New to telomere research? Check out the New Investigator Handbook, which provides an introduction to telomere research and methodologies of measuring telomere length!

TRN Recommendations

The Telomere Research Network (TRN) is a collaborative effort between telomere researchers and the NIH to establish best practices and methodologic guidelines for population-based studies of telomere length in relation to psychosocial and environmental exposures and a predictor of later health outcomes. As this multi-year effort conducts methodologically rigorous cross laboratory and cross method comparison studies, we expect to provide data-driven recommendations, as well as up-dates, as needed, to existing recommendations. These cross methodologic studies, and these recommendations are designed to enhance the reproducibility and rigor of the field.

Recommendation 1 – Key considerations for assay precision (Dec. 1, 2021)

Recommendation 2 – Key considerations for DNA extraction (Dec. 1, 2021)


Resources for Study Design & Analysis

Sample Collection & Storage Checklist (v.1) – guidance for key metrics to track related to sample collection, storage, and processing

Determining sample size in cross-sectional studies: The effect of intra-class correlation coefficient (ICC) on statistical power and required sample size based on various diference in TL. For questions/comments on this resource, please contact Simon Verhult ( 

Determining sample size in longitudinal studiesPower to detect a 33% change of telomere shortening rate, up or down, with p<0.05 relative to a baseline shortening rate of 25 bp/year, for a four-year and eight-year follow-up period.

To cite these resources, please reference Lindrose et al., 2020,

Telomere length measurement for longitudinal analysis: implications of assay precision: Nettle, et al. in press, American Journal of Epidemiology

Calculating repeatability of TL measures using ICC – instructions and script using R. exampledataset

Critical assay factors and recommendations for telomere length measurement by qPCR: Lin, et al. 2019 

How to design a pre-specified statistical analysis approach to limit p-hacking in clinical trials: Kahan, et al. 2020