Correction for both common and rare cell types in blood is important to identify genes that correlate with age

Journal 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;

Authors: Pellegrino-Coppola, D; Claringbould, A; Stutvoet, M; Boomsma, DI; Ikram, MA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Genomics
• Volume: 22
• Issue: 1
• Pages: 184-184
• Article number: 184
• Publication date: 2021-03-15
• DOI: 10.1186/s12864-020-07344-w
• EISSN: 1471-2164
• ISSN: 1471-2164
• Language: English
• Keywords: DNA microarray; Gene; Computational biology; Biology; Medicine; Gene expression; Gene expression profiling; Bioinformatics; Cell type; False positive paradox; Population; Genetics; Cell