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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
12/12/2017 |
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Actualizado : |
12/12/2017 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
LADO, B.; BATTENFIELD, S.; SILVA, P.; QUINCKE, M.; GUZMAN, C.; SINGH, R.P.; DREISIGACKER, S.; PEÑA, J.; FRITZ, A.; POLAND, J.; GUTIERREZ, L. |
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Afiliación : |
BETTINA LADO, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay.; SARAH BATTENFIELD, AgriPro Wheat, Syngenta, 11783 Ascher Rd. Junction City, KS, 66441, USA.; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GUZMAN, CIMMYT, El Batan, Mexico, Mexico.; RAVI P. SINGH, CIMMYT, El Batan, Mexico, Mexico.; SUSANNE DREISIGACKER, CIMMYT, El Batan, Mexico, Mexico.; JAVIER PEÑA, CIMMYT, El Batan, Mexico, Mexico.; ALLAN FRITZ, Wheat Genetics Resource Center, Department of Plant Pathology, 1712 Claflin Rd., Kansas State University, Manhattan, KS 66506, USA; .; JESSE POLAND, Wheat Genetics Resource Center, Department of Plant Pathology, 1712 Claflin Rd., Kansas State University, Manhattan, KS 66506, USA.; LUCIA GUTIERREZ, Department of Agronomy, University of Wisconsin, 1575 Linden Dr., Madison, WI 53706, USA. |
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Título : |
Comparing strategies to select crosses using genomic prediction in two wheat breeding programs. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
In: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. |
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Páginas : |
p.88-90. |
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Idioma : |
Español |
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Contenido : |
Key message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross.
In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2). Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses, but further understanding on optimizing the cross combinations is needed. MenosKey message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross.
In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2)... Presentar Todo |
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Palabras claves : |
WHEAT BREEDING PROGRAMS; WHEAT QUALITY. |
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Thesagro : |
MEJORAMIENTO GENETICO DE PLANTAS; TRIGO. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02689nam a2200289 a 4500
001 1057873
005 2017-12-12
008 2017 bl uuuu u01u1 u #d
100 1 $aLADO, B.
245 $aComparing strategies to select crosses using genomic prediction in two wheat breeding programs.
260 $aIn: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria.$c2017
300 $ap.88-90.
520 $aKey message: Evaluation of crosses prediction methods with and without accounting for progeny variance. Mid-parent values was a much larger factor determining genetic gain than increasing the progeny variance of a cross. In wheat breeding programs, a critical decision is to determine crosses that have high probability to deliver progenies with higher genetics gains (Zhong & Jannink 2007, Bernardo 2014). We present an application of genomic models for predicting parental cross combinations for grain yield, grain protein, and loaf volume across two wheat-breeding programs, INIA-Uruguay and CIMMYT. We evaluated three methods for selecting the ?best? crosses based on (1) mid-parents, (2) top 10% of the progeny within a cross, and (3) maximizing mean and variance within progeny using thresholds. The last two methods were evaluated with the predicted variances obtained through progeny simulation using the PopVar (Mohammadi et al. 2015, Tiede et al. 2015) package in R software. The first two methods showed 82% of crosses in common for yield, 55% for loaf volume and 53% for grain protein, even though only the second method accounts for the variance of the progeny (Figure 1). While the expected variance of the progeny is important to increase chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses selected was a much larger factor determining genetic gain than increasing the progeny variance of a cross (Figure 2). Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses, but further understanding on optimizing the cross combinations is needed.
650 $aMEJORAMIENTO GENETICO DE PLANTAS
650 $aTRIGO
653 $aWHEAT BREEDING PROGRAMS
653 $aWHEAT QUALITY
700 1 $aBATTENFIELD, S.
700 1 $aSILVA, P.
700 1 $aQUINCKE, M.
700 1 $aGUZMAN, C.
700 1 $aSINGH, R.P.
700 1 $aDREISIGACKER, S.
700 1 $aPEÑA, J.
700 1 $aFRITZ, A.
700 1 $aPOLAND, J.
700 1 $aGUTIERREZ, L.
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INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
09/11/2017 |
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Actualizado : |
25/11/2019 |
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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 : |
MASUDA, Y; MISZTAL, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; AGUILAR, I. |
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Afiliación : |
Y. MASUDA, Department of Animal and Dairy Science, University of Georgia; I. MISZTAL, Department of Animal and Dairy Science, University of Georgia; A. LEGARRA, INRA (Institut National de la Recherche Agronomique); S. TSURUTA, Department of Animal and Dairy Science, University of Georgia; D.A.L. LOURENCO, Department of Animal and Dairy Science, University of Georgia; B.O. FRAGOMENI, Department of Animal and Dairy Science, University of Georgia; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Journal of Animal Science, 2017, v. 95(1): 49-52. |
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DOI : |
10.2527/jas.2016.0699 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017.
This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936). |
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Contenido : |
ABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.
Copyright © 2016. American Society of Animal Science. MenosABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When t... Presentar Todo |
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Palabras claves : |
COMPUTATION; GENOMIC SELECTION; INVERSION; NUMERATOR RELATIONSHIP MATRIX; PRECONDITIONED CONJUGATE GRADIENT; SPARSE MATRIX. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02919naa a2200289 a 4500
001 1057743
005 2019-11-25
008 2017 bl uuuu u00u1 u #d
024 7 $a10.2527/jas.2016.0699$2DOI
100 1 $aMASUDA, Y
245 $aTechnical note$bAvoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017. This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936).
520 $aABSTRACT. This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations. Copyright © 2016. American Society of Animal Science.
653 $aCOMPUTATION
653 $aGENOMIC SELECTION
653 $aINVERSION
653 $aNUMERATOR RELATIONSHIP MATRIX
653 $aPRECONDITIONED CONJUGATE GRADIENT
653 $aSPARSE MATRIX
700 1 $aMISZTAL, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aLOURENCO, D.A.L.
700 1 $aFRAGOMENI, B.O.
700 1 $aAGUILAR, I.
773 $tJournal of Animal Science, 2017$gv. 95(1): 49-52.
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