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

Date Published: May 1, 2019

Publisher: Public Library of Science

Author(s): Daniel Nettle, Luise Seeker, Dan Nussey, Hannah Froy, Melissa Bateson, Suzannah Rutherford.

http://doi.org/10.1371/journal.pone.0216118

Abstract

The qPCR method provides an inexpensive, rapid method for estimating relative 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 (the TS 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 true telomere length, those in the TS ratio are positively skewed and proportional to true telomere length. The repeatability of the TS ratio declines sharply with increasing error in measurement of the Cq values for telomere and/or control gene. In simulated longitudinal data, measurement error alone can produce a pattern of low correlation between successive measures of relative telomere length, coupled with a strong negative dependency of the rate of change on initial relative telomere length. Our results illustrate the importance of reducing 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. The findings also illustrate the importance of characterising the measurement error 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.

Partial Text

The length of telomeres—DNA-protein caps on the ends of linear chromosomes—has emerged across several fields as a key integrative biomarker to be studied in relation to ageing [1,2], environmental exposures [3], early-life experience [4,5], social determinants of health [6], stress [7], disease [8], and reproduction [9]. The widespread use of telomere length as a biomarker in epidemiological and ecological studies depends on the availability of a convenient and high-throughput method of estimating the relative average telomere lengths of a sample of individuals. That method comes from quantitative PCR (qPCR) [10]. In a recent large meta-analysis of the human telomere epidemiology literature, qPCR was used in 80% of the 143 studies, including almost all the studies with a sample size greater than 100 individuals [11]. The cheapness and quickness of the qPCR method, as well as the fact that it requires only a small amount of DNA, are key factors enabling the explosion of interest in the field of in vivo telomere dynamics.

Using a combination of computer simulation and mathematical analysis, we were able to elucidate some important features of the potential impact of measurement error in datasets where relative telomere length is estimated by calculating a TS ratio from qPCR. First, because of the way two independent measurement errors (in the telomere and single-copy gene reaction) are exponentiated and combined, any error at the level of Cqs is magnified into a proportionately larger error in the TS ratio. Confirming this, papers reporting some estimate of measurement error for both the individual Cqs and the TS ratio do report proportionately greater error for the TS ratio (e.g. [4,20]). Repeatability of the TS ratio is high (greater than 0.75) as long as measurement errors in Cq are of the order of 0.075 or less, but it declines rapidly as error in the Cqs becomes greater. To illustrate with some concrete numbers, according to our simulations, repeatability of the TS ratio should be about 0.80 with error σ values of 0.05, 0.51 with error σ values of 0.1, and 0.28 with error σ values of 0.15. A widespread conclusion when surveying the qPCR telomere epidemiology literature is that there is a great deal of between-study variation in the observed strengths of associations [11,29]. Our findings suggest that small differentials in errors in the laboratory would be sufficient to drive large heterogeneity in outcomes. Nothing in our simulations suggests that the qPCR method cannot produce reliable estimates of relative telomere length. On the contrary, a number of multi-method studies have found moderate or high agreement between qPCR results and the results obtained with other methods [14,15,30]. What our results suggest is that it is easy, using qPCR, to produce datasets where the measured TS ratio does not reliably reflect relative telomere length: it merely requires the measurement errors in the Cqs to be slightly larger.

We have presented a simple simulation framework for exploring the impact of errors in measurement of Cq values on estimation of relative telomere length measurement using the TS ratio. The results illustrate the potentially large consequences for reliability of small increments in measurement error, and hence underline the need for researchers to both minimise and understand the measurement error that exists in their datasets. They also illustrate the value of simulation and mathematical analysis as tools for to guide empirical practices.

 

Source:

http://doi.org/10.1371/journal.pone.0216118

 

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