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Modeling dependency structures in 450k DNA methylation data
MOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependenc...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8796368/ https://www.ncbi.nlm.nih.gov/pubmed/34788815 http://dx.doi.org/10.1093/bioinformatics/btab774 |
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author | Nustad, Haakon E Steinsland, Ingelin Ollikainen, Miina Cazaly, Emma Kaprio, Jaakko Benjamini, Yuval Gervin, Kristina Lyle, Robert |
author_facet | Nustad, Haakon E Steinsland, Ingelin Ollikainen, Miina Cazaly, Emma Kaprio, Jaakko Benjamini, Yuval Gervin, Kristina Lyle, Robert |
author_sort | Nustad, Haakon E |
collection | PubMed |
description | MOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. AVAILABILITY AND IMPLEMENTATION: The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8796368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87963682022-01-31 Modeling dependency structures in 450k DNA methylation data Nustad, Haakon E Steinsland, Ingelin Ollikainen, Miina Cazaly, Emma Kaprio, Jaakko Benjamini, Yuval Gervin, Kristina Lyle, Robert Bioinformatics Original Papers MOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. AVAILABILITY AND IMPLEMENTATION: The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-11-12 /pmc/articles/PMC8796368/ /pubmed/34788815 http://dx.doi.org/10.1093/bioinformatics/btab774 Text en © The Author(s) 2021. 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 Papers Nustad, Haakon E Steinsland, Ingelin Ollikainen, Miina Cazaly, Emma Kaprio, Jaakko Benjamini, Yuval Gervin, Kristina Lyle, Robert Modeling dependency structures in 450k DNA methylation data |
title | Modeling dependency structures in 450k DNA methylation data |
title_full | Modeling dependency structures in 450k DNA methylation data |
title_fullStr | Modeling dependency structures in 450k DNA methylation data |
title_full_unstemmed | Modeling dependency structures in 450k DNA methylation data |
title_short | Modeling dependency structures in 450k DNA methylation data |
title_sort | modeling dependency structures in 450k dna methylation data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8796368/ https://www.ncbi.nlm.nih.gov/pubmed/34788815 http://dx.doi.org/10.1093/bioinformatics/btab774 |
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