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Inferring physiological age

Inferring physiological age. David Knowles Leo Parts Dan Glass John Winn. Setting the scene. TwinsUK cohort, based at Department of Twin Research, King’s College London. 12k female Caucasian twins across the UK Rich clinical data, measured on multiple visits over 15 years

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Inferring physiological age

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  1. Inferring physiological age David Knowles Leo Parts Dan Glass John Winn

  2. Setting the scene • TwinsUK cohort, based at Department of Twin Research, King’s College London. • 12k female Caucasian twins across the UK • Rich clinical data, measured on multiple visits over 15 years • Multiple Tissue Human Expression Resource (MuTHER): gene expression microarrays for three tissues in around 900 well phenotyped individuals (Wellcome Trust funded) • SNP, DNA sequence, methylation, small RNA data also available

  3. Global systemic ageing We derive a measure of global ageing based on:

  4. Linear model • : phenotype d for individual n • : intercept for phenotype d • : regression coefficient for phenotype d • : chronological age of individual n • : additional ageing of individual n (common across all phenotypes) • : Gaussian noise with standard deviation Jointly fit , , and using VB in Infer.NET Use logistic link for binary variables

  5. This 55 year old individual has the nuclear dip of a 79 year old =24 55 79 Expected value for this age

  6. The same 55 year old individual has the telomeres of a 72 year old =17 Expected value for this age 72 55

  7. Non-linear model • : phenotype d for individual n • : intercept for phenotype d in mixture component z • : regression coefficient for phenotype din mixture component z • : cluster assignment indictor for individual n • : chronological age of individual n • : additional ageing of individual n

  8. Genes correlated with ageing • Linear mixed effects model • [expression] = W x [age] + B x [confounders] + M x [family ID] Fixed effects Random effects [FDR=0.05]

  9. Future work • Multidimensional ageing: a different physiological age for • Explicit time series modelling: use longitudinal data fully. • “Three tier model”: associations with gene expression and genotype variation

  10. Held out phenotype test

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