In addition to DNA methylation, changes in gene expression have been shown to be associated with aging and aging-related outcomes.
This includes PhenoAge and GrimAge which aim to improve prediction of aging related outcomes (e.g., time-to-death, time-to-disease for cancer, Alzheimer’s disease and cardiovascular). While the first generation DNA methylation age estimators including Horvath’s clock and Hannum’s clock were developed based on chronological age, the second generation DNA methylation age estimators were obtained by optimizing the prediction error on phenotypic age derived from clinical attributes associated with mortality and morbidity. On the other hand, Hannum et al developed a 71-CpGs DNA methylation age calculator based on the whole blood of 656 human samples aged 19 to 101. The predictor was constructed using > 8,000 non-cancer samples from 82 DNA methylation datasets, including 51 healthy tissues and cell types based on elastic net model, a penalized regression statistical framework which retained 353 CpGs in the final model. Specifically, Horvath derived a multi-tissue and cell types DNA methylation age (DNAm age) estimator across the entire lifespan of human. Among the most widely used epigenetic age estimator are the DNA methylation age calculator of Horvath and Hannum et al, by regressing chronological age on DNA methylation.
Numerous estimators have been developed to predict human aging from DNA methylation data profiled on the Illumina Infinium HumanMethylation450K BeadChip. As aging is a multifactorial process determined by the dynamic nature of static genetics as well as stochastic epigenetic variation and transcriptomics regulation, both DNA methylation and gene expression have emerged as promising hallmark for understanding the aging process and its associated diseases. For example, the genome-wide association studies (GWAS) have identified genetic loci associated with longevity and several aging-related diseases. Over the last decade, there has been a growing body of research in identifying genetic and epigenetic biomarkers of aging to decipher the molecular mechanisms underpinning disease susceptibility. Increasing evidence has pointed to the interactions between genetics, epigenetics and environmental factors in the aging process. RNAAgeCalc is available at, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.Īging is among the most complex phenotype and is a well-known risk factor for a myriad of diseases including cardiovascular, diabetes, arthritis, neurodegeneration and cancer. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. Our results also indicate that both racial and tissue differences are associated with transcriptional age. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Based on these genes, we develop new across-tissue and tissue-specific age predictors. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality.
0 kommentar(er)
