plants) is transformed in ANPP and is known to vary according to temperature, precipitation and species composition. Objectives; The aims of this work were: a) to calibrate RUE and b) study the temporal variability of RUE for two contrasting grazing methods. Materials & Methods: The study was conducted on five livestock farms located in the Basaltic region. In each site, two contrasting pastures with different historical grazing management (controlled vs continuous stocking rate) were selected. Data was collected between september 2013 and february 2015. RUE coefficient was estimated following Monteith equation: RUE= ANPP / APAR and APAR= fPAR x PAR. ANPP was estimated using the technique of regrowth in three exclusion cages. Biomass was cut at 1cm in boxes 20 x 50cm with shears every 45-50 days and was dried in forced air oven at 60 ° C. fPAR was obtained as a function of ENVI images from MODIS sensor (US Geological Survey) and PAR was estimated from agro-climatic stations of INIA. RUE data were analyzed with a oneway ANOVA and the means were compared with T test for paired samples. Results: Between grazing methods, RUE average values were statistically different (p <0.05), with controlled management reporting values above 44%. When analysing seasonal variation between grazing methods, there were no statistical differences in RUE values. Seasonal variation of RUE for each grazing methods separately, was significantly different within seasons (p <0.05). Conclusions: The RUE values obtained could be used in the estimation of a more accurately ANPP in natural grasslands of this region.
650 $aPASTURAS
653 $aGRASSLAND PRODUCTIVITY
653 $aLIVESTOCK MANAGEMENT
653 $aPPNA
700 1 $aFORMOSO, D.
700 1 $aTISCORNIA, G.
700 1 $aBLUMETTO, O.
773 $tln: Encuentro de Investigadores de la Región Noreste: Cerro Largo-Rivera-Tacuarembó, 1., 12 de agosto de 2016, Campus Interinstitucional de Tacuarembó, Tacuarembó. Libro de Resúmenes. Tacuarembó: UDELAR; INIA, 2016.
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INIA Tacuarembó (TBO) |
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