Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets
Summary 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...
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crwiley:10.1111/j.1541-0420.2008.01115.x 2023-12-03T10:27:58+01:00 Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets Finley, Andrew O. Banerjee, Sudipto Waldmann, Patrik Ericsson, Tore 2009 http://dx.doi.org/10.1111/j.1541-0420.2008.01115.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1541-0420.2008.01115.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1541-0420.2008.01115.x en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Biometrics volume 65, issue 2, page 441-451 ISSN 0006-341X 1541-0420 Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability journal-article 2009 crwiley https://doi.org/10.1111/j.1541-0420.2008.01115.x 2023-11-09T13:57:14Z Summary 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 trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability. Article in Journal/Newspaper Northern Sweden Wiley Online Library (via Crossref) Biometrics 65 2 441 451 |
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Wiley Online Library (via Crossref) |
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English |
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Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
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Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability Finley, Andrew O. Banerjee, Sudipto Waldmann, Patrik Ericsson, Tore Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets |
topic_facet |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
description |
Summary 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 trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability. |
format |
Article in Journal/Newspaper |
author |
Finley, Andrew O. Banerjee, Sudipto Waldmann, Patrik Ericsson, Tore |
author_facet |
Finley, Andrew O. Banerjee, Sudipto Waldmann, Patrik Ericsson, Tore |
author_sort |
Finley, Andrew O. |
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 |
publisher |
Wiley |
publishDate |
2009 |
url |
http://dx.doi.org/10.1111/j.1541-0420.2008.01115.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1541-0420.2008.01115.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1541-0420.2008.01115.x |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Biometrics volume 65, issue 2, page 441-451 ISSN 0006-341X 1541-0420 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/j.1541-0420.2008.01115.x |
container_title |
Biometrics |
container_volume |
65 |
container_issue |
2 |
container_start_page |
441 |
op_container_end_page |
451 |
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1784277948864397312 |