Consequences of measurement error in qPCR telomere data: A simulation study

Abstract

AbstractThe qPCR method provides an inexpensive, rapid method for estimating relative average telomere length across a set of biological samples. Like all laboratory methods, it involves some degree of measurement error. The estimation of relative telomere length is done subjecting the actual measurements made (the Cq values for telomere and a control gene) to non-linear transformations and combining them into a ratio. Here, we use computer simulations, supported by mathematical analysis, to explore how errors in measurement affect qPCR estimates of relative telomere length, both in cross-sectional and longitudinal data. We show that errors introduced at the level of Cq values are magnified when the TS ratio is calculated. If the errors at the Cq level are normally distributed and independent of telomere length, those in the TS ratio are positively skewed and proportional to telomere length. The repeatability of the TS ratio declines abruptly with increasing error in measurement of the telomere sequence and/or the control gene. In simulated longitudinal data, measurement error alone can produce a pattern of low correlation between successive measures of telomere length, coupled with a strong dependency of the rate of change on initial telomere length. Our results illustrate the importance of control of measurement error: a small increase in error in Cq values can have large consequences for the power and interpretability of qPCR estimates of relative telomere length. They also illustrate the importance of characterising the measurement error that exists in each dataset—coefficients of variation are generally unhelpful, and researchers should report standard deviations of Cq values and/or repeatabilities of TS ratios—and allowing for the known effects of measurement error when interpreting patterns of TS ratio change over time.