New to telomere research? Check out the New Investigator Handbook, which provides an introduction to telomere research and methodologies of measuring telomere length!
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)
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 (email@example.com).
Determining sample size in longitudinal studies: Power 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, https://doi.org/10.1371/journal.pone.0245582
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