A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ...
Funder: Victorian Government’s Operational Infrastructure Support Program ... : Abstract: Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31‐year follow‐up of the Nort...
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Online Access: | https://dx.doi.org/10.17863/cam.69620 https://www.repository.cam.ac.uk/handle/1810/322161 |
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ftdatacite:10.17863/cam.69620 2023-12-03T10:27:46+01:00 A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... Bottolo, Leonardo Banterle, Marco Richardson, Sylvia Ala‐Korpela, Mika Järvelin, Marjo‐Riitta Lewin, Alex 2021 https://dx.doi.org/10.17863/cam.69620 https://www.repository.cam.ac.uk/handle/1810/322161 unknown Apollo - University of Cambridge Repository ORIGINAL ARTICLE ORIGINAL ARTICLES Bayesian computation covariance reparametrisation graphical models Markov chain Monte Carlo metabolomics quantitative trait loci ScholarlyArticle article-journal Article JournalArticle 2021 ftdatacite https://doi.org/10.17863/cam.69620 2023-11-03T10:27:20Z Funder: Victorian Government’s Operational Infrastructure Support Program ... : Abstract: Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31‐year follow‐up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high‐throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional data, with cell‐sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies ... Article in Journal/Newspaper Northern Finland DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ORIGINAL ARTICLE ORIGINAL ARTICLES Bayesian computation covariance reparametrisation graphical models Markov chain Monte Carlo metabolomics quantitative trait loci |
spellingShingle |
ORIGINAL ARTICLE ORIGINAL ARTICLES Bayesian computation covariance reparametrisation graphical models Markov chain Monte Carlo metabolomics quantitative trait loci Bottolo, Leonardo Banterle, Marco Richardson, Sylvia Ala‐Korpela, Mika Järvelin, Marjo‐Riitta Lewin, Alex A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
topic_facet |
ORIGINAL ARTICLE ORIGINAL ARTICLES Bayesian computation covariance reparametrisation graphical models Markov chain Monte Carlo metabolomics quantitative trait loci |
description |
Funder: Victorian Government’s Operational Infrastructure Support Program ... : Abstract: Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31‐year follow‐up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high‐throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional data, with cell‐sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies ... |
format |
Article in Journal/Newspaper |
author |
Bottolo, Leonardo Banterle, Marco Richardson, Sylvia Ala‐Korpela, Mika Järvelin, Marjo‐Riitta Lewin, Alex |
author_facet |
Bottolo, Leonardo Banterle, Marco Richardson, Sylvia Ala‐Korpela, Mika Järvelin, Marjo‐Riitta Lewin, Alex |
author_sort |
Bottolo, Leonardo |
title |
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
title_short |
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
title_full |
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
title_fullStr |
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
title_full_unstemmed |
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
title_sort |
computationally efficient bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery ... |
publisher |
Apollo - University of Cambridge Repository |
publishDate |
2021 |
url |
https://dx.doi.org/10.17863/cam.69620 https://www.repository.cam.ac.uk/handle/1810/322161 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_doi |
https://doi.org/10.17863/cam.69620 |
_version_ |
1784277659520335872 |