The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure

Abstract Background The single-step covariance matrix H combines the pedigree-based relationship matrix $${\mathbf {A}}$$ A with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix $${\mathbf {G}}$$ G . In particular, to impro...

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Main Authors: Johannes W. R. Martini, Matias F. Schrauf, Carolina A. Garcia-Baccino, Eduardo C. G. Pimentel, Sebastian Munilla, Andres Rogberg-Muñoz, Rodolfo J. C. Cantet, Christian Reimer, Ning Gao, Valentin Wimmer, Henner Simianer
Format: Article
Language:deu
Published: BMC 2018-04-01
Series:Genetics Selection Evolution
Online Access:http://link.springer.com/article/10.1186/s12711-018-0386-x
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spelling doaj-77220b7ad8a9418f9d1d4a01390b3d792020-11-24T20:45:01ZdeuBMCGenetics Selection Evolution1297-96862018-04-015011910.1186/s12711-018-0386-xThe effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedureJohannes W. R. Martini0Matias F. Schrauf1Carolina A. Garcia-Baccino2Eduardo C. G. Pimentel3Sebastian Munilla4Andres Rogberg-Muñoz5Rodolfo J. C. Cantet6Christian Reimer7Ning Gao8Valentin Wimmer9Henner Simianer10KWS SAAT SEDepartamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos AiresDepartamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos AiresInstitute of Animal Breeding, Bavarian State Research Center for AgricultureDepartamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos AiresDepartamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos AiresDepartamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos AiresAnimal Breeding and Genetics Group, Center for Integrated Breeding Research, University of GoettingenAnimal Breeding and Genetics Group, Center for Integrated Breeding Research, University of GoettingenKWS SAAT SEAnimal Breeding and Genetics Group, Center for Integrated Breeding Research, University of GoettingenAbstract Background The single-step covariance matrix H combines the pedigree-based relationship matrix $${\mathbf {A}}$$ A with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix $${\mathbf {G}}$$ G . In particular, to improve convergence behavior of iterative approaches and to reduce inflation, two weights $$\tau$$ τ and $$\omega$$ ω have been introduced in the definition of $${\mathbf {H}}^{-1}$$ H-1 , which blend the inverse of a part of $${\mathbf {A}}$$ A with the inverse of $${\mathbf {G}}$$ G . Since the definition of this blending is based on the equation describing $${\mathbf {H}}^{-1}$$ H-1 , its impact on the structure of $${\mathbf {H}}$$ H is not obvious. In a joint discussion, we considered the question of the shape of $${\mathbf {H}}$$ H for non-trivial $$\tau$$ τ and $$\omega$$ ω . Results Here, we present the general matrix $${\mathbf {H}}$$ H as a function of these parameters and discuss its structure and properties. Moreover, we screen for optimal values of $$\tau$$ τ and $$\omega$$ ω with respect to predictive ability, inflation and iterations up to convergence on a well investigated, publicly available wheat data set. Conclusion Our results may help the reader to develop a better understanding for the effects of changes of $$\tau$$ τ and $$\omega$$ ω on the covariance model. In particular, we give theoretical arguments that as a general tendency, inflation will be reduced by increasing $$\tau$$ τ or by decreasing $$\omega$$ ω .http://link.springer.com/article/10.1186/s12711-018-0386-x
collection DOAJ
language deu
format Article
sources DOAJ
author Johannes W. R. Martini
Matias F. Schrauf
Carolina A. Garcia-Baccino
Eduardo C. G. Pimentel
Sebastian Munilla
Andres Rogberg-Muñoz
Rodolfo J. C. Cantet
Christian Reimer
Ning Gao
Valentin Wimmer
Henner Simianer
spellingShingle Johannes W. R. Martini
Matias F. Schrauf
Carolina A. Garcia-Baccino
Eduardo C. G. Pimentel
Sebastian Munilla
Andres Rogberg-Muñoz
Rodolfo J. C. Cantet
Christian Reimer
Ning Gao
Valentin Wimmer
Henner Simianer
The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
Genetics Selection Evolution
author_facet Johannes W. R. Martini
Matias F. Schrauf
Carolina A. Garcia-Baccino
Eduardo C. G. Pimentel
Sebastian Munilla
Andres Rogberg-Muñoz
Rodolfo J. C. Cantet
Christian Reimer
Ning Gao
Valentin Wimmer
Henner Simianer
author_sort Johannes W. R. Martini
title The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
title_short The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
title_full The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
title_fullStr The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
title_full_unstemmed The effect of the H −1 scaling factors τ and ω on the structure of H in the single-step procedure
title_sort effect of the h −1 scaling factors τ and ω on the structure of h in the single-step procedure
publisher BMC
series Genetics Selection Evolution
issn 1297-9686
publishDate 2018-04-01
description Abstract Background The single-step covariance matrix H combines the pedigree-based relationship matrix $${\mathbf {A}}$$ A with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix $${\mathbf {G}}$$ G . In particular, to improve convergence behavior of iterative approaches and to reduce inflation, two weights $$\tau$$ τ and $$\omega$$ ω have been introduced in the definition of $${\mathbf {H}}^{-1}$$ H-1 , which blend the inverse of a part of $${\mathbf {A}}$$ A with the inverse of $${\mathbf {G}}$$ G . Since the definition of this blending is based on the equation describing $${\mathbf {H}}^{-1}$$ H-1 , its impact on the structure of $${\mathbf {H}}$$ H is not obvious. In a joint discussion, we considered the question of the shape of $${\mathbf {H}}$$ H for non-trivial $$\tau$$ τ and $$\omega$$ ω . Results Here, we present the general matrix $${\mathbf {H}}$$ H as a function of these parameters and discuss its structure and properties. Moreover, we screen for optimal values of $$\tau$$ τ and $$\omega$$ ω with respect to predictive ability, inflation and iterations up to convergence on a well investigated, publicly available wheat data set. Conclusion Our results may help the reader to develop a better understanding for the effects of changes of $$\tau$$ τ and $$\omega$$ ω on the covariance model. In particular, we give theoretical arguments that as a general tendency, inflation will be reduced by increasing $$\tau$$ τ or by decreasing $$\omega$$ ω .
url http://link.springer.com/article/10.1186/s12711-018-0386-x
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