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How to make DNA methylome wide association studies more powerful
Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Future Medicine Ltd
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066141/ https://www.ncbi.nlm.nih.gov/pubmed/27052998 http://dx.doi.org/10.2217/epi-2016-0017 |
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author | Lin, Xinyi Barton, Sheila Holbrook, Joanna D |
author_facet | Lin, Xinyi Barton, Sheila Holbrook, Joanna D |
author_sort | Lin, Xinyi |
collection | PubMed |
description | Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered. |
format | Online Article Text |
id | pubmed-5066141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Future Medicine Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-50661412017-08-01 How to make DNA methylome wide association studies more powerful Lin, Xinyi Barton, Sheila Holbrook, Joanna D Epigenomics Review Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered. Future Medicine Ltd 2016-08 2016-04-07 /pmc/articles/PMC5066141/ /pubmed/27052998 http://dx.doi.org/10.2217/epi-2016-0017 Text en © Joanna Holbrook This work is licensed under a Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Review Lin, Xinyi Barton, Sheila Holbrook, Joanna D How to make DNA methylome wide association studies more powerful |
title | How to make DNA methylome wide association studies more powerful |
title_full | How to make DNA methylome wide association studies more powerful |
title_fullStr | How to make DNA methylome wide association studies more powerful |
title_full_unstemmed | How to make DNA methylome wide association studies more powerful |
title_short | How to make DNA methylome wide association studies more powerful |
title_sort | how to make dna methylome wide association studies more powerful |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066141/ https://www.ncbi.nlm.nih.gov/pubmed/27052998 http://dx.doi.org/10.2217/epi-2016-0017 |
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