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Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation
Sex is a modulator of health that has been historically overlooked in biomedical research. Recognizing this knowledge gap, funding agencies now mandate the inclusion of sex as a biological variable with the goal of stimulating efforts to illuminate the molecular underpinnings of sex biases in health...
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/PMC8450084/ https://www.ncbi.nlm.nih.gov/pubmed/34403484 http://dx.doi.org/10.1093/nar/gkab682 |
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author | Gatev, Evan Inkster, Amy M Negri, Gian Luca Konwar, Chaini Lussier, Alexandre A Skakkebaek, Anne Sokolowski, Marla B Gravholt, Claus H Dunn, Erin C Kobor, Michael S Aristizabal, Maria J |
author_facet | Gatev, Evan Inkster, Amy M Negri, Gian Luca Konwar, Chaini Lussier, Alexandre A Skakkebaek, Anne Sokolowski, Marla B Gravholt, Claus H Dunn, Erin C Kobor, Michael S Aristizabal, Maria J |
author_sort | Gatev, Evan |
collection | PubMed |
description | Sex is a modulator of health that has been historically overlooked in biomedical research. Recognizing this knowledge gap, funding agencies now mandate the inclusion of sex as a biological variable with the goal of stimulating efforts to illuminate the molecular underpinnings of sex biases in health and disease. DNA methylation (DNAm) is a strong molecular candidate for mediating such sex biases; however, a robust and well characterized annotation of sex differences in DNAm is yet to emerge. Beginning with a large (n = 3795) dataset of DNAm profiles from normative adult whole blood samples, we identified, validated and characterized autosomal sex-associated co-methylated genomic regions (sCMRs). Strikingly, sCMRs showed consistent sex differences in DNAm over the life course and a subset were also consistent across cell, tissue and cancer types. sCMRs included sites with known sex differences in DNAm and links to health conditions with sex biased effects. The robustness of sCMRs enabled the generation of an autosomal DNAm-based predictor of sex with 96% accuracy. Testing this tool on blood DNAm profiles from individuals with sex chromosome aneuploidies (Klinefelter [47,XXY], Turner [45,X] and 47,XXX syndrome) revealed an intimate relationship between sex chromosomes and sex-biased autosomal DNAm. |
format | Online Article Text |
id | pubmed-8450084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84500842021-09-20 Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation Gatev, Evan Inkster, Amy M Negri, Gian Luca Konwar, Chaini Lussier, Alexandre A Skakkebaek, Anne Sokolowski, Marla B Gravholt, Claus H Dunn, Erin C Kobor, Michael S Aristizabal, Maria J Nucleic Acids Res Data Resources and Analyses Sex is a modulator of health that has been historically overlooked in biomedical research. Recognizing this knowledge gap, funding agencies now mandate the inclusion of sex as a biological variable with the goal of stimulating efforts to illuminate the molecular underpinnings of sex biases in health and disease. DNA methylation (DNAm) is a strong molecular candidate for mediating such sex biases; however, a robust and well characterized annotation of sex differences in DNAm is yet to emerge. Beginning with a large (n = 3795) dataset of DNAm profiles from normative adult whole blood samples, we identified, validated and characterized autosomal sex-associated co-methylated genomic regions (sCMRs). Strikingly, sCMRs showed consistent sex differences in DNAm over the life course and a subset were also consistent across cell, tissue and cancer types. sCMRs included sites with known sex differences in DNAm and links to health conditions with sex biased effects. The robustness of sCMRs enabled the generation of an autosomal DNAm-based predictor of sex with 96% accuracy. Testing this tool on blood DNAm profiles from individuals with sex chromosome aneuploidies (Klinefelter [47,XXY], Turner [45,X] and 47,XXX syndrome) revealed an intimate relationship between sex chromosomes and sex-biased autosomal DNAm. Oxford University Press 2021-08-17 /pmc/articles/PMC8450084/ /pubmed/34403484 http://dx.doi.org/10.1093/nar/gkab682 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Data Resources and Analyses Gatev, Evan Inkster, Amy M Negri, Gian Luca Konwar, Chaini Lussier, Alexandre A Skakkebaek, Anne Sokolowski, Marla B Gravholt, Claus H Dunn, Erin C Kobor, Michael S Aristizabal, Maria J Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title | Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title_full | Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title_fullStr | Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title_full_unstemmed | Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title_short | Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation |
title_sort | autosomal sex-associated co-methylated regions predict biological sex from dna methylation |
topic | Data Resources and Analyses |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450084/ https://www.ncbi.nlm.nih.gov/pubmed/34403484 http://dx.doi.org/10.1093/nar/gkab682 |
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