Telomere length measurement for longitudinal analysis: implications of assay precision.

Researchers increasingly wish to test hypotheses concerning the impact of environmental or disease exposures on telomere length (TL), and use longitudinal study designs to do so. In population studies, TL is usually measured using a quantitative polymerase chain reaction (qPCR)-based method. This method has been validated by presenting a correlation with a gold standard method such as Southern blotting (SB) in cross-sectional datasets. However, in a cross-section, the range of true variation in TL is large, and measurement error is introduced only once. In a longitudinal study, the target variation of interest is small, and measurement error is introduced both at baseline and follow-up. We present a small dataset (n = 20) where leukocyte TL was measured 6.6 years apart by both qPCR and SB. The cross-sectional correlations between qPCR and SB were high both at baseline (r = 0.90) and follow-up (r = 0.85), yet their correlation for TL change was poor (r = 0.48). Moreover, the qPCR but not SB data showed strong signatures of measurement error. Through simulation, we show that the statistical power gain from performing a longitudinal analysis is much greater for SB than qPCR. We discuss implications for optimal study design and analysis.