ArticleBMC genomics2021
Correction for both common and rare cell types in blood is important to identify genes that correlate with age.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Whole blood transcriptional signatures of age and survival identified in long life family and integrative longevity omics studies.GeroScience · 2026Article
- Naïve T-cell decline is a significant contributor to expression changes in ageing blood.Frontiers in aging · 2024Article
- The landscape of expression and alternative splicing variation across human traits.Cell genomics · 2023Article
- Quantifying the role of transcript levels in mediating DNA methylation effects on complex traits and diseases.Nature communications · 2022Article
- Genetic, parental and lifestyle factors influence telomere length.Communications biology · 2022Article
- Regulation of the mitochondrial permeability transition pore and its effects on aging.Microbial cell (Graz, Austria) · 2020Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAging 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.
resultsHere, 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
conclusionsWe 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.
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