Some recent findings
Refining epigenetic prediction of chronological and biological age. Genome Medicine, 2023.
Building the most accurate DNAm predictors of chronological age to date (median error of 1.7 years in a test set). We also generate an updated version of GrimAge by incorporating more protein EpiScores in our training model.
Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals. PLoS Medicine, 2023
Using eHR data from primary and secondary care records, we run EWASs of 19 common disease outcomes (both prevalent and incident analyses). We then compare our results with those reported in the literature.
Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes. Nature Aging, 2023
We build DNAm predictors of incident type 2 diabetes (ascertained via eHR linkage in Generation Scotland) and test in an independent subset of GS plus the German KORA cohort.
Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology, 2022.
We test if brain health can be estimated by blood-based DNA methylation signatures. This could impact studies of cognitive function across the lifespan where pen-and-paper testing is not possible or is unreliable (e.g., neurodevelopment or neurodegeneration).
Building the most accurate DNAm predictors of chronological age to date (median error of 1.7 years in a test set). We also generate an updated version of GrimAge by incorporating more protein EpiScores in our training model.
Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals. PLoS Medicine, 2023
Using eHR data from primary and secondary care records, we run EWASs of 19 common disease outcomes (both prevalent and incident analyses). We then compare our results with those reported in the literature.
Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes. Nature Aging, 2023
We build DNAm predictors of incident type 2 diabetes (ascertained via eHR linkage in Generation Scotland) and test in an independent subset of GS plus the German KORA cohort.
Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology, 2022.
We test if brain health can be estimated by blood-based DNA methylation signatures. This could impact studies of cognitive function across the lifespan where pen-and-paper testing is not possible or is unreliable (e.g., neurodevelopment or neurodegeneration).
Epigenetic scores for the circulating proteome as tools for disease prediction. ELife, 2022
We show that blood-based DNA methylation can be used to approximate levels of 109 proteins. These epigenetic scores predict the incidence of multiple diseases, up to 14 years prior to diagnosis. Click here to watch Danni's summary of the paper.
MethylDetectR: a software for methylation-based health profiling. Wellcome Open Research, 2021.
A ShinyApp to project and visualise epigenetic scores (EpiScores) for complex traits in your own cohort. Click here to watch Rob's summary of the paper.
Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biology, 2021.
A multi-ancestry GWAS meta-analysis of 4 epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge) in >40,000 individuals.
We show that blood-based DNA methylation can be used to approximate levels of 109 proteins. These epigenetic scores predict the incidence of multiple diseases, up to 14 years prior to diagnosis. Click here to watch Danni's summary of the paper.
MethylDetectR: a software for methylation-based health profiling. Wellcome Open Research, 2021.
A ShinyApp to project and visualise epigenetic scores (EpiScores) for complex traits in your own cohort. Click here to watch Rob's summary of the paper.
Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biology, 2021.
A multi-ancestry GWAS meta-analysis of 4 epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge) in >40,000 individuals.
