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A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer’s disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to...

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Autores principales: Duan, Jiefang, Zhang, Jiayu, Liu, Long, Wen, Yalu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581223/
https://www.ncbi.nlm.nih.gov/pubmed/36276959
http://dx.doi.org/10.3389/fgene.2022.1017380
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author Duan, Jiefang
Zhang, Jiayu
Liu, Long
Wen, Yalu
author_facet Duan, Jiefang
Zhang, Jiayu
Liu, Long
Wen, Yalu
author_sort Duan, Jiefang
collection PubMed
description Brain imaging outcomes are important for Alzheimer’s disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer’s Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.
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spelling pubmed-95812232022-10-20 A guidance of model selection for genomic prediction based on linear mixed models for complex traits Duan, Jiefang Zhang, Jiayu Liu, Long Wen, Yalu Front Genet Genetics Brain imaging outcomes are important for Alzheimer’s disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer’s Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9581223/ /pubmed/36276959 http://dx.doi.org/10.3389/fgene.2022.1017380 Text en Copyright © 2022 Duan, Zhang, Liu and Wen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Duan, Jiefang
Zhang, Jiayu
Liu, Long
Wen, Yalu
A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title_full A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title_fullStr A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title_full_unstemmed A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title_short A guidance of model selection for genomic prediction based on linear mixed models for complex traits
title_sort guidance of model selection for genomic prediction based on linear mixed models for complex traits
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581223/
https://www.ncbi.nlm.nih.gov/pubmed/36276959
http://dx.doi.org/10.3389/fgene.2022.1017380
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