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DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce D...

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Autores principales: McCaw, Zachary R., Colthurst, Thomas, Yun, Taedong, Furlotte, Nicholas A., Carroll, Andrew, Alipanahi, Babak, McLean, Cory Y., Hormozdiari, Farhad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752755/
https://www.ncbi.nlm.nih.gov/pubmed/35017556
http://dx.doi.org/10.1038/s41467-021-27930-0
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author McCaw, Zachary R.
Colthurst, Thomas
Yun, Taedong
Furlotte, Nicholas A.
Carroll, Andrew
Alipanahi, Babak
McLean, Cory Y.
Hormozdiari, Farhad
author_facet McCaw, Zachary R.
Colthurst, Thomas
Yun, Taedong
Furlotte, Nicholas A.
Carroll, Andrew
Alipanahi, Babak
McLean, Cory Y.
Hormozdiari, Farhad
author_sort McCaw, Zachary R.
collection PubMed
description Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).
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spelling pubmed-87527552022-01-20 DeepNull models non-linear covariate effects to improve phenotypic prediction and association power McCaw, Zachary R. Colthurst, Thomas Yun, Taedong Furlotte, Nicholas A. Carroll, Andrew Alipanahi, Babak McLean, Cory Y. Hormozdiari, Farhad Nat Commun Article Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average). Nature Publishing Group UK 2022-01-11 /pmc/articles/PMC8752755/ /pubmed/35017556 http://dx.doi.org/10.1038/s41467-021-27930-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
McCaw, Zachary R.
Colthurst, Thomas
Yun, Taedong
Furlotte, Nicholas A.
Carroll, Andrew
Alipanahi, Babak
McLean, Cory Y.
Hormozdiari, Farhad
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title_full DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title_fullStr DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title_full_unstemmed DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title_short DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
title_sort deepnull models non-linear covariate effects to improve phenotypic prediction and association power
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752755/
https://www.ncbi.nlm.nih.gov/pubmed/35017556
http://dx.doi.org/10.1038/s41467-021-27930-0
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