Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials
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 of multiple traits of interest. Direct appl...
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ftpubmed:oai:pubmedcentral.nih.gov:2911798 2023-05-15T17:44:50+02:00 Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials Banerjee, Sudipto Finley, Andrew O. Waldmann, Patrik Ericsson, Tore 2010-06-01 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911798 http://www.ncbi.nlm.nih.gov/pubmed/20676229 https://doi.org/10.1198/jasa.2009.ap09068 en eng http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911798 http://www.ncbi.nlm.nih.gov/pubmed/20676229 http://dx.doi.org/10.1198/jasa.2009.ap09068 Article Text 2010 ftpubmed https://doi.org/10.1198/jasa.2009.ap09068 2013-09-03T03:07:20Z 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 of multiple traits of interest. Direct application of such multivariate models to large spatial datasets is often 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 thousand 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 negates the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects we discuss a multivariate predictive process that reduces the computational burden by projecting the original process onto a subspace generated by realizations of the original process at a specified set of locations (or knots). We illustrate the proposed methods using a synthetic dataset with multivariate additive and dominant 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. Text Northern Sweden PubMed Central (PMC) Journal of the American Statistical Association 105 490 506 521 |
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Article Banerjee, Sudipto Finley, Andrew O. Waldmann, Patrik Ericsson, Tore Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
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Article |
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 of multiple traits of interest. Direct application of such multivariate models to large spatial datasets is often 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 thousand 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 negates the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects we discuss a multivariate predictive process that reduces the computational burden by projecting the original process onto a subspace generated by realizations of the original process at a specified set of locations (or knots). We illustrate the proposed methods using a synthetic dataset with multivariate additive and dominant 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 |
Text |
author |
Banerjee, Sudipto Finley, Andrew O. Waldmann, Patrik Ericsson, Tore |
author_facet |
Banerjee, Sudipto Finley, Andrew O. Waldmann, Patrik Ericsson, Tore |
author_sort |
Banerjee, Sudipto |
title |
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
title_short |
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
title_full |
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
title_fullStr |
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
title_full_unstemmed |
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials |
title_sort |
hierarchical spatial process models for multiple traits in large genetic trials |
publishDate |
2010 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911798 http://www.ncbi.nlm.nih.gov/pubmed/20676229 https://doi.org/10.1198/jasa.2009.ap09068 |
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Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911798 http://www.ncbi.nlm.nih.gov/pubmed/20676229 http://dx.doi.org/10.1198/jasa.2009.ap09068 |
op_doi |
https://doi.org/10.1198/jasa.2009.ap09068 |
container_title |
Journal of the American Statistical Association |
container_volume |
105 |
container_issue |
490 |
container_start_page |
506 |
op_container_end_page |
521 |
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1766147131998994432 |