Predicting Age from the Transcriptome of Human Dermal Fibroblasts

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2018 Dec 21

PMID 30567591

Citations 95

Authors

Jason G Fleischer

Roberta Schulte

Hsiao H Tsai

Swati Tyagi

Arkaitz Ibarra

Maxim N Shokhirev

Ling Huang

Martin W Hetzer

Saket Navlakha

Affiliations

Soon will be listed here.

Abstract

Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

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