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Correction for both common and rare cell types in blood is important to identify genes that correlate with age
BACKGROUND: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechan...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958454/ https://www.ncbi.nlm.nih.gov/pubmed/33722199 http://dx.doi.org/10.1186/s12864-020-07344-w |
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author | Pellegrino-Coppola, Damiano Claringbould, Annique Stutvoet, Maartje Boomsma, Dorret I. Ikram, M. Arfan Slagboom, P. Eline Westra, Harm-Jan Franke, Lude |
author_facet | Pellegrino-Coppola, Damiano Claringbould, Annique Stutvoet, Maartje Boomsma, Dorret I. Ikram, M. Arfan Slagboom, P. Eline Westra, Harm-Jan Franke, Lude |
author_sort | Pellegrino-Coppola, Damiano |
collection | PubMed |
description | BACKGROUND: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. RESULTS: Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18–81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10(−6)). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. CONCLUSIONS: We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-020-07344-w. |
format | Online Article Text |
id | pubmed-7958454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79584542021-03-16 Correction for both common and rare cell types in blood is important to identify genes that correlate with age Pellegrino-Coppola, Damiano Claringbould, Annique Stutvoet, Maartje Boomsma, Dorret I. Ikram, M. Arfan Slagboom, P. Eline Westra, Harm-Jan Franke, Lude BMC Genomics Research Article BACKGROUND: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. RESULTS: Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18–81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10(−6)). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. CONCLUSIONS: We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-020-07344-w. BioMed Central 2021-03-15 /pmc/articles/PMC7958454/ /pubmed/33722199 http://dx.doi.org/10.1186/s12864-020-07344-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Pellegrino-Coppola, Damiano Claringbould, Annique Stutvoet, Maartje Boomsma, Dorret I. Ikram, M. Arfan Slagboom, P. Eline Westra, Harm-Jan Franke, Lude Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title | Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title_full | Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title_fullStr | Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title_full_unstemmed | Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title_short | Correction for both common and rare cell types in blood is important to identify genes that correlate with age |
title_sort | correction for both common and rare cell types in blood is important to identify genes that correlate with age |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958454/ https://www.ncbi.nlm.nih.gov/pubmed/33722199 http://dx.doi.org/10.1186/s12864-020-07344-w |
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