Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784631942864437248 |
---|---|
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). |
format | Online Article Text |
id | pubmed-8752755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT mccawzacharyr deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT colthurstthomas deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT yuntaedong deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT furlottenicholasa deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT carrollandrew deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT alipanahibabak deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT mcleancoryy deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower AT hormozdiarifarhad deepnullmodelsnonlinearcovariateeffectstoimprovephenotypicpredictionandassociationpower |