Introduction

Telomeres, the protective nucleic acid and protein cap found at the end of all eukaryotic chromosomes, have captured the attention of scientists, medical and public health professionals, biotechnology companies, and the media over the last two decades. In 1973, Olovnikov proposed his theory of marginotomy, which reasoned that during DNA replication, DNA polymerase would not be able to completely copy the first DNA segment and, to prevent the loss of critical DNA sequences in genes, a noncoding set of DNA nucleotides would be required to act as a buffer protecting the loss of important, gene-encoding, sequences [1]. Subsequently in 1978, Blackburn et al. first reported the actual DNA sequences of telomeres in yeast [2], followed by the first sequencing of the human telomere in 1988 [3]. The sequencing of telomeric DNA paved the way for the development of methods that measured the length of telomeres, beginning with the first report of telomere length measurement using the Southern blot method for mammalian chromosomes in 1988 [4]. Since then, thousands of papers assessing telomere length (TL) in human cells have been published across a myriad of different scientific fields (Fig 1). As a result of the broad scientific interest in both the role of TL in disease processes and the influence of environmental factors on TL dynamics, the number of studies evaluating TL in human cells continues to increase, in part facilitated by the regular development of new methods and modifications of existing assays.

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Fig 1. Telomere publications 1988–2019.

All publications mentioning telomere length of human DNA from 1988 to 2019 obtained by searching “telomere,” “length,” and “human” in PubMed. Search was completed on May 25, 2020.

https://doi.org/10.1371/journal.pone.0245582.g001

Currently, over two dozen assays have been developed to measure TL (Fig 2) [4–35]. These assays are classifiable into four broad categories: hybridization-based, polymerase chain reaction (PCR)-based, sequence-based, and mixed methods (e.g. hybridization/PCR combination). These assays vary in the information they yield on TL. While most focus on the measurement of the average TL within the sample, assays also measure chromosome-specific TL [6, 17], the complete distribution of TL in a cell population [33], or the shortest TL [26]. The shortest TL has received considerable attention, given evidence from in vitro and preclinical models suggesting that the shortest TL is most predictive of cellular senescence [36, 37]. Several recent reviews have discussed the overall advantages and disadvantages of each method [38–40] focusing on cost, scalability, constraints of starting biological samples (e.g. living cells, amount of DNA, etc) and, to some extent, inter and intra-lab precision as specific challenges facing the field, including the use of coefficient of variation (CV) compared to intraclass correlation coefficients (ICC) [41]. While studies of basic telomere biology continue to explore the complex role that telomeres play in cellular and organismal function, studies testing TL as markers of disease risk or environmental exposure must balance biological relevance, methodologic precision, and experimental practicality, similar to other epigenetic markers such as DNA methylation [42].

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Fig 2. Novel telomere methods developed from 1988 to 2020.

Data were obtained by literature search and through references of telomere review papers and method comparison papers.

https://doi.org/10.1371/journal.pone.0245582.g002

Over the last decade, debates have arisen over the utility and measurement of TL, particularly with regards to qPCR-based methods. This debate is partially fueled by concerns related to the reproducibility and replicability of TL measurements across studies, methods, and laboratories, and is accentuated by new method development and adaptations of existing protocols without sufficient consensus on the required quality control as more laboratories begin to perform TL assays independently. In response to this debate, several studies have attempted to compare TL measured across different assay methods or laboratories. Some of these method comparison studies examined the direct correlation of TL measurement in the same sample using different assay methods and/or tested the repeatability of TL with the same method (e.g. the amount of within assay variation) [43, 44]. Others tested the relative correlation of the TL measured by different assays with an expected phenotype (e.g. aging, parent-offspring correlation) [45], or examined the relative ability of different assays of TL to predict a specific disease or health outcome [46, 47]. Each of these approaches requires a different analytic strategy and study design and comparison of outcomes is not straightforward. To date, the existing evidence remains insufficient to answer key methodologic questions related to differences in reproducibility and replicability across measurement assays and laboratories, and how/whether these differences affect the ability of TL to serve as a biological indicator of exposure or a predictor of disease or health risk [48]. Beyond these concerns, there remains a lack of consensus as to which, if any, methodology is the “gold standard,” as even the classic Southern blot method is challenged by its inability to capture potentially critical metrics (e.g. full distribution, shortest telomere length, inclusion of the subtelomeric region). To ensure reliability in the widespread utilization of TL as a biomarker of environmental exposure and/or a predictor of a disease, measured by any method that is applicable to population studies, it is critical to systematically test fundamental issues related to assay reproducibility and replicability [49].

As part of a joint National Institute of Aging and National Institute of Environmental Health Science initiative that funded a U24 cooperative award and four separate U01 awards, a Telomere Research Network (TRN) was established in 2019 (trn.tulane.edu). The TRN is coordinating cross-method comparison studies with the long-term goal of developing methodological guidelines and recommendations for telomere research applicable to population-based studies. As a first step towards the goals of this network, we undertook a systematic literature review of published studies that directly compared TL measured using at least one PCR-based method and another approach to determine how these studies might inform the field, with particular attention to assay precision and accuracy of different measurement assays and what research gaps remain.

As defined by the Committee on Reproducibility and Replicability in Science, precision is the closeness of agreement between measured quantities obtained by replicate measurements, while accuracy is the closeness of agreement between a measured quantity and a true value [49]. Reproducibility is defined as precision in measurement under conditions that involve different locations or different measurement procedures, while repeatability is defined as precision in measurements that include the same procedures/locations. Beginning from this perspective, this systematic review evaluated the existing literature related to cross-method comparisons. This review focuses on PCR-based methodologies due to their increasing use in population-based studies, their central role in the debate related to assay precision, and the existence of two reporting guidelines—one created through the TRN (S1 Table), and a second one created by a separate group in a recently published manuscript [50]. The majority of PCR-based methods are derived from two seminal methodologic papers by Richard Cawthon, the first describing a monoplex based assay (qPCR) and the second describing a multiplex assay (MMqPCR) [14, 19]. Our review focuses specifically on the comprehensiveness of methodologic reporting, correlation between TL measured by different assays, assay repeatability and reproducibility, and overall scientific design of methodological comparisons. Finally, we suggest areas of needed scientific examination and provide some guidance related to study design, necessary sample size, and analytic approach, to address key remaining questions: (1) What is known about the relationship between TL measured using PCR-based methods and other assays? (2) What is known about the reproducibility and repeatability of PCR-based methods and how does this relate to other TL measurement techniques? (3) What are the implications of methodologic precision for sample size and power? (4) What are appropriate guidelines to systematically evaluate the precision of both existing and future TL assays? Addressing these important questions is a requisite step in advancing our understanding of the ability of TL, measured by any approach, to serve as a sentinel of psychosocial and environmental exposures and a predictor of future disease.

Methods

Manuscript search

To identify relevant papers that reported on cross method comparisons of any qPCR-based method (qPCR, absolute TL (aTL), and MMqPCR) and another method of TL assessment (PCR-based or otherwise) or the same PCR-based method conducted in separate laboratories, we conducted a critical review beginning with a literature search (Fig 3). The following key terms “telomere,” “telomere length,” and “human” were searched in PubMed and Web of Science. From these initial results, a second search included the keyword “PCR” to identify the initial titles for screening. Search criteria included papers published since January 1, 2002 (the year the first method to measure TL by qPCR was published) through May 10, 2020. The references of selected papers were also reviewed to identify any additional papers. A list of identified papers was presented to the TRN Steering Committee, who also suggested additional papers. Initial review of papers for inclusion was accomplished through evaluation of both the abstract and methods section, as some manuscripts were not directly focused on methodological comparison and instead only reported the cross-method comparison on a subset of samples. Final inclusion in this review met the following baseline requirements:

1. Article published in a peer-reviewed journal (abstracts and pre-prints not included).
2. Article was not presenting the initial development of a new method or a substantial refinement of an existing methodology. This type of study was excluded due to the expectation that these new methodologic manuscripts were utilizing cross method comparison as a measure of external validity for the methodologic design. As we did not find any papers describing the failed development of a new method for TL measurement, and would not expect that to be readily publishable, to avoid any potential intrinsic bias in these highly specialized reports we opted to exclude them.
3. Included a direct comparison of TL using the same biological sample measured with two distinct TL assays or the same assay in two or more separate laboratories.
4. At least one of the methods used to measure TL was solely qPCR-based. TeSLA and STELA were not considered due to the additional hybridization component of the assay.

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Fig 3. PRISMA flow diagram of literature search and article selection for inclusion.

Date range searched was between Jan 1 2002 to May 10 2020. Other sources of article identification included suggestions from participants of the TRN and review of reference lists of selected papers.

https://doi.org/10.1371/journal.pone.0245582.g003

Results

The initial search revealed 5427 articles and the inclusion of “PCR” as a search term limited the results to 767 articles, whose abstracts and methods were read (Fig 3). An additional 30 articles were identified through assessment of the references of method validation papers and other included cross-method validation studies. A review of these 797 abstracts identified 70 articles for assessment of the full text and supplemental information to determine inclusion in this review. Twenty-six articles were determined to be novel method validation and excluded. Eighteen articles were excluded as they were either reviews or did not include direct method comparisons. One article was excluded due to the determination that the DNA samples used for cross-method comparison were obtained at different time points. We also included nine papers that, while not specifically designed as a cross method comparison, included sufficient details comparing TL measurement using different assays. This resulted in a total of 25 articles included in this review (Table 1).

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Table 1. Characteristics of included papers.

https://doi.org/10.1371/journal.pone.0245582.t001

Overall quality of reporting of PCR assay methodology

Of the 25 studies included in this systematic review, the average completion score across both reporting guidelines was 51%, with an average of 52% for the TRN guidelines and 50% for the Morinha guidelines (Table 2). Overall, papers included between 26–75% and 29–78% of the recommended reporting metrics for the TRN and Morinha guidelines, respectively. Some metrics were consistently reported in nearly all papers, including the sample type, single copy gene name, and type of PCR method utilized. However, only about 10% of the included papers reported on sample storage, PCR efficiencies, or the number of samples excluded due to quality concerns with the assay.

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Table 2. Results of reporting guidelines analysis for TRN and Morinha reporting guidelines.

https://doi.org/10.1371/journal.pone.0245582.t002

DNA processing.

For both the TRN guidelines and Morinha guidelines, reporting of sample type, storage, DNA extraction, and DNA quality/integrity was poor, with an average of 37% for the TRN Sample/DNA category, 46% for the Morinha Sample category, and 32% for the DNA category of the Morinha guidelines. Storage conditions for both the biological samples and extracted DNA were poorly reported, with 24% or less of studies providing this information (Table 3). Fewer than half of the studies reported on metrics related to DNA integrity.

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Table 3. Item-specific reporting results of the TRN reporting guidelines.

https://doi.org/10.1371/journal.pone.0245582.t003

PCR assay.

Reporting on PCR assay conditions and quality control varied. While many metrics of the PCR assay were well-reported, only 18 of 25 studies reported the full cycling conditions. The lowest reporting metric related to PCR was experimental efficiency, with only 12% reporting actual PCR efficiencies. Additionally, just over half (56%) of studies reported the source of their control samples.

Analytic approaches.

Several key reporting gaps were noted in relation to assay quality control and analytic approaches to determining final TL. As with any biologic assay, there is the potential that a specific sample will fail quality control metrics and need to be repeated. Only six papers reported on the number of samples repeated and/or the number of samples that failed quality control. While all but one study reported the numbers of sample replicates, surprisingly, only 14 studies reported on the level of independence of sample replicates (e.g. replicates run on the same plate or on different plates/different times) and only half of the studies reported the means and standard deviations (or median and range) of the T/S ratio.

Cross-laboratory studies.

Only six studies compared analyses across more than one laboratory (Table 1). Of these studies, three described how samples were blinded before analyses. Further, of these cross-laboratory studies, only three studies included the same assay performed in different laboratories [44, 46, 69].

Reproducibility

Reproducibility, a critical criterion for biologic assays, refers to the relation between measurements using the same assay in different locations or the comparison of values generated using different measurement procedures. This systematic review attempted to assess the relative reproducibility of PCR-based measure of TL in different laboratories as well as the reproducibility precision, e.g. the closeness of two or more measurements, in TL measurement using different methods.

Relative reproducibility.

The current literature does not provide sufficient data to address the relative reproducibility, as, to date, only three studies have tested this directly by performing the same assay in different laboratories or settings. In one study that blinded comparison samples before they were sent to the external laboratories, the median CV across laboratories for qPCR was 18.3%, while the median CV for STELA/TRF based TL measurement was 9.2% 44]. However given the dependence of CVs on the y-intercept, the interpretation of these CVs remains challenging [41]. In the second study, where samples were not blinded before being assayed, the reported within-lab CVs for replicate qPCR measurements were 2.5% and 8.6% [46]. As the laboratories involved utilized different PCR primers, and slightly different methods, it was not possible to directly compare cross-laboratory reproducibility. The third study found inter-assay CVs of 12.0 and 1.2% in two participating labs performing qPCR, but an additional laboratory’s results were excluded from analysis due to an extremely high CV of 27%. Correlation between each laboratory and TRF results were calculated, but no correlation results were provided for the two qPCR assays, and ICC estimates were not reported.

Reproducibility precision.

Reproducibility precision, i.e. the closeness of two or more measurements using different techniques, was addressed to some extent in 19 of the 25 studies reviewed. However, only six studies involved assays performed in different laboratories. The correlation of TL measurement with qPCR-based assays to other assays was, for the most part, reported as linear regression and correlation coefficients (Fig 4). Other papers reported Bland-Altman analyses or did not report a measure of correlation at all. One paper reported mean LTL values for both TRF and qPCR, but did not report a measure of correlation [69]. When examining these results, it should be kept in mind that these methodological studies were generally done in laboratories with extensive experience in the focal technique, and as such are unlikely to be representative of the field at large.

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Fig 4. Weighted average correlation (Pearson’s r) and 95% CI for each method compared with qPCR, MMqPCR, or aTL.

LCL: lower confidence limit. UCL: upper confidence limit. The point sizes for each comparison are weighted by the number of individual papers with that comparison.

https://doi.org/10.1371/journal.pone.0245582.g004

As qPCR and TRF were the most common methods compared, these studies typically reported high correlation, with a weighted correlation coefficient for all studies around 0.75. Correlation of other methods with qPCR or MMqPCR were more variable. No studies have compared MMqPCR to aTL, or whole genome sequencing (WGS). Only one paper each compared qPCR and qPCR (in separate labs), aTL and TRF, qPCR and aTL, or qPCR with WGS. To our knowledge no studies have compared WGS data with aTL, although studies have compared TRF and WGS [24].

In five of the papers in this review, linear regression was used to extrapolate TL into kilobases (kb) from the T/S ratio using TRF values. One paper converted T/S ratio to kb before analysis of the correlation between methods [47]. In two cases, T/S ratio were converted to kb prior to TL comparison utilizing Bland-Altman analysis [71, 76]. In two of the five papers, the conversion of the T/S ratio to bp was based on analyses extrapolated from different data or measured on a different sample type, raising substantial concerns on the true measurement with uncertain implications for the r value [47, 76]. Beyond concerns related to the source of the data utilized for conversion from T/S to bp before comparison across methods, this analytic approach likely to leads to inaccurate reporting [47]. Only two studies utilized the TRN-recommended procedure of transformation to z-score before comparison [44, 69]. When comparing relative TL estimates such as the T/S ratio generated from qPCR, transformation of these values to z-scores will yield more informative results and improve ability to compare results between laboratories or assays [78].

Determination of effects of ICC variability on sample size and study power

Our systematic review revealed wide variation in TL measurement repeatability. No biologic assay is perfect, and laboratories measuring any biologic substrate vary in their own internal quality control and repeatability. To provide general guidance for investigators, we therefore conducted analyses to evaluate the impact on power and sample size across a range of ICCs.

In Fig 5A, we present the sample size required to test effect sizes of 150, 200, and 300 bp with a t-test with a power of 0.9, as a function of measurement error as expressed in the ICC. To contextualize the differences: 150 bp is the approximate difference found between the sexes, and 300 bp is the approximate difference observed between individuals with and without atherosclerotic cardiovascular disease [80]. As directly converting bp to T/S ratios is not feasible in this analysis and the analyses below, we suggest that investigators using T/S or other relative TL measurements use standard deviation (SD) differences to estimate power. For example. a difference of 150 bp is equal to 150 / 650 = 0.23 SD, which can be converted to a T/S difference when the SD of the T/S measurements is known. Estimates of potential difference can be extracted from existing literature related to their exposure or outcome of interest when considering study design and sample size.

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Fig 5.

A. The sample size required to test effect sizes of 150, 200 and 300 bp with a t-test with a power of 0.9, as a function of measurement error as expressed in the ICC. Calculations assumed a realistic (true) standard deviation of 650 bp and power analysis was done using G*Power [57]. N is the combined n of the two groups to be compared and was assumed to be equally distributed among the two groups. B, C. 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. D. Four-year follow-up period. E. Eight-year follow-up period. Power was calculated for sample sizes as shown (200–2800), equally divided over the two levels of telomere shortening rate. Baseline telomere shortening was simulated assuming a Poisson distribution with mean/variance of 25, and population SD of telomere length was maintained at 0.65 kb at both time points.

https://doi.org/10.1371/journal.pone.0245582.g005

Finally, we present the statistical power of different sample sizes to detect a significant difference in telomere shortening rate of 33% using longitudinal data, as a function of measurement reliability expressed as ICC (Fig 5B and 5C). This analysis revealed that even with a high ICC (>0.9), large sample sizes are required to yield sufficient statistical power to detect differences in telomere shortening rate, in particular when the follow-up period is short. This is due to the mean rate of telomere shortening being low (here 25 bp/year) compared to the TL variation between individuals (here an SD of 650 bp). The rate of base pair loss in infants and children is likely significantly different and, but as of yet is poorly characterized (but see [81]).

Discussion

This systematic review found a total of 25 papers documenting comparison between TL measured using a PCR-based methodology and another TL assay. Until recently, no publication reporting guidelines existed for qPCR-based TL measurement. Our review focused on method comparison studies with the expectation that critical assay parameters and methodologic description would be more detailed and specific. Our review, using two separately developed reporting guidelines, found that, on average, only half of the recommended factors were documented, indicating the need for increased methodologic reporting and wider awareness of reporting recommendations. The lowest reporting was related to information about the validation of PCR-based assays outlined in the Morinha guidelines, with only seven papers including any of the recommended factors. PCR efficiencies, a key reporting requirement in both guidelines and the MIQE guidelines, was absent from the majority of papers with only six mentioning the PCR efficiency parameters and only three documenting the actual PCR efficiencies. Given that all PCR-based methods either assume or specifically calculate the PCR efficiency when determining the T/S ratio, and that, in general, the determination of the T/S ratio assumes similar efficiencies for the single gene and the telomeric primers, the absence of this key metric is concerning. Fewer than half of studies failed to comment on key pre-analytic factors, specifically sample storage time and conditions, freeze-thaw cycles, and evaluation of DNA quality and integrity, all potential sources of assay variability for both PCR and non-PCR-based TL assays that may contribute to current debates in the field about the utility of TL [77, 82, 83]. Lastly, the reporting of the number of samples failing initial quality control, repeated, or unable to be assayed was low. In laboratories routinely performing TL measurement using any assay, a certain percentage of samples for each study will require repeating and regularly a small subset may be unanalyzable for various reasons. While it is possible that these factors were considered and monitored, the lack of reporting for this metric heightens the need for increased attention to the proposed reporting recommendations. Moving forward, the widespread dissemination of these qPCR reporting guidelines to study sections, peer reviewers, and scientists represents an important next step in enhancing the scientific rigor of the field.

At this time, evaluation of the existing literature fails to provide sufficient evidence of the relative or precision reproducibility of different TL assays. Our review identified only six studies that included cross laboratory comparisons and, of these, only three evaluated PCR-based assays performed in more than one lab. As the number of laboratories performing TL studies using PCR and other methods continue to increase, the lack of clear data about cross-laboratory reproducibility and the absence of existing DNA standards or other methods to account for cross-laboratory variation substantially limits the ability to characterize relative reproducibility. In terms of reproducibility across different methods (e.g. PCR and TRF, or PCR and FISH), the current variability in findings, particularly when coupled with limited methodologic reporting, highlights the need for additional rigorous and blinded cross laboratory studies that are adequately powered to accurately determine how TL in a population measured using different assays truly relates. Although 17 studies evaluated the relationship between qPCR and TRF, due to the wide range in reported correlations between TL measurements, the relatively small samples sizes, and the insufficient analytic and assay blinding, there is currently insufficient data to draw firm conclusions on the general correlation between TL measured with different assays. The analytic consequences of using CVs to test the relationship between TL measured using different assays has been discussed previously, as has the issues caused by the use of analytic strategies such as conversion to base pairs instead of z scores, especially when extrapolating from data produced in different laboratories or using different samples [41, 52]. In this review, we utilized existing raw data from 3 included studies to provide preliminary data about precision reproducibility for PCR and TRF studies. The wide range of ICCs calculated from these few studies, particularly for PCR-based methods, and the low reporting of ICCs in the papers included in this review highlights the need to increase attention to the importance of reporting ICC statistics. For many of the existing studies, the small sample size and the lack of reporting of the means and standard deviations of TL prevents objective determination of whether any of the current studies were adequately powered. Beyond these concerns, the over-representation of data from specialized laboratories, particularly for TRF, the applicability of much of the existing data to the wider telomere field is uncertain. For aTL and MMqPCR-based TL measurements, the current paucity of published cross method comparisons limits the ability to form an opinion of how TL measured with these assays relates to other methods.

Measurement precision is critical, in particular for longitudinal studies. Methodologies that are low cost, practical, and simple to implement with standard laboratory equipment, especially when they are innovative or high impact, are often rapidly implemented across laboratories with various levels of expertise in the new methodology. Invariably this results in diverse protocols, analyses, and methodologic reporting–consequences that are even more problematic when there is an absence of consensus on best practices [51]. As with many other biologic assays, the development of reporting guidelines for TL measurement has lagged behind the broad implementation of the methods themselves [84–88]. The lack of consolidated guidance about factors, both pre-analytic and within the assay itself, that contribute to measurement error when combined with the wide popularity of PCR-based TL measurement undoubtedly contributed to discrepancies in the existing literature and failed study replications. Similar to the MIQE guidelines, the reporting guidelines presented and tested in this systematic review for PCR-based TL assays are meant as minimal reporting recommendations focused on enhancing the reliability of results, consistency between different laboratories performing the same assay, and increased experimental transparency and accuracy [51]. To assist investigators and reviewers we highlight the overlapping recommendations with the MIQE guidelines, indicate whether a particular requirement is desirable or essential, and provide references that support the selection of the particular reporting requirement. Over the course of the next four years, the TRN expects to develop similar reporting recommendations for other types of TL assays while conducting adequately powered and scientifically rigorous studies to support these reporting guidelines, recognizing that individual recommendations have varying levels of initial empirical support [89].

Despite the strengths of this review, there are several limitations. First, this review only focused on assays applicable to population-based studies in humans. It does not address issues in other species or assays that may have clinical utility but for which the requirements for sample types (e.g. fresh tissue and/or live cells) or the cost/labor/expertise requirements (e.g. TeSLA, STELA) limit utilization in population based studies. A second limitation is that we utilized reporting guidelines for qPCR-based assays only. To date, specific protocol recommendations and reporting guidelines have not been published for other TL assays (e.g. TRF, FISH) although detailed methodologic protocols do exist [90]. Additionally, it is possible that additional articles comparing TL assays may be available in other databases or pre-print servers. However, many of the articles included in this review were not specifically designed solely to compare TL measurement methods and would not be found through standardized database searches. Further, it is unlikely that additional articles would change the general picture emerging from this review. Finally, we note that while this article focused on precision and reproducibility, accuracy of measurement is as important. Precisely inaccurate measures will be of limited use to the scientific field, a factor that becomes more problematic when using relative estimates and not true values as is the case in many TL assays. In the absence of a clear gold standard measurement technique, accuracy is difficult to discern.

Conclusions

After careful examination of the existing literature, it is apparent that rigorous cross laboratory and methodological studies must be an immediate priority for the field. To assist the field moving forward, we include reporting guidelines for PCR-based TL assays and indicate specific scientific papers that support these recommendations originally developed through consensus of the initial TRN members and consultants. These guidelines do not outline a specific PCR methodology and, at this time, we do not believe there is sufficient data to provide guidance on specific assay approaches or components. Rather, these guidelines are provided to ensure reviewers and readers can adequately assess the methodology and consider the implications of these factors for each study’s findings. The consistency in results across reporting guidelines (TRN, Morinha, MIQE) related to the integrity and quality of both the initial biological sample and the DNA itself support the critical nature of this reporting metric. In terms of assay reporting, increased attention of investigators and reviewers to ensuring complete reporting of assay reagents and PCR efficiencies is also expected to enhance the rigor of the field. TRN investigators are currently testing the impact of different pre-analytic factors, DNA integrity, and PCR conditions to provide evidence of the importance of these parameters in relation to precision and reproducibility. We recommend that studies be required to report ICCs in lieu of CVs, as well as either the median or mean and standard deviation of TL. We also provide specific guidance related to sample size and power that is contingent upon the ICC given the substantial impact of differences in assay precision on the ability to determine true relationships and with the expectation that this will be of use for investigators as they embark on new research studies. It is important to balance assay cost, in terms of both time and reagents, with the needed sample size and statistical power. Moving forward, investigators should carefully consider study design from this perspective, recognizing that there is currently no “ideal” approach. Telomere research offers significant potential across a diverse range of scientific fields with potential mechanistic insight into overlapping biological pathways contributing to many of the leading causes of morbidity and mortality. Ensuring the highest scientific rigor and precision, through accurate methodological reporting and rigorous testing of the factors that contribute to assay variability, are requisite steps to ensuring that potential is achieved.

Supporting information

Acknowledgments

The authors would like to acknowledge the TRN Advisory Committee (Daniel Belsky, Eileen Crimmins, Sonja Entringer, Daniel Nettle, Jerry Shay), U01 grantee P.I.s (Abraham Aviv, Jue Lin, Idan Shalev, Yun-Ling Zheng), and NIA/NIEHS partners (Max Guo, Michelle Heacock, Lisbeth Nielsen) for their input in creating the TRN qPCR Reporting Guidelines.