Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets
This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.150.1460 2023-05-15T17:44:46+02:00 Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets Andrew O. Finley Sudipto Banerjee Patrik Waldmann Tore Ericsson The Pennsylvania State University CiteSeerX Archives 2009 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.1460 http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.1460 http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf text 2009 ftciteseerx 2016-01-07T15:18:47Z This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein–Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large Text Northern Sweden Unknown |
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English |
description |
This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein–Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Andrew O. Finley Sudipto Banerjee Patrik Waldmann Tore Ericsson |
spellingShingle |
Andrew O. Finley Sudipto Banerjee Patrik Waldmann Tore Ericsson Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
author_facet |
Andrew O. Finley Sudipto Banerjee Patrik Waldmann Tore Ericsson |
author_sort |
Andrew O. Finley |
title |
Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
title_short |
Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
title_full |
Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
title_fullStr |
Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
title_full_unstemmed |
Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
title_sort |
hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets |
publishDate |
2009 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.1460 http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.1460 http://www.biostat.umn.edu/~sudiptob/ResearchPapers/FBWE.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766147052912246784 |