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Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data
MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed signific...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627352/ https://www.ncbi.nlm.nih.gov/pubmed/37882747 http://dx.doi.org/10.1093/bioinformatics/btad647 |
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author | Hai, Yang Ma, Jixiang Yang, Kaixin Wen, Yalu |
author_facet | Hai, Yang Ma, Jixiang Yang, Kaixin Wen, Yalu |
author_sort | Hai, Yang |
collection | PubMed |
description | MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer’s Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM). |
format | Online Article Text |
id | pubmed-10627352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106273522023-11-07 Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data Hai, Yang Ma, Jixiang Yang, Kaixin Wen, Yalu Bioinformatics Original Paper MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer’s Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM). Oxford University Press 2023-10-26 /pmc/articles/PMC10627352/ /pubmed/37882747 http://dx.doi.org/10.1093/bioinformatics/btad647 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Hai, Yang Ma, Jixiang Yang, Kaixin Wen, Yalu Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title | Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title_full | Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title_fullStr | Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title_full_unstemmed | Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title_short | Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
title_sort | bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627352/ https://www.ncbi.nlm.nih.gov/pubmed/37882747 http://dx.doi.org/10.1093/bioinformatics/btad647 |
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