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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 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. |
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Fecha actual : |
18/12/2017 |
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Actualizado : |
15/10/2019 |
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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 : |
LOURENCO, D.A.L.; FRAGOMENI, B.O.; BRADFORD, H.L.; MENEZES I.R.; FERRAZ, J.B.S.; AGUILAR, I.; MISZTAL, I. |
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Afiliación : |
D.A.L. LOURENCO, Universidad de Georgia (UG); B.O. FRAGOMENI, Universidad de Georgia (UG); H.L. BRADFORD, Universidad de Georgia (UG); I.R. MENEZES, FZEA, University of Sao Paulo.; J.B.S. FERRAZ, FZEA, University of Sao Paulo.; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; I. MISZTAL, Universidad de Georgia (UG). |
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Título : |
Implications of SNP weighting on single-step genomic predictions for different reference population sizes. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Journal of Animal Breeding and Genetics, 2017, v. 134 (6), p. 463-471. |
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DOI : |
10.1111/jbg.12288 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 28 February 2017 / Accepted: 19 July 2017.
This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI) and by Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the US Department of Agriculture's National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers, and we thank Andra H. Nelson for assisting with data analysis. |
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Contenido : |
ABSTRACT.
We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single-step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix (G). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome-wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G. Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important.
© 2017 Blackwell Verlag GmbH |
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Palabras claves : |
ACCURAY; BAYES B; SNP WEIGHTING; VARIABLE SELECTION; WEIGTED SSGBLUP. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02612naa a2200277 a 4500
001 1057902
005 2019-10-15
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1111/jbg.12288$2DOI
100 1 $aLOURENCO, D.A.L.
245 $aImplications of SNP weighting on single-step genomic predictions for different reference population sizes.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: 28 February 2017 / Accepted: 19 July 2017. This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI) and by Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the US Department of Agriculture's National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers, and we thank Andra H. Nelson for assisting with data analysis.
520 $aABSTRACT. We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single-step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix (G). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome-wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G. Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important. © 2017 Blackwell Verlag GmbH
653 $aACCURAY
653 $aBAYES B
653 $aSNP WEIGHTING
653 $aVARIABLE SELECTION
653 $aWEIGTED SSGBLUP
700 1 $aFRAGOMENI, B.O.
700 1 $aBRADFORD, H.L.
700 1 $aMENEZES I.R.
700 1 $aFERRAZ, J.B.S.
700 1 $aAGUILAR, I.
700 1 $aMISZTAL, I.
773 $tJournal of Animal Breeding and Genetics, 2017$gv. 134 (6), p. 463-471.
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