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Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits

As popularised by PrediXcan (and related methods), transcriptome‐wide association studies (TWAS), in which gene expression is imputed from single‐nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression...

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Autores principales: Fryett, James J., Morris, Andrew P., Cordell, Heather J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9804820/
https://www.ncbi.nlm.nih.gov/pubmed/35930604
http://dx.doi.org/10.1002/gepi.22496
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author Fryett, James J.
Morris, Andrew P.
Cordell, Heather J.
author_facet Fryett, James J.
Morris, Andrew P.
Cordell, Heather J.
author_sort Fryett, James J.
collection PubMed
description As popularised by PrediXcan (and related methods), transcriptome‐wide association studies (TWAS), in which gene expression is imputed from single‐nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome‐wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.
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spelling pubmed-98048202023-01-06 Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits Fryett, James J. Morris, Andrew P. Cordell, Heather J. Genet Epidemiol Research Articles As popularised by PrediXcan (and related methods), transcriptome‐wide association studies (TWAS), in which gene expression is imputed from single‐nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome‐wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits. John Wiley and Sons Inc. 2022-08-05 2022-12 /pmc/articles/PMC9804820/ /pubmed/35930604 http://dx.doi.org/10.1002/gepi.22496 Text en © 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Fryett, James J.
Morris, Andrew P.
Cordell, Heather J.
Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title_full Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title_fullStr Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title_full_unstemmed Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title_short Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits
title_sort investigating the prediction of cpg methylation levels from snp genotype data to help elucidate relationships between methylation, gene expression and complex traits
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9804820/
https://www.ncbi.nlm.nih.gov/pubmed/35930604
http://dx.doi.org/10.1002/gepi.22496
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