Electronic supplementary material
SUPPLEMENTARY MATERIAL AND METHODS
Relative telomere length (RTL)
We used a quantitative PCR method [1,2] to estimate the quantity of telomere repeated sequences relative to a reference gene that was predetermined to be non-variable in copy number (non-VCN) among the sample genomes. This ratio of telomere-to-non-VCN gene was also standardised among runs by division by the telomere-to-non-VCN gene ratio for a control sample included in each qPCR run (see equation below). The reference gene (c-Myc) for the relative measure was determined to be non-variable in copy number as per Smith et al. (2011). Primers for the non-VCN gene (5’-GAG GGC
CAA GTT GGA CAG TG-3’, and 5’-TTG CGG TTG TTG CTG ATC TG-3’) amplified a 54 bp portion of the c-myc proto-oncogene. Forward and reverse telomeric primers were 5’- CGG TTT GTT
TGG GTT TGG GTT TGG GTT TGG GTT TGG GTT-3’ and 5’- GGC TTG CCT TAC CCT TAC
CCT TAC CCT TAC CCT TAC CCT-3’, respectively [2].
Samples were diluted to a standard DNA concentration of ~20 ng μl
-1
. Reactions were prepared using the Qiagility PCR robot (Qiagen, Germany) in 20 μl volumes and contained 10 μl SensiMix™
SYBR no-Rox Kit (Bioline, Germany), and 300 nM of each primer for telomeres or 200 nM of each primer for non-VCN gene reactions. Cycling conditions included an initial hold at 95°C for 5 mins followed by 40 cycles of 94°C for 10 secs, 58°C for 15 secs, and 72°C for 20 secs with fluorescence readings recorded at this step. A final melt step was included on each run with the temperature ramping from 65°C to 95°C in 1°C increments. Cycling was performed on a Rotorgene 6000 real-time PCR instrument (Qiagen, Germany). In each of five runs, we included three replicates of DNA samples taken on day 0 and 180 from an even number of individuals from each treatment group, five replicates of a control DNA sample derived from a non-experimental individual and a no-template control. Mean
PCR efficiencies were 1.00 and 0.99 for the telomere and non-VCN gene reactions, and mean coefficients of variation among replicates were 4.9% and 5.2% for the telomere and non-VCN gene qPCR products, respectively.
We analysed non-baseline-corrected raw data exported from the instrument using the
LinRegPCR (V.12.5) program [3], which uses linear regression to estimate sample reaction efficiencies. Using the mean PCR efficiency per amplicon (E) and the cycle threshold (Cq) value per sample, we calculated the RTL using the formula [4]:

RTL
S
= (E
CT
^Cq
CT
/ E
ST
^Cq
ST
) / (E
CN
^Cq
CN
/ E
SN
^Cq
SN
)
Where: RTL
S
= relative telomere length for a given sample; E
CT
= mean efficiency of the control telomere reactions; Cq
CT
= mean cycle threshold of the control telomere replicates; E
ST
= mean efficiency of all sample telomere reactions; Cq ST = mean cycle threshold of within-sample telomere replicates; E
CN
= mean efficiency of the control non-VCN gene reactions; Cq
CN
= mean cycle threshold of the control non-VCN gene replicates; E SN = average efficiency of all sample non-VCN gene reactions; and Cq SN = average cycle threshold of within-sample non-VCN gene replicates.
Multi-model inference
Models were ranked by minimizing the value of Akaike’s Information Criterion corrected for small samples size (i.e. AICc) [5]. We also calculated Akaike weights, which represent the relative likelihood of each model given the data and the entire set of models [5]. To identify influential parameters, we made robust inferences based on a set of well-supported models (∆AICc < 2). We could then estimate the relative explanatory strength of each parameter while simultaneously accounting for uncertainty in model selection. We calculated model-averaged coefficients for all parameters by averaging the estimates, weighted by multiplication with that model’s Akaike weight, over the set of well-supported models (∆AICc < 2) including each parameter. We also derived the unconditional standard error and
95% CI associated with each coefficient (i.e. not dependent on a single model). The relative importance of all parameters was calculated by summing the Akaike weights of each model containing that parameter.
REFERENCES
1. Cawthon, R. M. 2002 Telomere measurement by quantitative PCR. Nucleic Acids Research 30, e47.
2. Callicott, R. J. & Womack, J. E. 2006 Real-time PCR assay for measurement of mouse telomeres.
Comparative Medicine 56, 17-22.
3. Ruijter, J. M., Ramakers, C., Hoogaars, W. M. H., Karlen, Y., Bakker, O., van den Hoff, M. J. B. &
Moorman, A. F. M. 2009 Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Research 37, e45.
4. Ehrlenbach, S., Willeit, P., Kiechl, S., Willeit, J., Reindl, M., Schanda, K., Kronenberg, F. &
Brandstaetter, A. 2009 Influences on the reduction of relative telomere length over 10 years in the population-based Bruneck Study: introduction of a well-controlled high-throughput assay.
International Journal of Epidemiology 38, 1725-1734.

5. Burnham, K. P. & Anderson, D. R. 2002 Model selection and multi-model inference: a practical information-theoretic approach. 2nd Ed.
New York: Springer-Verlag.