Research
Our work sits at the intersection of computational biology, biostatistics, and human genetics, applied to a single question: how do we measure aging well enough to change it?
Epigenetic clocks
DNA methylation changes with age in a remarkably regular way. Epigenetic clocks are mathematical models that read those changes and estimate biological age, in humans across tissues and, since 2023, across all mammalian species.
Penalized regression and related estimators trained on CpG methylation to predict chronological age, mortality, and healthspan; multi-tissue human clocks, second-generation clocks (PhenoAge, GrimAge), and the universal pan-mammalian clock built on conserved CpGs.
Key papers: DNA methylation age of human tissues and cell types · Universal DNA methylation age across mammalian tissues
Biomarkers of aging
A clock is only useful if it predicts something that matters. We build and validate biomarkers that track healthspan and mortality risk in large cohorts, and we study what they do and do not tell us about the underlying biology.
Second- and third-generation DNA methylation biomarkers trained on clinical and mortality outcomes; validation in independent cohorts; causal anchoring of surrogate biomarkers; comparison with proteomic, transcriptomic, and clinical measures.
Key papers: DNA methylation GrimAge strongly predicts lifespan and healthspan · An epigenetic biomarker of aging for lifespan and healthspan
Epigenetic rejuvenation
If the epigenome records age, can it be reset? We use clocks to test whether reprogramming and small-molecule interventions genuinely move biological age, and we hold those tests to a high evidential bar.
Clock-based readouts for partial reprogramming and pharmacological interventions in cells, animals, and human trials; distinguishing rejuvenation from selection, dedifferentiation, and measurement artifact.
Methods and software
The field can only measure biological age reproducibly if the tools are open. We publish the statistical methods and software behind our clocks so that other laboratories can apply, test, and improve them.
Open-source estimators, normalization pipelines, and array designs, including the mammalian methylation array; reproducible analysis code accompanying publications.