Increasing evidence has pointed to the interactions between genetics, epigenetics and environmental factors in the aging process. Contact: Data Access Committee for National Human Genome Research Institute ( PK: CDC/NIOSH award U01 OH011478 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.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. The authors had no special access privileges and other researchers will be able to access the data in the same manner as the authors. These data are available at dbGaP ( ) for researchers who meet the criteria for access to confidential data. The GTEx chronological age and self-reported race cannot be shared publicly because they are GTEx protected data. These datasets can also be downloaded from under the TCGA tab. TCGA clinical, DNA methylation and RNA-Seq data are publicly available at. This dataset can also be downloaded from under the GTEx tab. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: GTEx RNA-Seq data is publicly available at under GTEx Analysis V6. Received: Accepted: JPublished: August 4, 2020Ĭopyright: © 2020 Ren, Kuan. University of North Carolina at Chapel Hill, UNITED STATES RNAAgeCalc is available at, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.Ĭitation: Ren X, Kuan PF (2020) RNAAgeCalc: A multi-tissue transcriptional age calculator. 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.
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