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

Lookup NU author(s): Professor Daniel Nettle, Professor Melissa Bateson

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2019 Nettle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.


Publication metadata

Author(s): Nettle D, Seeker L, Nussey D, Froy H, Bateson M

Publication type: Article

Publication status: Published

Journal: PLoS ONE

Year: 2019

Volume: 14

Issue: 5

Online publication date: 01/05/2019

Acceptance date: 14/04/2019

Date deposited: 29/05/2019

ISSN (electronic): 1932-6203

Publisher: Public Library of Science

URL: https://doi.org/10.1371/journal.pone.0216118

DOI: 10.1371/journal.pone.0216118

PubMed id: 31042766


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