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Unveiling the Epigenetic Clock: Age Prediction in 51 Tissues

Dive into the Horvath paper on epigenetic clocks using public data, exploring age prediction in various tissues and potential applications in genetics, disease studies, and forensics. Discover the far-reaching impact and implications of this remarkable research.

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Unveiling the Epigenetic Clock: Age Prediction in 51 Tissues

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  1. Why this paper? Cool stuff Potentially far-reaching consequences Of relevance to our own work

  2. Take-home message Novel and important science can be done using data that are in the public domain

  3. 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

  4. 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

  5. Measuring predictive accuracy • Age correlation • r(age, DNAm age) • Median error • median |DNAm age – chronological age| • Age acceleration • {DNAmage – chronological age}

  6. A multi-tissue age prediction works remarkably well

  7. A multi-tissue age prediction works remarkably well (test data)

  8. Epigenetic clock is reset in iPS and ES cells

  9. The clock tick rate is non-linear: fast until adulthood

  10. Age-acceleration is heritable!

  11. In cancers: more somatic mutations = less age acceleration

  12. 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

  13. 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

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