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Why this paper? Cool stuff Potentially far-reaching consequences Of relevance to our own work. Take-home message. Novel and important science can be done using data that are in the public domain. Horvath paper: data. 7844 non-cancer samples from 82 datasets
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Why this paper? Cool stuff Potentially far-reaching consequences Of relevance to our own work
Take-home message Novel and important science can be done using data that are in the public domain
Horvath paper: data • 7844 non-cancer samples from 82 datasets • Illumina 27k or 450k methylation arrays • 51 tissues / cell types • Also cancer Genome Atlas (TCGA) datasets • 21369 CpGs (probes) studied • overlap of the two arrays
Statistical method • chronological age = CpG + noise • Training set of 39 datasets, rest Test • Penalised regression model (‘elastic net’) • 353 CpG selected in the model = ‘epigenetic clock’ • Predictor = DNAm age
Measuring predictive accuracy • Age correlation • r(age, DNAm age) • Median error • median |DNAm age – chronological age| • Age acceleration • {DNAmage – chronological age}
A multi-tissue age prediction works remarkably well (test data)
Conclusions • There is an epigenetic clock • ~similar across healthy tissues • reset each generation and reset in iPS/ES cells • heritable • evolutionary conserved • Predictions of chronological age are reasonably accurate
Discussion • Is DNAm Age a marker or effector of ageing? • What is DNAm Age a biomarker for? • Applications • Genetic studies • heritability, GWAS • Association • disease (dementia?) • gene expression? • Forensics