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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
21/02/2014 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LEGARRA, A.; AGUILAR, I.; MISZTAL, I. |
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Afiliación : |
A. LEGARRA, INRA, France; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; I. MISZTAL, Department of Animal and Dairy Science, University of Georgia, US. |
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Título : |
A relationship matrix including full pedigree and genomic information. |
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Fecha de publicación : |
2009 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2009, 92 (9): 4656-4663. OPEN ACCESS. |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2009-2061 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received January 26, 2009. // Accepted April 28, 2009. |
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Contenido : |
ABSTRACT.
Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-de- rived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.
© American Dairy Science Association, 2009. MenosABSTRACT.
Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-de- rived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternat... Presentar Todo |
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Palabras claves : |
GENETIC EVALUATION; GENOMIC SELECTION; MIXED MODEL; RELATIONSHIP MATRIX. |
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Thesagro : |
EVALUACIÓN GENÉTICA. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12192/1/1-s2.0-S0022030209707933-main.pdf
https://www.journalofdairyscience.org/article/S0022-0302(09)70793-3/pdf
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Marc : |
LEADER 02476naa a2200241 a 4500
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008 2009 bl uuuu u00u1 u #d
022 $a0022-0302
024 7 $a10.3168/jds.2009-2061$2DOI
100 1 $aLEGARRA, A.
245 $aA relationship matrix including full pedigree and genomic information.$h[electronic resource]
260 $c2009
500 $aArticle history: Received January 26, 2009. // Accepted April 28, 2009.
520 $aABSTRACT. Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-de- rived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients. © American Dairy Science Association, 2009.
650 $aEVALUACIÓN GENÉTICA
653 $aGENETIC EVALUATION
653 $aGENOMIC SELECTION
653 $aMIXED MODEL
653 $aRELATIONSHIP MATRIX
700 1 $aAGUILAR, I.
700 1 $aMISZTAL, I.
773 $tJournal of Dairy Science, 2009, 92 (9): 4656-4663. OPEN ACCESS.
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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 : |
27/04/2023 |
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Actualizado : |
27/04/2023 |
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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 : |
GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. |
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Afiliación : |
ANDRÉ GARCÍA, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA Toulouse, Castanet Tolosan, 31326, France; SHOGO TSURUTA, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States. |
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Título : |
Correction: Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP (Genetics, selection, evolution : GSE (2022) 54:1 (66)). |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Genetics, Selection, Evolution : GSE, 2023, Volume 55, Issue 1, Pages 26. OPEN ACCESS. https://doi.org/10.1186/s12711-023-00799-x |
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ISSN : |
1297-9686 |
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DOI : |
10.1186/s12711-023-00799-x |
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Idioma : |
Inglés |
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Notas : |
Article history: Published online 17 April 2023. -- Document: Erratum - Gold Open Access. -- The original article can be found online at https://doi.org/10.1186/s12711-022-00752-4 |
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Contenido : |
Correction: Genetics Selection Evolution (2022) 54(1):66 - https://doi.org/10.1186/s12711-022-00752-4 |
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Palabras claves : |
Algorithm; Breeding; Erratum. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00799-x.pdf
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Marc : |
LEADER 01119naa a2200253 a 4500
001 1064061
005 2023-04-27
008 2023 bl uuuu u00u1 u #d
022 $a1297-9686
024 7 $a10.1186/s12711-023-00799-x$2DOI
100 1 $aGARCÍA, A.
245 $aCorrection$bTheoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP (Genetics, selection, evolution : GSE (2022) 54:1 (66)).$h[electronic resource]
260 $c2023
500 $aArticle history: Published online 17 April 2023. -- Document: Erratum - Gold Open Access. -- The original article can be found online at https://doi.org/10.1186/s12711-022-00752-4
520 $aCorrection: Genetics Selection Evolution (2022) 54(1):66 - https://doi.org/10.1186/s12711-022-00752-4
653 $aAlgorithm
653 $aBreeding
653 $aErratum
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aMISZTAL, I.
700 1 $aLOURENCO, D.
773 $tGenetics, Selection, Evolution : GSE, 2023, Volume 55, Issue 1, Pages 26. OPEN ACCESS. https://doi.org/10.1186/s12711-023-00799-x
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