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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
23/05/2016 |
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Actualizado : |
11/12/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. |
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Afiliación : |
Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; INRA (Institut National de la Recherche Agronomique); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Holstein Association USA Inc. |
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Título : |
Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS |
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DOI : |
10.3168/jds.2015-10540 |
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Idioma : |
Inglés |
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Notas : |
OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016 |
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Contenido : |
ABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals.
© 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). MenosABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 rand... Presentar Todo |
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Palabras claves : |
FINAL SCORE; GENOMIC EVALUATION; GENOMIC RELATIONSHIP MATRIX. |
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Thesagro : |
SsGBLUP; TORO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12160/1/1-s2.0-S0022030216000825-main.pdf
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Marc : |
LEADER 03610naa a2200289 a 4500
001 1054839
005 2018-12-11
008 2016 bl uuuu u00u1 u #d
024 7 $a10.3168/jds.2015-10540$2DOI
100 1 $aMASUDA, Y.
245 $aImplementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.$h[electronic resource]
260 $c2016
500 $aOPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016
520 $aABSTRACT. The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. © 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
650 $aSsGBLUP
650 $aTORO
653 $aFINAL SCORE
653 $aGENOMIC EVALUATION
653 $aGENOMIC RELATIONSHIP MATRIX
700 1 $aMISZTAL, I.
700 1 $aTSURUTA, S.
700 1 $aLEGARRA, A.
700 1 $aAGUILAR, I.
700 1 $aLOURENCO, D.A.L.
700 1 $aFRAGOMENI, B.O.
700 1 $aLAWLOR, T.J.
773 $tJournal of Dairy Science, 2016$gv.99, no.3, p.1968-1974. OPEN ACCESS
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INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
28/06/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
B - 1 |
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Autor : |
TERRA, J.A.; SHAW, N.J.; REEVES, D.W.; RAPER, R.L.; VAN SANTEN, E.; MASK, P.L. |
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Afiliación : |
JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Soil Carbon relationships with terrain attributes, electrical conductivity, and a soil survey in a coastal plain landscape. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Soil Science, 2004, V. 169, No. 12, p. 819-831. |
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DOI : |
10.1097/00010694-200412000-00001 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received May 3, 2004 // Accepted Sept. 30, 2004, Publishing Dec. 2004. |
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Contenido : |
Soil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration. MenosSoil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased... Presentar Todo |
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Thesagro : |
MANEJO DEL SUELO; SUELO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02522naa a2200229 a 4500
001 1032966
005 2021-06-28
008 2004 bl uuuu u00u1 u #d
024 7 $a10.1097/00010694-200412000-00001$2DOI
100 1 $aTERRA, J.A.
245 $aSoil Carbon relationships with terrain attributes, electrical conductivity, and a soil survey in a coastal plain landscape.$h[electronic resource]
260 $c2004
500 $aArticle history: Received May 3, 2004 // Accepted Sept. 30, 2004, Publishing Dec. 2004.
520 $aSoil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration.
650 $aMANEJO DEL SUELO
650 $aSUELO
700 1 $aSHAW, N.J.
700 1 $aREEVES, D.W.
700 1 $aRAPER, R.L.
700 1 $aVAN SANTEN, E.
700 1 $aMASK, P.L.
773 $tSoil Science, 2004, V. 169, No. 12, p. 819-831.
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